From 1a8e007066a2577bc774805e127d016f46c96911 Mon Sep 17 00:00:00 2001
From: Lincoln Stein <lincoln.stein@gmail.com>
Date: Sat, 12 Nov 2022 17:17:07 +0000
Subject: [PATCH] merge release-candidate-1-3-2 into main.
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Squashed commit of the following:

commit 9a1fe8e7fbbd81d9e9e47c4fda6bde6f66fba30c
Author: Lincoln Stein <lincoln.stein@gmail.com>
Date:   Sat Nov 12 17:07:40 2022 +0000

    swap in release URLs for installers

commit ff56f5251ba8502833cb561317e82b028cd2da5d
Author: Lincoln Stein <lincoln.stein@gmail.com>
Date:   Sat Nov 12 17:03:21 2022 +0000

    fix up bad unicode chars in invoke.py

commit ed943bd6c785d968d1e31fbbb814fea8688fc8bb
Author: Lincoln Stein <lincoln.stein@gmail.com>
Date:   Sat Nov 12 16:05:45 2022 +0000

    outcrop improvements, hand-added

commit 7ad2355b1da53861bb75312edead8f9db0b0f44d
Author: Lincoln Stein <lincoln.stein@gmail.com>
Date:   Sat Nov 12 15:14:33 2022 +0000

    documentation fixes

commit 66c920fc197d9206be9e0e2d1eb2a1cb86362887
Author: Lincoln Stein <lincoln.stein@gmail.com>
Date:   Thu Nov 10 16:49:25 2022 -0500

    Revert "Resize hires as an image"

    This reverts commit d05b1b3544dfa7b3c071ac05b4f014a52e08fa50.

commit 3fc5cb09f8bc03ea96e67a3e49fb087019d2f9d5
Author: Lincoln Stein <lincoln.stein@gmail.com>
Date:   Sat Nov 12 12:43:17 2022 +0000

    fix incorrect link in install

commit 1345ec77ab58f70e1bf5a0f587ca40aba4e906b7
Author: tildebyte <337875+tildebyte@users.noreply.github.com>
Date:   Sun Nov 6 19:07:31 2022 -0500

    toil(repo): add tildebyte as owner of installer/ directory

commit b116715490f390a59e5bee1022a6a758aee0918d
Author: Kyle Schouviller <kyle0654@hotmail.com>
Date:   Thu Nov 10 21:43:56 2022 -0800

    Fix performance issue introduced by torch cuda cache clear during generation

commit fa3670270e7aae6bfd8d47fb7bdd32b707310c23
Author: mauwii <Mauwii@outlook.de>
Date:   Fri Nov 11 12:42:03 2022 +0100

    small update to dockers huggingface section

commit c304250ef663b3e202b4da8332365288589d94c7
Author: mauwii <Mauwii@outlook.de>
Date:   Fri Nov 11 12:19:27 2022 +0100

    fix format and Link in INSTALL_INVOKE.md

commit 802ce5dde5ca68f24aa4609cdba428044e0c7fa1
Author: mauwii <Mauwii@outlook.de>
Date:   Fri Nov 11 11:17:49 2022 +0100

    small fixex to format and a link in INSTALL_MANUAL

commit 311ee320ecac892cb4a1bcd012374fa051b1d011
Author: Lincoln Stein <lincoln.stein@gmail.com>
Date:   Fri Nov 11 07:23:35 2022 +0000

    ignore installer intermediate files

commit e9df17b374df58a44d3ae3ba33cbdc58794bcf5d
Author: Lincoln Stein <lincoln.stein@gmail.com>
Date:   Fri Nov 11 07:19:25 2022 +0000

    fix backslash-related syntax error

commit 061fb4ef00482bdd4fba635aeaeff4ca7510190d
Merge: 52be0d23 4095acd1
Author: Lincoln Stein <lincoln.stein@gmail.com>
Date:   Fri Nov 11 06:50:04 2022 +0000

    Merge branch 'release-candidate-2-1-3' of github.com:/invoke-ai/InvokeAI into release-candidate-2-1-3

commit 52be0d2396de2de02030855c92e3367008b50377
Author: Lincoln Stein <lincoln.stein@gmail.com>
Date:   Fri Nov 11 06:49:45 2022 +0000

    add WindowsLongFileName batfile to source installer

commit 4095acd10ebae05a8cb14cc2faf7c79936d9e6a9
Author: mauwii <Mauwii@outlook.de>
Date:   Fri Nov 11 07:05:17 2022 +0100

    Doc Updates
    A lot of re-formating of new Installation Docs
    also some content updates/corrections

commit 201eb22d7650b00f55249c2646fa4f47ac3e0ec2
Author: Lincoln Stein <lincoln.stein@gmail.com>
Date:   Fri Nov 11 04:41:02 2022 +0000

    prevent two models from being marked default in models.yaml

commit 17ab98220034dfbb798a9a0460bc2837f0c1e2cb
Author: Lincoln Stein <lincoln.stein@gmail.com>
Date:   Fri Nov 11 03:56:54 2022 +0000

    installers download branch HEAD not tag

commit a04965b0e9e9d1170cf284af9acbd1186d681255
Author: Lincoln Stein <lincoln.stein@gmail.com>
Date:   Fri Nov 11 03:48:21 2022 +0000

    improve messaging during installation process

commit 0b529f0c574dd91fb9593e924ebc41c8c219b6ce
Author: Lincoln Stein <lincoln.stein@gmail.com>
Date:   Tue Nov 8 15:22:32 2022 +0000

    enable outcropping of random JPG/PNG images

    - Works best with runwayML inpainting model
    - Numerous code changes required to propagate seed to final metadata.
      Original code predicated on the image being generated within InvokeAI.

commit 6f9f84834557eb3c48562dc5d0ace51ca9e9cca2
Author: Lincoln Stein <lincoln.stein@gmail.com>
Date:   Tue Nov 8 17:27:42 2022 +0000

    enhance outcropping with ability to direct contents of new regions

    - When outcropping an image you can now add a `--new_prompt` option, to specify
      a new prompt to be used instead of the original one used to generate the image.

    - Similarly you can provide a new seed using `--seed` (or `-S`). A seed of zero
      will pick one randomly.

    - This PR also fixes the crash that happened when trying to outcrop an image
      that does not contain InvokeAI metadata.

commit 918c1589ef30595668899f4259ece6a4ad605c69
Author: Lincoln Stein <lincoln.stein@gmail.com>
Date:   Thu Nov 10 20:16:47 2022 +0000

    fix #1402

commit 116415b3fcec8415f8e6de9b23c0bbd0dbc6845b
Author: Lincoln Stein <lincoln.stein@gmail.com>
Date:   Thu Nov 10 21:27:25 2022 +0000

    fix invoke.py crash if no models.yaml file present

    - Script will now offer the user the ability to create a
      minimal models.yaml and then gracefully exit.
    - Closes #1420

commit b4b6eabaac94896e38123be331c44d1b63065b0d
Author: Lincoln Stein <lincoln.stein@gmail.com>
Date:   Thu Nov 10 16:49:25 2022 -0500

    Revert "Log strength with hires"

    This reverts commit 82d4904c07670d514e9a541de7c9ecd8489eeae2.

commit 4ef1f4a854717c953f9192454d0adabef32af36c
Author: Lincoln Stein <lincoln.stein@gmail.com>
Date:   Thu Nov 10 20:01:49 2022 +0000

    remove temporary directory from repo

commit 510fc4ebaa5f9dc0e77b47cf04e0c265423e0a44
Author: Lincoln Stein <lincoln.stein@gmail.com>
Date:   Thu Nov 10 19:59:03 2022 +0000

    remove -e from clipseg load in installer

commit a20914434bb3609cf1d883cabf048d4cc25f881a
Author: Lincoln Stein <lincoln.stein@gmail.com>
Date:   Thu Nov 10 19:37:07 2022 +0000

    change clipseg repo branch to avoid clipseg not found error

commit 0d134195fda5c69fce5a95d6da90248eba8d226d
Author: Lincoln Stein <lincoln.stein@gmail.com>
Date:   Thu Nov 10 18:39:29 2022 +0000

    update repo URL to point to rc

commit 649d8c8573896fdb5a73453b69ee4b54cf4d6241
Author: Lincoln Stein <lincoln.stein@gmail.com>
Date:   Thu Nov 10 18:13:28 2022 +0000

    integrate tildebyte installer

commit a358d370a05ea985a9e26fa9c1101d9c14fdb3ab
Author: Lincoln Stein <lincoln.stein@gmail.com>
Date:   Thu Nov 10 17:48:14 2022 +0000

    add @tildebyte compiled pip installer

commit 94a9033c4f6214e604be149a3de6d7c747af7f3a
Author: Lincoln Stein <lincoln.stein@gmail.com>
Date:   Thu Nov 10 14:52:00 2022 +0000

    ignore source installer zip files

commit 18a947c503c83c95cd83f27424696219ff3c4c95
Author: Lincoln Stein <lincoln.stein@gmail.com>
Date:   Thu Nov 10 14:46:36 2022 +0000

    documentation and environment file fixes

    - Have clarified the relationship between the @tildebyte and @cmdr2 installers;
      However, @tildebyte installer merge is still a WIP due to conflicts over
      such things as `invoke.sh`.
    - Rechristened 1click installer as "source" installer. @tildebyte installer will be
      "the" installer. (We'll see which one generates the least support requests and
      maintenance work.)
    - Sync'd `environment-mac.yml` with `development`. The former was failing with a
      taming-transformers error as per https://discord.com/channels/@me/1037201214154231899/1040060947378749460

commit a23b03189526a49da8ddae90ce71b0e902784d29
Author: Mike DiGiovanni <vinblau@gmail.com>
Date:   Wed Nov 9 16:44:59 2022 -0500

    Fixes typos in README.md

commit 23af68c7d79d947f1bd3441c91bb44d655238f01
Author: Lincoln Stein <lincoln.stein@gmail.com>
Date:   Thu Nov 10 07:02:27 2022 -0500

    downgrade win installs to basicsr==1.4.1

commit e258beeb51f3e645b9ea60e52d69d18722221cc5
Merge: 7460c069 e481bfac
Author: Lincoln Stein <lincoln.stein@gmail.com>
Date:   Thu Nov 10 06:37:45 2022 -0500

    Merge branch 'release-candidate-2-1-3' of github.com:invoke-ai/InvokeAI into release-candidate-2-1-3

commit 7460c069b880b10c84b55f2e0c0998e350d6cf12
Author: Lincoln Stein <lincoln.stein@gmail.com>
Date:   Thu Nov 10 06:36:48 2022 -0500

    remove --prefer-binary from requirements-base.txt

    It appears that some versions of pip do not recognize this option
    when it appears in the requirements file. Did not explore this further
    but recommend --prefer-binary in the manual install instructions on
    the command line.

commit e481bfac615e0c798ceb5e59e8fbe3038f85b431
Merge: 5040747c d1ab65a4
Author: Lincoln Stein <lincoln.stein@gmail.com>
Date:   Thu Nov 10 11:21:56 2022 +0000

    Merge branch 'release-candidate-2-1-3' of github.com:/invoke-ai/InvokeAI into release-candidate-2-1-3

commit 5040747c6700c9b949e3f3e1d2c3f21daffcec6c
Author: Lincoln Stein <lincoln.stein@gmail.com>
Date:   Thu Nov 10 11:21:43 2022 +0000

    fix windows install instructions & bat file

commit d1ab65a43122f73c20533d5815590ec10bc05eca
Author: mauwii <Mauwii@outlook.de>
Date:   Thu Nov 10 07:18:59 2022 +0100

    update WEBUIHOTKEYS.md

commit af4ee7feb8a7d2528bf4e2b3f7deee7a7c4c3dec
Author: mauwii <Mauwii@outlook.de>
Date:   Thu Nov 10 06:33:49 2022 +0100

    update INSTALL_DOCKER.md

commit 764fb29aded8114984d05932c4266ea24614b932
Author: mauwii <Mauwii@outlook.de>
Date:   Thu Nov 10 06:30:15 2022 +0100

    fix formatting in INSTALL.md

commit 1014d3ba44aa4cd4cfdc6e3650c24a0dc9568a6e
Author: mauwii <Mauwii@outlook.de>
Date:   Thu Nov 10 06:29:14 2022 +0100

    fix build.sh invokeai_conda_env_file default value

commit 40a48aca88e337e9f4475f80a03b7d6d4751d09b
Author: mauwii <Mauwii@outlook.de>
Date:   Thu Nov 10 05:25:30 2022 +0100

    fix environment-mac.yml
    moved taming-transformers-rom1504 to pip dependencies

commit 92abc00f16a34a586dd16d3c64408f00e23fcb53
Author: mauwii <Mauwii@outlook.de>
Date:   Thu Nov 10 05:19:52 2022 +0100

    fix test-invoke-conda
    - copy required conda environment yaml
    - use environment.yml
    - I use cp instead of ln since would be compatible for windows runners

commit a5719aabf89c8c5f29c6044e2882ccdcd0c6916b
Author: mauwii <Mauwii@outlook.de>
Date:   Thu Nov 10 04:14:35 2022 +0100

    update Dockerfile
    - link environment.yml from new environemnts path
    - change default conda_env_file
    - quote all variables to avoid splitting
    - also remove paths from conda-env-files in build-container.yml

commit 44a18511fa7accfec6a20345774583a1ce745b02
Author: Lincoln Stein <lincoln.stein@gmail.com>
Date:   Wed Nov 9 20:51:06 2022 +0000

    update paths in container build workflow

commit b850dbadafba052310327ca759d96f49a9d25d03
Author: Lincoln Stein <lincoln.stein@gmail.com>
Date:   Wed Nov 9 20:16:57 2022 +0000

    finished reorganization of install docs

commit 9ef8b944d53afb1fed94c418b2fd936d2d743797
Author: Lincoln Stein <lincoln.stein@gmail.com>
Date:   Wed Nov 9 18:50:58 2022 +0000

    tweaks to manual install documentation

    --prefer-binary is an iffy option in the requirements file. It isn't
    supported by some versions of pip, so I removed it from
    requirements-base.txt and inserted it into the manual install
    instructions where it seems to do what it is supposed to.

commit efc5a9848803b4250d726bc1913b9fd5a33fad75
Author: Lincoln Stein <lincoln.stein@gmail.com>
Date:   Wed Nov 9 18:20:03 2022 +0000

    manual installation documentation tested on Linux

commit 1417c87928e13b0ed21bf1c5604f8636d3b50a8d
Author: Lincoln Stein <lincoln.stein@gmail.com>
Date:   Wed Nov 9 17:37:06 2022 +0000

    change name of requirements.txt to avoid confusion

commit 2dd6fc2b93ff7bbb4038568a3b28d36a8fafaeb8
Merge: 22213612 71ee44a8
Author: Lincoln Stein <lincoln.stein@gmail.com>
Date:   Wed Nov 9 17:26:24 2022 +0000

    Merge branch 'release-candidate-2-1-3' of github.com:/invoke-ai/InvokeAI into release-candidate-2-1-3

commit 22213612a08bbece46eafd48994f985b097cb141
Author: Lincoln Stein <lincoln.stein@gmail.com>
Date:   Wed Nov 9 17:25:59 2022 +0000

    directory cleanup; working on install docs

commit 71ee44a827898759fd457f774c0b2ac7a1ef6519
Author: Lincoln Stein <lincoln.stein@gmail.com>
Date:   Wed Nov 9 02:07:13 2022 +0000

    prevent crash when switching to an invalid model

commit b17ca0a5e731c142101bbdbf34d121df949e86d7
Author: damian0815 <null@damianstewart.com>
Date:   Tue Nov 8 14:28:38 2022 +0100

    don't suppress exceptions when doing cross-attention control

commit 71bbfe4a1a5b70f90e5f41c94df50fb104dbdd79
Author: damian0815 <null@damianstewart.com>
Date:   Tue Nov 8 12:59:34 2022 +0100

    Fix #1362 by improving VRAM usage patterns when doing .swap()

    commit ef3f7a26e242b73c2beb0195c7fd8f654ef47f55
    Author: damian0815 <null@damianstewart.com>
    Date:   Tue Nov 8 12:18:37 2022 +0100

        remove log spam

    commit 7189d649622d4668b120b0dd278388ad672142c4
    Author: damian0815 <null@damianstewart.com>
    Date:   Tue Nov 8 12:10:28 2022 +0100

        change the way saved slicing strategy is applied

    commit 01c40f751ab72955140165c16f95ae411732265b
    Author: damian0815 <null@damianstewart.com>
    Date:   Tue Nov 8 12:04:43 2022 +0100

        fix slicing_strategy_getter callsite

    commit f8cfe25150a346958903316bc710737d99839923
    Author: damian0815 <null@damianstewart.com>
    Date:   Tue Nov 8 11:56:22 2022 +0100

        cleanup, consistent dim=0 also tested

    commit 5bf9b1e890d48e962afd4a668a219b68271e5dc1
    Author: damian0815 <null@damianstewart.com>
    Date:   Tue Nov 8 11:34:09 2022 +0100

        refactored context, tested with non-sliced cross attention control

    commit d58a46e39bf562e7459290d2444256e8c08ad0b6
    Author: damian0815 <null@damianstewart.com>
    Date:   Sun Nov 6 00:41:52 2022 +0100

        cleanup

    commit 7e2c658b4c06fe239311b65b9bb16fa3adec7fd7
    Author: damian0815 <null@damianstewart.com>
    Date:   Sat Nov 5 22:57:31 2022 +0100

        disable logs

    commit 20ee89d93841b070738b3d8a4385c93b097d92eb
    Author: damian0815 <null@damianstewart.com>
    Date:   Sat Nov 5 22:36:58 2022 +0100

        slice saved attention if necessary

    commit 0a7684a22c880ec0f48cc22bfed4526358f71546
    Author: damian0815 <null@damianstewart.com>
    Date:   Sat Nov 5 22:32:38 2022 +0100

        raise instead of asserting

    commit 7083104c7f3a0d8fd96e94a2f391de50a3c942e4
    Author: damian0815 <null@damianstewart.com>
    Date:   Sat Nov 5 22:31:00 2022 +0100

        store dim when saving slices

    commit f7c0808ed383ec1dc70645288a798ed2aa4fa85c
    Author: damian0815 <null@damianstewart.com>
    Date:   Sat Nov 5 22:27:16 2022 +0100

        don't retry on exception

    commit 749a721e939b3fe7c1741e7998dab6bd2c85a0cb
    Author: damian0815 <null@damianstewart.com>
    Date:   Sat Nov 5 22:24:50 2022 +0100

        stuff

    commit 032ab90e9533be8726301ec91b97137e2aadef9a
    Author: damian0815 <null@damianstewart.com>
    Date:   Sat Nov 5 22:20:17 2022 +0100

        more logging

    commit 3dc34b387f033482305360e605809d95a40bf6f8
    Author: damian0815 <null@damianstewart.com>
    Date:   Sat Nov 5 22:16:47 2022 +0100

        logs

    commit 901c4c1aa4b9bcef695a6551867ec8149e6e6a93
    Author: damian0815 <null@damianstewart.com>
    Date:   Sat Nov 5 22:12:39 2022 +0100

        actually set save_slicing_strategy to True

    commit f780e0a0a7c6b6a3db320891064da82589358c8a
    Author: damian0815 <null@damianstewart.com>
    Date:   Sat Nov 5 22:10:35 2022 +0100

        store slicing strategy

    commit 93bb6d566fd18c5c69ef7dacc8f74ba2cf671cb7
    Author: damian <git@damianstewart.com>
    Date:   Sat Nov 5 20:43:48 2022 +0100

        still not it

    commit 5e3a9541f8ae00bde524046963910323e20c40b7
    Author: damian <git@damianstewart.com>
    Date:   Sat Nov 5 17:20:02 2022 +0100

        wip offloading attention slices on-demand

    commit 4c2966aa856b6f3b446216da3619ae931552ef08
    Author: damian0815 <null@damianstewart.com>
    Date:   Sat Nov 5 15:47:40 2022 +0100

        pre-emptive offloading, idk if it works

    commit 572576755e9f0a878d38e8173e485126c0efbefb
    Author: root <you@example.com>
    Date:   Sat Nov 5 11:25:32 2022 +0000

        push attention slices to cpu. slow but saves memory.

    commit b57c83a68f2ac03976ebc89ce2ff03812d6d185f
    Author: damian0815 <null@damianstewart.com>
    Date:   Sat Nov 5 12:04:22 2022 +0100

        verbose logging

    commit 3a5dae116f110a96585d9eb71d713b5ed2bc3d2b
    Author: damian0815 <null@damianstewart.com>
    Date:   Sat Nov 5 11:50:48 2022 +0100

        wip fixing mem strategy crash (4 test on runpod)

    commit 3cf237db5fae0c7b0b4cc3c47c81830bdb2ae7de
    Author: damian0815 <null@damianstewart.com>
    Date:   Fri Nov 4 09:02:40 2022 +0100

        wip, only works on cuda

commit 5702271991e126c026aa1ff41c44f8ad37b26e3a
Author: Lincoln Stein <lincoln.stein@gmail.com>
Date:   Wed Nov 9 14:09:36 2022 +0000

    speculative reorganization of the requirements & environment files

    - This is only a test!
    - The various environment*.yml and requirements*.txt files have all
      been moved into a directory named "environments-and-requirements".
    - The idea is to clean up our root directory so that the github home
      page is tidy.
    - The manual install instructions will start with the instructions to
      create a symbolic link from environment.yml to the appropriate file
      for OS and GPU.
    - The 1-click installers have been updated to accommodate this change.

commit 10781e7dc4f4551583a110d14f2a1f60393112b8
Author: Lincoln Stein <lincoln.stein@gmail.com>
Date:   Wed Nov 9 01:59:45 2022 +0000

    refactoring requirements

commit 099d1157c579c7b9fea20945e8504cc15745c598
Author: mauwii <Mauwii@outlook.de>
Date:   Wed Nov 9 00:16:18 2022 +0100

    better way to make sure if conda is useable

commit ab825bf7ee5f0b82d2273da1cda64fc9266a978e
Author: Lincoln Stein <lincoln.stein@gmail.com>
Date:   Tue Nov 8 22:05:33 2022 +0000

    add back --prefer-binaries to requirements

commit 10cfeb5ada7bcfc9f3aff3716ab8ea4961a8b528
Author: mauwii <Mauwii@outlook.de>
Date:   Tue Nov 8 22:27:19 2022 +0100

    add quotes to set and use `$environment_file`

commit e97515d0453583e013674b4dd1f3739c7de402cc
Author: mauwii <Mauwii@outlook.de>
Date:   Tue Nov 8 22:24:21 2022 +0100

    set environment file for conda update

commit 0f04bc5789c712bf7c7319b1d93064914993f4ec
Author: mauwii <Mauwii@outlook.de>
Date:   Tue Nov 8 22:21:25 2022 +0100

    use conda env update

commit 3f74aabecd08ed25fac158463ae12b775bd1fca2
Author: mauwii <Mauwii@outlook.de>
Date:   Tue Nov 8 22:20:44 2022 +0100

    use command instead of hash

commit b1a99a51b7ca23c4b895855bd7b4c82dbdc431e4
Author: Lincoln Stein <lincoln.stein@gmail.com>
Date:   Tue Nov 8 14:44:44 2022 -0500

    remove --global git config from 1-click installers

commit 8004f8a6d99f6ff75e00c170da40f1dec93a8914
Author: Lincoln Stein <lincoln.stein@gmail.com>
Date:   Mon Nov 7 09:07:20 2022 -0500

    Revert "Use array slicing to calc ddim timesteps"

    This reverts commit 1f0c5b4cf196716c21f2db74f92c8750ad8f889d.

commit ff8ff2212afb1e1c2dce4f6a61f29e2bc8302eb5
Merge: 8e5363cd 636620b1
Author: Lincoln Stein <lincoln.stein@gmail.com>
Date:   Tue Nov 8 14:01:40 2022 +0000

    add initfile support from PR #1386

commit 8e5363cd83bff0e6be0f002c644283955f7fcbfd
Author: Lincoln Stein <lincoln.stein@gmail.com>
Date:   Tue Nov 8 13:26:18 2022 +0000

    move 'installer/' to '1-click-installer' to make room for tildebyte installer

commit 1450779146e89510d12b8c90eb316d9861c3375e
Author: Lincoln Stein <lincoln.stein@gmail.com>
Date:   Tue Nov 8 12:56:36 2022 +0000

    update branch for installer to pull against

commit 8cd5d95b8a1cb213cce2f949c394c841a09d9cab
Author: Lincoln Stein <lincoln.stein@gmail.com>
Date:   Tue Nov 8 05:30:20 2022 +0000

    move all models into subdirectories of ./models

    - this required an update to the invoke-ai fork of gfpgan
    - simultaneously reverted consolidation of environment and
      requirements files, as their presence in a directory
      triggered setup.py to try to install a sub-package.

commit abd6407394d55d4f6c064a05f3ac989dd3817c59
Author: Lincoln Stein <lincoln.stein@gmail.com>
Date:   Tue Nov 8 03:52:46 2022 +0000

    leave a copy of environment-cuda.yml at top level

    - named it environment.yml
    - need to avoid a big change for users and breaking older support
      instructions.

commit 734dacfbe9d8c86ca943c6541e7b5ff5ef787ff4
Author: Lincoln Stein <lincoln.stein@gmail.com>
Date:   Tue Nov 8 03:50:07 2022 +0000

    consolidate environment files

    - starting to remove unneeded entries and pins
    - no longer require -e in front of github dependencies
    - update setup.py with release number
    - update manual installation instructions

commit 636620b1d5e2466613d0dcc2a604522be12fa3a4
Author: Lincoln Stein <lincoln.stein@gmail.com>
Date:   Tue Nov 8 03:26:16 2022 +0000

    change initfile to ~/.invokeai

    - adjust documentation
    - also fix 'clipseg_models' to 'clipseg', which seems to be working now

commit 1fe41146f0975bbc9d552208473490a9ccb9fc56
Author: Lincoln Stein <lincoln.stein@gmail.com>
Date:   Sat Nov 5 14:28:01 2022 -0400

    add support for an initialization file, invokeai.init

    - Place preferred startup command switches in a file named
      "invokeai.init". The file can consist of a single line of switches
      such as "--web --steps=28", a series of switches on each
      line, or any combination of the two.

     Example:
     ```
       --web
       --host=0.0.0.0
       --steps=28
       --grid
       -f 0.6 -C 11.0 -A k_euler_a
    ```

    - The following options, which were previously only available within
      the CLI, are now available on the command line as well:

      --steps
      --strength
      --cfg_scale
      --width
      --height
      --fit

commit 2ad6ef355a9b9c4b17bae71edea2346c2e8708c5
Merge: 865502ee 8b47c829
Author: Lincoln Stein <lincoln.stein@gmail.com>
Date:   Sun Nov 6 18:08:36 2022 +0000

    update discord link

commit 865502ee4f681de4516d231370ae603983354d8f
Author: mauwii <Mauwii@outlook.de>
Date:   Sun Nov 6 18:00:16 2022 +0100

    update changelog

commit c7984f3299c5c8f56a70d80907d527af458674af
Author: mauwii <Mauwii@outlook.de>
Date:   Sun Nov 6 17:07:27 2022 +0100

    update TROUBLESHOOT.md

commit 7f150ed83302ba875c3e0c0f39d8ac0f32c6e362
Author: mauwii <Mauwii@outlook.de>
Date:   Sun Nov 6 16:56:58 2022 +0100

    remove `:`from headlines in CONTRIBUTORS.md

commit badf4e256ce6e3201df1a9a87a7072b36b18d699
Author: mauwii <Mauwii@outlook.de>
Date:   Sun Nov 6 16:56:37 2022 +0100

    enable navigation tabs
    Since the docs are growing, this way they look cleaner

commit e64c60bbb38fc1620e08ce52bf28bd17003c7ef6
Author: mauwii <Mauwii@outlook.de>
Date:   Sun Nov 6 16:18:59 2022 +0100

    remove preflight checks from assets
    seems like somebody executed tests and commited them

commit 178061854300632b024810e65922b54b64d95939
Author: mauwii <Mauwii@outlook.de>
Date:   Sun Nov 6 16:15:06 2022 +0100

    update INSTALLING_MODELS.md

commit f91fd276242a0c92b301e99d02982d30f7b712c3
Author: Kyle Schouviller <kyle0654@hotmail.com>
Date:   Sat Nov 5 14:47:53 2022 -0700

    Bug fix for inpaint size

commit 09e41e8f769457b9ffd56901aae13991cdc6eeb8
Author: Kyle Schouviller <kyle0654@hotmail.com>
Date:   Sat Nov 5 14:34:52 2022 -0700

    Add inpaint size options to inpaint at a larger size than the actual inpaint image, then scale back down for recombination

commit 6eeb2107b300fc27cfe6134b43e5570c246b8e96
Author: mauwii <Mauwii@outlook.de>
Date:   Sat Nov 5 21:01:14 2022 +0100

    remove create-caches.yml since not used anywhere

commit 17053ad8b7efecb21a9b4eb32695089f683f72e3
Author: Lincoln Stein <lincoln.stein@gmail.com>
Date:   Sat Nov 5 16:01:55 2022 -0400

    fix duplicated argument introduced by conflict resolution

commit fefb4dc1f88bc6551a39602a2bc79b278cd83c59
Merge: 762ca60a d05b1b35
Author: Lincoln Stein <lincoln.stein@gmail.com>
Date:   Sat Nov 5 12:47:35 2022 -0700

    Merge branch 'development' into fix_generate.py

commit d05b1b3544dfa7b3c071ac05b4f014a52e08fa50
Author: Craig <cwallen@users.noreply.github.com>
Date:   Sat Oct 29 20:40:30 2022 -0400

    Resize hires as an image

commit 82d4904c07670d514e9a541de7c9ecd8489eeae2
Author: Craig <cwallen@users.noreply.github.com>
Date:   Sat Oct 29 20:37:40 2022 -0400

    Log strength with hires

commit 1cdcf33cfa39e90f9660fabf8f5936dcfb6c3aa5
Merge: 6616fa83 cbc029c6
Author: Lincoln Stein <lincoln.stein@gmail.com>
Date:   Sat Nov 5 09:57:38 2022 -0400

    Merge branch 'main' into development

    - this synchronizes recent document fixes by mauwii

commit 6616fa835ac4318bf53419f2b54b88c962559578
Author: Lincoln Stein <lincoln.stein@gmail.com>
Date:   Fri Nov 4 00:47:03 2022 -0400

    fix Windows library dependency issues

    This commit addresses two bugs:

    1) invokeai.py crashes immediately with a message about an undefined
       attritube sigKILL (closes #1288). The fix is to pin torch at 1.12.1.

    2) Version 1.4.2 of basicsr fails to load properly on Windows, and is
       a requirement of realesrgan, however 1.4.1 works. Pinning basicsr
       in our requirements file resulted in a dependency conflict, so I
       ended up cloning realesrgan into the invoke-ai Git space and changing
       the requirements file there.

    If there is a more elegant solution, please advise.

commit 7b9a4564b11f4181b9b3c06ecda2a4a25fd1849e
Author: Matthias Wild <40327258+mauwii@users.noreply.github.com>
Date:   Sat Nov 5 14:36:45 2022 +0100

    Update-docs (#1382)

    * update IMG2IMG.md

    * update INPAINTING.md

    * update WEBUIHOTKEYS.md

    * more doc updates (mostly fix formatting):
    - OUTPAINTING.md
    - POSTPROCESS.md
    - PROMPTS.md
    - VARIATIONS.md
    - WEB.md
    - WEBUIHOTKEYS.md

commit fcdefa0620761472718afa14b2955a5d0603cfcc
Author: Matthias Wild <40327258+mauwii@users.noreply.github.com>
Date:   Fri Nov 4 20:47:31 2022 +0100

    Hotifx docs (#1376) (#1377)

commit ef8b3ce639954259a95a38f44fa5329687bee781
Merge: b7042095 36870a8f
Author: Lincoln Stein <lincoln.stein@gmail.com>
Date:   Fri Nov 4 12:08:44 2022 -0400

    Merge-main-into-development (#1373)

    To get the rid of the difference between main and development.

    Since otherwise it will be a pain to start fixing the documentatino
    (when the state between main and development is not the same ...)

    Also this should fix the problem of all tests failing since environment
    yamls get updated.

commit 36870a8f53217d4b733e9bb8b119190b5e36da59
Merge: 6b89adfa b7042095
Author: Matthias Wild <40327258+mauwii@users.noreply.github.com>
Date:   Fri Nov 4 16:25:00 2022 +0100

    Merge branch 'development' into merge-main-into-development

commit b70420951d5b70937c0c07e4f8a552a01c3008bc
Author: damian0815 <null@damianstewart.com>
Date:   Thu Nov 3 12:39:45 2022 +0100

    fix parsing error doing eg `forest ().swap(in winter)`

commit 1f0c5b4cf196716c21f2db74f92c8750ad8f889d
Author: wfng92 <43742196+wfng92@users.noreply.github.com>
Date:   Thu Nov 3 17:13:52 2022 +0800

    Use array slicing to calc ddim timesteps

commit 8648da811190fc8bcf237d83aff38cf605851300
Author: mauwii <Mauwii@outlook.de>
Date:   Fri Nov 4 00:06:19 2022 +0100

    update environment-linux-aarch64 to use python 3.9

commit 45b459356399f8cfb7e9af571c1643fab04218fe
Author: mauwii <Mauwii@outlook.de>
Date:   Thu Nov 3 22:31:46 2022 +0100

    update environment-linux-aarch64.yml
    - move getpass_asterisk to pip

commit 41b04316cf5118ea9518dc77934d135a489dd684
Author: mauwii <Mauwii@outlook.de>
Date:   Thu Nov 3 20:40:08 2022 +0100

    rename job, remove debug branch from triggers

commit e97c6db2a3c0157a8a5ab74cd28ebee480a2d078
Author: mauwii <Mauwii@outlook.de>
Date:   Thu Nov 3 20:34:01 2022 +0100

    include build matrix to build x86_64 and aarch64

commit 896820a349292b0e14bbc32f0c557fe8a6b45395
Author: mauwii <Mauwii@outlook.de>
Date:   Tue Nov 1 05:01:15 2022 +0100

    disable caching

commit 06c8f468bff7e1dfecc1a1990164be1005b114a7
Author: mauwii <Mauwii@outlook.de>
Date:   Tue Nov 1 04:26:39 2022 +0100

    disable PR-Validation
    since there are no files passed from context this is unecesarry

commit 61920e2701c4a9cea94c03ab933c4482458215b7
Author: mauwii <Mauwii@outlook.de>
Date:   Tue Nov 1 04:09:39 2022 +0100

    update action to use current branch
    also update build-args of dockerfile and build.sh

commit f34ba7ca70f1c417ee97e8b0c57abff57614d78f
Author: mauwii <Mauwii@outlook.de>
Date:   Tue Nov 1 02:30:24 2022 +0100

    remove unecesarry mkdir command again

commit c30ef0895dbf0565345ac6ccb136d98a174470a6
Author: mauwii <Mauwii@outlook.de>
Date:   Tue Nov 1 01:51:12 2022 +0100

    remove symlink to GFPGANv1.4
    also re-add mkdir to prevent action from failing

commit aa3a774f73d5004571ea42fea14cd2b525fffb1c
Author: mauwii <Mauwii@outlook.de>
Date:   Tue Nov 1 01:48:59 2022 +0100

    update build-container.yml to use cachev3

commit 2c30555b844293764123baa4137fcf952d40a4e7
Author: mauwii <Mauwii@outlook.de>
Date:   Tue Nov 1 01:34:20 2022 +0100

    update Dockerfile
    - create models.yaml from models.yaml.example
    - run preload_models.py with --no-interactive

commit 743f60577364d43237eeb46aefd523371c36e56b
Author: mauwii <Mauwii@outlook.de>
Date:   Tue Nov 1 01:21:15 2022 +0100

    update build.sh to download sd-v1.5 model

commit 519c661abbae83aa62b3ee536635c39192890441
Author: mauwii <Mauwii@outlook.de>
Date:   Tue Oct 25 01:26:50 2022 +0200

    replace old fashined markdown templates with forms
    this will help the readability of issues a lot 🤓

commit 22c956c75f4797ddc2dffa944e3ea84b4eb3477c
Merge: 13696adc 0196571a
Author: Lincoln Stein <lincoln.stein@gmail.com>
Date:   Thu Nov 3 10:20:21 2022 -0400

    Merge branch 'development' of github.com:invoke-ai/InvokeAI into development

commit 13696adc3a3a2b169e68048a5b511660d0430fae
Author: Lincoln Stein <lincoln.stein@gmail.com>
Date:   Thu Nov 3 10:20:10 2022 -0400

    speculative change to solve windows esrgan issues

commit 0196571a12992eb8207d36e022721fe3ad2cfd19
Author: Lincoln Stein <lincoln.stein@gmail.com>
Date:   Wed Nov 2 22:39:35 2022 -0400

    remove merge markers from preload_models.py

commit 9666f466ab1c710dfda2e59be9e8a26fcd7e8718
Author: Lincoln Stein <lincoln.stein@gmail.com>
Date:   Wed Nov 2 18:29:34 2022 -0400

    use refined model by default

commit 240e5486c89552ef480c3443a63283fb86a493c3
Merge: 8164b6b9 aa247e68
Author: Lincoln Stein <lincoln.stein@gmail.com>
Date:   Wed Nov 2 18:35:00 2022 -0400

    Merge branch 'spezialspezial-patch-9' into development

commit 8164b6b9cff077e4131384195af65e30a40733a7
Merge: 4fc82d55 dd5a88dc
Author: Lincoln Stein <lincoln.stein@gmail.com>
Date:   Wed Nov 2 17:06:46 2022 -0400

    Merge branch 'development' of github.com:invoke-ai/InvokeAI into development

commit 4fc82d554ff573780c0af562a6bc9586aa56012d
Author: blessedcoolant <54517381+blessedcoolant@users.noreply.github.com>
Date:   Thu Nov 3 04:17:28 2022 +1300

    [WebUI] Final 2.1 Release Build

commit 96b34c0f85f6ed52707fe43456d20b81822c7d1d
Author: damian0815 <null@damianstewart.com>
Date:   Wed Nov 2 09:08:11 2022 +0100

    Final WebUI build for Release 2.1
    - squashed commit of 52 commits from PR #1327

    don't log base64 progress images

    Fresh Build For WebUI

    [WebUI] Loopback Default False

    Fixes bugs/styling

    - Fixes missing web app state on new version:
    Adds stateReconciler to redux-persist.

    When we add more values to the state and then release the update app, they will be automatically merged in.

    Reseting web UI will be needed far less.
    7159ec

    - Fixes console z-index
    - Moves reset web UI button to visible area

    Decreases gallery width on inpainting

    Increases workarea split padding to 1rem

    Adds missing tooltips to site header

    Changes inpainting controls settings to hover

    Fixes hotkeys and settings buttons not working

    Improves bounding box interactions

    - Bounding box can now be moved by dragging any of its edges
    - Bounding box does not affect drawing if already drawing a stroke
    - Can lock bounding box to draw directly on the bounding box edges
    - Removes spacebar-hold behaviour due to technical issues

    Fixes silent crash when init image too large

    To send the mask to the server, the UI rendered the mask onto the init image and sent the whole image. The mask was then cropped by the server.

    If the image was too large, the app silently failed. Maybe it exceeds the websocket size limit.

    Fixed by cropping the mask in the UI layer, sending only bounding-box-sized mask image data.

    Disabled bounding box settings when locked

    Styles image uploader

    Builds fresh bundle

    Improves bounding box interaction

    Added spacebar-hold-to-transform back.

    Address bounding box feedback

    - Adds back toggle to hide bounding box
    - Box quick toggle = q, normal toggle = shift + q
    - Styles canvas alert icons

    Adds hints when unable to invoke

    - Popover on Invoke button indicates why exactly it is disabled, e.g. prompt is empty, something else is processing, etc.
    - There may be more than one reason; all are displayed.

    Fix Inpainting Alerts Styling

    Preventing unnecessary re-renders across the app

    Code Split Inpaint Options

    Isolate features to their own components so they dont re-render the other stuff each time.

    [TESTING] Remove  global isReady checking

    I dont believe this is need at all because the isready state is constantly updated when needed and tracked real time in the Redux store. This causes massive re-renders. @psychedelicious If this is absolutely essential for a reason that I do not see, please hit me up on Discord.

    Fresh Bundle

    Fix Bounding Box Settings re-rendering on brush stroke

    [Code Splitting] Bounding Box Options

    Isolated all bounding box components to trigger unnecessary re-renders. Still need to fix  bounding box  triggering re-renders on the control panel inside the canvas itself. But the options panel should be a good to go with this change.

    Inpainting Controls Code Spitting and Performance

    Codesplit the entirety of the inpainting controls. Created new selectors for each and every component to ensure there are no unnecessary re-renders. App feels a lot smoother.

    Fixes rerenders on ClearBrushHistory

    Fixes crash when requesting post-generation upscale/face restoration

    - Moves the inpainting paste to before the postprocessing.

    Removes unused isReady state

    Changes Report Bug icon to a bug

    Restores shift+q bounding box shortcut

    Adds alert for bounding box size to status icons

    Adds asCheckbox to IAIIconButton

    Rough draft of this. Not happy with the styling but it's clearer than having them look just like buttons.

    Fixes crash related to old value of progress_latents in state

    Styling changes and settings modal minor refactor

    Fixes: uploaded JPG images not loading

    Reworks CurrentImageButtons.tsx

    - Change all icons to FA iconset for consistency
    - Refactors IAIIconButton, IAIButton, IAIPopover to handle ref forwarding
    - Redesigns buttons into group

    Only generate 1 iteration when seed fixed & variations disabled

    Fixes progress images select

    Fixes edge case: upload over gets stuck while alt tabbing

    - Press esc to close it now

    Fixes display progress images select typing

    Fixes current image button rerenders

    Adds min width to ImageUploader

    Makes fast-latents in progress default

    Update Icon Button Checkbox Style Styling

    Fixes next/prev image buttons

    Refactor canvas buttons + more

    Add Save Intermediates Step Count

    For accurate mode only.

    Co-Authored-By: Richard Macarthy <richardmacarthy@protonmail.com>

    Restores "initial image" text

    Address feedback

    - moves mask clear button
    - fixes intermediates
    - shrinks inpainting icons by 10%

    Fix Loopback Styling

    Adds escape hotkey to close floating panels

    Readd Hotkey for Dual Display

    Updated Current Image Button Styling

commit dd5a88dceeec53ceb1722a62e3689ae3dd871154
Author: blessedcoolant <54517381+blessedcoolant@users.noreply.github.com>
Date:   Thu Nov 3 04:17:28 2022 +1300

    [WebUI] Final 2.1 Release Build

commit 95ed56bf82825a47cfe406fa34eaf4e32c129143
Author: blessedcoolant <54517381+blessedcoolant@users.noreply.github.com>
Date:   Thu Nov 3 04:16:31 2022 +1300

    Updated Current Image Button Styling

commit 1ae80f5ab9d00a25e0c3a21e2eccd318c5343401
Author: blessedcoolant <54517381+blessedcoolant@users.noreply.github.com>
Date:   Thu Nov 3 04:07:57 2022 +1300

    Readd Hotkey for Dual Display

commit 1f0bd3ca6cda4221d1a7a46325f235cd23bd0af9
Author: psychedelicious <4822129+psychedelicious@users.noreply.github.com>
Date:   Thu Nov 3 02:07:00 2022 +1100

    Adds escape hotkey to close floating panels

commit a1971f6830306b3d748f7f36c2243f6dee682f97
Author: blessedcoolant <54517381+blessedcoolant@users.noreply.github.com>
Date:   Thu Nov 3 03:38:15 2022 +1300

    Fix Loopback Styling

commit c6118e889895ba35a191124ce1beb5d2b719366d
Author: psychedelicious <4822129+psychedelicious@users.noreply.github.com>
Date:   Thu Nov 3 01:29:51 2022 +1100

    Address feedback

    - moves mask clear button
    - fixes intermediates
    - shrinks inpainting icons by 10%

commit 7ba958cf7ff4e804a127b71c88666084f81e7340
Author: psychedelicious <4822129+psychedelicious@users.noreply.github.com>
Date:   Thu Nov 3 01:10:38 2022 +1100

    Restores "initial image" text

commit 383905d5d24359aa1824de96ff124a8f8a87e015
Author: blessedcoolant <54517381+blessedcoolant@users.noreply.github.com>
Date:   Thu Nov 3 02:59:11 2022 +1300

    Add Save Intermediates Step Count

    For accurate mode only.

    Co-Authored-By: Richard Macarthy <richardmacarthy@protonmail.com>

commit 6173e3e9cabd5e74fb466d55d7bb6a80a77b3fa8
Author: psychedelicious <4822129+psychedelicious@users.noreply.github.com>
Date:   Thu Nov 3 00:53:53 2022 +1100

    Refactor canvas buttons + more

commit 3feb7d8922b0e4c9f944e96b95211003f40d9705
Author: psychedelicious <4822129+psychedelicious@users.noreply.github.com>
Date:   Thu Nov 3 00:49:23 2022 +1100

    Fixes next/prev image buttons

commit 1d9edbd0ddd6e10cf120fe0de3db0b56ae374db4
Author: blessedcoolant <54517381+blessedcoolant@users.noreply.github.com>
Date:   Thu Nov 3 00:50:44 2022 +1300

    Update Icon Button Checkbox Style Styling

commit d439abdb89db73e652add9df371234861189d90c
Author: psychedelicious <4822129+psychedelicious@users.noreply.github.com>
Date:   Wed Nov 2 21:37:24 2022 +1100

    Makes fast-latents in progress default

commit ee47ea0c891a33049bdec12f1c254926f341eff2
Author: psychedelicious <4822129+psychedelicious@users.noreply.github.com>
Date:   Wed Nov 2 21:37:09 2022 +1100

    Adds min width to ImageUploader

commit 300bb2e6279c1c477131b9e57425db348062f19c
Author: psychedelicious <4822129+psychedelicious@users.noreply.github.com>
Date:   Wed Nov 2 21:28:22 2022 +1100

    Fixes current image button rerenders

commit ccf8593501d23e00979f7340f8991514e6ca3428
Author: psychedelicious <4822129+psychedelicious@users.noreply.github.com>
Date:   Wed Nov 2 21:27:43 2022 +1100

    Fixes display progress images select typing

commit 0fda612f3ff8ad75932e158b9b29cf64c3f30eb6
Author: psychedelicious <4822129+psychedelicious@users.noreply.github.com>
Date:   Wed Nov 2 21:02:01 2022 +1100

    Fixes edge case: upload over gets stuck while alt tabbing

    - Press esc to close it now

commit 5afff65b71bb60d067392c120efff1a17adc66af
Author: psychedelicious <4822129+psychedelicious@users.noreply.github.com>
Date:   Wed Nov 2 20:33:19 2022 +1100

    Fixes progress images select

commit 7e55bdefce8920831cac7b9fa857bfbffe9e9d5d
Author: psychedelicious <4822129+psychedelicious@users.noreply.github.com>
Date:   Wed Nov 2 20:27:47 2022 +1100

    Only generate 1 iteration when seed fixed & variations disabled

commit 620cf84d3dd54b8498e84574f33040a26d4d315f
Author: psychedelicious <4822129+psychedelicious@users.noreply.github.com>
Date:   Wed Nov 2 19:51:38 2022 +1100

    Reworks CurrentImageButtons.tsx

    - Change all icons to FA iconset for consistency
    - Refactors IAIIconButton, IAIButton, IAIPopover to handle ref forwarding
    - Redesigns buttons into group

commit cfe567c62ad3fd0cff5e3e579a2250ca5dfa2d53
Author: psychedelicious <4822129+psychedelicious@users.noreply.github.com>
Date:   Wed Nov 2 16:14:50 2022 +1100

    Fixes: uploaded JPG images not loading

commit cefe12f1df72e051f109ed77ce46f18093b3b8bb
Author: psychedelicious <4822129+psychedelicious@users.noreply.github.com>
Date:   Wed Nov 2 15:31:18 2022 +1100

    Styling changes and settings modal minor refactor

commit 1e51c3992853ad4643d8eda2443a3bd0d65f5f90
Author: psychedelicious <4822129+psychedelicious@users.noreply.github.com>
Date:   Wed Nov 2 15:27:46 2022 +1100

    Fixes crash related to old value of progress_latents in state

commit 42a02bbb8087cd46a1b5c795b1b41e38c08ba229
Author: psychedelicious <4822129+psychedelicious@users.noreply.github.com>
Date:   Wed Nov 2 13:15:06 2022 +1100

    Adds asCheckbox to IAIIconButton

    Rough draft of this. Not happy with the styling but it's clearer than having them look just like buttons.

commit f1ae6dae4c36e74ca8dedf8b57eb8933c059dcf4
Author: psychedelicious <4822129+psychedelicious@users.noreply.github.com>
Date:   Wed Nov 2 13:13:56 2022 +1100

    Adds alert for bounding box size to status icons

commit 6195579910a91fb2ab7e1186aa1c1145566c3122
Author: psychedelicious <4822129+psychedelicious@users.noreply.github.com>
Date:   Wed Nov 2 11:52:19 2022 +1100

    Restores shift+q bounding box shortcut

commit 16c8b23b347792d7811cff66a27d350fe5d0d62c
Author: psychedelicious <4822129+psychedelicious@users.noreply.github.com>
Date:   Wed Nov 2 11:32:07 2022 +1100

    Changes Report Bug icon to a bug

commit 07ae626b22554b5222a3b71d1ce3974e2c3fb284
Author: psychedelicious <4822129+psychedelicious@users.noreply.github.com>
Date:   Wed Nov 2 11:17:16 2022 +1100

    Removes unused isReady state

commit 8d171bb044f578d4b9cc4911edfa05b20a01d373
Author: psychedelicious <4822129+psychedelicious@users.noreply.github.com>
Date:   Wed Nov 2 11:13:26 2022 +1100

    Fixes crash when requesting post-generation upscale/face restoration

    - Moves the inpainting paste to before the postprocessing.

commit 6e33ca7e9e64fb355430511e28bb08c32c9c68b1
Author: psychedelicious <4822129+psychedelicious@users.noreply.github.com>
Date:   Wed Nov 2 10:59:01 2022 +1100

    Fixes rerenders on ClearBrushHistory

commit db46e12f2b8c98dac8895ea4d04242d3754dbec4
Author: blessedcoolant <54517381+blessedcoolant@users.noreply.github.com>
Date:   Wed Nov 2 11:36:28 2022 +1300

    Inpainting Controls Code Spitting and Performance

    Codesplit the entirety of the inpainting controls. Created new selectors for each and every component to ensure there are no unnecessary re-renders. App feels a lot smoother.

commit 868e4b2db82fa8556b423111f2f8d565a70ec129
Author: blessedcoolant <54517381+blessedcoolant@users.noreply.github.com>
Date:   Wed Nov 2 07:40:31 2022 +1300

    [Code Splitting] Bounding Box Options

    Isolated all bounding box components to trigger unnecessary re-renders. Still need to fix  bounding box  triggering re-renders on the control panel inside the canvas itself. But the options panel should be a good to go with this change.

commit 2e562742c13988a1fc299c673a97ba4e4a645aaf
Author: blessedcoolant <54517381+blessedcoolant@users.noreply.github.com>
Date:   Wed Nov 2 06:40:27 2022 +1300

    Fix Bounding Box Settings re-rendering on brush stroke

commit 68e69580091fc1dc1d8b6e03c9e7ef777c7ebf61
Author: blessedcoolant <54517381+blessedcoolant@users.noreply.github.com>
Date:   Wed Nov 2 06:28:34 2022 +1300

    Fresh Bundle

commit ea6e3a7949f2743711516261cb7e450517d39702
Author: blessedcoolant <54517381+blessedcoolant@users.noreply.github.com>
Date:   Wed Nov 2 06:26:56 2022 +1300

    [TESTING] Remove  global isReady checking

    I dont believe this is need at all because the isready state is constantly updated when needed and tracked real time in the Redux store. This causes massive re-renders. @psychedelicious If this is absolutely essential for a reason that I do not see, please hit me up on Discord.

commit b2879ca99f88869277c474185ade049914b0bf26
Author: blessedcoolant <54517381+blessedcoolant@users.noreply.github.com>
Date:   Wed Nov 2 06:08:59 2022 +1300

    Code Split Inpaint Options

    Isolate features to their own components so they dont re-render the other stuff each time.

commit 4e911566c33989392eb3279049b15331ca98594a
Author: blessedcoolant <54517381+blessedcoolant@users.noreply.github.com>
Date:   Wed Nov 2 03:50:56 2022 +1300

    Preventing unnecessary re-renders across the app

commit 9bafda6a152778893df143776ae859a3b8b7f192
Author: blessedcoolant <54517381+blessedcoolant@users.noreply.github.com>
Date:   Wed Nov 2 03:02:35 2022 +1300

    Fix Inpainting Alerts Styling

commit 871a8a5375cae891f22eb853ffd2d22298298b11
Author: psychedelicious <4822129+psychedelicious@users.noreply.github.com>
Date:   Tue Nov 1 23:52:07 2022 +1100

    Adds hints when unable to invoke

    - Popover on Invoke button indicates why exactly it is disabled, e.g. prompt is empty, something else is processing, etc.
    - There may be more than one reason; all are displayed.

commit 0eef74bc00d3c0cedc5037c1deeef1d9a5b6d33d
Author: psychedelicious <4822129+psychedelicious@users.noreply.github.com>
Date:   Tue Nov 1 23:40:11 2022 +1100

    Address bounding box feedback

    - Adds back toggle to hide bounding box
    - Box quick toggle = q, normal toggle = shift + q
    - Styles canvas alert icons

commit 423ae3209782f1d8d321b2ed9ed00712b726adf7
Author: psychedelicious <4822129+psychedelicious@users.noreply.github.com>
Date:   Tue Nov 1 17:06:07 2022 +1100

    Improves bounding box interaction

    Added spacebar-hold-to-transform back.

commit 8282e5d0458ca1bba5565c069093b5389f2228d4
Author: psychedelicious <4822129+psychedelicious@users.noreply.github.com>
Date:   Tue Nov 1 16:57:07 2022 +1100

    Builds fresh bundle

commit 19305cdbdf6ac74833bd6011bff0ced8e9d36f69
Author: psychedelicious <4822129+psychedelicious@users.noreply.github.com>
Date:   Tue Nov 1 16:51:11 2022 +1100

    Styles image uploader

commit eb9028ab3069bca9d747aca664ca60ae13ba6df8
Author: psychedelicious <4822129+psychedelicious@users.noreply.github.com>
Date:   Tue Nov 1 16:51:03 2022 +1100

    Disabled bounding box settings when locked

commit 21483f5d0731bce5aa97985e722a64bf0d7cd12c
Author: psychedelicious <4822129+psychedelicious@users.noreply.github.com>
Date:   Tue Nov 1 16:50:24 2022 +1100

    Fixes silent crash when init image too large

    To send the mask to the server, the UI rendered the mask onto the init image and sent the whole image. The mask was then cropped by the server.

    If the image was too large, the app silently failed. Maybe it exceeds the websocket size limit.

    Fixed by cropping the mask in the UI layer, sending only bounding-box-sized mask image data.

commit 82dcbac28f140aa7bd89d967bcc7fa31cb03a267
Author: psychedelicious <4822129+psychedelicious@users.noreply.github.com>
Date:   Tue Nov 1 16:28:30 2022 +1100

    Improves bounding box interactions

    - Bounding box can now be moved by dragging any of its edges
    - Bounding box does not affect drawing if already drawing a stroke
    - Can lock bounding box to draw directly on the bounding box edges
    - Removes spacebar-hold behaviour due to technical issues

commit d43bd4625d64a03d90de0458ad5fcacef211a8a6
Author: psychedelicious <4822129+psychedelicious@users.noreply.github.com>
Date:   Tue Nov 1 15:10:49 2022 +1100

    Fixes hotkeys and settings buttons not working

commit ea891324a2086faf8eeeeac7eaf47ae2935322fb
Author: psychedelicious <4822129+psychedelicious@users.noreply.github.com>
Date:   Tue Nov 1 11:04:02 2022 +1100

    Changes inpainting controls settings to hover

commit 8fd9ea219390493f392e690eadb23c4209a4ecfc
Author: psychedelicious <4822129+psychedelicious@users.noreply.github.com>
Date:   Tue Nov 1 11:03:41 2022 +1100

    Adds missing tooltips to site header

commit fb026668563b3179805819a1630c654c9ce260d9
Author: psychedelicious <4822129+psychedelicious@users.noreply.github.com>
Date:   Tue Nov 1 11:03:25 2022 +1100

    Increases workarea split padding to 1rem

commit f6f5c2731b0c37089c5d7382736f973e857df712
Author: psychedelicious <4822129+psychedelicious@users.noreply.github.com>
Date:   Tue Nov 1 11:03:10 2022 +1100

    Decreases gallery width on inpainting

commit b4e3f771e0314e0a18ad5cedc80b28f246fced59
Author: psychedelicious <4822129+psychedelicious@users.noreply.github.com>
Date:   Tue Nov 1 10:54:59 2022 +1100

    Fixes bugs/styling

    - Fixes missing web app state on new version:
    Adds stateReconciler to redux-persist.

    When we add more values to the state and then release the update app, they will be automatically merged in.

    Reseting web UI will be needed far less.
    7159ec

    - Fixes console z-index
    - Moves reset web UI button to visible area

commit 99bb9491ac97f1f733accab839ea38cdf4870ba0
Author: blessedcoolant <54517381+blessedcoolant@users.noreply.github.com>
Date:   Tue Nov 1 08:35:45 2022 +1300

    [WebUI] Loopback Default False

commit 0453f211278a533a49fb1aee54dd989b69179c10
Author: blessedcoolant <54517381+blessedcoolant@users.noreply.github.com>
Date:   Wed Nov 2 23:23:51 2022 +1300

    Fresh Build For WebUI

commit 9fc09aa4bda3b3e6139b026f24733a37747d373f
Author: damian0815 <null@damianstewart.com>
Date:   Wed Nov 2 09:08:11 2022 +0100

    don't log base64 progress images

commit 5e87062cf809a08242d3b006b66d3bbb834ddb16
Author: spezialspezial <75758219+spezialspezial@users.noreply.github.com>
Date:   Wed Nov 2 00:21:27 2022 +0100

    Option to directly invert the grayscale heatmap - fix

commit 3e7a459990407ad1679f38d4027c19d50a9d36e3
Author: spezialspezial <75758219+spezialspezial@users.noreply.github.com>
Date:   Tue Nov 1 21:37:33 2022 +0100

    Update txt2mask.py

commit bbf4c03e500bafc8627c2a70f8b3396efeddabb3
Author: spezialspezial <75758219+spezialspezial@users.noreply.github.com>
Date:   Tue Nov 1 21:11:19 2022 +0100

    Option to directly invert the grayscale heatmap

    Theoretically less work inverting the image while it's small but I can't measure a significant difference. Though, handy option to have in some cases.

commit 611a3a975328a7e276c2781214d5e14108708ed7
Author: mauwii <Mauwii@outlook.de>
Date:   Wed Nov 2 02:23:09 2022 +0100

    fix name of caching step

commit 1611f0d1815022fe90d33f41c21d670251a75f29
Author: mauwii <Mauwii@outlook.de>
Date:   Wed Nov 2 02:18:46 2022 +0100

    readd caching of sd-models
    - this would remove the necesarrity of the secret availability in PRs

commit 08835115e4c5c0ee3587ce07ab769a04d55cb3c5
Author: Lincoln Stein <lincoln.stein@gmail.com>
Date:   Tue Nov 1 22:10:12 2022 -0400

    pin pytorch_lightning to 1.7.7, issue #1331

commit 2d84e28d329be282b51a4cbc22f7c200d80c8f0e
Merge: 533fd04e ef17aae8
Author: Lincoln Stein <lincoln.stein@gmail.com>
Date:   Tue Nov 1 22:11:04 2022 -0400

    Merge branch 'development' of github.com:invoke-ai/InvokeAI into development

commit ef17aae8abe03fd87dc6f91b3c6d2fe03375f6c5
Author: damian0815 <null@damianstewart.com>
Date:   Wed Nov 2 00:39:48 2022 +0100

    add damian0815 to contributors list

commit 0cc39f01a3c67f32873b7888e1e032d33626e269
Author: damian0815 <null@damianstewart.com>
Date:   Wed Nov 2 01:18:50 2022 +0100

    report full size for fast latents and update conversion matrix for v1.5

commit 688d7258f185035a5924bee23322b001574da931
Author: damian0815 <null@damianstewart.com>
Date:   Wed Nov 2 00:33:00 2022 +0100

    fix a bug that broke cross attention control index mapping

commit 4513320bf161d3efdd99e2aef4f7c37472131373
Author: damian0815 <null@damianstewart.com>
Date:   Wed Nov 2 00:31:58 2022 +0100

    save VRAM by not recombining tensors that have been sliced to save VRAM

commit 533fd04ef0d1ba9d2cb25d556991b999e7d44aba
Merge: 6215592b dff5681c
Author: Lincoln Stein <lincoln.stein@gmail.com>
Date:   Tue Nov 1 17:40:36 2022 -0400

    Merge branch 'development' of github.com:invoke-ai/InvokeAI into development

commit dff5681cf0e24faf67d5eb5903203c4ea39f89bd
Author: damian0815 <d@d.com>
Date:   Tue Nov 1 13:56:03 2022 +0100

    shorter strings

commit 5a2790a69b11e049d2507bc22eab7812915d867f
Author: damian0815 <d@d.com>
Date:   Tue Nov 1 13:19:20 2022 +0100

    convert progress display to a drop-down

commit 7c5305ccbaf7926162536aa3a9e51dcdd8574d0d
Author: damian0815 <d@d.com>
Date:   Tue Nov 1 12:54:46 2022 +0100

    do not try to save base64 intermediates in gallery on cancellation

commit 4013e8ad6fa5e5c2a710c607fe7e1d040f353299
Author: psychedelicious <4822129+psychedelicious@users.noreply.github.com>
Date:   Tue Nov 1 21:54:35 2022 +1100

    Fixes b64 image sending and displaying

commit d1dfd257f9e1cafd352d9007a56194226b9d6b63
Author: damian <d@d.com>
Date:   Tue Nov 1 11:40:40 2022 +0100

    wip base64

commit 5322d735ee4515d89293b7fb015b2ce048262f68
Author: damian <d@d.com>
Date:   Tue Nov 1 11:31:42 2022 +0100

    update frontend

commit cdb107dcda858a8bb64455cc60a196a42345f8f7
Author: damian <d@d.com>
Date:   Tue Nov 1 11:17:43 2022 +0100

    add option to show intermediate latent space

commit be1393a41ce7a75ed8de7f648a93c5e5974a50d2
Author: damian <d@d.com>
Date:   Tue Nov 1 10:16:55 2022 +0100

    ensure existing exception handling code also handles new exception class

commit e554c2607f00a5e51c03052acb9861ff21ec1c12
Author: Damian at mba <damian@frey.NOSPAMco.nz>
Date:   Tue Nov 1 10:08:42 2022 +0100

    Rebuilt prompt parsing logic

    Complete re-write of the prompt parsing logic to be more readable and
    logical, and therefore also hopefully easier to debug, maintain, and
    augment.

    In the process it has also become more robust to badly-formed prompts.

    Squashed commit of the following:

    commit 8fcfa88a16e1390d41717e940d72aed64712171c
    Author: Damian at mba <damian@frey.NOSPAMco.nz>
    Date:   Sun Oct 30 17:05:57 2022 +0100

        further cleanup

    commit 1a1fd78bcfeb49d072e3e6d5808aa8df15441629
    Author: Damian at mba <damian@frey.NOSPAMco.nz>
    Date:   Sun Oct 30 16:07:57 2022 +0100

        cleanup and document

    commit 099c9659fa8b8135876f9a5a50fe80b20bc0635c
    Author: Damian at mba <damian@frey.NOSPAMco.nz>
    Date:   Sun Oct 30 15:54:58 2022 +0100

        works fully

    commit 5e6887ea8c25a1e21438ff6defb381fd027d25fd
    Author: Damian at mba <damian@frey.NOSPAMco.nz>
    Date:   Sun Oct 30 15:24:31 2022 +0100

        further...

    commit 492fda120844d9bc1ad4ec7dd408a3374762d0ff
    Author: Damian at mba <damian@frey.NOSPAMco.nz>
    Date:   Sun Oct 30 14:08:57 2022 +0100

        getting there...

    commit c6aab05a8450cc3c95c8691daf38fdc64c74f52d
    Author: Damian at mba <damian@frey.NOSPAMco.nz>
    Date:   Fri Oct 28 14:29:03 2022 +0200

        wip doesn't compile

    commit 5e533f731cfd20cd435330eeb0012e5689e87e81
    Author: Damian at mba <damian@frey.NOSPAMco.nz>
    Date:   Fri Oct 28 13:21:43 2022 +0200

        working with CrossAttentionCtonrol but no Attention support yet

    commit 9678348773431e500e110e8aede99086bb7b5955
    Author: Damian at mba <damian@frey.NOSPAMco.nz>
    Date:   Fri Oct 28 13:04:52 2022 +0200

        wip rebuiling prompt parser

commit 6215592b12dea3822c727913b771012789a52477
Merge: ef24d76a 349cc254
Author: Lincoln Stein <lincoln.stein@gmail.com>
Date:   Tue Nov 1 17:34:55 2022 -0400

    Merge branch 'development' of github.com:invoke-ai/InvokeAI into development

commit 349cc25433e1d358c6c61cca4a6c300ecc8ca3d7
Author: damian0815 <d@d.com>
Date:   Tue Nov 1 20:08:52 2022 +0100

    fix crash (be a little less aggressive clearing out the attention slice)

commit 214d276379387ae0fa2259602ab00884ec1ad03e
Author: damian0815 <d@d.com>
Date:   Tue Nov 1 19:57:55 2022 +0100

    be more aggressive at clearing out saved_attn_slice

commit ef24d76adc5a192297714707cd6794d4f44bcf67
Author: Lincoln Stein <lincoln.stein@gmail.com>
Date:   Tue Nov 1 14:34:23 2022 -0400

    fix library problems in preload_modules

commit ab2b5a691dadb292f7cecf5db94a165db3e3e126
Author: Lincoln Stein <lincoln.stein@gmail.com>
Date:   Tue Nov 1 17:22:48 2022 -0400

    fix model_cache memory management issues

commit c7de2b2801ad5d6ce52a5f44e88c5705f1dbf524
Author: mauwii <Mauwii@outlook.de>
Date:   Tue Nov 1 02:02:14 2022 +0100

    disable checks with sd-V1.4 model...
    ...to save some resources, since V1.5 is the default now

commit e8075658acf675346d35435d0cfb7710becab42a
Author: mauwii <Mauwii@outlook.de>
Date:   Mon Oct 31 22:20:51 2022 +0100

    update test-invoke-conda.yml
    - fix model dl path for sd-v1-4.ckpt
    - copy configs/models.yaml.example to configs/models.yaml

commit 4202dabee1143f597f4c3946420fbfa7b8eccddf
Author: mauwii <Mauwii@outlook.de>
Date:   Mon Oct 31 22:17:21 2022 +0100

    fix models example weights for sd-v1.4

commit d67db2bcf1ac26d8bb7c5f8bfd056f2172597780
Author: blessedcoolant <54517381+blessedcoolant@users.noreply.github.com>
Date:   Tue Nov 1 08:35:45 2022 +1300

    [WebUI] Loopback Default False

commit 7159ec885faa1117a3506689d547618be1d91808
Author: Lincoln Stein <lincoln.stein@gmail.com>
Date:   Mon Oct 31 11:33:05 2022 -0400

    further improvements to preload_models.py

    - Faster startup for command line switch processing
    - Specify configuration file to modify using --config option:

      ./scripts/preload_models.ply --config models/my-models-file.yaml

commit b5cf734ba9f1aeaab14075b55cc13ed9a0ae31e2
Author: Lincoln Stein <lincoln.stein@gmail.com>
Date:   Mon Oct 31 11:08:19 2022 -0400

    improve behavior of preload_models.py

    - NEVER overwrite user's existing models.yaml
    - Instead, merge its contents into new config file,
      and rename original to models.yaml.orig (with
      message)
    - models.yaml has been removed from repository and renamed
      models.yaml.example

commit f7dc8eafeef991e135b5541c01d0ff740dda9591
Author: Lincoln Stein <lincoln.stein@gmail.com>
Date:   Mon Oct 31 10:47:35 2022 -0400

    restore models.yaml to virgin state

commit 762ca60a309f70daaf1b9e79b6273f76fd9474b6
Author: Lincoln Stein <lincoln.stein@gmail.com>
Date:   Tue Oct 4 22:55:10 2022 -0400

    Update INPAINTING.md

commit e7fb9f342cb5ed0c09e6215026d60d45eef3ebcb
Author: Hideyuki Katsushiro <h.katsushiro@qualia.tokyo.jp>
Date:   Wed Oct 5 10:08:53 2022 +0900

    add argument --outdir
---
 .github/CODEOWNERS                            |    1 +
 .github/workflows/build-container.yml         |   32 +-
 .github/workflows/create-caches.yml           |   80 -
 .github/workflows/test-invoke-conda.yml       |   17 +-
 .gitignore                                    |   28 +-
 {data => assets/data}/DejaVuSans.ttf          |  Bin
 .../superresolution/sample_0.jpg              |  Bin
 .../text_conditional/sample_0.txt             |    0
 .../data}/imagenet_clsidx_to_label.txt        |    0
 .../data}/imagenet_train_hr_indices.p         |  Bin
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 {data => assets/data}/index_synset.yaml       |    0
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 docker-build/Dockerfile                       |   46 +-
 docker-build/build.sh                         |   17 +-
 docs/CHANGELOG.md                             |  334 ++-
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 docs/features/CLI.md                          |   31 +-
 docs/features/IMG2IMG.md                      |  173 +-
 docs/features/OUTPAINTING.md                  |  171 +-
 docs/features/POSTPROCESS.md                  |    6 +-
 docs/features/PROMPTS.md                      |  248 +-
 docs/features/VARIATIONS.md                   |    8 +-
 docs/features/WEB.md                          |  434 ++--
 docs/features/WEBUIHOTKEYS.md                 |   92 +-
 docs/help/TROUBLESHOOT.md                     |   99 +-
 docs/index.md                                 |  200 +-
 docs/installation/INSTALLING_MODELS.md        |  351 ++-
 docs/installation/INSTALL_DOCKER.md           |   90 +-
 docs/installation/INSTALL_INVOKE.md           |   55 +
 docs/installation/INSTALL_JUPYTER.md          |   27 +
 docs/installation/INSTALL_MANUAL.md           |  416 ++++
 docs/installation/INSTALL_PCP.md              |   17 +
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 .../requirements-lin-arm64.txt                |    3 +
 .../requirements-lin-cuda.txt                 |    2 +
 .../requirements-mac-mps-cpu.txt              |    2 +-
 .../requirements-win-colab-cuda.txt           |    3 +-
 frontend/README.md                            |    4 +-
 frontend/dist/assets/index.8eb7dfe4.js        |  501 ++++
 .../SettingsModal/SettingsModalItem.tsx       |   50 -
 installer/WinLongPathsEnabled.reg             |  Bin 0 -> 284 bytes
 installer/create_installers.sh                |   35 +-
 installer/install.bat                         |  197 +-
 installer/install.sh                          |  241 +-
 installer/invoke.bat                          |   27 +
 installer/invoke.sh                           |   24 +
 installer/py3.10-darwin-arm64-mps-reqs.txt    | 2069 ++++++++++++++++
 installer/py3.10-darwin-x86_64-cpu-reqs.txt   | 2069 ++++++++++++++++
 installer/py3.10-linux-x86_64-cuda-reqs.txt   | 2066 ++++++++++++++++
 installer/py3.10-windows-x86_64-cuda-reqs.txt | 2072 +++++++++++++++++
 installer/readme.txt                          |   12 +-
 installer/requirements.in                     |   24 +
 ldm/generate.py                               |   46 +-
 ldm/invoke/args.py                            |  104 +-
 ldm/invoke/conditioning.py                    |    1 -
 ldm/invoke/generator/base.py                  |   20 +-
 ldm/invoke/generator/inpaint.py               |   23 +-
 ldm/invoke/model_cache.py                     |   20 +-
 ldm/invoke/prompt_parser.py                   |  479 ++--
 ldm/invoke/readline.py                        |    2 +
 ldm/invoke/restoration/gfpgan.py              |    6 +-
 ldm/invoke/restoration/outcrop.py             |    7 +-
 ldm/invoke/txt2mask.py                        |    6 +-
 .../diffusion/cross_attention_control.py      |  269 ++-
 .../diffusion/shared_invokeai_diffusion.py    |   43 +-
 ldm/modules/attention.py                      |   66 +-
 mkdocs.yml                                    |   10 +
 pyproject.toml.hide                           |    3 -
 requirements-linux-arm64.txt                  |   27 -
 scripts/invoke.py                             |   66 +-
 scripts/preload_models.py                     |   67 +-
 setup.py                                      |    5 +-
 source_installer/create_installers.sh         |   23 +
 source_installer/install.bat                  |  118 +
 source_installer/install.sh                   |  138 ++
 invoke.bat => source_installer/invoke.bat     |    0
 invoke.sh => source_installer/invoke.sh       |    3 +-
 source_installer/readme.txt                   |   16 +
 update.bat => source_installer/update.bat     |    0
 update.sh => source_installer/update.sh       |    2 +
 tests/test_prompt_parser.py                   |  135 +-
 144 files changed, 12727 insertions(+), 2226 deletions(-)
 delete mode 100644 .github/workflows/create-caches.yml
 rename {data => assets/data}/DejaVuSans.ttf (100%)
 rename {data => assets/data}/example_conditioning/superresolution/sample_0.jpg (100%)
 rename {data => assets/data}/example_conditioning/text_conditional/sample_0.txt (100%)
 rename {data => assets/data}/imagenet_clsidx_to_label.txt (100%)
 rename {data => assets/data}/imagenet_train_hr_indices.p (100%)
 rename {data => assets/data}/imagenet_val_hr_indices.p (100%)
 rename {data => assets/data}/index_synset.yaml (100%)
 rename {data => assets/data}/inpainting_examples/6458524847_2f4c361183_k.png (100%)
 rename {data => assets/data}/inpainting_examples/6458524847_2f4c361183_k_mask.png (100%)
 rename {data => assets/data}/inpainting_examples/8399166846_f6fb4e4b8e_k.png (100%)
 rename {data => assets/data}/inpainting_examples/8399166846_f6fb4e4b8e_k_mask.png (100%)
 rename {data => assets/data}/inpainting_examples/alex-iby-G_Pk4D9rMLs.png (100%)
 rename {data => assets/data}/inpainting_examples/alex-iby-G_Pk4D9rMLs_mask.png (100%)
 rename {data => assets/data}/inpainting_examples/bench2.png (100%)
 rename {data => assets/data}/inpainting_examples/bench2_mask.png (100%)
 rename {data => assets/data}/inpainting_examples/bertrand-gabioud-CpuFzIsHYJ0.png (100%)
 rename {data => assets/data}/inpainting_examples/bertrand-gabioud-CpuFzIsHYJ0_mask.png (100%)
 rename {data => assets/data}/inpainting_examples/billow926-12-Wc-Zgx6Y.png (100%)
 rename {data => assets/data}/inpainting_examples/billow926-12-Wc-Zgx6Y_mask.png (100%)
 rename {data => assets/data}/inpainting_examples/overture-creations-5sI6fQgYIuo.png (100%)
 rename {data => assets/data}/inpainting_examples/overture-creations-5sI6fQgYIuo_mask.png (100%)
 rename {data => assets/data}/inpainting_examples/photo-1583445095369-9c651e7e5d34.png (100%)
 rename {data => assets/data}/inpainting_examples/photo-1583445095369-9c651e7e5d34_mask.png (100%)
 delete mode 100644 docs/assets/preflight-checks/inputs/Lincoln-and-Parrot-512-transparent.png
 delete mode 100644 docs/assets/preflight-checks/inputs/Lincoln-and-Parrot-512.png
 delete mode 100644 docs/assets/preflight-checks/inputs/curly.png
 delete mode 100644 docs/assets/preflight-checks/outputs/000001.1863159593.png
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 delete mode 100644 docs/assets/preflight-checks/outputs/000010.2028635318.png
 delete mode 100644 docs/assets/preflight-checks/outputs/000011.1111168647.png
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 delete mode 100644 docs/assets/preflight-checks/outputs/curly.942491079.outpaint.png
 delete mode 100644 docs/assets/preflight-checks/outputs/invoke_log.md
 delete mode 100644 docs/assets/preflight-checks/outputs/invoke_log.txt
 delete mode 100644 docs/assets/preflight-checks/preflight_prompts.txt
 create mode 100644 docs/installation/INSTALL_INVOKE.md
 create mode 100644 docs/installation/INSTALL_JUPYTER.md
 create mode 100644 docs/installation/INSTALL_MANUAL.md
 create mode 100644 docs/installation/INSTALL_PCP.md
 create mode 100644 docs/installation/INSTALL_SOURCE.md
 create mode 100644 docs/installation/index.md
 rename docs/installation/{ => older_docs_to_be_removed}/INSTALL_LINUX.md (83%)
 rename docs/installation/{ => older_docs_to_be_removed}/INSTALL_MAC.md (100%)
 rename docs/installation/{ => older_docs_to_be_removed}/INSTALL_WINDOWS.md (82%)
 delete mode 100644 environment-mac.yml
 rename environment-linux-aarch64.yml => environments-and-requirements/environment-lin-aarch64.yml (59%)
 create mode 100644 environments-and-requirements/environment-lin-amd.yml
 rename environment.yml => environments-and-requirements/environment-lin-cuda.yml (64%)
 create mode 100644 environments-and-requirements/environment-mac.yml
 create mode 100644 environments-and-requirements/environment-win-cuda.yml
 rename requirements.txt => environments-and-requirements/requirements-base.txt (67%)
 rename requirements-lin-AMD.txt => environments-and-requirements/requirements-lin-amd.txt (76%)
 create mode 100644 environments-and-requirements/requirements-lin-arm64.txt
 create mode 100644 environments-and-requirements/requirements-lin-cuda.txt
 rename requirements-mac-MPS-CPU.txt => environments-and-requirements/requirements-mac-mps-cpu.txt (50%)
 rename requirements-lin-win-colab-CUDA.txt => environments-and-requirements/requirements-win-colab-cuda.txt (72%)
 create mode 100644 frontend/dist/assets/index.8eb7dfe4.js
 delete mode 100644 frontend/src/features/system/SettingsModal/SettingsModalItem.tsx
 create mode 100644 installer/WinLongPathsEnabled.reg
 create mode 100644 installer/invoke.bat
 create mode 100755 installer/invoke.sh
 create mode 100644 installer/py3.10-darwin-arm64-mps-reqs.txt
 create mode 100644 installer/py3.10-darwin-x86_64-cpu-reqs.txt
 create mode 100644 installer/py3.10-linux-x86_64-cuda-reqs.txt
 create mode 100644 installer/py3.10-windows-x86_64-cuda-reqs.txt
 create mode 100644 installer/requirements.in
 delete mode 100644 pyproject.toml.hide
 delete mode 100644 requirements-linux-arm64.txt
 mode change 100644 => 100755 scripts/invoke.py
 create mode 100755 source_installer/create_installers.sh
 create mode 100644 source_installer/install.bat
 create mode 100755 source_installer/install.sh
 rename invoke.bat => source_installer/invoke.bat (100%)
 rename invoke.sh => source_installer/invoke.sh (93%)
 create mode 100644 source_installer/readme.txt
 rename update.bat => source_installer/update.bat (100%)
 rename update.sh => source_installer/update.sh (99%)

diff --git a/.github/CODEOWNERS b/.github/CODEOWNERS
index 8d93c26b19..7957684466 100644
--- a/.github/CODEOWNERS
+++ b/.github/CODEOWNERS
@@ -2,3 +2,4 @@ ldm/invoke/pngwriter.py @CapableWeb
 ldm/invoke/server_legacy.py @CapableWeb
 scripts/legacy_api.py @CapableWeb
 tests/legacy_tests.sh @CapableWeb
+installer/ @tildebyte
diff --git a/.github/workflows/build-container.yml b/.github/workflows/build-container.yml
index 2aa9433d01..ec4a105a9e 100644
--- a/.github/workflows/build-container.yml
+++ b/.github/workflows/build-container.yml
@@ -6,14 +6,22 @@ on:
     branches:
       - 'main'
       - 'development'
-  pull_request:
-    branches:
-      - 'main'
-      - 'development'
 
 jobs:
   docker:
+    strategy:
+      fail-fast: false
+      matrix:
+        arch:
+          - x86_64
+          - aarch64
+        include:
+          - arch: x86_64
+            conda-env-file: environment-lin-cuda.yml
+          - arch: aarch64
+            conda-env-file: environment-lin-aarch64.yml
     runs-on: ubuntu-latest
+    name: ${{ matrix.arch }}
     steps:
       - name: prepare docker-tag
         env:
@@ -25,18 +33,16 @@ jobs:
         uses: docker/setup-qemu-action@v2
       - name: Set up Docker Buildx
         uses: docker/setup-buildx-action@v2
-      - name: Cache Docker layers
-        uses: actions/cache@v2
-        with:
-          path: /tmp/.buildx-cache
-          key: buildx-${{ hashFiles('docker-build/Dockerfile') }}
       - name: Build container
         uses: docker/build-push-action@v3
         with:
           context: .
           file: docker-build/Dockerfile
-          platforms: linux/amd64
+          platforms: Linux/${{ matrix.arch }}
           push: false
-          tags: ${{ env.dockertag }}:latest
-          cache-from: type=local,src=/tmp/.buildx-cache
-          cache-to: type=local,dest=/tmp/.buildx-cache
+          tags: ${{ env.dockertag }}:${{ matrix.arch }}
+          build-args: |
+            conda_env_file=${{ matrix.conda-env-file }}
+            conda_version=py39_4.12.0-Linux-${{ matrix.arch }}
+            invokeai_git=${{ github.repository }}
+            invokeai_branch=${{ github.ref_name }}
diff --git a/.github/workflows/create-caches.yml b/.github/workflows/create-caches.yml
deleted file mode 100644
index 946025d78b..0000000000
--- a/.github/workflows/create-caches.yml
+++ /dev/null
@@ -1,80 +0,0 @@
-name: Create Caches
-
-on: workflow_dispatch
-
-jobs:
-  os_matrix:
-    strategy:
-      matrix:
-        os: [ubuntu-latest, macos-latest]
-        include:
-          - os: ubuntu-latest
-            environment-file: environment.yml
-            default-shell: bash -l {0}
-          - os: macos-latest
-            environment-file: environment-mac.yml
-            default-shell: bash -l {0}
-    name: Test invoke.py on ${{ matrix.os }} with conda
-    runs-on: ${{ matrix.os }}
-    defaults:
-      run:
-        shell: ${{ matrix.default-shell }}
-    steps:
-      - name: Checkout sources
-        uses: actions/checkout@v3
-
-      - name: setup miniconda
-        uses: conda-incubator/setup-miniconda@v2
-        with:
-          auto-activate-base: false
-          auto-update-conda: false
-          miniconda-version: latest
-
-      - name: set environment
-        run: |
-          [[ "$GITHUB_REF" == 'refs/heads/main' ]] \
-            && echo "TEST_PROMPTS=tests/preflight_prompts.txt" >> $GITHUB_ENV \
-            || echo "TEST_PROMPTS=tests/dev_prompts.txt" >> $GITHUB_ENV
-          echo "CONDA_ROOT=$CONDA" >> $GITHUB_ENV
-          echo "CONDA_ENV_NAME=invokeai" >> $GITHUB_ENV
-
-      - name: Use Cached Stable Diffusion v1.4 Model
-        id: cache-sd-v1-4
-        uses: actions/cache@v3
-        env:
-          cache-name: cache-sd-v1-4
-        with:
-          path: models/ldm/stable-diffusion-v1/model.ckpt
-          key: ${{ env.cache-name }}
-          restore-keys: ${{ env.cache-name }}
-
-      - name: Download Stable Diffusion v1.4 Model
-        if: ${{ steps.cache-sd-v1-4.outputs.cache-hit != 'true' }}
-        run: |
-          [[ -d models/ldm/stable-diffusion-v1 ]] \
-            || mkdir -p models/ldm/stable-diffusion-v1
-          [[ -r models/ldm/stable-diffusion-v1/model.ckpt ]] \
-            || curl \
-              -H "Authorization: Bearer ${{ secrets.HUGGINGFACE_TOKEN }}" \
-              -o models/ldm/stable-diffusion-v1/model.ckpt \
-              -L https://huggingface.co/CompVis/stable-diffusion-v-1-4-original/resolve/main/sd-v1-4.ckpt
-
-      - name: Activate Conda Env
-        uses: conda-incubator/setup-miniconda@v2
-        with:
-          activate-environment: ${{ env.CONDA_ENV_NAME }}
-          environment-file: ${{ matrix.environment-file }}
-
-      - name: Use Cached Huggingface and Torch models
-        id: cache-hugginface-torch
-        uses: actions/cache@v3
-        env:
-          cache-name: cache-hugginface-torch
-        with:
-          path: ~/.cache
-          key: ${{ env.cache-name }}
-          restore-keys: |
-            ${{ env.cache-name }}-${{ hashFiles('scripts/preload_models.py') }}
-
-      - name: run preload_models.py
-        run: python scripts/preload_models.py
diff --git a/.github/workflows/test-invoke-conda.yml b/.github/workflows/test-invoke-conda.yml
index 76c061e3a1..8fc3b10fef 100644
--- a/.github/workflows/test-invoke-conda.yml
+++ b/.github/workflows/test-invoke-conda.yml
@@ -23,7 +23,7 @@ jobs:
           - macOS-12
         include:
           - os: ubuntu-latest
-            environment-file: environment.yml
+            environment-file: environment-lin-cuda.yml
             default-shell: bash -l {0}
           - os: macOS-12
             environment-file: environment-mac.yml
@@ -49,6 +49,9 @@ jobs:
       - name: create models.yaml from example
         run: cp configs/models.yaml.example configs/models.yaml
 
+      - name: create environment.yml
+        run: cp environments-and-requirements/${{ matrix.environment-file }} environment.yml
+
       - name: Use cached conda packages
         id: use-cached-conda-packages
         uses: actions/cache@v3
@@ -61,7 +64,7 @@ jobs:
         uses: conda-incubator/setup-miniconda@v2
         with:
           activate-environment: ${{ env.CONDA_ENV_NAME }}
-          environment-file: ${{ matrix.environment-file }}
+          environment-file: environment.yml
           miniconda-version: latest
 
       - name: set test prompt to main branch validation
@@ -76,8 +79,18 @@ jobs:
         if: ${{ github.ref != 'refs/heads/main' && github.ref != 'refs/heads/development' }}
         run: echo "TEST_PROMPTS=tests/validate_pr_prompt.txt" >> $GITHUB_ENV
 
+      - name: Use Cached Stable Diffusion Model
+        id: cache-sd-model
+        uses: actions/cache@v3
+        env:
+          cache-name: cache-${{ matrix.stable-diffusion-model-switch }}
+        with:
+          path: ${{ matrix.stable-diffusion-model-dl-path }}
+          key: ${{ env.cache-name }}
+
       - name: Download ${{ matrix.stable-diffusion-model-switch }}
         id: download-stable-diffusion-model
+        if: ${{ steps.cache-sd-model.outputs.cache-hit != 'true' }}
         run: |
           [[ -d models/ldm/stable-diffusion-v1 ]] \
             || mkdir -p models/ldm/stable-diffusion-v1
diff --git a/.gitignore b/.gitignore
index 54433c86dc..02cebdb4f6 100644
--- a/.gitignore
+++ b/.gitignore
@@ -194,6 +194,10 @@ checkpoints
 
 # Let the frontend manage its own gitignore
 !frontend/*
+frontend/apt-get
+frontend/dist
+frontend/sudo
+frontend/update
 
 # Scratch folder
 .scratch/
@@ -201,6 +205,7 @@ checkpoints
 gfpgan/
 models/ldm/stable-diffusion-v1/*.sha256
 
+
 # GFPGAN model files
 gfpgan/
 
@@ -208,4 +213,25 @@ gfpgan/
 configs/models.yaml
 
 # weights (will be created by installer)
-models/ldm/stable-diffusion-v1/*.ckpt
\ No newline at end of file
+models/ldm/stable-diffusion-v1/*.ckpt
+models/clipseg
+models/gfpgan
+
+# ignore initfile
+invokeai.init
+
+# ignore environment.yml and requirements.txt
+# these are links to the real files in environments-and-requirements
+environment.yml
+requirements.txt
+
+# source installer files
+source_installer/*zip
+source_installer/invokeAI
+install.bat
+install.sh
+update.bat
+update.sh
+
+# this may be present if the user created a venv
+invokeai
diff --git a/data/DejaVuSans.ttf b/assets/data/DejaVuSans.ttf
similarity index 100%
rename from data/DejaVuSans.ttf
rename to assets/data/DejaVuSans.ttf
diff --git a/data/example_conditioning/superresolution/sample_0.jpg b/assets/data/example_conditioning/superresolution/sample_0.jpg
similarity index 100%
rename from data/example_conditioning/superresolution/sample_0.jpg
rename to assets/data/example_conditioning/superresolution/sample_0.jpg
diff --git a/data/example_conditioning/text_conditional/sample_0.txt b/assets/data/example_conditioning/text_conditional/sample_0.txt
similarity index 100%
rename from data/example_conditioning/text_conditional/sample_0.txt
rename to assets/data/example_conditioning/text_conditional/sample_0.txt
diff --git a/data/imagenet_clsidx_to_label.txt b/assets/data/imagenet_clsidx_to_label.txt
similarity index 100%
rename from data/imagenet_clsidx_to_label.txt
rename to assets/data/imagenet_clsidx_to_label.txt
diff --git a/data/imagenet_train_hr_indices.p b/assets/data/imagenet_train_hr_indices.p
similarity index 100%
rename from data/imagenet_train_hr_indices.p
rename to assets/data/imagenet_train_hr_indices.p
diff --git a/data/imagenet_val_hr_indices.p b/assets/data/imagenet_val_hr_indices.p
similarity index 100%
rename from data/imagenet_val_hr_indices.p
rename to assets/data/imagenet_val_hr_indices.p
diff --git a/data/index_synset.yaml b/assets/data/index_synset.yaml
similarity index 100%
rename from data/index_synset.yaml
rename to assets/data/index_synset.yaml
diff --git a/data/inpainting_examples/6458524847_2f4c361183_k.png b/assets/data/inpainting_examples/6458524847_2f4c361183_k.png
similarity index 100%
rename from data/inpainting_examples/6458524847_2f4c361183_k.png
rename to assets/data/inpainting_examples/6458524847_2f4c361183_k.png
diff --git a/data/inpainting_examples/6458524847_2f4c361183_k_mask.png b/assets/data/inpainting_examples/6458524847_2f4c361183_k_mask.png
similarity index 100%
rename from data/inpainting_examples/6458524847_2f4c361183_k_mask.png
rename to assets/data/inpainting_examples/6458524847_2f4c361183_k_mask.png
diff --git a/data/inpainting_examples/8399166846_f6fb4e4b8e_k.png b/assets/data/inpainting_examples/8399166846_f6fb4e4b8e_k.png
similarity index 100%
rename from data/inpainting_examples/8399166846_f6fb4e4b8e_k.png
rename to assets/data/inpainting_examples/8399166846_f6fb4e4b8e_k.png
diff --git a/data/inpainting_examples/8399166846_f6fb4e4b8e_k_mask.png b/assets/data/inpainting_examples/8399166846_f6fb4e4b8e_k_mask.png
similarity index 100%
rename from data/inpainting_examples/8399166846_f6fb4e4b8e_k_mask.png
rename to assets/data/inpainting_examples/8399166846_f6fb4e4b8e_k_mask.png
diff --git a/data/inpainting_examples/alex-iby-G_Pk4D9rMLs.png b/assets/data/inpainting_examples/alex-iby-G_Pk4D9rMLs.png
similarity index 100%
rename from data/inpainting_examples/alex-iby-G_Pk4D9rMLs.png
rename to assets/data/inpainting_examples/alex-iby-G_Pk4D9rMLs.png
diff --git a/data/inpainting_examples/alex-iby-G_Pk4D9rMLs_mask.png b/assets/data/inpainting_examples/alex-iby-G_Pk4D9rMLs_mask.png
similarity index 100%
rename from data/inpainting_examples/alex-iby-G_Pk4D9rMLs_mask.png
rename to assets/data/inpainting_examples/alex-iby-G_Pk4D9rMLs_mask.png
diff --git a/data/inpainting_examples/bench2.png b/assets/data/inpainting_examples/bench2.png
similarity index 100%
rename from data/inpainting_examples/bench2.png
rename to assets/data/inpainting_examples/bench2.png
diff --git a/data/inpainting_examples/bench2_mask.png b/assets/data/inpainting_examples/bench2_mask.png
similarity index 100%
rename from data/inpainting_examples/bench2_mask.png
rename to assets/data/inpainting_examples/bench2_mask.png
diff --git a/data/inpainting_examples/bertrand-gabioud-CpuFzIsHYJ0.png b/assets/data/inpainting_examples/bertrand-gabioud-CpuFzIsHYJ0.png
similarity index 100%
rename from data/inpainting_examples/bertrand-gabioud-CpuFzIsHYJ0.png
rename to assets/data/inpainting_examples/bertrand-gabioud-CpuFzIsHYJ0.png
diff --git a/data/inpainting_examples/bertrand-gabioud-CpuFzIsHYJ0_mask.png b/assets/data/inpainting_examples/bertrand-gabioud-CpuFzIsHYJ0_mask.png
similarity index 100%
rename from data/inpainting_examples/bertrand-gabioud-CpuFzIsHYJ0_mask.png
rename to assets/data/inpainting_examples/bertrand-gabioud-CpuFzIsHYJ0_mask.png
diff --git a/data/inpainting_examples/billow926-12-Wc-Zgx6Y.png b/assets/data/inpainting_examples/billow926-12-Wc-Zgx6Y.png
similarity index 100%
rename from data/inpainting_examples/billow926-12-Wc-Zgx6Y.png
rename to assets/data/inpainting_examples/billow926-12-Wc-Zgx6Y.png
diff --git a/data/inpainting_examples/billow926-12-Wc-Zgx6Y_mask.png b/assets/data/inpainting_examples/billow926-12-Wc-Zgx6Y_mask.png
similarity index 100%
rename from data/inpainting_examples/billow926-12-Wc-Zgx6Y_mask.png
rename to assets/data/inpainting_examples/billow926-12-Wc-Zgx6Y_mask.png
diff --git a/data/inpainting_examples/overture-creations-5sI6fQgYIuo.png b/assets/data/inpainting_examples/overture-creations-5sI6fQgYIuo.png
similarity index 100%
rename from data/inpainting_examples/overture-creations-5sI6fQgYIuo.png
rename to assets/data/inpainting_examples/overture-creations-5sI6fQgYIuo.png
diff --git a/data/inpainting_examples/overture-creations-5sI6fQgYIuo_mask.png b/assets/data/inpainting_examples/overture-creations-5sI6fQgYIuo_mask.png
similarity index 100%
rename from data/inpainting_examples/overture-creations-5sI6fQgYIuo_mask.png
rename to assets/data/inpainting_examples/overture-creations-5sI6fQgYIuo_mask.png
diff --git a/data/inpainting_examples/photo-1583445095369-9c651e7e5d34.png b/assets/data/inpainting_examples/photo-1583445095369-9c651e7e5d34.png
similarity index 100%
rename from data/inpainting_examples/photo-1583445095369-9c651e7e5d34.png
rename to assets/data/inpainting_examples/photo-1583445095369-9c651e7e5d34.png
diff --git a/data/inpainting_examples/photo-1583445095369-9c651e7e5d34_mask.png b/assets/data/inpainting_examples/photo-1583445095369-9c651e7e5d34_mask.png
similarity index 100%
rename from data/inpainting_examples/photo-1583445095369-9c651e7e5d34_mask.png
rename to assets/data/inpainting_examples/photo-1583445095369-9c651e7e5d34_mask.png
diff --git a/docker-build/Dockerfile b/docker-build/Dockerfile
index 2f0c892730..3f20e4b6f0 100644
--- a/docker-build/Dockerfile
+++ b/docker-build/Dockerfile
@@ -39,36 +39,46 @@ RUN apt-get update \
   && apt-get clean \
   && rm -rf /var/lib/apt/lists/*
 
-# clone repository and create symlinks
-ARG invokeai_git=https://github.com/invoke-ai/InvokeAI.git
+# clone repository, create models.yaml and create symlinks
+ARG invokeai_git=invoke-ai/InvokeAI
+ARG invokeai_branch=main
 ARG project_name=invokeai
-RUN git clone ${invokeai_git} /${project_name} \
-  && mkdir /${project_name}/models/ldm/stable-diffusion-v1 \
-  && ln -s /data/models/sd-v1-4.ckpt /${project_name}/models/ldm/stable-diffusion-v1/model.ckpt \
-  && ln -s /data/outputs/ /${project_name}/outputs
+ARG conda_env_file=environment-lin-cuda.yml
+RUN git clone -b ${invokeai_branch} https://github.com/${invokeai_git}.git "/${project_name}" \
+  && cp \
+    "/${project_name}/configs/models.yaml.example" \
+    "/${project_name}/configs/models.yaml" \
+  && ln -sf \
+    "/${project_name}/environments-and-requirements/${conda_env_file}" \
+    "/${project_name}/environment.yml" \
+  && ln -sf \
+    /data/models/v1-5-pruned-emaonly.ckpt \
+    "/${project_name}/models/ldm/stable-diffusion-v1/v1-5-pruned-emaonly.ckpt" \
+  && ln -sf \
+    /data/outputs/ \
+    "/${project_name}/outputs"
 
 # set workdir
-WORKDIR /${project_name}
+WORKDIR "/${project_name}"
 
 # install conda env and preload models
 ARG conda_prefix=/opt/conda
-ARG conda_env_file=environment.yml
-COPY --from=get_miniconda ${conda_prefix} ${conda_prefix}
-RUN source ${conda_prefix}/etc/profile.d/conda.sh \
+COPY --from=get_miniconda "${conda_prefix}" "${conda_prefix}"
+RUN source "${conda_prefix}/etc/profile.d/conda.sh" \
   && conda init bash \
   && source ~/.bashrc \
   && conda env create \
-    --name ${project_name} \
-    --file ${conda_env_file} \
+    --name "${project_name}" \
   && rm -Rf ~/.cache \
   && conda clean -afy \
-  && echo "conda activate ${project_name}" >> ~/.bashrc \
-  && ln -s /data/models/GFPGANv1.4.pth ./src/gfpgan/experiments/pretrained_models/GFPGANv1.4.pth \
-  && conda activate ${project_name} \
-  && python scripts/preload_models.py
+  && echo "conda activate ${project_name}" >> ~/.bashrc
+
+RUN source ~/.bashrc \
+  && python scripts/preload_models.py \
+    --no-interactive
 
 # Copy entrypoint and set env
-ENV CONDA_PREFIX=${conda_prefix}
-ENV PROJECT_NAME=${project_name}
+ENV CONDA_PREFIX="${conda_prefix}"
+ENV PROJECT_NAME="${project_name}"
 COPY docker-build/entrypoint.sh /
 ENTRYPOINT [ "/entrypoint.sh" ]
diff --git a/docker-build/build.sh b/docker-build/build.sh
index fea311ae82..c7b94d7c0e 100755
--- a/docker-build/build.sh
+++ b/docker-build/build.sh
@@ -8,8 +8,9 @@ source ./docker-build/env.sh || echo "please run from repository root" || exit 1
 
 invokeai_conda_version=${INVOKEAI_CONDA_VERSION:-py39_4.12.0-${platform/\//-}}
 invokeai_conda_prefix=${INVOKEAI_CONDA_PREFIX:-\/opt\/conda}
-invokeai_conda_env_file=${INVOKEAI_CONDA_ENV_FILE:-environment.yml}
-invokeai_git=${INVOKEAI_GIT:-https://github.com/invoke-ai/InvokeAI.git}
+invokeai_conda_env_file=${INVOKEAI_CONDA_ENV_FILE:-environment-lin-cuda.yml}
+invokeai_git=${INVOKEAI_GIT:-invoke-ai/InvokeAI}
+invokeai_branch=${INVOKEAI_BRANCH:-main}
 huggingface_token=${HUGGINGFACE_TOKEN?}
 
 # print the settings
@@ -38,11 +39,12 @@ _copyCheckpoints() {
   echo "creating subfolders for models and outputs"
   _runAlpine mkdir models
   _runAlpine mkdir outputs
-  echo -n "downloading sd-v1-4.ckpt"
-  _runAlpine wget --header="Authorization: Bearer ${huggingface_token}" -O models/sd-v1-4.ckpt https://huggingface.co/CompVis/stable-diffusion-v-1-4-original/resolve/main/sd-v1-4.ckpt
+  echo "downloading v1-5-pruned-emaonly.ckpt"
+  _runAlpine wget \
+    --header="Authorization: Bearer ${huggingface_token}" \
+    -O models/v1-5-pruned-emaonly.ckpt \
+    https://huggingface.co/runwayml/stable-diffusion-v1-5/resolve/main/v1-5-pruned-emaonly.ckpt
   echo "done"
-  echo "downloading GFPGANv1.4.pth"
-  _runAlpine wget -O models/GFPGANv1.4.pth https://github.com/TencentARC/GFPGAN/releases/download/v1.3.0/GFPGANv1.4.pth
 }
 
 _checkVolumeContent() {
@@ -51,7 +53,7 @@ _checkVolumeContent() {
 
 _getModelMd5s() {
   _runAlpine  \
-    alpine sh -c "md5sum /data/models/*"
+    alpine sh -c "md5sum /data/models/*.ckpt"
 }
 
 if [[ -n "$(docker volume ls -f name="${volumename}" -q)" ]]; then
@@ -77,5 +79,6 @@ docker build \
   --build-arg conda_prefix="${invokeai_conda_prefix}" \
   --build-arg conda_env_file="${invokeai_conda_env_file}" \
   --build-arg invokeai_git="${invokeai_git}" \
+  --build-arg invokeai_branch="${invokeai_branch}" \
   --file ./docker-build/Dockerfile \
   .
diff --git a/docs/CHANGELOG.md b/docs/CHANGELOG.md
index 9b4dfe1cfc..a4fce8d5ef 100644
--- a/docs/CHANGELOG.md
+++ b/docs/CHANGELOG.md
@@ -4,45 +4,228 @@ title: Changelog
 
 # :octicons-log-16: **Changelog**
 
-## v2.0.1 (13 October 2022)
+## v2.1.0 <small>(2 November 2022)</small>
 
-- fix noisy images at high step count when using k* samplers
-- dream.py script now calls invoke.py module directly rather than
-    via a new python process (which could break the environment)
+- update mac instructions to use invokeai for env name by @willwillems in
+  https://github.com/invoke-ai/InvokeAI/pull/1030
+- Update .gitignore by @blessedcoolant in
+  https://github.com/invoke-ai/InvokeAI/pull/1040
+- reintroduce fix for m1 from https://github.com/invoke-ai/InvokeAI/pull/579
+  missing after merge by @skurovec in
+  https://github.com/invoke-ai/InvokeAI/pull/1056
+- Update Stable_Diffusion_AI_Notebook.ipynb (Take 2) by @ChloeL19 in
+  https://github.com/invoke-ai/InvokeAI/pull/1060
+- Print out the device type which is used by @manzke in
+  https://github.com/invoke-ai/InvokeAI/pull/1073
+- Hires Addition by @hipsterusername in
+  https://github.com/invoke-ai/InvokeAI/pull/1063
+- fix for "1 leaked semaphore objects to clean up at shutdown" on M1 by
+  @skurovec in https://github.com/invoke-ai/InvokeAI/pull/1081
+- Forward dream.py to invoke.py using the same interpreter, add deprecation
+  warning by @db3000 in https://github.com/invoke-ai/InvokeAI/pull/1077
+- fix noisy images at high step counts by @lstein in
+  https://github.com/invoke-ai/InvokeAI/pull/1086
+- Generalize facetool strength argument by @db3000 in
+  https://github.com/invoke-ai/InvokeAI/pull/1078
+- Enable fast switching among models at the invoke> command line by @lstein in
+  https://github.com/invoke-ai/InvokeAI/pull/1066
+- Fix Typo, committed changing ldm environment to invokeai by @jdries3 in
+  https://github.com/invoke-ai/InvokeAI/pull/1095
+- Update generate.py by @unreleased in
+  https://github.com/invoke-ai/InvokeAI/pull/1109
+- Update 'ldm' env to 'invokeai' in troubleshooting steps by @19wolf in
+  https://github.com/invoke-ai/InvokeAI/pull/1125
+- Fixed documentation typos and resolved merge conflicts by @rupeshs in
+  https://github.com/invoke-ai/InvokeAI/pull/1123
+- Fix broken doc links, fix malaprop in the project subtitle by @majick in
+  https://github.com/invoke-ai/InvokeAI/pull/1131
+- Only output facetool parameters if enhancing faces by @db3000 in
+  https://github.com/invoke-ai/InvokeAI/pull/1119
+- Update gitignore to ignore codeformer weights at new location by
+  @spezialspezial in https://github.com/invoke-ai/InvokeAI/pull/1136
+- fix links to point to invoke-ai.github.io #1117 by @mauwii in
+  https://github.com/invoke-ai/InvokeAI/pull/1143
+- Rework-mkdocs by @mauwii in https://github.com/invoke-ai/InvokeAI/pull/1144
+- add option to CLI and pngwriter that allows user to set PNG compression level
+  by @lstein in https://github.com/invoke-ai/InvokeAI/pull/1127
+- Fix img2img DDIM index out of bound by @wfng92 in
+  https://github.com/invoke-ai/InvokeAI/pull/1137
+- Fix gh actions by @mauwii in https://github.com/invoke-ai/InvokeAI/pull/1128
+- update mac instructions to use invokeai for env name by @willwillems in
+  https://github.com/invoke-ai/InvokeAI/pull/1030
+- Update .gitignore by @blessedcoolant in
+  https://github.com/invoke-ai/InvokeAI/pull/1040
+- reintroduce fix for m1 from https://github.com/invoke-ai/InvokeAI/pull/579
+  missing after merge by @skurovec in
+  https://github.com/invoke-ai/InvokeAI/pull/1056
+- Update Stable_Diffusion_AI_Notebook.ipynb (Take 2) by @ChloeL19 in
+  https://github.com/invoke-ai/InvokeAI/pull/1060
+- Print out the device type which is used by @manzke in
+  https://github.com/invoke-ai/InvokeAI/pull/1073
+- Hires Addition by @hipsterusername in
+  https://github.com/invoke-ai/InvokeAI/pull/1063
+- fix for "1 leaked semaphore objects to clean up at shutdown" on M1 by
+  @skurovec in https://github.com/invoke-ai/InvokeAI/pull/1081
+- Forward dream.py to invoke.py using the same interpreter, add deprecation
+  warning by @db3000 in https://github.com/invoke-ai/InvokeAI/pull/1077
+- fix noisy images at high step counts by @lstein in
+  https://github.com/invoke-ai/InvokeAI/pull/1086
+- Generalize facetool strength argument by @db3000 in
+  https://github.com/invoke-ai/InvokeAI/pull/1078
+- Enable fast switching among models at the invoke> command line by @lstein in
+  https://github.com/invoke-ai/InvokeAI/pull/1066
+- Fix Typo, committed changing ldm environment to invokeai by @jdries3 in
+  https://github.com/invoke-ai/InvokeAI/pull/1095
+- Fixed documentation typos and resolved merge conflicts by @rupeshs in
+  https://github.com/invoke-ai/InvokeAI/pull/1123
+- Only output facetool parameters if enhancing faces by @db3000 in
+  https://github.com/invoke-ai/InvokeAI/pull/1119
+- add option to CLI and pngwriter that allows user to set PNG compression level
+  by @lstein in https://github.com/invoke-ai/InvokeAI/pull/1127
+- Fix img2img DDIM index out of bound by @wfng92 in
+  https://github.com/invoke-ai/InvokeAI/pull/1137
+- Add text prompt to inpaint mask support by @lstein in
+  https://github.com/invoke-ai/InvokeAI/pull/1133
+- Respect http[s] protocol when making socket.io middleware by @damian0815 in
+  https://github.com/invoke-ai/InvokeAI/pull/976
+- WebUI: Adds Codeformer support by @psychedelicious in
+  https://github.com/invoke-ai/InvokeAI/pull/1151
+- Skips normalizing prompts for web UI metadata by @psychedelicious in
+  https://github.com/invoke-ai/InvokeAI/pull/1165
+- Add Asymmetric Tiling by @carson-katri in
+  https://github.com/invoke-ai/InvokeAI/pull/1132
+- Web UI: Increases max CFG Scale to 200 by @psychedelicious in
+  https://github.com/invoke-ai/InvokeAI/pull/1172
+- Corrects color channels in face restoration; Fixes #1167 by @psychedelicious
+  in https://github.com/invoke-ai/InvokeAI/pull/1175
+- Flips channels using array slicing instead of using OpenCV by @psychedelicious
+  in https://github.com/invoke-ai/InvokeAI/pull/1178
+- Fix typo in docs: s/Formally/Formerly by @noodlebox in
+  https://github.com/invoke-ai/InvokeAI/pull/1176
+- fix clipseg loading problems by @lstein in
+  https://github.com/invoke-ai/InvokeAI/pull/1177
+- Correct color channels in upscale using array slicing by @wfng92 in
+  https://github.com/invoke-ai/InvokeAI/pull/1181
+- Web UI: Filters existing images when adding new images; Fixes #1085 by
+  @psychedelicious in https://github.com/invoke-ai/InvokeAI/pull/1171
+- fix a number of bugs in textual inversion by @lstein in
+  https://github.com/invoke-ai/InvokeAI/pull/1190
+- Improve !fetch, add !replay command by @ArDiouscuros in
+  https://github.com/invoke-ai/InvokeAI/pull/882
+- Fix generation of image with s>1000 by @holstvoogd in
+  https://github.com/invoke-ai/InvokeAI/pull/951
+- Web UI: Gallery improvements by @psychedelicious in
+  https://github.com/invoke-ai/InvokeAI/pull/1198
+- Update CLI.md by @krummrey in https://github.com/invoke-ai/InvokeAI/pull/1211
+- outcropping improvements by @lstein in
+  https://github.com/invoke-ai/InvokeAI/pull/1207
+- add support for loading VAE autoencoders by @lstein in
+  https://github.com/invoke-ai/InvokeAI/pull/1216
+- remove duplicate fix_func for MPS by @wfng92 in
+  https://github.com/invoke-ai/InvokeAI/pull/1210
+- Metadata storage and retrieval fixes by @lstein in
+  https://github.com/invoke-ai/InvokeAI/pull/1204
+- nix: add shell.nix file by @Cloudef in
+  https://github.com/invoke-ai/InvokeAI/pull/1170
+- Web UI: Changes vite dist asset paths to relative by @psychedelicious in
+  https://github.com/invoke-ai/InvokeAI/pull/1185
+- Web UI: Removes isDisabled from PromptInput by @psychedelicious in
+  https://github.com/invoke-ai/InvokeAI/pull/1187
+- Allow user to generate images with initial noise as on M1 / mps system by
+  @ArDiouscuros in https://github.com/invoke-ai/InvokeAI/pull/981
+- feat: adding filename format template by @plucked in
+  https://github.com/invoke-ai/InvokeAI/pull/968
+- Web UI: Fixes broken bundle by @psychedelicious in
+  https://github.com/invoke-ai/InvokeAI/pull/1242
+- Support runwayML custom inpainting model by @lstein in
+  https://github.com/invoke-ai/InvokeAI/pull/1243
+- Update IMG2IMG.md by @talitore in
+  https://github.com/invoke-ai/InvokeAI/pull/1262
+- New dockerfile - including a build- and a run- script as well as a GH-Action
+  by @mauwii in https://github.com/invoke-ai/InvokeAI/pull/1233
+- cut over from karras to model noise schedule for higher steps by @lstein in
+  https://github.com/invoke-ai/InvokeAI/pull/1222
+- Prompt tweaks by @lstein in https://github.com/invoke-ai/InvokeAI/pull/1268
+- Outpainting implementation by @Kyle0654 in
+  https://github.com/invoke-ai/InvokeAI/pull/1251
+- fixing aspect ratio on hires by @tjennings in
+  https://github.com/invoke-ai/InvokeAI/pull/1249
+- Fix-build-container-action by @mauwii in
+  https://github.com/invoke-ai/InvokeAI/pull/1274
+- handle all unicode characters by @damian0815 in
+  https://github.com/invoke-ai/InvokeAI/pull/1276
+- adds models.user.yml to .gitignore by @JakeHL in
+  https://github.com/invoke-ai/InvokeAI/pull/1281
+- remove debug branch, set fail-fast to false by @mauwii in
+  https://github.com/invoke-ai/InvokeAI/pull/1284
+- Protect-secrets-on-pr by @mauwii in
+  https://github.com/invoke-ai/InvokeAI/pull/1285
+- Web UI: Adds initial inpainting implementation by @psychedelicious in
+  https://github.com/invoke-ai/InvokeAI/pull/1225
+- fix environment-mac.yml - tested on x64 and arm64 by @mauwii in
+  https://github.com/invoke-ai/InvokeAI/pull/1289
+- Use proper authentication to download model by @mauwii in
+  https://github.com/invoke-ai/InvokeAI/pull/1287
+- Prevent indexing error for mode RGB by @spezialspezial in
+  https://github.com/invoke-ai/InvokeAI/pull/1294
+- Integrate sd-v1-5 model into test matrix (easily expandable), remove
+  unecesarry caches by @mauwii in
+  https://github.com/invoke-ai/InvokeAI/pull/1293
+- add --no-interactive to preload_models step by @mauwii in
+  https://github.com/invoke-ai/InvokeAI/pull/1302
+- 1-click installer and updater. Uses micromamba to install git and conda into a
+  contained environment (if necessary) before running the normal installation
+  script by @cmdr2 in https://github.com/invoke-ai/InvokeAI/pull/1253
+- preload_models.py script downloads the weight files by @lstein in
+  https://github.com/invoke-ai/InvokeAI/pull/1290
+
+## v2.0.1 <small>(13 October 2022)</small>
+
+- fix noisy images at high step count when using k\* samplers
+- dream.py script now calls invoke.py module directly rather than via a new
+  python process (which could break the environment)
 
 ## v2.0.0 <small>(9 October 2022)</small>
 
-- `dream.py` script renamed `invoke.py`. A `dream.py` script wrapper remains
-    for backward compatibility.
+- `dream.py` script renamed `invoke.py`. A `dream.py` script wrapper remains for
+  backward compatibility.
 - Completely new WebGUI - launch with `python3 scripts/invoke.py --web`
-- Support for [inpainting](features/INPAINTING.md) and [outpainting](features/OUTPAINTING.md)
-- img2img runs on all k* samplers
-- Support for [negative prompts](features/PROMPTS.md#negative-and-unconditioned-prompts)
+- Support for [inpainting](features/INPAINTING.md) and
+  [outpainting](features/OUTPAINTING.md)
+- img2img runs on all k\* samplers
+- Support for
+  [negative prompts](features/PROMPTS.md#negative-and-unconditioned-prompts)
 - Support for CodeFormer face reconstruction
 - Support for Textual Inversion on Macintoshes
-- Support in both WebGUI and CLI for [post-processing of previously-generated images](features/POSTPROCESS.md)
-    using facial reconstruction, ESRGAN upscaling, outcropping (similar to DALL-E infinite canvas),
-    and "embiggen" upscaling. See the `!fix` command.
-- New `--hires` option on `invoke>` line allows [larger images to be created without duplicating elements](features/CLI.md#this-is-an-example-of-txt2img), at the cost of some performance.
-- New `--perlin` and `--threshold` options allow you to add and control variation
-    during image generation (see [Thresholding and Perlin Noise Initialization](features/OTHER.md#thresholding-and-perlin-noise-initialization-options))
-- Extensive metadata now written into PNG files, allowing reliable regeneration of images
-    and tweaking of previous settings.
-- Command-line completion in `invoke.py` now works on Windows, Linux and Mac platforms.
-- Improved [command-line completion behavior](features/CLI.md)
-    New commands added:
+- Support in both WebGUI and CLI for
+  [post-processing of previously-generated images](features/POSTPROCESS.md)
+  using facial reconstruction, ESRGAN upscaling, outcropping (similar to DALL-E
+  infinite canvas), and "embiggen" upscaling. See the `!fix` command.
+- New `--hires` option on `invoke>` line allows
+  [larger images to be created without duplicating elements](features/CLI.md#this-is-an-example-of-txt2img),
+  at the cost of some performance.
+- New `--perlin` and `--threshold` options allow you to add and control
+  variation during image generation (see
+  [Thresholding and Perlin Noise Initialization](features/OTHER.md#thresholding-and-perlin-noise-initialization-options))
+- Extensive metadata now written into PNG files, allowing reliable regeneration
+  of images and tweaking of previous settings.
+- Command-line completion in `invoke.py` now works on Windows, Linux and Mac
+  platforms.
+- Improved [command-line completion behavior](features/CLI.md) New commands
+  added:
   - List command-line history with `!history`
   - Search command-line history with `!search`
   - Clear history with `!clear`
 - Deprecated `--full_precision` / `-F`. Simply omit it and `invoke.py` will auto
-    configure. To switch away from auto use the new flag like `--precision=float32`.
+  configure. To switch away from auto use the new flag like
+  `--precision=float32`.
 
 ## v1.14 <small>(11 September 2022)</small>
 
 - Memory optimizations for small-RAM cards. 512x512 now possible on 4 GB GPUs.
 - Full support for Apple hardware with M1 or M2 chips.
 - Add "seamless mode" for circular tiling of image. Generates beautiful effects.
-    ([prixt](https://github.com/prixt)).
+  ([prixt](https://github.com/prixt)).
 - Inpainting support.
 - Improved web server GUI.
 - Lots of code and documentation cleanups.
@@ -50,16 +233,17 @@ title: Changelog
 ## v1.13 <small>(3 September 2022)</small>
 
 - Support image variations (see [VARIATIONS](features/VARIATIONS.md)
-    ([Kevin Gibbons](https://github.com/bakkot) and many contributors and reviewers)
-- Supports a Google Colab notebook for a standalone server running on Google hardware
-    [Arturo Mendivil](https://github.com/artmen1516)
+  ([Kevin Gibbons](https://github.com/bakkot) and many contributors and
+  reviewers)
+- Supports a Google Colab notebook for a standalone server running on Google
+  hardware [Arturo Mendivil](https://github.com/artmen1516)
 - WebUI supports GFPGAN/ESRGAN facial reconstruction and upscaling
-    [Kevin Gibbons](https://github.com/bakkot)
+  [Kevin Gibbons](https://github.com/bakkot)
 - WebUI supports incremental display of in-progress images during generation
-    [Kevin Gibbons](https://github.com/bakkot)
+  [Kevin Gibbons](https://github.com/bakkot)
 - A new configuration file scheme that allows new models (including upcoming
-    stable-diffusion-v1.5) to be added without altering the code.
-    ([David Wager](https://github.com/maddavid12))
+  stable-diffusion-v1.5) to be added without altering the code.
+  ([David Wager](https://github.com/maddavid12))
 - Can specify --grid on invoke.py command line as the default.
 - Miscellaneous internal bug and stability fixes.
 - Works on M1 Apple hardware.
@@ -71,49 +255,59 @@ title: Changelog
 
 - Improved file handling, including ability to read prompts from standard input.
   (kudos to [Yunsaki](https://github.com/yunsaki)
-- The web server is now integrated with the invoke.py script. Invoke by adding --web to
-  the invoke.py command arguments.
+- The web server is now integrated with the invoke.py script. Invoke by adding
+  --web to the invoke.py command arguments.
 - Face restoration and upscaling via GFPGAN and Real-ESGAN are now automatically
   enabled if the GFPGAN directory is located as a sibling to Stable Diffusion.
-  VRAM requirements are modestly reduced. Thanks to both [Blessedcoolant](https://github.com/blessedcoolant) and
+  VRAM requirements are modestly reduced. Thanks to both
+  [Blessedcoolant](https://github.com/blessedcoolant) and
   [Oceanswave](https://github.com/oceanswave) for their work on this.
-- You can now swap samplers on the invoke> command line. [Blessedcoolant](https://github.com/blessedcoolant)
+- You can now swap samplers on the invoke> command line.
+  [Blessedcoolant](https://github.com/blessedcoolant)
 
 ---
 
 ## v1.11 <small>(26 August 2022)</small>
 
-- NEW FEATURE: Support upscaling and face enhancement using the GFPGAN module. (kudos to [Oceanswave](https://github.com/Oceanswave)
-- You now can specify a seed of -1 to use the previous image's seed, -2 to use the seed for the image generated before that, etc.
-  Seed memory only extends back to the previous command, but will work on all images generated with the -n# switch.
+- NEW FEATURE: Support upscaling and face enhancement using the GFPGAN module.
+  (kudos to [Oceanswave](https://github.com/Oceanswave)
+- You now can specify a seed of -1 to use the previous image's seed, -2 to use
+  the seed for the image generated before that, etc. Seed memory only extends
+  back to the previous command, but will work on all images generated with the
+  -n# switch.
 - Variant generation support temporarily disabled pending more general solution.
-- Created a feature branch named **yunsaki-morphing-invoke** which adds experimental support for
-  iteratively modifying the prompt and its parameters. Please see[Pull Request #86](https://github.com/lstein/stable-diffusion/pull/86)
-  for a synopsis of how this works. Note that when this feature is eventually added to the main branch, it will may be modified
-  significantly.
+- Created a feature branch named **yunsaki-morphing-invoke** which adds
+  experimental support for iteratively modifying the prompt and its parameters.
+  Please
+  see[Pull Request #86](https://github.com/lstein/stable-diffusion/pull/86) for
+  a synopsis of how this works. Note that when this feature is eventually added
+  to the main branch, it will may be modified significantly.
 
 ---
 
 ## v1.10 <small>(25 August 2022)</small>
 
-- A barebones but fully functional interactive web server for online generation of txt2img and img2img.
+- A barebones but fully functional interactive web server for online generation
+  of txt2img and img2img.
 
 ---
 
 ## v1.09 <small>(24 August 2022)</small>
 
 - A new -v option allows you to generate multiple variants of an initial image
-  in img2img mode. (kudos to [Oceanswave](https://github.com/Oceanswave). [
-  See this discussion in the PR for examples and details on use](https://github.com/lstein/stable-diffusion/pull/71#issuecomment-1226700810))
-- Added ability to personalize text to image generation (kudos to [Oceanswave](https://github.com/Oceanswave) and [nicolai256](https://github.com/nicolai256))
+  in img2img mode. (kudos to [Oceanswave](https://github.com/Oceanswave).
+  [ See this discussion in the PR for examples and details on use](https://github.com/lstein/stable-diffusion/pull/71#issuecomment-1226700810))
+- Added ability to personalize text to image generation (kudos to
+  [Oceanswave](https://github.com/Oceanswave) and
+  [nicolai256](https://github.com/nicolai256))
 - Enabled all of the samplers from k_diffusion
 
 ---
 
 ## v1.08 <small>(24 August 2022)</small>
 
-- Escape single quotes on the invoke> command before trying to parse. This avoids
-  parse errors.
+- Escape single quotes on the invoke> command before trying to parse. This
+  avoids parse errors.
 - Removed instruction to get Python3.8 as first step in Windows install.
   Anaconda3 does it for you.
 - Added bounds checks for numeric arguments that could cause crashes.
@@ -123,34 +317,36 @@ title: Changelog
 
 ## v1.07 <small>(23 August 2022)</small>
 
-- Image filenames will now never fill gaps in the sequence, but will be assigned the
-  next higher name in the chosen directory. This ensures that the alphabetic and chronological
-  sort orders are the same.
+- Image filenames will now never fill gaps in the sequence, but will be assigned
+  the next higher name in the chosen directory. This ensures that the alphabetic
+  and chronological sort orders are the same.
 
 ---
 
 ## v1.06 <small>(23 August 2022)</small>
 
-- Added weighted prompt support contributed by [xraxra](https://github.com/xraxra)
-- Example of using weighted prompts to tweak a demonic figure contributed by [bmaltais](https://github.com/bmaltais)
+- Added weighted prompt support contributed by
+  [xraxra](https://github.com/xraxra)
+- Example of using weighted prompts to tweak a demonic figure contributed by
+  [bmaltais](https://github.com/bmaltais)
 
 ---
 
 ## v1.05 <small>(22 August 2022 - after the drop)</small>
 
-- Filenames now use the following formats:
-  000010.95183149.png -- Two files produced by the same command (e.g. -n2),
-  000010.26742632.png -- distinguished by a different seed.
+- Filenames now use the following formats: 000010.95183149.png -- Two files
+  produced by the same command (e.g. -n2), 000010.26742632.png -- distinguished
+  by a different seed.
 
   000011.455191342.01.png -- Two files produced by the same command using
   000011.455191342.02.png -- a batch size>1 (e.g. -b2). They have the same seed.
 
-  000011.4160627868.grid#1-4.png -- a grid of four images (-g); the whole grid can
-  be regenerated with the indicated key
+  000011.4160627868.grid#1-4.png -- a grid of four images (-g); the whole grid
+  can be regenerated with the indicated key
 
 - It should no longer be possible for one image to overwrite another
-- You can use the "cd" and "pwd" commands at the invoke> prompt to set and retrieve
-  the path of the output directory.
+- You can use the "cd" and "pwd" commands at the invoke> prompt to set and
+  retrieve the path of the output directory.
 
 ---
 
@@ -164,26 +360,28 @@ title: Changelog
 
 ## v1.03 <small>(22 August 2022)</small>
 
-- The original txt2img and img2img scripts from the CompViz repository have been moved into
-  a subfolder named "orig_scripts", to reduce confusion.
+- The original txt2img and img2img scripts from the CompViz repository have been
+  moved into a subfolder named "orig_scripts", to reduce confusion.
 
 ---
 
 ## v1.02 <small>(21 August 2022)</small>
 
-- A copy of the prompt and all of its switches and options is now stored in the corresponding
-  image in a tEXt metadata field named "Dream". You can read the prompt using scripts/images2prompt.py,
-  or an image editor that allows you to explore the full metadata.
-  **Please run "conda env update" to load the k_lms dependencies!!**
+- A copy of the prompt and all of its switches and options is now stored in the
+  corresponding image in a tEXt metadata field named "Dream". You can read the
+  prompt using scripts/images2prompt.py, or an image editor that allows you to
+  explore the full metadata. **Please run "conda env update" to load the k_lms
+  dependencies!!**
 
 ---
 
 ## v1.01 <small>(21 August 2022)</small>
 
-- added k_lms sampling.
-  **Please run "conda env update" to load the k_lms dependencies!!**
-- use half precision arithmetic by default, resulting in faster execution and lower memory requirements
-  Pass argument --full_precision to invoke.py to get slower but more accurate image generation
+- added k_lms sampling. **Please run "conda env update" to load the k_lms
+  dependencies!!**
+- use half precision arithmetic by default, resulting in faster execution and
+  lower memory requirements Pass argument --full_precision to invoke.py to get
+  slower but more accurate image generation
 
 ---
 
diff --git a/docs/assets/preflight-checks/inputs/Lincoln-and-Parrot-512-transparent.png b/docs/assets/preflight-checks/inputs/Lincoln-and-Parrot-512-transparent.png
deleted file mode 100644
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diff --git a/docs/assets/preflight-checks/outputs/invoke_log.md b/docs/assets/preflight-checks/outputs/invoke_log.md
deleted file mode 100644
index 75ab95e49c..0000000000
--- a/docs/assets/preflight-checks/outputs/invoke_log.md
+++ /dev/null
@@ -1,116 +0,0 @@
-## 000001.1863159593.png
-![](000001.1863159593.png)
-
-banana sushi -s 50 -S 1863159593 -W 512 -H 512 -C 7.5 -A k_lms
-## 000002.1151955949.png
-![](000002.1151955949.png)
-
-banana sushi -s 50 -S 1151955949 -W 512 -H 512 -C 7.5 -A plms
-## 000003.2736230502.png
-![](000003.2736230502.png)
-
-banana sushi -s 50 -S 2736230502 -W 512 -H 512 -C 7.5 -A ddim
-## 000004.42.png
-![](000004.42.png)
-
-banana sushi -s 50 -S 42 -W 512 -H 512 -C 7.5 -A k_lms
-## 000005.42.png
-![](000005.42.png)
-
-banana sushi -s 50 -S 42 -W 512 -H 512 -C 7.5 -A k_lms
-## 000006.478163327.png
-![](000006.478163327.png)
-
-banana sushi -s 50 -S 478163327 -W 640 -H 448 -C 7.5 -A k_lms
-## 000007.2407640369.png
-![](000007.2407640369.png)
-
-banana sushi -s 50 -S 42 -W 512 -H 512 -C 7.5 -A k_lms -V 2407640369:0.1
-## 000008.2772421987.png
-![](000008.2772421987.png)
-
-banana sushi -s 50 -S 42 -W 512 -H 512 -C 7.5 -A k_lms -V 2772421987:0.1
-## 000009.3532317557.png
-![](000009.3532317557.png)
-
-banana sushi -s 50 -S 42 -W 512 -H 512 -C 7.5 -A k_lms -V 3532317557:0.1
-## 000010.2028635318.png
-![](000010.2028635318.png)
-
-banana sushi -s 50 -S 2028635318 -W 512 -H 512 -C 7.5 -A k_lms
-## 000011.1111168647.png
-![](000011.1111168647.png)
-
-pond with waterlillies -s 50 -S 1111168647 -W 512 -H 512 -C 7.5 -A k_lms
-## 000012.1476370516.png
-![](000012.1476370516.png)
-
-pond with waterlillies -s 50 -S 1476370516 -W 512 -H 512 -C 7.5 -A k_lms
-## 000013.4281108706.png
-![](000013.4281108706.png)
-
-banana sushi -s 50 -S 4281108706 -W 960 -H 960 -C 7.5 -A k_lms
-## 000014.2396987386.png
-![](000014.2396987386.png)
-
-old sea captain with crow on shoulder -s 50 -S 2396987386 -W 512 -H 512 -C 7.5 -I docs/assets/preflight-checks/inputs/Lincoln-and-Parrot-512.png -A k_lms -f 0.75
-## 000015.1252923272.png
-![](000015.1252923272.png)
-
-old sea captain with crow on shoulder -s 50 -S 1252923272 -W 512 -H 512 -C 7.5 -I docs/assets/preflight-checks/inputs/Lincoln-and-Parrot-512-transparent.png -A k_lms -f 0.75
-## 000016.2633891320.png
-![](000016.2633891320.png)
-
-old sea captain with crow on shoulder -s 50 -S 2633891320 -W 512 -H 512 -C 7.5 -I docs/assets/preflight-checks/inputs/Lincoln-and-Parrot-512.png -A plms -f 0.75
-## 000017.1134411920.png
-![](000017.1134411920.png)
-
-old sea captain with crow on shoulder -s 50 -S 1134411920 -W 512 -H 512 -C 7.5 -I docs/assets/preflight-checks/inputs/Lincoln-and-Parrot-512.png -A k_euler_a -f 0.75
-## 000018.47.png
-![](000018.47.png)
-
-big red dog playing with cat -s 50 -S 47 -W 512 -H 512 -C 7.5 -A k_lms
-## 000019.47.png
-![](000019.47.png)
-
-big red++++ dog playing with cat -s 50 -S 47 -W 512 -H 512 -C 7.5 -A k_lms
-## 000020.47.png
-![](000020.47.png)
-
-big red dog playing with cat+++ -s 50 -S 47 -W 512 -H 512 -C 7.5 -A k_lms
-## 000021.47.png
-![](000021.47.png)
-
-big (red dog).swap(tiger) playing with cat -s 50 -S 47 -W 512 -H 512 -C 7.5 -A k_lms
-## 000022.47.png
-![](000022.47.png)
-
-dog:1,cat:2 -s 50 -S 47 -W 512 -H 512 -C 7.5 -A k_lms
-## 000023.47.png
-![](000023.47.png)
-
-dog:2,cat:1 -s 50 -S 47 -W 512 -H 512 -C 7.5 -A k_lms
-## 000024.1029061431.png
-![](000024.1029061431.png)
-
-medusa with cobras -s 50 -S 1029061431 -W 512 -H 512 -C 7.5 -I docs/assets/preflight-checks/inputs/curly.png -A k_lms -f 0.75 -tm hair
-## 000025.1284519352.png
-![](000025.1284519352.png)
-
-bearded man -s 50 -S 1284519352 -W 512 -H 512 -C 7.5 -I docs/assets/preflight-checks/inputs/curly.png -A k_lms -f 0.75 -tm face
-## curly.942491079.gfpgan.png
-![](curly.942491079.gfpgan.png)
-
-!fix ./docs/assets/preflight-checks/inputs/curly.png -s 50 -S 942491079 -W 512 -H 512 -C 7.5 -A k_lms -G 0.8 -ft gfpgan -U 2.0 0.75
-## curly.942491079.outcrop.png
-![](curly.942491079.outcrop.png)
-
-!fix ./docs/assets/preflight-checks/inputs/curly.png -s 50 -S 942491079 -W 512 -H 512 -C 7.5 -A k_lms -c top 64
-## curly.942491079.outpaint.png
-![](curly.942491079.outpaint.png)
-
-!fix ./docs/assets/preflight-checks/inputs/curly.png -s 50 -S 942491079 -W 512 -H 512 -C 7.5 -A k_lms -D top 64
-## curly.942491079.outcrop-01.png
-![](curly.942491079.outcrop-01.png)
-
-!fix ./docs/assets/preflight-checks/inputs/curly.png -s 50 -S 942491079 -W 512 -H 512 -C 7.5 -A k_lms -c top 64
diff --git a/docs/assets/preflight-checks/outputs/invoke_log.txt b/docs/assets/preflight-checks/outputs/invoke_log.txt
deleted file mode 100644
index 081afe2822..0000000000
--- a/docs/assets/preflight-checks/outputs/invoke_log.txt
+++ /dev/null
@@ -1,29 +0,0 @@
-outputs/preflight/000001.1863159593.png: banana sushi -s 50 -S 1863159593 -W 512 -H 512 -C 7.5 -A k_lms
-outputs/preflight/000002.1151955949.png: banana sushi -s 50 -S 1151955949 -W 512 -H 512 -C 7.5 -A plms
-outputs/preflight/000003.2736230502.png: banana sushi -s 50 -S 2736230502 -W 512 -H 512 -C 7.5 -A ddim
-outputs/preflight/000004.42.png: banana sushi -s 50 -S 42 -W 512 -H 512 -C 7.5 -A k_lms
-outputs/preflight/000005.42.png: banana sushi -s 50 -S 42 -W 512 -H 512 -C 7.5 -A k_lms
-outputs/preflight/000006.478163327.png: banana sushi -s 50 -S 478163327 -W 640 -H 448 -C 7.5 -A k_lms
-outputs/preflight/000007.2407640369.png: banana sushi -s 50 -S 42 -W 512 -H 512 -C 7.5 -A k_lms -V 2407640369:0.1
-outputs/preflight/000008.2772421987.png: banana sushi -s 50 -S 42 -W 512 -H 512 -C 7.5 -A k_lms -V 2772421987:0.1
-outputs/preflight/000009.3532317557.png: banana sushi -s 50 -S 42 -W 512 -H 512 -C 7.5 -A k_lms -V 3532317557:0.1
-outputs/preflight/000010.2028635318.png: banana sushi -s 50 -S 2028635318 -W 512 -H 512 -C 7.5 -A k_lms
-outputs/preflight/000011.1111168647.png: pond with waterlillies -s 50 -S 1111168647 -W 512 -H 512 -C 7.5 -A k_lms
-outputs/preflight/000012.1476370516.png: pond with waterlillies -s 50 -S 1476370516 -W 512 -H 512 -C 7.5 -A k_lms
-outputs/preflight/000013.4281108706.png: banana sushi -s 50 -S 4281108706 -W 960 -H 960 -C 7.5 -A k_lms
-outputs/preflight/000014.2396987386.png: old sea captain with crow on shoulder -s 50 -S 2396987386 -W 512 -H 512 -C 7.5 -I docs/assets/preflight-checks/inputs/Lincoln-and-Parrot-512.png -A k_lms -f 0.75
-outputs/preflight/000015.1252923272.png: old sea captain with crow on shoulder -s 50 -S 1252923272 -W 512 -H 512 -C 7.5 -I docs/assets/preflight-checks/inputs/Lincoln-and-Parrot-512-transparent.png -A k_lms -f 0.75
-outputs/preflight/000016.2633891320.png: old sea captain with crow on shoulder -s 50 -S 2633891320 -W 512 -H 512 -C 7.5 -I docs/assets/preflight-checks/inputs/Lincoln-and-Parrot-512.png -A plms -f 0.75
-outputs/preflight/000017.1134411920.png: old sea captain with crow on shoulder -s 50 -S 1134411920 -W 512 -H 512 -C 7.5 -I docs/assets/preflight-checks/inputs/Lincoln-and-Parrot-512.png -A k_euler_a -f 0.75
-outputs/preflight/000018.47.png: big red dog playing with cat -s 50 -S 47 -W 512 -H 512 -C 7.5 -A k_lms
-outputs/preflight/000019.47.png: big red++++ dog playing with cat -s 50 -S 47 -W 512 -H 512 -C 7.5 -A k_lms
-outputs/preflight/000020.47.png: big red dog playing with cat+++ -s 50 -S 47 -W 512 -H 512 -C 7.5 -A k_lms
-outputs/preflight/000021.47.png: big (red dog).swap(tiger) playing with cat -s 50 -S 47 -W 512 -H 512 -C 7.5 -A k_lms
-outputs/preflight/000022.47.png: dog:1,cat:2 -s 50 -S 47 -W 512 -H 512 -C 7.5 -A k_lms
-outputs/preflight/000023.47.png: dog:2,cat:1 -s 50 -S 47 -W 512 -H 512 -C 7.5 -A k_lms
-outputs/preflight/000024.1029061431.png: medusa with cobras -s 50 -S 1029061431 -W 512 -H 512 -C 7.5 -I docs/assets/preflight-checks/inputs/curly.png -A k_lms -f 0.75 -tm hair
-outputs/preflight/000025.1284519352.png: bearded man -s 50 -S 1284519352 -W 512 -H 512 -C 7.5 -I docs/assets/preflight-checks/inputs/curly.png -A k_lms -f 0.75 -tm face
-outputs/preflight/curly.942491079.gfpgan.png: !fix ./docs/assets/preflight-checks/inputs/curly.png -s 50 -S 942491079 -W 512 -H 512 -C 7.5 -A k_lms -G 0.8 -ft gfpgan -U 2.0 0.75
-outputs/preflight/curly.942491079.outcrop.png: !fix ./docs/assets/preflight-checks/inputs/curly.png -s 50 -S 942491079 -W 512 -H 512 -C 7.5 -A k_lms -c top 64
-outputs/preflight/curly.942491079.outpaint.png: !fix ./docs/assets/preflight-checks/inputs/curly.png -s 50 -S 942491079 -W 512 -H 512 -C 7.5 -A k_lms -D top 64
-outputs/preflight/curly.942491079.outcrop-01.png: !fix ./docs/assets/preflight-checks/inputs/curly.png -s 50 -S 942491079 -W 512 -H 512 -C 7.5 -A k_lms -c top 64
diff --git a/docs/assets/preflight-checks/preflight_prompts.txt b/docs/assets/preflight-checks/preflight_prompts.txt
deleted file mode 100644
index 3ec421e9e9..0000000000
--- a/docs/assets/preflight-checks/preflight_prompts.txt
+++ /dev/null
@@ -1,61 +0,0 @@
-# outputs/preflight/000001.1863159593.png
-banana sushi -s 50 -S 1863159593 -W 512 -H 512 -C 7.5 -A k_lms
-# outputs/preflight/000002.1151955949.png
-banana sushi -s 50 -S 1151955949 -W 512 -H 512 -C 7.5 -A plms
-# outputs/preflight/000003.2736230502.png
-banana sushi -s 50 -S 2736230502 -W 512 -H 512 -C 7.5 -A ddim
-# outputs/preflight/000004.42.png
-banana sushi -s 50 -S 42 -W 512 -H 512 -C 7.5 -A k_lms
-# outputs/preflight/000005.42.png
-banana sushi -s 50 -S 42 -W 512 -H 512 -C 7.5 -A k_lms
-# outputs/preflight/000006.478163327.png
-banana sushi -s 50 -S 478163327 -W 640 -H 448 -C 7.5 -A k_lms
-# outputs/preflight/000007.2407640369.png
-banana sushi -s 50 -S 42 -W 512 -H 512 -C 7.5 -A k_lms -V 2407640369:0.1
-# outputs/preflight/000007.2772421987.png
-banana sushi -s 50 -S 42 -W 512 -H 512 -C 7.5 -A k_lms -V 2772421987:0.1
-# outputs/preflight/000007.3532317557.png
-banana sushi -s 50 -S 42 -W 512 -H 512 -C 7.5 -A k_lms -V 3532317557:0.1
-# outputs/preflight/000008.2028635318.png
-banana sushi -s 50 -S 2028635318 -W 512 -H 512 -C 7.5 -A k_lms
-# outputs/preflight/000009.1111168647.png
-pond with waterlillies -s 50 -S 1111168647 -W 512 -H 512 -C 7.5 -A k_lms
-# outputs/preflight/000010.1476370516.png
-pond with waterlillies -s 50 -S 1476370516 -W 512 -H 512 -C 7.5 -A k_lms --seamless
-# outputs/preflight/000011.4281108706.png
-banana sushi -s 50 -S 4281108706 -W 960 -H 960 -C 7.5 -A k_lms
-# outputs/preflight/000012.2396987386.png
-old sea captain with crow on shoulder -s 50 -S 2396987386 -W 512 -H 512 -C 7.5 -I docs/assets/preflight-checks/inputs/Lincoln-and-Parrot-512.png -A k_lms -f 0.75
-# outputs/preflight/000013.1252923272.png
-old sea captain with crow on shoulder -s 50 -S 1252923272 -W 512 -H 512 -C 7.5 -I docs/assets/preflight-checks/inputs/Lincoln-and-Parrot-512-transparent.png -A k_lms -f 0.75
-# outputs/preflight/000014.2633891320.png
-old sea captain with crow on shoulder -s 50 -S 2633891320 -W 512 -H 512 -C 7.5 -I docs/assets/preflight-checks/inputs/Lincoln-and-Parrot-512.png -A plms -f 0.75
-# outputs/preflight/000015.1134411920.png
-old sea captain with crow on shoulder -s 50 -S 1134411920 -W 512 -H 512 -C 7.5 -I docs/assets/preflight-checks/inputs/Lincoln-and-Parrot-512.png -A k_euler_a -f 0.75
-# outputs/preflight/000016.42.png
-big red dog playing with cat -s 50 -S 47 -W 512 -H 512 -C 7.5 -A k_lms
-# outputs/preflight/000017.42.png
-big red++++ dog playing with cat -s 50 -S 47 -W 512 -H 512 -C 7.5 -A k_lms
-# outputs/preflight/000018.42.png
-big red dog playing with cat+++ -s 50 -S 47 -W 512 -H 512 -C 7.5 -A k_lms
-# outputs/preflight/000019.42.png
-big (red dog).swap(tiger) playing with cat -s 50 -S 47 -W 512 -H 512 -C 7.5 -A k_lms
-# outputs/preflight/000020.42.png
-dog:1,cat:2 -s 50 -S 47 -W 512 -H 512 -C 7.5 -A k_lms
-# outputs/preflight/000021.42.png
-dog:2,cat:1 -s 50 -S 47 -W 512 -H 512 -C 7.5 -A k_lms
-# outputs/preflight/000022.1029061431.png
-medusa with cobras -s 50 -S 1029061431 -W 512 -H 512 -C 7.5 -I docs/assets/preflight-checks/inputs/curly.png -A k_lms -f 0.75 -tm hair
-# outputs/preflight/000023.1284519352.png
-bearded man -s 50 -S 1284519352 -W 512 -H 512 -C 7.5 -I docs/assets/preflight-checks/inputs/curly.png -A k_lms -f 0.75 -tm face
-# outputs/preflight/000024.curly.hair.deselected.png
-!mask -I docs/assets/preflight-checks/inputs/curly.png -tm hair
-# outputs/preflight/curly.942491079.gfpgan.png
-!fix ./docs/assets/preflight-checks/inputs/curly.png -U2 -G0.8
-# outputs/preflight/curly.942491079.outcrop.png
-!fix ./docs/assets/preflight-checks/inputs/curly.png -c top 64
-# outputs/preflight/curly.942491079.outpaint.png
-!fix ./docs/assets/preflight-checks/inputs/curly.png -D top 64
-# outputs/preflight/curly.942491079.outcrop-01.png
-!switch inpainting-1.5
-!fix ./docs/assets/preflight-checks/inputs/curly.png -c top 64
diff --git a/docs/features/CLI.md b/docs/features/CLI.md
index 26d0ecc8d5..b8de156412 100644
--- a/docs/features/CLI.md
+++ b/docs/features/CLI.md
@@ -1,7 +1,5 @@
 ---
 title: CLI
-hide:
-  - toc
 ---
 
 # :material-bash: CLI
@@ -93,6 +91,10 @@ overridden on a per-prompt basis (see
 | `--port <port>`                           |                                           | `9090`                                         | Which port web server should listen for requests on.                                                 |
 | `--config <path>`                         |                                           | `configs/models.yaml`                          | Configuration file for models and their weights.                                                     |
 | `--iterations <int>`                      | `-n<int>`                                 | `1`                                            | How many images to generate per prompt.                                                              |
+| `--width <int>`                           | `-W<int>`                                 | `512`                                    | Width of generated image                                                                                                                                                                                                                         |
+| `--height <int>`                          | `-H<int>`                                 | `512`                                    | Height of generated image                                                                                                        | `--steps <int>`                           | `-s<int>`                                 | `50`                                     | How many steps of refinement to apply                                                                                                                                                                                                            |
+| `--strength <float>`                      | `-s<float>` | `0.75`  | For img2img: how hard to try to match the prompt to the initial image. Ranges from 0.0-0.99, with higher values replacing the initial image completely. |
+| `--fit`                                   | `-F`        | `False` | For img2img: scale the init image to fit into the specified -H and -W dimensions                                                                             |
 | `--grid`                                  | `-g`                                      | `False`                                        | Save all image series as a grid rather than individually.                                            |
 | `--sampler <sampler>`                     | `-A<sampler>`                             | `k_lms`                                        | Sampler to use. Use `-h` to get list of available samplers.                                          |
 | `--seamless`                              |                                           | `False`                                        | Create interesting effects by tiling elements of the image.                                          |
@@ -108,7 +110,7 @@ overridden on a per-prompt basis (see
 
     | Argument           |  Shortcut  |  Default            |  Description |
     |--------------------|------------|---------------------|--------------|
-    | `--weights <path>`   |            | `None`                | Pth to weights file; use `--model stable-diffusion-1.4` instead |
+    | `--weights <path>`   |            | `None`                | Path to weights file; use `--model stable-diffusion-1.4` instead |
     | `--laion400m`        | `-l`         | `False`               | Use older LAION400m weights; use `--model=laion400m` instead |
 
     </div>
@@ -121,6 +123,29 @@ overridden on a per-prompt basis (see
       You can either double your slashes (ick): `C:\\path\\to\\my\\file`, or
       use Linux/Mac style forward slashes (better): `C:/path/to/my/file`.
 
+## The .invokeai initialization file
+
+To start up invoke.py with your preferred settings, place your desired
+startup options in a file in your home directory named `.invokeai` The
+file should contain the startup options as you would type them on the
+command line (`--steps=10 --grid`), one argument per line, or a
+mixture of both using any of the accepted command switch formats:
+
+!!! example ""
+
+    ```bash
+    --web
+    --steps=28
+    --grid
+    -f 0.6 -C 11.0 -A k_euler_a
+    ```
+
+Note that the initialization file only accepts the command line arguments.
+There are additional arguments that you can provide on the `invoke>` command
+line (such as `-n` or `--iterations`) that cannot be entered into this file.
+Also be alert for empty blank lines at the end of the file, which will cause
+an arguments error at startup time.
+
 ## List of prompt arguments
 
 After the invoke.py script initializes, it will present you with a `invoke>`
diff --git a/docs/features/IMG2IMG.md b/docs/features/IMG2IMG.md
index a390a836c5..6589027761 100644
--- a/docs/features/IMG2IMG.md
+++ b/docs/features/IMG2IMG.md
@@ -6,10 +6,11 @@ title: Image-to-Image
 
 ## `img2img`
 
-This script also provides an `img2img` feature that lets you seed your creations with an initial
-drawing or photo. This is a really cool feature that tells stable diffusion to build the prompt on
-top of the image you provide, preserving the original's basic shape and layout. To use it, provide
-the `--init_img` option as shown here:
+This script also provides an `img2img` feature that lets you seed your creations
+with an initial drawing or photo. This is a really cool feature that tells
+stable diffusion to build the prompt on top of the image you provide, preserving
+the original's basic shape and layout. To use it, provide the `--init_img`
+option as shown here:
 
 ```commandline
 tree on a hill with a river, nature photograph, national geographic -I./test-pictures/tree-and-river-sketch.png -f 0.85
@@ -18,63 +19,76 @@ tree on a hill with a river, nature photograph, national geographic -I./test-pic
 This will take the original image shown here:
 
 <figure markdown>
-<img src="https://user-images.githubusercontent.com/50542132/193946000-c42a96d8-5a74-4f8a-b4c3-5213e6cadcce.png" width=350>
+![original-image](https://user-images.githubusercontent.com/50542132/193946000-c42a96d8-5a74-4f8a-b4c3-5213e6cadcce.png){ width=320 }
 </figure>
 
 and generate a new image based on it as shown here:
 
 <figure markdown>
-<img src="https://user-images.githubusercontent.com/111189/194135515-53d4c060-e994-4016-8121-7c685e281ac9.png" width=350>
+![generated-image](https://user-images.githubusercontent.com/111189/194135515-53d4c060-e994-4016-8121-7c685e281ac9.png){ width=320 }
 </figure>
 
-The `--init_img` (`-I`) option gives the path to the seed picture. `--strength` (`-f`) controls how much
-the original will be modified, ranging from `0.0` (keep the original intact), to `1.0` (ignore the
-original completely). The default is `0.75`, and ranges from `0.25-0.90` give interesting results. 
-Other relevant options include `-C` (classification free guidance scale), and `-s` (steps). Unlike `txt2img`, 
-adding steps will continuously change the resulting image and it will not converge.
+The `--init_img` (`-I`) option gives the path to the seed picture. `--strength`
+(`-f`) controls how much the original will be modified, ranging from `0.0` (keep
+the original intact), to `1.0` (ignore the original completely). The default is
+`0.75`, and ranges from `0.25-0.90` give interesting results. Other relevant
+options include `-C` (classification free guidance scale), and `-s` (steps).
+Unlike `txt2img`, adding steps will continuously change the resulting image and
+it will not converge.
 
-You may also pass a `-v<variation_amount>` option to generate `-n<iterations>` count variants on
-the original image. This is done by passing the first generated image
-back into img2img the requested number of times. It generates
+You may also pass a `-v<variation_amount>` option to generate `-n<iterations>`
+count variants on the original image. This is done by passing the first
+generated image back into img2img the requested number of times. It generates
 interesting variants.
 
-Note that the prompt makes a big difference. For example, this slight variation on the prompt produces
-a very different image:
+Note that the prompt makes a big difference. For example, this slight variation
+on the prompt produces a very different image:
 
 <figure markdown>
-<img src="https://user-images.githubusercontent.com/111189/194135220-16b62181-b60c-4248-8989-4834a8fd7fbd.png" width=350>
+![](https://user-images.githubusercontent.com/111189/194135220-16b62181-b60c-4248-8989-4834a8fd7fbd.png){ width=320 }
 <caption markdown>photograph of a tree on a hill with a river</caption>
 </figure>
 
 !!! tip
 
-    When designing prompts, think about how the images scraped from the internet were captioned. Very few photographs will
-    be labeled "photograph" or "photorealistic." They will, however, be captioned with the publication, photographer, camera
-    model, or film settings.
+    When designing prompts, think about how the images scraped from the internet were
+    captioned. Very few photographs will be labeled "photograph" or "photorealistic."
+    They will, however, be captioned with the publication, photographer, camera model,
+    or film settings.
 
-If the initial image contains transparent regions, then Stable Diffusion will only draw within the
-transparent regions, a process called [`inpainting`](./INPAINTING.md#creating-transparent-regions-for-inpainting). However, for this to work correctly, the color
-information underneath the transparent needs to be preserved, not erased.
+If the initial image contains transparent regions, then Stable Diffusion will
+only draw within the transparent regions, a process called
+[`inpainting`](./INPAINTING.md#creating-transparent-regions-for-inpainting).
+However, for this to work correctly, the color information underneath the
+transparent needs to be preserved, not erased.
 
-!!! warning
+!!! warning "**IMPORTANT ISSUE** "
 
-**IMPORTANT ISSUE** `img2img` does not work properly on initial images smaller than 512x512. Please scale your
-image to at least 512x512 before using it. Larger images are not a problem, but may run out of VRAM on your
-GPU card. To fix this, use the --fit option, which downscales the initial image to fit within the box specified
-by width x height:
-~~~
-tree on a hill with a river, national geographic -I./test-pictures/big-sketch.png -H512 -W512 --fit
-~~~
+    `img2img` does not work properly on initial images smaller
+    than 512x512. Please scale your image to at least 512x512 before using it.
+    Larger images are not a problem, but may run out of VRAM on your GPU card. To
+    fix this, use the --fit option, which downscales the initial image to fit within
+    the box specified by width x height:
+
+    ```
+    tree on a hill with a river, national geographic -I./test-pictures/big-sketch.png -H512 -W512 --fit
+    ```
 
 ## How does it actually work, though?
 
-The main difference between `img2img` and `prompt2img` is the starting point. While `prompt2img` always starts with pure
-gaussian noise and progressively refines it over the requested number of steps, `img2img` skips some of these earlier steps
-(how many it skips is indirectly controlled by the `--strength` parameter), and uses instead your initial image mixed with gaussian noise as the starting image.
+The main difference between `img2img` and `prompt2img` is the starting point.
+While `prompt2img` always starts with pure gaussian noise and progressively
+refines it over the requested number of steps, `img2img` skips some of these
+earlier steps (how many it skips is indirectly controlled by the `--strength`
+parameter), and uses instead your initial image mixed with gaussian noise as the
+starting image.
 
-**Let's start** by thinking about vanilla `prompt2img`, just generating an image from a prompt. If the step count is 10, then the "latent space" (Stable Diffusion's internal representation of the image) for the prompt "fire" with seed `1592514025` develops something like this:
+**Let's start** by thinking about vanilla `prompt2img`, just generating an image
+from a prompt. If the step count is 10, then the "latent space" (Stable
+Diffusion's internal representation of the image) for the prompt "fire" with
+seed `1592514025` develops something like this:
 
-```commandline
+```bash
 invoke> "fire" -s10 -W384 -H384 -S1592514025
 ```
 
@@ -82,9 +96,16 @@ invoke> "fire" -s10 -W384 -H384 -S1592514025
 ![latent steps](../assets/img2img/000019.steps.png)
 </figure>
 
-Put simply: starting from a frame of fuzz/static, SD finds details in each frame that it thinks look like "fire" and brings them a little bit more into focus, gradually scrubbing out the fuzz until a clear image remains.
+Put simply: starting from a frame of fuzz/static, SD finds details in each frame
+that it thinks look like "fire" and brings them a little bit more into focus,
+gradually scrubbing out the fuzz until a clear image remains.
 
-**When you use `img2img`** some of the earlier steps are cut, and instead an initial image of your choice is used. But because of how the maths behind Stable Diffusion works, this image needs to be mixed with just the right amount of noise (fuzz/static) for where it is being inserted. This is where the strength parameter comes in. Depending on the set strength, your image will be inserted into the sequence at the appropriate point, with just the right amount of noise.
+**When you use `img2img`** some of the earlier steps are cut, and instead an
+initial image of your choice is used. But because of how the maths behind Stable
+Diffusion works, this image needs to be mixed with just the right amount of
+noise (fuzz/static) for where it is being inserted. This is where the strength
+parameter comes in. Depending on the set strength, your image will be inserted
+into the sequence at the appropriate point, with just the right amount of noise.
 
 ### A concrete example
 
@@ -94,7 +115,9 @@ I want SD to draw a fire based on this hand-drawn image:
 ![drawing of a fireplace](../assets/img2img/fire-drawing.png)
 </figure>
 
-Let's only do 10 steps, to make it easier to see what's happening. If strength is `0.7`, this is what the internal steps the algorithm has to take will look like:
+Let's only do 10 steps, to make it easier to see what's happening. If strength
+is `0.7`, this is what the internal steps the algorithm has to take will look
+like:
 
 <figure markdown>
 ![gravity32](../assets/img2img/000032.steps.gravity.png)
@@ -106,33 +129,49 @@ With strength `0.4`, the steps look more like this:
 ![gravity30](../assets/img2img/000030.steps.gravity.png)
 </figure>
 
-Notice how much more fuzzy the starting image is for strength `0.7` compared to `0.4`, and notice also how much longer the sequence is with `0.7`:
+Notice how much more fuzzy the starting image is for strength `0.7` compared to
+`0.4`, and notice also how much longer the sequence is with `0.7`:
 
-|  | strength = 0.7 | strength = 0.4 |
-| -- | -- | -- |
-| initial image that SD sees | ![](../assets/img2img/000032.step-0.png) | ![](../assets/img2img/000030.step-0.png) |
-| steps argument to `invoke>` | `-S10` | `-S10` |
-| steps actually taken | 7 | 4 |
-| latent space at each step | ![gravity32](../assets/img2img/000032.steps.gravity.png) | ![gravity30](../assets/img2img/000030.steps.gravity.png) |
-| output | ![000032.1592514025](../assets/img2img/000032.1592514025.png) | ![000030.1592514025](../assets/img2img/000030.1592514025.png) |
+|                             | strength = 0.7                                                | strength = 0.4                                                |
+| --------------------------- | ------------------------------------------------------------- | ------------------------------------------------------------- |
+| initial image that SD sees  | ![step-0](../assets/img2img/000032.step-0.png)                | ![step-0](../assets/img2img/000030.step-0.png)                |
+| steps argument to `invoke>` | `-S10`                                                        | `-S10`                                                        |
+| steps actually taken        | `7`                                                           | `4`                                                           |
+| latent space at each step   | ![gravity32](../assets/img2img/000032.steps.gravity.png)      | ![gravity30](../assets/img2img/000030.steps.gravity.png)      |
+| output                      | ![000032.1592514025](../assets/img2img/000032.1592514025.png) | ![000030.1592514025](../assets/img2img/000030.1592514025.png) |
 
-Both of the outputs look kind of like what I was thinking of. With the strength higher, my input becomes more vague, *and* Stable Diffusion has more steps to refine its output. But it's not really making what I want, which is a picture of cheery open fire. With the strength lower, my input is more clear, *but* Stable Diffusion has less chance to refine itself, so the result ends up inheriting all the problems of my bad drawing.
+Both of the outputs look kind of like what I was thinking of. With the strength
+higher, my input becomes more vague, _and_ Stable Diffusion has more steps to
+refine its output. But it's not really making what I want, which is a picture of
+cheery open fire. With the strength lower, my input is more clear, _but_ Stable
+Diffusion has less chance to refine itself, so the result ends up inheriting all
+the problems of my bad drawing.
 
-If you want to try this out yourself, all of these are using a seed of `1592514025` with a width/height of `384`, step count `10`, the default sampler (`k_lms`), and the single-word prompt `"fire"`:
+If you want to try this out yourself, all of these are using a seed of
+`1592514025` with a width/height of `384`, step count `10`, the default sampler
+(`k_lms`), and the single-word prompt `"fire"`:
 
-```commandline
+```bash
 invoke> "fire" -s10 -W384 -H384 -S1592514025 -I /tmp/fire-drawing.png --strength 0.7
 ```
 
-The code for rendering intermediates is on my (damian0815's) branch [document-img2img](https://github.com/damian0815/InvokeAI/tree/document-img2img) - run `invoke.py` and check your `outputs/img-samples/intermediates` folder while generating an image. 
+The code for rendering intermediates is on my (damian0815's) branch
+[document-img2img](https://github.com/damian0815/InvokeAI/tree/document-img2img) -
+run `invoke.py` and check your `outputs/img-samples/intermediates` folder while
+generating an image.
 
 ### Compensating for the reduced step count
 
-After putting this guide together I was curious to see how the difference would be if I increased the step count to compensate, so that SD could have the same amount of steps to develop the image regardless of the strength. So I ran the generation again using the same seed, but this time adapting the step count to give each generation 20 steps.
+After putting this guide together I was curious to see how the difference would
+be if I increased the step count to compensate, so that SD could have the same
+amount of steps to develop the image regardless of the strength. So I ran the
+generation again using the same seed, but this time adapting the step count to
+give each generation 20 steps.
 
-Here's strength `0.4` (note step count `50`, which is `20 ÷ 0.4` to make sure SD does `20` steps from my image):
+Here's strength `0.4` (note step count `50`, which is `20 ÷ 0.4` to make sure SD
+does `20` steps from my image):
 
-```commandline
+```bash
 invoke> "fire" -s50 -W384 -H384 -S1592514025 -I /tmp/fire-drawing.png -f 0.4
 ```
 
@@ -140,7 +179,8 @@ invoke> "fire" -s50 -W384 -H384 -S1592514025 -I /tmp/fire-drawing.png -f 0.4
 ![000035.1592514025](../assets/img2img/000035.1592514025.png)
 </figure>
 
-and here is strength `0.7` (note step count `30`, which is roughly `20 ÷ 0.7` to make sure SD does `20` steps from my image):
+and here is strength `0.7` (note step count `30`, which is roughly `20 ÷ 0.7` to
+make sure SD does `20` steps from my image):
 
 ```commandline
 invoke> "fire" -s30 -W384 -H384 -S1592514025 -I /tmp/fire-drawing.png -f 0.7
@@ -150,7 +190,11 @@ invoke> "fire" -s30 -W384 -H384 -S1592514025 -I /tmp/fire-drawing.png -f 0.7
 ![000046.1592514025](../assets/img2img/000046.1592514025.png)
 </figure>
 
-In both cases the image is nice and clean and "finished", but because at strength `0.7` Stable Diffusion has been give so much more freedom to improve on my badly-drawn flames, they've come out looking much better. You can really see the difference when looking at the latent steps. There's more noise on the first image with strength `0.7`:
+In both cases the image is nice and clean and "finished", but because at
+strength `0.7` Stable Diffusion has been give so much more freedom to improve on
+my badly-drawn flames, they've come out looking much better. You can really see
+the difference when looking at the latent steps. There's more noise on the first
+image with strength `0.7`:
 
 <figure markdown>
 ![gravity46](../assets/img2img/000046.steps.gravity.png)
@@ -162,15 +206,19 @@ than there is for strength `0.4`:
 ![gravity35](../assets/img2img/000035.steps.gravity.png)
 </figure>
 
-and that extra noise gives the algorithm more choices when it is evaluating how to denoise any particular pixel in the image.
+and that extra noise gives the algorithm more choices when it is evaluating how
+to denoise any particular pixel in the image.
 
-Unfortunately, it seems that `img2img` is very sensitive to the step count. Here's strength `0.7` with a step count of `29` (SD did 19 steps from my image):
+Unfortunately, it seems that `img2img` is very sensitive to the step count.
+Here's strength `0.7` with a step count of `29` (SD did 19 steps from my image):
 
 <figure markdown>
 ![gravity45](../assets/img2img/000045.1592514025.png)
 </figure>
 
-By comparing the latents we can sort of see that something got interpreted differently enough on the third or fourth step to lead to a rather different interpretation of the flames.
+By comparing the latents we can sort of see that something got interpreted
+differently enough on the third or fourth step to lead to a rather different
+interpretation of the flames.
 
 <figure markdown>
 ![gravity46](../assets/img2img/000046.steps.gravity.png)
@@ -180,4 +228,9 @@ By comparing the latents we can sort of see that something got interpreted diffe
 ![gravity45](../assets/img2img/000045.steps.gravity.png)
 </figure>
 
-This is the result of a difference in the de-noising "schedule" - basically the noise has to be cleaned by a certain degree each step or the model won't "converge" on the image properly (see [stable diffusion blog](https://huggingface.co/blog/stable_diffusion) for more about that). A different step count means a different schedule, which means things get interpreted slightly differently at every step.
+This is the result of a difference in the de-noising "schedule" - basically the
+noise has to be cleaned by a certain degree each step or the model won't
+"converge" on the image properly (see
+[stable diffusion blog](https://huggingface.co/blog/stable_diffusion) for more
+about that). A different step count means a different schedule, which means
+things get interpreted slightly differently at every step.
diff --git a/docs/features/OUTPAINTING.md b/docs/features/OUTPAINTING.md
index 58cbc0aa43..b53ff545f3 100644
--- a/docs/features/OUTPAINTING.md
+++ b/docs/features/OUTPAINTING.md
@@ -6,61 +6,63 @@ title: Outpainting
 
 ## Outpainting and outcropping
 
-Outpainting is a process by which the AI generates parts of the image
-that are outside its original frame. It can be used to fix up images
-in which the subject is off center, or when some detail (often the top
-of someone's head!) is cut off.
+Outpainting is a process by which the AI generates parts of the image that are
+outside its original frame. It can be used to fix up images in which the subject
+is off center, or when some detail (often the top of someone's head!) is cut
+off.
 
-InvokeAI supports two versions of outpainting, one called "outpaint"
-and the other "outcrop." They work slightly differently and each has
-its advantages and drawbacks.
+InvokeAI supports two versions of outpainting, one called "outpaint" and the
+other "outcrop." They work slightly differently and each has its advantages and
+drawbacks.
 
 ### Outpainting
 
-Outpainting is the same as inpainting, except that the painting occurs
-in the regions outside of the original image. To outpaint using the
-`invoke.py` command line script, prepare an image in which the borders
-to be extended are pure black. Add an alpha channel (if there isn't one
-already), and make the borders completely transparent and the interior
-completely opaque. If you wish to modify the interior as well, you may
-create transparent holes in the transparency layer, which `img2img` will
-paint into as usual.
+Outpainting is the same as inpainting, except that the painting occurs in the
+regions outside of the original image. To outpaint using the `invoke.py` command
+line script, prepare an image in which the borders to be extended are pure
+black. Add an alpha channel (if there isn't one already), and make the borders
+completely transparent and the interior completely opaque. If you wish to modify
+the interior as well, you may create transparent holes in the transparency
+layer, which `img2img` will paint into as usual.
 
-Pass the image as the argument to the `-I` switch as you would for
-regular inpainting:
+Pass the image as the argument to the `-I` switch as you would for regular
+inpainting:
 
- invoke> a stream by a river -I /path/to/transparent_img.png
+```bash
+invoke> a stream by a river -I /path/to/transparent_img.png
+```
 
 You'll likely be delighted by the results.
 
 ### Tips
 
-1. Do not try to expand the image too much at once. Generally it is best
-   to expand the margins in 64-pixel increments. 128 pixels often works,
-   but your mileage may vary depending on the nature of the image you are
-   trying to outpaint into.
+1. Do not try to expand the image too much at once. Generally it is best to
+   expand the margins in 64-pixel increments. 128 pixels often works, but your
+   mileage may vary depending on the nature of the image you are trying to
+   outpaint into.
 
-2. There are a series of switches that can be used to adjust how the
-   inpainting algorithm operates. In particular, you can use these to
-   minimize the seam that sometimes appears between the original image
-   and the extended part. These switches are:
+2. There are a series of switches that can be used to adjust how the inpainting
+   algorithm operates. In particular, you can use these to minimize the seam
+   that sometimes appears between the original image and the extended part.
+   These switches are:
 
-  --seam_size SEAM_SIZE    Size of the mask around the seam between original and outpainted image (0)
-  --seam_blur SEAM_BLUR    The amount to blur the seam inwards (0)
-  --seam_strength STRENGTH The img2img strength to use when filling the seam (0.7)
-  --seam_steps SEAM_STEPS  The number of steps to use to fill the seam. (10)
-  --tile_size TILE_SIZE    The tile size to use for filling outpaint areas (32)
+| switch                     | default | description                                                            |
+| -------------------------- | ------- | ---------------------------------------------------------------------- |
+| `--seam_size SEAM_SIZE `   | `0`     | Size of the mask around the seam between original and outpainted image |
+| `--seam_blur SEAM_BLUR`    | `0`     | The amount to blur the seam inwards                                    |
+| `--seam_strength STRENGTH` | `0.7`   | The img2img strength to use when filling the seam                      |
+| `--seam_steps SEAM_STEPS`  | `10`    | The number of steps to use to fill the seam.                           |
+| `--tile_size TILE_SIZE`    | `32`    | The tile size to use for filling outpaint areas                        |
 
 ### Outcrop
 
-The `outcrop` extension gives you a convenient `!fix` postprocessing
-command that allows you to extend a previously-generated image in 64
-pixel increments in any direction. You can apply the module to any
-image previously-generated by InvokeAI. Note that it works with
-arbitrary PNG photographs, but not currently with JPG or other
-formats. Outcropping is particularly effective when combined with the
-[runwayML custom inpainting
-model](INPAINTING.md#using-the-runwayml-inpainting-model).
+The `outcrop` extension gives you a convenient `!fix` postprocessing command
+that allows you to extend a previously-generated image in 64 pixel increments in
+any direction. You can apply the module to any image previously-generated by
+InvokeAI. Note that it works with arbitrary PNG photographs, but not currently
+with JPG or other formats. Outcropping is particularly effective when combined
+with the
+[runwayML custom inpainting model](INPAINTING.md#using-the-runwayml-inpainting-model).
 
 Consider this image:
 
@@ -68,18 +70,17 @@ Consider this image:
 ![curly_woman](../assets/outpainting/curly.png)
 </figure>
 
-Pretty nice, but it's annoying that the top of her head is cut
-off. She's also a bit off center. Let's fix that!
+Pretty nice, but it's annoying that the top of her head is cut off. She's also a
+bit off center. Let's fix that!
 
 ```bash
 invoke> !fix images/curly.png --outcrop top 128 right 64 bottom 64
 ```
 
-This is saying to apply the `outcrop` extension by extending the top
-of the image by 128 pixels, and the right and bottom of the image by
-64 pixels. You can use any combination of top|left|right|bottom, and
-specify any number of pixels to extend. You can also abbreviate
-`--outcrop` to `-c`.
+This is saying to apply the `outcrop` extension by extending the top of the
+image by 128 pixels, and the right and bottom of the image by 64 pixels. You can
+use any combination of top|left|right|bottom, and specify any number of pixels
+to extend. You can also abbreviate `--outcrop` to `-c`.
 
 The result looks like this:
 
@@ -87,39 +88,53 @@ The result looks like this:
 ![curly_woman_outcrop](../assets/outpainting/curly-outcrop-2.png)
 </figure>
 
-The new image is larger than the original (576x704)
-because 64 pixels were added to the top and right sides. You will
-need enough VRAM to process an image of this size.
+The new image is larger than the original (576x704) because 64 pixels were added
+to the top and right sides. You will need enough VRAM to process an image of
+this size.
+
+#### Outcropping non-InvokeAI images
+
+You can outcrop an arbitrary image that was not generated by InvokeAI,
+but your results will vary. The `inpainting-1.5` model is highly
+recommended, but if not feasible, then you may be able to improve the
+output by conditioning the outcropping with a text prompt that
+describes the scene using the `--new_prompt` argument:
+
+```bash
+invoke> !fix images/vacation.png --outcrop top 128 --new_prompt "family vacation"
+```
+
+You may also provide a different seed for outcropping to use by passing
+`-S<seed>`. A negative seed will generate a new random seed.
 
 A number of caveats:
 
-1. Although you can specify any pixel values, they will be rounded up
-to the nearest multiple of 64. Smaller values are better. Larger
-extensions are more likely to generate artefacts. However, if you wish
-you can run the !fix command repeatedly to cautiously expand the
-image.
+1. Although you can specify any pixel values, they will be rounded up to the
+   nearest multiple of 64. Smaller values are better. Larger extensions are more
+   likely to generate artefacts. However, if you wish you can run the !fix
+   command repeatedly to cautiously expand the image.
 
-2. The extension is stochastic, meaning that each time you run it
-you'll get a slightly different result. You can run it repeatedly
-until you get an image you like. Unfortunately `!fix` does not
-currently respect the `-n` (`--iterations`) argument.
+2. The extension is stochastic, meaning that each time you run it you'll get a
+   slightly different result. You can run it repeatedly until you get an image
+   you like. Unfortunately `!fix` does not currently respect the `-n`
+   (`--iterations`) argument.
 
-3. Your results will be _much_ better if you use the `inpaint-1.5`
-model released by runwayML and installed by default by
-`scripts/preload_models.py`. This model was trained specifically to
-harmoniously fill in image gaps. The standard model will work as well,
-but you may notice color discontinuities at the border.
+3. Your results will be _much_ better if you use the `inpaint-1.5` model
+   released by runwayML and installed by default by `scripts/preload_models.py`.
+   This model was trained specifically to harmoniously fill in image gaps. The
+   standard model will work as well, but you may notice color discontinuities at
+   the border.
 
-4. When using the `inpaint-1.5` model, you may notice subtle changes
-to the area within the original image. This is because the model
-performs an encoding/decoding on the image as a whole. This does not
-occur with the standard model.
+4. When using the `inpaint-1.5` model, you may notice subtle changes to the area
+   outside the masked region. This is because the model performs an
+   encoding/decoding on the image as a whole. This does not occur with the
+   standard model.
 
 ## Outpaint
 
-The `outpaint` extension does the same thing, but with subtle
-differences. Starting with the same image, here is how we would add an
-additional 64 pixels to the top of the image:
+The `outpaint` extension does the same thing, but with subtle differences.
+Starting with the same image, here is how we would add an additional 64 pixels
+to the top of the image:
 
 ```bash
 invoke> !fix images/curly.png --out_direction top 64
@@ -138,15 +153,15 @@ outcropping:
 
 - You can only specify one direction to extend at a time.
 - The image is **not** resized. Instead, the image is shifted by the specified
-number of pixels. If you look carefully, you'll see that less of the lady's
-torso is visible in the image.
-- Because the image dimensions remain the same, there's no rounding
-to multiples of 64.
-- Attempting to outpaint larger areas will frequently give rise to ugly
-   ghosting effects.
+  number of pixels. If you look carefully, you'll see that less of the lady's
+  torso is visible in the image.
+- Because the image dimensions remain the same, there's no rounding to multiples
+  of 64.
+- Attempting to outpaint larger areas will frequently give rise to ugly ghosting
+  effects.
 - For best results, try increasing the step number.
-- If you don't specify a pixel value in `-D`, it will default to half
-   of the whole image, which is likely not what you want.
+- If you don't specify a pixel value in `-D`, it will default to half of the
+  whole image, which is likely not what you want.
 
 !!! tip
 
diff --git a/docs/features/POSTPROCESS.md b/docs/features/POSTPROCESS.md
index 29c63ffd14..74a48c4d8f 100644
--- a/docs/features/POSTPROCESS.md
+++ b/docs/features/POSTPROCESS.md
@@ -19,14 +19,14 @@ applied after the fact.
 
 The default face restoration module is GFPGAN. The default upscale is
 Real-ESRGAN. For an alternative face restoration module, see [CodeFormer
-Support] below.
+Support](#codeformer-support) below.
 
 As of version 1.14, environment.yaml will install the Real-ESRGAN
 package into the standard install location for python packages, and
 will put GFPGAN into a subdirectory of "src" in the InvokeAI
 directory. Upscaling with Real-ESRGAN should "just work" without
-further intervention. Simply pass the --upscale (-U) option on the
-invoke> command line, or indicate the desired scale on the popup in
+further intervention. Simply pass the `--upscale` (`-U`) option on the
+`invoke>` command line, or indicate the desired scale on the popup in
 the Web GUI.
 
 **GFPGAN** requires a series of downloadable model files to
diff --git a/docs/features/PROMPTS.md b/docs/features/PROMPTS.md
index a6eb310e77..d18a5c8b75 100644
--- a/docs/features/PROMPTS.md
+++ b/docs/features/PROMPTS.md
@@ -6,14 +6,15 @@ title: Prompting-Features
 
 ## **Reading Prompts from a File**
 
-You can automate `invoke.py` by providing a text file with the prompts you want to run, one line per
-prompt. The text file must be composed with a text editor (e.g. Notepad) and not a word processor.
-Each line should look like what you would type at the invoke> prompt:
+You can automate `invoke.py` by providing a text file with the prompts you want
+to run, one line per prompt. The text file must be composed with a text editor
+(e.g. Notepad) and not a word processor. Each line should look like what you
+would type at the invoke> prompt:
 
 ```bash
-a beautiful sunny day in the park, children playing -n4 -C10
-stormy weather on a mountain top, goats grazing     -s100
-innovative packaging for a squid's dinner           -S137038382
+"a beautiful sunny day in the park, children playing" -n4 -C10
+"stormy weather on a mountain top, goats grazing" -s100
+"innovative packaging for a squid's dinner" -S137038382
 ```
 
 Then pass this file's name to `invoke.py` when you invoke it:
@@ -22,7 +23,8 @@ Then pass this file's name to `invoke.py` when you invoke it:
 (invokeai) ~/stable-diffusion$ python3 scripts/invoke.py --from_file "path/to/prompts.txt"
 ```
 
-You may read a series of prompts from standard input by providing a filename of `-`:
+You may read a series of prompts from standard input by providing a filename of
+`-`:
 
 ```bash
 (invokeai) ~/stable-diffusion$ echo "a beautiful day" | python3 scripts/invoke.py --from_file -
@@ -32,14 +34,15 @@ You may read a series of prompts from standard input by providing a filename of
 
 ## **Negative and Unconditioned Prompts**
 
-Any words between a pair of square brackets will instruct Stable
-Diffusion to attempt to ban the concept from the generated image.
+Any words between a pair of square brackets will instruct Stable Diffusion to
+attempt to ban the concept from the generated image.
 
 ```text
 this is a test prompt [not really] to make you understand [cool] how this works.
 ```
 
-In the above statement, the words 'not really cool` will be ignored by Stable Diffusion.
+In the above statement, the words 'not really cool` will be ignored by Stable
+Diffusion.
 
 Here's a prompt that depicts what it does.
 
@@ -51,7 +54,9 @@ original prompt:
 ![step1](../assets/negative_prompt_walkthru/step1.png)
 </figure>
 
-That image has a woman, so if we want the horse without a rider, we can influence the image not to have a woman by putting [woman] in the prompt, like this:
+That image has a woman, so if we want the horse without a rider, we can
+influence the image not to have a woman by putting [woman] in the prompt, like
+this:
 
 `#!bash "A fantastical translucent poney made of water and foam, ethereal, radiant, hyperalism, scottish folklore, digital painting, artstation, concept art, smooth, 8 k frostbite 3 engine, ultra detailed, art by artgerm and greg rutkowski and magali villeneuve [woman]" -s 20 -W 512 -H 768 -C 7.5 -A k_euler_a -S 1654590180`
 
@@ -59,7 +64,8 @@ That image has a woman, so if we want the horse without a rider, we can influenc
 ![step2](../assets/negative_prompt_walkthru/step2.png)
 </figure>
 
-That's nice - but say we also don't want the image to be quite so blue. We can add "blue" to the list of negative prompts, so it's now [woman blue]:
+That's nice - but say we also don't want the image to be quite so blue. We can
+add "blue" to the list of negative prompts, so it's now [woman blue]:
 
 `#!bash "A fantastical translucent poney made of water and foam, ethereal, radiant, hyperalism, scottish folklore, digital painting, artstation, concept art, smooth, 8 k frostbite 3 engine, ultra detailed, art by artgerm and greg rutkowski and magali villeneuve [woman blue]" -s 20 -W 512 -H 768 -C 7.5 -A k_euler_a -S 1654590180`
 
@@ -67,7 +73,8 @@ That's nice - but say we also don't want the image to be quite so blue. We can a
 ![step3](../assets/negative_prompt_walkthru/step3.png)
 </figure>
 
-Getting close - but there's no sense in having a saddle when our horse doesn't have a rider, so we'll add one more negative prompt: [woman blue saddle].
+Getting close - but there's no sense in having a saddle when our horse doesn't
+have a rider, so we'll add one more negative prompt: [woman blue saddle].
 
 `#!bash "A fantastical translucent poney made of water and foam, ethereal, radiant, hyperalism, scottish folklore, digital painting, artstation, concept art, smooth, 8 k frostbite 3 engine, ultra detailed, art by artgerm and greg rutkowski and magali villeneuve [woman blue saddle]" -s 20 -W 512 -H 768 -C 7.5 -A k_euler_a -S 1654590180`
 
@@ -88,91 +95,128 @@ Getting close - but there's no sense in having a saddle when our horse doesn't h
 
 The InvokeAI prompting language has the following features:
 
-### Attention weighting 
-Append a word or phrase with `-` or `+`, or a weight between `0` and `2` (`1`=default), to decrease or increase "attention" (= a mix of per-token CFG weighting multiplier and, for `-`, a weighted blend with the prompt without the term).
+### Attention weighting
+
+Append a word or phrase with `-` or `+`, or a weight between `0` and `2`
+(`1`=default), to decrease or increase "attention" (= a mix of per-token CFG
+weighting multiplier and, for `-`, a weighted blend with the prompt without the
+term).
 
 The following syntax is recognised:
-  * single words without parentheses: `a tall thin man picking apricots+`
-  * single or multiple words with parentheses: `a tall thin man picking (apricots)+` `a tall thin man picking (apricots)-` `a tall thin man (picking apricots)+` `a tall thin man (picking apricots)-` 
-  * more effect with more symbols `a tall thin man (picking apricots)++`
-  * nesting `a tall thin man (picking apricots+)++` (`apricots` effectively gets `+++`)
-  * all of the above with explicit numbers `a tall thin man picking (apricots)1.1` `a tall thin man (picking (apricots)1.3)1.1`. (`+` is equivalent to 1.1, `++` is pow(1.1,2), `+++` is pow(1.1,3), etc; `-` means 0.9, `--` means pow(0.9,2), etc.)
-  * attention also applies to `[unconditioning]` so `a tall thin man picking apricots [(ladder)0.01]` will *very gently* nudge SD away from trying to draw the man on a ladder
 
-You can use this to increase or decrease the amount of something. Starting from this prompt of `a man picking apricots from a tree`, let's see what happens if we increase and decrease how much attention we want Stable Diffusion to pay to the word `apricots`:
+- single words without parentheses: `a tall thin man picking apricots+`
+- single or multiple words with parentheses:
+  `a tall thin man picking (apricots)+` `a tall thin man picking (apricots)-`
+  `a tall thin man (picking apricots)+` `a tall thin man (picking apricots)-`
+- more effect with more symbols `a tall thin man (picking apricots)++`
+- nesting `a tall thin man (picking apricots+)++` (`apricots` effectively gets
+  `+++`)
+- all of the above with explicit numbers `a tall thin man picking (apricots)1.1`
+  `a tall thin man (picking (apricots)1.3)1.1`. (`+` is equivalent to 1.1, `++`
+  is pow(1.1,2), `+++` is pow(1.1,3), etc; `-` means 0.9, `--` means pow(0.9,2),
+  etc.)
+- attention also applies to `[unconditioning]` so
+  `a tall thin man picking apricots [(ladder)0.01]` will _very gently_ nudge SD
+  away from trying to draw the man on a ladder
+
+You can use this to increase or decrease the amount of something. Starting from
+this prompt of `a man picking apricots from a tree`, let's see what happens if
+we increase and decrease how much attention we want Stable Diffusion to pay to
+the word `apricots`:
 
 ![an AI generated image of a man picking apricots from a tree](../assets/prompt_syntax/apricots-0.png)
 
 Using `-` to reduce apricot-ness:
 
-| `a man picking apricots- from a tree` | `a man picking apricots-- from a tree` | `a man picking apricots--- from a tree` |
-| -- | -- | -- |
+| `a man picking apricots- from a tree`                                                                                          | `a man picking apricots-- from a tree`                                                                                                        | `a man picking apricots--- from a tree`                                                                                                    |
+| ------------------------------------------------------------------------------------------------------------------------------ | --------------------------------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------ |
 | ![an AI generated image of a man picking apricots from a tree, with smaller apricots](../assets/prompt_syntax/apricots--1.png) | ![an AI generated image of a man picking apricots from a tree, with even smaller and fewer apricots](../assets/prompt_syntax/apricots--2.png) | ![an AI generated image of a man picking apricots from a tree, with very few very small apricots](../assets/prompt_syntax/apricots--3.png) |
 
 Using `+` to increase apricot-ness:
 
-| `a man picking apricots+ from a tree` | `a man picking apricots++ from a tree` | `a man picking apricots+++ from a tree` | `a man picking apricots++++ from a tree` | `a man picking apricots+++++ from a tree` |
-| -- | -- | -- | -- | -- |
+| `a man picking apricots+ from a tree`                                                                                                      | `a man picking apricots++ from a tree`                                                                                                              | `a man picking apricots+++ from a tree`                                                                                                                     | `a man picking apricots++++ from a tree`                                                                                                                                           | `a man picking apricots+++++ from a tree`                                                                                                                                                                           |
+| ------------------------------------------------------------------------------------------------------------------------------------------ | --------------------------------------------------------------------------------------------------------------------------------------------------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
 | ![an AI generated image of a man picking apricots from a tree, with larger, more vibrant apricots](../assets/prompt_syntax/apricots-1.png) | ![an AI generated image of a man picking apricots from a tree with even larger, even more vibrant apricots](../assets/prompt_syntax/apricots-2.png) | ![an AI generated image of a man picking apricots from a tree, but the man has been replaced by a pile of apricots](../assets/prompt_syntax/apricots-3.png) | ![an AI generated image of a man picking apricots from a tree, but the man has been replaced by a mound of giant melting-looking apricots](../assets/prompt_syntax/apricots-4.png) | ![an AI generated image of a man picking apricots from a tree, but the man and the leaves and parts of the ground have all been replaced by giant melting-looking apricots](../assets/prompt_syntax/apricots-5.png) |
 
-You can also change the balance between different parts of a prompt. For example, below is a `mountain man`:
+You can also change the balance between different parts of a prompt. For
+example, below is a `mountain man`:
 
 ![an AI generated image of a mountain man](../assets/prompt_syntax/mountain-man.png)
 
 And here he is with more mountain:
 
-| `mountain+ man` | `mountain++ man` | `mountain+++ man` | 
-| -- | -- | -- |
+| `mountain+ man`                                | `mountain++ man`                               | `mountain+++ man`                              |
+| ---------------------------------------------- | ---------------------------------------------- | ---------------------------------------------- |
 | ![](../assets/prompt_syntax/mountain1-man.png) | ![](../assets/prompt_syntax/mountain2-man.png) | ![](../assets/prompt_syntax/mountain3-man.png) |
 
 Or, alternatively, with more man:
 
-| `mountain man+` | `mountain man++` | `mountain man+++` | `mountain man++++` | 
-| -- | -- | -- | -- |
+| `mountain man+`                                | `mountain man++`                               | `mountain man+++`                              | `mountain man++++`                             |
+| ---------------------------------------------- | ---------------------------------------------- | ---------------------------------------------- | ---------------------------------------------- |
 | ![](../assets/prompt_syntax/mountain-man1.png) | ![](../assets/prompt_syntax/mountain-man2.png) | ![](../assets/prompt_syntax/mountain-man3.png) | ![](../assets/prompt_syntax/mountain-man4.png) |
 
 ### Blending between prompts
 
-* `("a tall thin man picking apricots", "a tall thin man picking pears").blend(1,1)`
-* The existing prompt blending using `:<weight>` will continue to be supported - `("a tall thin man picking apricots", "a tall thin man picking pears").blend(1,1)` is equivalent to `a tall thin man picking apricots:1 a tall thin man picking pears:1` in the old syntax.
-* Attention weights can be nested inside blends.
-* Non-normalized blends are supported by passing `no_normalize` as an additional argument to the blend weights, eg `("a tall thin man picking apricots", "a tall thin man picking pears").blend(1,-1,no_normalize)`. very fun to explore local maxima in the feature space, but also easy to produce garbage output.
+- `("a tall thin man picking apricots", "a tall thin man picking pears").blend(1,1)`
+- The existing prompt blending using `:<weight>` will continue to be supported -
+  `("a tall thin man picking apricots", "a tall thin man picking pears").blend(1,1)`
+  is equivalent to
+  `a tall thin man picking apricots:1 a tall thin man picking pears:1` in the
+  old syntax.
+- Attention weights can be nested inside blends.
+- Non-normalized blends are supported by passing `no_normalize` as an additional
+  argument to the blend weights, eg
+  `("a tall thin man picking apricots", "a tall thin man picking pears").blend(1,-1,no_normalize)`.
+  very fun to explore local maxima in the feature space, but also easy to
+  produce garbage output.
 
-See the section below on "Prompt Blending" for more information about how this works.
+See the section below on "Prompt Blending" for more information about how this
+works.
 
 ### Cross-Attention Control ('prompt2prompt')
 
-Sometimes an image you generate is almost right, and you just want to
-change one detail without affecting the rest. You could use a photo editor and inpainting
-to overpaint the area, but that's a pain. Here's where `prompt2prompt`
-comes in handy.
+Sometimes an image you generate is almost right, and you just want to change one
+detail without affecting the rest. You could use a photo editor and inpainting
+to overpaint the area, but that's a pain. Here's where `prompt2prompt` comes in
+handy.
 
-Generate an image with a given prompt, record the seed of the image,
-and then use the `prompt2prompt` syntax to substitute words in the
-original prompt for words in a new prompt. This works for `img2img` as well.
+Generate an image with a given prompt, record the seed of the image, and then
+use the `prompt2prompt` syntax to substitute words in the original prompt for
+words in a new prompt. This works for `img2img` as well.
 
-* `a ("fluffy cat").swap("smiling dog") eating a hotdog`.
-  * quotes optional: `a (fluffy cat).swap(smiling dog) eating a hotdog`.
-  * for single word substitutions parentheses are also optional: `a cat.swap(dog) eating a hotdog`.
-* Supports options `s_start`, `s_end`, `t_start`, `t_end` (each 0-1) loosely corresponding to bloc97's `prompt_edit_spatial_start/_end` and `prompt_edit_tokens_start/_end` but with the math swapped to make it easier to intuitively understand.
-  * Example usage:`a (cat).swap(dog, s_end=0.3) eating a hotdog` - the `s_end` argument means that the "spatial" (self-attention) edit will stop having any effect after 30% (=0.3) of the steps have been done, leaving Stable Diffusion with 70% of the steps where it is free to decide for itself how to reshape the cat-form into a dog form. 
-  * The numbers represent a percentage through the step sequence where the edits should happen. 0 means the start (noisy starting image), 1 is the end (final image).
-    * For img2img, the step sequence does not start at 0 but instead at (1-strength) - so if strength is 0.7, s_start and s_end must both be greater than 0.3 (1-0.7) to have any effect. 
-* Convenience option `shape_freedom` (0-1) to specify how much "freedom" Stable Diffusion should have to change the shape of the subject being swapped. 
-  * `a (cat).swap(dog, shape_freedom=0.5) eating a hotdog`.
+- `a ("fluffy cat").swap("smiling dog") eating a hotdog`.
+  - quotes optional: `a (fluffy cat).swap(smiling dog) eating a hotdog`.
+  - for single word substitutions parentheses are also optional:
+    `a cat.swap(dog) eating a hotdog`.
+- Supports options `s_start`, `s_end`, `t_start`, `t_end` (each 0-1) loosely
+  corresponding to bloc97's `prompt_edit_spatial_start/_end` and
+  `prompt_edit_tokens_start/_end` but with the math swapped to make it easier to
+  intuitively understand.
+  - Example usage:`a (cat).swap(dog, s_end=0.3) eating a hotdog` - the `s_end`
+    argument means that the "spatial" (self-attention) edit will stop having any
+    effect after 30% (=0.3) of the steps have been done, leaving Stable
+    Diffusion with 70% of the steps where it is free to decide for itself how to
+    reshape the cat-form into a dog form.
+  - The numbers represent a percentage through the step sequence where the edits
+    should happen. 0 means the start (noisy starting image), 1 is the end (final
+    image).
+    - For img2img, the step sequence does not start at 0 but instead at
+      (1-strength) - so if strength is 0.7, s_start and s_end must both be
+      greater than 0.3 (1-0.7) to have any effect.
+- Convenience option `shape_freedom` (0-1) to specify how much "freedom" Stable
+  Diffusion should have to change the shape of the subject being swapped.
+  - `a (cat).swap(dog, shape_freedom=0.5) eating a hotdog`.
 
+The `prompt2prompt` code is based off
+[bloc97's colab](https://github.com/bloc97/CrossAttentionControl).
 
+Note that `prompt2prompt` is not currently working with the runwayML inpainting
+model, and may never work due to the way this model is set up. If you attempt to
+use `prompt2prompt` you will get the original image back. However, since this
+model is so good at inpainting, a good substitute is to use the `clipseg` text
+masking option:
 
-The `prompt2prompt` code is based off [bloc97's
-colab](https://github.com/bloc97/CrossAttentionControl).
-
-Note that `prompt2prompt` is not currently working with the runwayML
-inpainting model, and may never work due to the way this model is set
-up. If you attempt to use `prompt2prompt` you will get the original
-image back. However, since this model is so good at inpainting, a
-good substitute is to use the `clipseg` text masking option:
-
-```
+```bash
 invoke> a fluffy cat eating a hotdot
 Outputs:
 [1010] outputs/000025.2182095108.png: a fluffy cat eating a hotdog
@@ -181,94 +225,96 @@ invoke> a smiling dog eating a hotdog -I 000025.2182095108.png -tm cat
 
 ### Escaping parantheses () and speech marks ""
 
-If the model you are using has parentheses () or speech marks "" as
-part of its syntax, you will need to "escape" these using a backslash,
-so that`(my_keyword)` becomes `\(my_keyword\)`. Otherwise, the prompt
-parser will attempt to interpret the parentheses as part of the prompt
-syntax and it will get confused.
+If the model you are using has parentheses () or speech marks "" as part of its
+syntax, you will need to "escape" these using a backslash, so that`(my_keyword)`
+becomes `\(my_keyword\)`. Otherwise, the prompt parser will attempt to interpret
+the parentheses as part of the prompt syntax and it will get confused.
+
+---
 
 ## **Prompt Blending**
 
-You may blend together different sections of the prompt to explore the
-AI's latent semantic space and generate interesting (and often
-surprising!) variations. The syntax is:
+You may blend together different sections of the prompt to explore the AI's
+latent semantic space and generate interesting (and often surprising!)
+variations. The syntax is:
 
 ```bash
 blue sphere:0.25 red cube:0.75 hybrid
 ```
 
-This will tell the sampler to blend 25% of the concept of a blue
-sphere with 75% of the concept of a red cube. The blend weights can
-use any combination of integers and floating point numbers, and they
-do not need to add up to 1. Everything to the left of the `:XX` up to
-the previous `:XX` is used for merging, so the overall effect is:
+This will tell the sampler to blend 25% of the concept of a blue sphere with 75%
+of the concept of a red cube. The blend weights can use any combination of
+integers and floating point numbers, and they do not need to add up to 1.
+Everything to the left of the `:XX` up to the previous `:XX` is used for
+merging, so the overall effect is:
 
 ```bash
 0.25 * "blue sphere" + 0.75 * "white duck" + hybrid
 ```
 
-Because you are exploring the "mind" of the AI, the AI's way of mixing
-two concepts may not match yours, leading to surprising effects. To
-illustrate, here are three images generated using various combinations
-of blend weights. As usual, unless you fix the seed, the prompts will give you
-different results each time you run them.
-
----
+Because you are exploring the "mind" of the AI, the AI's way of mixing two
+concepts may not match yours, leading to surprising effects. To illustrate, here
+are three images generated using various combinations of blend weights. As
+usual, unless you fix the seed, the prompts will give you different results each
+time you run them.
 
 <figure markdown>
 ### "blue sphere, red cube, hybrid"
 </figure>
 
-This example doesn't use melding at all and represents the default way
-of mixing concepts.
+This example doesn't use melding at all and represents the default way of mixing
+concepts.
 
 <figure markdown>
 ![blue-sphere-red-cube-hyprid](../assets/prompt-blending/blue-sphere-red-cube-hybrid.png)
 </figure>
 
-It's interesting to see how the AI expressed the concept of "cube" as
-the four quadrants of the enclosing frame. If you look closely, there
-is depth there, so the enclosing frame is actually a cube.
+It's interesting to see how the AI expressed the concept of "cube" as the four
+quadrants of the enclosing frame. If you look closely, there is depth there, so
+the enclosing frame is actually a cube.
 
 <figure markdown>
 ### "blue sphere:0.25 red cube:0.75 hybrid"
 
 ![blue-sphere-25-red-cube-75](../assets/prompt-blending/blue-sphere-0.25-red-cube-0.75-hybrid.png)
+
 </figure>
 
-Now that's interesting. We get neither a blue sphere nor a red cube,
-but a red sphere embedded in a brick wall, which represents a melding
-of concepts within the AI's "latent space" of semantic
-representations. Where is Ludwig Wittgenstein when you need him?
+Now that's interesting. We get neither a blue sphere nor a red cube, but a red
+sphere embedded in a brick wall, which represents a melding of concepts within
+the AI's "latent space" of semantic representations. Where is Ludwig
+Wittgenstein when you need him?
 
 <figure markdown>
 ### "blue sphere:0.75 red cube:0.25 hybrid"
 
 ![blue-sphere-75-red-cube-25](../assets/prompt-blending/blue-sphere-0.75-red-cube-0.25-hybrid.png)
+
 </figure>
 
-Definitely more blue-spherey. The cube is gone entirely, but it's
-really cool abstract art.
+Definitely more blue-spherey. The cube is gone entirely, but it's really cool
+abstract art.
 
 <figure markdown>
 ### "blue sphere:0.5 red cube:0.5 hybrid"
 
 ![blue-sphere-5-red-cube-5-hybrid](../assets/prompt-blending/blue-sphere-0.5-red-cube-0.5-hybrid.png)
+
 </figure>
 
-Whoa...! I see blue and red, but no spheres or cubes. Is the word
-"hybrid" summoning up the concept of some sort of scifi creature?
-Let's find out.
+Whoa...! I see blue and red, but no spheres or cubes. Is the word "hybrid"
+summoning up the concept of some sort of scifi creature? Let's find out.
 
 <figure markdown>
 ### "blue sphere:0.5 red cube:0.5"
 
 ![blue-sphere-5-red-cube-5](../assets/prompt-blending/blue-sphere-0.5-red-cube-0.5.png)
+
 </figure>
 
-Indeed, removing the word "hybrid" produces an image that is more like
-what we'd expect.
+Indeed, removing the word "hybrid" produces an image that is more like what we'd
+expect.
 
-In conclusion, prompt blending is great for exploring creative space,
-but can be difficult to direct. A forthcoming release of InvokeAI will
-feature more deterministic prompt weighting.
+In conclusion, prompt blending is great for exploring creative space, but can be
+difficult to direct. A forthcoming release of InvokeAI will feature more
+deterministic prompt weighting.
diff --git a/docs/features/VARIATIONS.md b/docs/features/VARIATIONS.md
index 70ba84429b..aeeb57d52c 100644
--- a/docs/features/VARIATIONS.md
+++ b/docs/features/VARIATIONS.md
@@ -16,12 +16,10 @@ You are able to do the following:
 2. Given two or more variations that you like, you can combine them in a
    weighted fashion.
 
----
+!!! Information ""
 
-This cheat sheet provides a quick guide for how this works in practice, using
-variations to create the desired image of Xena, Warrior Princess.
-
----
+    This cheat sheet provides a quick guide for how this works in practice, using
+    variations to create the desired image of Xena, Warrior Princess.
 
 ## Step 1 -- Find a base image that you like
 
diff --git a/docs/features/WEB.md b/docs/features/WEB.md
index 9250e32541..795d9cf962 100644
--- a/docs/features/WEB.md
+++ b/docs/features/WEB.md
@@ -4,56 +4,55 @@ title: InvokeAI Web Server
 
 # :material-web: InvokeAI Web Server
 
-As of version 2.0.0, this distribution comes with a full-featured web
-server (see screenshot). To use it, run the `invoke.py` script by
-adding the `--web` option:
+As of version 2.0.0, this distribution comes with a full-featured web server
+(see screenshot). To use it, run the `invoke.py` script by adding the `--web`
+option:
 
 ```bash
 (invokeai) ~/InvokeAI$ python3 scripts/invoke.py --web
 ```
 
 You can then connect to the server by pointing your web browser at
-http://localhost:9090. To reach the server from a different machine on
-your LAN, you may launch the web server with the `--host` argument and
-either the IP address of the host you are running it on, or the
-wildcard `0.0.0.0`. For example:
+http://localhost:9090. To reach the server from a different machine on your LAN,
+you may launch the web server with the `--host` argument and either the IP
+address of the host you are running it on, or the wildcard `0.0.0.0`. For
+example:
 
 ```bash
 (invokeai) ~/InvokeAI$ python3 scripts/invoke.py --web --host 0.0.0.0
 ```
 
-# Quick guided walkthrough of the WebGUI's features
+## Quick guided walkthrough of the WebGUI's features
 
-While most of the WebGUI's features are intuitive, here is a guided
-walkthrough through its various components.
+While most of the WebGUI's features are intuitive, here is a guided walkthrough
+through its various components.
 
 ![Invoke Web Server - Major Components](../assets/invoke-web-server-1.png){:width="640px"}
 
-The screenshot above shows the Text to Image tab of the WebGUI. There
-are three main sections:
+The screenshot above shows the Text to Image tab of the WebGUI. There are three
+main sections:
 
-1. A **control panel** on the left, which contains various settings
-for text to image generation. The most important part is the text
-field (currently showing `strawberry sushi`) for entering the text
-prompt, and the camera icon directly underneath that will render the
-image. We'll call this the *Invoke* button from now on.
+1. A **control panel** on the left, which contains various settings for text to
+   image generation. The most important part is the text field (currently
+   showing `strawberry sushi`) for entering the text prompt, and the camera icon
+   directly underneath that will render the image. We'll call this the _Invoke_
+   button from now on.
 
-2. The **current image** section in the middle, which shows a large
-format version of the image you are currently working on. A series of
-buttons at the top ("image to image", "Use All", "Use Seed", etc) lets
-you modify the image in various ways.
+2. The **current image** section in the middle, which shows a large format
+   version of the image you are currently working on. A series of buttons at the
+   top ("image to image", "Use All", "Use Seed", etc) lets you modify the image
+   in various ways.
 
-3. A **gallery* section on the left that contains a history of the
-images you have generated. These images are read and written to the
-directory specified at launch time in `--outdir`.
+3. A \*_gallery_ section on the left that contains a history of the images you
+   have generated. These images are read and written to the directory specified
+   at launch time in `--outdir`.
 
-In addition to these three elements, there are a series of icons for
-changing global settings, reporting bugs, and changing the theme on
-the upper right.
+In addition to these three elements, there are a series of icons for changing
+global settings, reporting bugs, and changing the theme on the upper right.
 
 There are also a series of icons to the left of the control panel (see
-highlighted area in the screenshot below) which select among a series
-of tabs for performing different types of operations.
+highlighted area in the screenshot below) which select among a series of tabs
+for performing different types of operations.
 
 <figure markdown>
 ![Invoke Web Server - Control Panel](../assets/invoke-web-server-2.png){:width="512px"}
@@ -61,174 +60,167 @@ of tabs for performing different types of operations.
 
 From top to bottom, these are:
 
-1. Text to Image  - generate images from text
-2. Image to Image - from an uploaded starting image (drawing or photograph) generate a new one, modified by the text prompt
-3. Inpainting (pending) - Interactively erase portions of a starting image and have the AI fill in the erased region from a text prompt.
-4. Outpainting (pending) - Interactively add blank space to the borders of a starting image and fill in the background from a text prompt.
-5. Postprocessing (pending) - Interactively postprocess generated images using a variety of filters.
+1. Text to Image - generate images from text
+2. Image to Image - from an uploaded starting image (drawing or photograph)
+   generate a new one, modified by the text prompt
+3. Inpainting (pending) - Interactively erase portions of a starting image and
+   have the AI fill in the erased region from a text prompt.
+4. Outpainting (pending) - Interactively add blank space to the borders of a
+   starting image and fill in the background from a text prompt.
+5. Postprocessing (pending) - Interactively postprocess generated images using a
+   variety of filters.
 
 The inpainting, outpainting and postprocessing tabs are currently in
-development. However, limited versions of their features can already
-be accessed through the Text to Image and Image to Image tabs.
+development. However, limited versions of their features can already be accessed
+through the Text to Image and Image to Image tabs.
 
 ## Walkthrough
 
-The following walkthrough will exercise most (but not all) of the
-WebGUI's feature set.
+The following walkthrough will exercise most (but not all) of the WebGUI's
+feature set.
 
 ### Text to Image
 
-1. Launch the WebGUI using `python scripts/invoke.py --web` and
-connect to it with your browser by accessing
-`http://localhost:9090`. If the browser and server are running on
-different machines on your LAN, add the option `--host 0.0.0.0` to the
-launch command line and connect to the machine hosting the web server
-using its IP address or domain name.
+1. Launch the WebGUI using `python scripts/invoke.py --web` and connect to it
+   with your browser by accessing `http://localhost:9090`. If the browser and
+   server are running on different machines on your LAN, add the option
+   `--host 0.0.0.0` to the launch command line and connect to the machine
+   hosting the web server using its IP address or domain name.
 
 2. If all goes well, the WebGUI should come up and you'll see a green
-`connected` message on the upper right.
+   `connected` message on the upper right.
 
 #### Basics
 
-1. Generate an image by typing *strawberry sushi* into the large
-prompt field on the upper left and then clicking on the Invoke button
-(the one with the Camera icon). After a short wait, you'll see a large
-image of sushi in the image panel, and a new thumbnail in the gallery
-on the right.
+1.  Generate an image by typing _strawberry sushi_ into the large prompt field
+    on the upper left and then clicking on the Invoke button (the one with the
+    Camera icon). After a short wait, you'll see a large image of sushi in the
+    image panel, and a new thumbnail in the gallery on the right.
 
-    If you need more room on the screen, you can turn the gallery  off
-    by clicking on the **x** to the right of "Your Invocations". You can
-    turn it back on later by clicking the image icon that appears in the
-    gallery's place.
+    If you need more room on the screen, you can turn the gallery off by
+    clicking on the **x** to the right of "Your Invocations". You can turn it
+    back on later by clicking the image icon that appears in the gallery's
+    place.
 
-    The images are written into the directory indicated by the `--outdir`
-    option provided at script launch time. By default, this is
-    `outputs/img-samples` under the InvokeAI directory.
+    The images are written into the directory indicated by the `--outdir` option
+    provided at script launch time. By default, this is `outputs/img-samples`
+    under the InvokeAI directory.
 
-2. Generate a bunch of strawberry sushi images by increasing the
-number of requested images by adjusting the Images counter just below
-the Camera button. As each is generated, it will be added to the
-gallery. You can switch the active image by clicking on the gallery
-thumbnails.
+2.  Generate a bunch of strawberry sushi images by increasing the number of
+    requested images by adjusting the Images counter just below the Camera
+    button. As each is generated, it will be added to the gallery. You can
+    switch the active image by clicking on the gallery thumbnails.
 
-3. Try playing with different settings, including image width and
-height, the Sampler, the Steps and the CFG scale.
+3.  Try playing with different settings, including image width and height, the
+    Sampler, the Steps and the CFG scale.
 
-    Image *Width* and *Height* do what you'd expect. However, be aware that
+    Image _Width_ and _Height_ do what you'd expect. However, be aware that
     larger images consume more VRAM memory and take longer to generate.
 
-    The *Sampler* controls how the AI selects the image to display. Some
-    samplers are more "creative" than others and will produce a wider
-    range of variations (see next section). Some samplers run faster than
-    others.
+    The _Sampler_ controls how the AI selects the image to display. Some
+    samplers are more "creative" than others and will produce a wider range of
+    variations (see next section). Some samplers run faster than others.
 
-    *Steps* controls how many noising/denoising/sampling steps the AI will
-    take. The higher this value, the more refined the image will be, but
-    the longer the image will take to generate. A typical strategy is to
-    generate images with a low number of steps in order to select one to
-    work on further, and then regenerate it using a higher number of
-    steps.
+    _Steps_ controls how many noising/denoising/sampling steps the AI will take.
+    The higher this value, the more refined the image will be, but the longer
+    the image will take to generate. A typical strategy is to generate images
+    with a low number of steps in order to select one to work on further, and
+    then regenerate it using a higher number of steps.
 
-    The *CFG Scale* controls how hard the AI tries to match the generated
-    image to the input prompt. You can go as high or low as you like, but
-    generally values greater than 20 won't improve things much, and values
-    lower than 5 will produce unexpected images. There are complex
-    interactions between *Steps*, *CFG Scale* and the *Sampler*, so
-    experiment to find out what works for you.
+    The _CFG Scale_ controls how hard the AI tries to match the generated image
+    to the input prompt. You can go as high or low as you like, but generally
+    values greater than 20 won't improve things much, and values lower than 5
+    will produce unexpected images. There are complex interactions between
+    _Steps_, _CFG Scale_ and the _Sampler_, so experiment to find out what works
+    for you.
 
-6. To regenerate a previously-generated image, select the image you
-want and click *Use All*. This loads the text prompt and other
-original settings into the control panel. If you then press *Invoke*
-it will regenerate the image exactly. You can also selectively modify
-the prompt or other settings to tweak the image.
+4.  To regenerate a previously-generated image, select the image you want and
+    click _Use All_. This loads the text prompt and other original settings into
+    the control panel. If you then press _Invoke_ it will regenerate the image
+    exactly. You can also selectively modify the prompt or other settings to
+    tweak the image.
 
-    Alternatively, you may click on *Use Seed* to load just the image's
-    seed, and leave other settings unchanged.
+    Alternatively, you may click on _Use Seed_ to load just the image's seed,
+    and leave other settings unchanged.
 
-7. To regenerate a Stable Diffusion image that was generated by
-another SD package, you need to know its text prompt and its
-*Seed*. Copy-paste the prompt into the prompt box, unset the
-*Randomize Seed* control in the control panel, and copy-paste the
-desired *Seed* into its text field. When you Invoke, you will get
-something similar to the original image. It will not be exact unless
-you also set the correct values for the original sampler, CFG,
-steps and dimensions, but it will (usually) be close.
+5.  To regenerate a Stable Diffusion image that was generated by another SD
+    package, you need to know its text prompt and its _Seed_. Copy-paste the
+    prompt into the prompt box, unset the _Randomize Seed_ control in the
+    control panel, and copy-paste the desired _Seed_ into its text field. When
+    you Invoke, you will get something similar to the original image. It will
+    not be exact unless you also set the correct values for the original
+    sampler, CFG, steps and dimensions, but it will (usually) be close.
 
 #### Variations on a theme
 
-1. Let's try generating some variations. Select your favorite sushi
-image from the gallery to load it. Then select "Use All" from the list
-of buttons above. This will load up all the settings used to generate
-this image, including its unique seed.
+1.  Let's try generating some variations. Select your favorite sushi image from
+    the gallery to load it. Then select "Use All" from the list of buttons
+    above. This will load up all the settings used to generate this image,
+    including its unique seed.
 
-    Go down to the Variations section of the Control Panel and set the
-    button to On. Set Variation Amount to 0.2 to generate a modest
-    number of variations on the image, and also set the Image counter to
-    `4`. Press the `invoke` button. This will generate a series of related
-    images. To obtain smaller variations, just lower the Variation
-    Amount. You may also experiment with changing the Sampler. Some
-    samplers generate more variability than others. *k_euler_a* is
-    particularly creative, while *ddim* is pretty conservative.
+    Go down to the Variations section of the Control Panel and set the button to
+    On. Set Variation Amount to 0.2 to generate a modest number of variations on
+    the image, and also set the Image counter to `4`. Press the `invoke` button.
+    This will generate a series of related images. To obtain smaller variations,
+    just lower the Variation Amount. You may also experiment with changing the
+    Sampler. Some samplers generate more variability than others. _k_euler_a_ is
+    particularly creative, while _ddim_ is pretty conservative.
 
-2. For even more variations, experiment with increasing the setting
-for *Perlin*. This adds a bit of noise to the image generation
-process. Note that values of Perlin noise greater than 0.15 produce
-poor images for several of the samplers.
+2.  For even more variations, experiment with increasing the setting for
+    _Perlin_. This adds a bit of noise to the image generation process. Note
+    that values of Perlin noise greater than 0.15 produce poor images for
+    several of the samplers.
 
 #### Facial reconstruction and upscaling
 
-Stable Diffusion frequently produces mangled faces, particularly when
-there are multiple figures in the same scene. Stable Diffusion has
-particular issues with generating reallistic eyes. InvokeAI provides
-the ability to reconstruct faces using either the GFPGAN or CodeFormer
-libraries. For more information see [POSTPROCESS](POSTPROCESS.md).
-  
-1. Invoke a prompt that generates a mangled face. A prompt that often
-gives this is "portrait of a lawyer, 3/4 shot" (this is not intended
-as a slur against lawyers!) Once you have an image that needs some
-touching up, load it into the Image panel, and press the button with
-the face icon (highlighted in the first screenshot below). A dialog
-box will appear. Leave *Strength* at 0.8 and press *Restore Faces". If
-all goes well, the eyes and other aspects of the face will be improved
-(see the second screenshot)
+Stable Diffusion frequently produces mangled faces, particularly when there are
+multiple figures in the same scene. Stable Diffusion has particular issues with
+generating reallistic eyes. InvokeAI provides the ability to reconstruct faces
+using either the GFPGAN or CodeFormer libraries. For more information see
+[POSTPROCESS](POSTPROCESS.md).
+
+1.  Invoke a prompt that generates a mangled face. A prompt that often gives
+    this is "portrait of a lawyer, 3/4 shot" (this is not intended as a slur
+    against lawyers!) Once you have an image that needs some touching up, load
+    it into the Image panel, and press the button with the face icon
+    (highlighted in the first screenshot below). A dialog box will appear. Leave
+    _Strength_ at 0.8 and press \*Restore Faces". If all goes well, the eyes and
+    other aspects of the face will be improved (see the second screenshot)
 
     ![Invoke Web Server - Original Image](../assets/invoke-web-server-3.png)
 
     ![Invoke Web Server - Retouched Image](../assets/invoke-web-server-4.png)
 
-    The facial reconstruction *Strength* field adjusts how aggressively
-    the face library will try to alter the face. It can be as high as 1.0,
-    but be aware that this often softens the face airbrush style, losing
-    some details. The default 0.8 is usually sufficient.
+    The facial reconstruction _Strength_ field adjusts how aggressively the face
+    library will try to alter the face. It can be as high as 1.0, but be aware
+    that this often softens the face airbrush style, losing some details. The
+    default 0.8 is usually sufficient.
 
-2. "Upscaling" is the process of increasing the size of an image while
-retaining the sharpness. InvokeAI uses an external library called
-"ESRGAN" to do this. To invoke upscaling, simply select an image and
-press the *HD* button above it. You can select between 2X and 4X
-upscaling, and adjust the upscaling strength, which has much the same
-meaning as in facial reconstruction. Try running this on one of your
-previously-generated images.
+2.  "Upscaling" is the process of increasing the size of an image while
+    retaining the sharpness. InvokeAI uses an external library called "ESRGAN"
+    to do this. To invoke upscaling, simply select an image and press the _HD_
+    button above it. You can select between 2X and 4X upscaling, and adjust the
+    upscaling strength, which has much the same meaning as in facial
+    reconstruction. Try running this on one of your previously-generated images.
 
-3. Finally, you can run facial reconstruction and/or upscaling
-automatically after each Invocation. Go to the Advanced Options
-section of the Control Panel and turn on *Restore Face* and/or
-*Upscale*.
+3.  Finally, you can run facial reconstruction and/or upscaling automatically
+    after each Invocation. Go to the Advanced Options section of the Control
+    Panel and turn on _Restore Face_ and/or _Upscale_.
 
 ### Image to Image
 
-InvokeAI lets you take an existing image and use it as the basis for a
-new creation. You can use any sort of image, including a photograph, a
-scanned sketch, or a digital drawing, as long as it is in PNG or JPEG
-format.
+InvokeAI lets you take an existing image and use it as the basis for a new
+creation. You can use any sort of image, including a photograph, a scanned
+sketch, or a digital drawing, as long as it is in PNG or JPEG format.
 
 For this tutorial, we'll use files named
-[Lincoln-and-Parrot-512.png](../assets/Lincoln-and-Parrot-512.png),
-and
+[Lincoln-and-Parrot-512.png](../assets/Lincoln-and-Parrot-512.png), and
 [Lincoln-and-Parrot-512-transparent.png](../assets/Lincoln-and-Parrot-512-transparent.png).
-Download these images to your local machine now to continue with the walkthrough.
+Download these images to your local machine now to continue with the
+walkthrough.
 
-1. Click on the *Image to Image* tab icon, which is the second icon
-from the top on the left-hand side of the screen:
+1.  Click on the _Image to Image_ tab icon, which is the second icon from the
+    top on the left-hand side of the screen:
 
     <figure markdown>
     ![Invoke Web Server - Image to Image Icon](../assets/invoke-web-server-5.png)
@@ -240,93 +232,92 @@ from the top on the left-hand side of the screen:
     ![Invoke Web Server - Image to Image Tab](../assets/invoke-web-server-6.png){:width="640px"}
     </figure>
 
-2. Drag-and-drop the Lincoln-and-Parrot image into the Image panel, or
-click the blank area to get an upload dialog. The image will load into
-an area marked *Initial Image*. (The WebGUI will also load the most
-recently-generated image from the gallery into a section on the left,
-but this image will be replaced in the next step.)
+2.  Drag-and-drop the Lincoln-and-Parrot image into the Image panel, or click
+    the blank area to get an upload dialog. The image will load into an area
+    marked _Initial Image_. (The WebGUI will also load the most
+    recently-generated image from the gallery into a section on the left, but
+    this image will be replaced in the next step.)
 
-3. Go to the prompt box and type *old sea captain with raven on
-shoulder* and press Invoke. A derived image will appear to the right
-of the original one:
+3.  Go to the prompt box and type _old sea captain with raven on shoulder_ and
+    press Invoke. A derived image will appear to the right of the original one:
 
     ![Invoke Web Server - Image to Image example](../assets/invoke-web-server-7.png){:width="640px"}
 
-4. Experiment with the different settings. The most influential one
-in Image to Image is *Image to Image Strength* located about midway
-down the control panel. By default it is set to 0.75, but can range
-from 0.0 to 0.99. The higher the value, the more of the original image
-the AI will replace. A value of 0 will leave the initial image
-completely unchanged, while 0.99 will replace it completely. However,
-the Sampler and CFG Scale also influence the final result. You can
-also generate variations in the same way as described in Text to
-Image.
+4.  Experiment with the different settings. The most influential one in Image to
+    Image is _Image to Image Strength_ located about midway down the control
+    panel. By default it is set to 0.75, but can range from 0.0 to 0.99. The
+    higher the value, the more of the original image the AI will replace. A
+    value of 0 will leave the initial image completely unchanged, while 0.99
+    will replace it completely. However, the Sampler and CFG Scale also
+    influence the final result. You can also generate variations in the same way
+    as described in Text to Image.
 
-5. What if we only want to change certain part(s) of the image and
-leave the rest intact? This is called Inpainting, and a future version
-of the InvokeAI web server will provide an interactive painting canvas
-on which you can directly draw the areas you wish to Inpaint into. For
-now, you can achieve this effect by using an external photoeditor tool
-to make one or more regions of the image transparent as described in
-[INPAINTING.md] and uploading that.
+5.  What if we only want to change certain part(s) of the image and leave the
+    rest intact? This is called Inpainting, and a future version of the InvokeAI
+    web server will provide an interactive painting canvas on which you can
+    directly draw the areas you wish to Inpaint into. For now, you can achieve
+    this effect by using an external photoeditor tool to make one or more
+    regions of the image transparent as described in [INPAINTING.md] and
+    uploading that.
 
     The file
     [Lincoln-and-Parrot-512-transparent.png](../assets/Lincoln-and-Parrot-512-transparent.png)
-    is a version of the earlier image in which the area around the parrot
-    has been replaced with transparency. Click on the "x" in the upper
-    right of the Initial Image and upload the transparent version. Using
-    the same prompt "old sea captain with raven on shoulder" try Invoking
-    an image. This time, only the parrot will be replaced, leaving the
-    rest of the original image intact:
+    is a version of the earlier image in which the area around the parrot has
+    been replaced with transparency. Click on the "x" in the upper right of the
+    Initial Image and upload the transparent version. Using the same prompt "old
+    sea captain with raven on shoulder" try Invoking an image. This time, only
+    the parrot will be replaced, leaving the rest of the original image intact:
 
-<figure markdown>
-![Invoke Web Server - Inpainting](../assets/invoke-web-server-8.png){:width="640px"}
-</figure>
+    <figure markdown>
+    ![Invoke Web Server - Inpainting](../assets/invoke-web-server-8.png){:width="640px"}
+    </figure>
 
-6. Would you like to modify a previously-generated image using the
-Image to Image facility? Easy! While in the Image to Image panel,
-hover over any of the gallery images to see a little menu of icons pop
-up.  Click the picture icon to instantly send the selected image to
-Image to Image as the initial image.
+6.  Would you like to modify a previously-generated image using the Image to
+    Image facility? Easy! While in the Image to Image panel, hover over any of
+    the gallery images to see a little menu of icons pop up. Click the picture
+    icon to instantly send the selected image to Image to Image as the initial
+    image.
 
-You can do the same from the Text to Image tab by clicking on the
-picture icon above the central image panel. The screenshot below
-shows where the "use as initial image" icons are located.
+You can do the same from the Text to Image tab by clicking on the picture icon
+above the central image panel. The screenshot below shows where the "use as
+initial image" icons are located.
 
 ![Invoke Web Server - Use as Image Links](../assets/invoke-web-server-9.png){:width="640px"}
 
 ## Parting remarks
 
-This concludes the walkthrough, but there are several more features that you
-can explore. Please check out the [Command Line Interface](CLI.md)
-documentation for further explanation of the advanced features that
-were not covered here.
+This concludes the walkthrough, but there are several more features that you can
+explore. Please check out the [Command Line Interface](CLI.md) documentation for
+further explanation of the advanced features that were not covered here.
 
-The WebGUI is only rapid development. Check back regularly for
-updates!
+The WebGUI is only rapid development. Check back regularly for updates!
 
 ## Reference
 
 ### Additional Options
 
- parameter <img width=160 align="right"> | effect
--- | --
-`--web_develop` | Starts the web server in development mode.
-`--web_verbose` | Enables verbose logging
-`--cors [CORS ...]` | Additional allowed origins, comma-separated
-`--host HOST` | Web server: Host or IP to listen on. Set to 0.0.0.0 to accept traffic from other devices on your network.
-`--port PORT` | Web server: Port to listen on
-`--gui` | Start InvokeAI GUI - This is the "desktop mode" version of the web app. It uses Flask to create a desktop app experience of the webserver.
+| parameter <img width=160 align="right"> | effect                                                                                                                                     |
+| --------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------ |
+| `--web_develop`                         | Starts the web server in development mode.                                                                                                 |
+| `--web_verbose`                         | Enables verbose logging                                                                                                                    |
+| `--cors [CORS ...]`                     | Additional allowed origins, comma-separated                                                                                                |
+| `--host HOST`                           | Web server: Host or IP to listen on. Set to 0.0.0.0 to accept traffic from other devices on your network.                                  |
+| `--port PORT`                           | Web server: Port to listen on                                                                                                              |
+| `--gui`                                 | Start InvokeAI GUI - This is the "desktop mode" version of the web app. It uses Flask to create a desktop app experience of the webserver. |
 
 ### Web Specific Features
 
-The web experience offers an incredibly easy-to-use experience for interacting with the InvokeAI toolkit. 
-For detailed guidance on individual features, see the Feature-specific help documents available in this directory.
-Note that the latest functionality available in the CLI may not always be available in the Web interface.
+The web experience offers an incredibly easy-to-use experience for interacting
+with the InvokeAI toolkit. For detailed guidance on individual features, see the
+Feature-specific help documents available in this directory. Note that the
+latest functionality available in the CLI may not always be available in the Web
+interface.
 
 #### Dark Mode & Light Mode
 
-The InvokeAI interface is available in a nano-carbon black & purple Dark Mode, and a "burn your eyes out Nosferatu" Light Mode. These can be toggled by clicking the Sun/Moon icons at the top right of the interface. 
+The InvokeAI interface is available in a nano-carbon black & purple Dark Mode,
+and a "burn your eyes out Nosferatu" Light Mode. These can be toggled by
+clicking the Sun/Moon icons at the top right of the interface.
 
 ![InvokeAI Web Server - Dark Mode](../assets/invoke_web_dark.png)
 
@@ -334,7 +325,10 @@ The InvokeAI interface is available in a nano-carbon black & purple Dark Mode, a
 
 #### Invocation Toolbar
 
-The left side of the InvokeAI interface is available for customizing the prompt and the settings used for invoking your new image. Typing your prompt into the open text field and clicking the Invoke button will produce the image based on the settings configured in the toolbar.
+The left side of the InvokeAI interface is available for customizing the prompt
+and the settings used for invoking your new image. Typing your prompt into the
+open text field and clicking the Invoke button will produce the image based on
+the settings configured in the toolbar.
 
 See below for additional documentation related to each feature:
 
@@ -347,11 +341,17 @@ See below for additional documentation related to each feature:
 
 #### Invocation Gallery
 
-The currently selected --outdir (or the default outputs folder) will display all previously generated files on load. As new invocations are generated, these will be dynamically added to the gallery, and can be previewed by selecting them. Each image also has a simple set of actions (e.g., Delete, Use Seed, Use All Parameters, etc.) that can be accessed by hovering over the image.
+The currently selected --outdir (or the default outputs folder) will display all
+previously generated files on load. As new invocations are generated, these will
+be dynamically added to the gallery, and can be previewed by selecting them.
+Each image also has a simple set of actions (e.g., Delete, Use Seed, Use All
+Parameters, etc.) that can be accessed by hovering over the image.
 
 #### Image Workspace
 
-When an image from the Invocation Gallery is selected, or is generated, the image will be displayed within the center of the interface. A quickbar of common image interactions are displayed along the top of the image, including:
+When an image from the Invocation Gallery is selected, or is generated, the
+image will be displayed within the center of the interface. A quickbar of common
+image interactions are displayed along the top of the image, including:
 
 - Use image in the `Image to Image` workflow
 - Initialize Face Restoration on the selected file
@@ -361,9 +361,9 @@ When an image from the Invocation Gallery is selected, or is generated, the imag
 
 ## Acknowledgements
 
-A huge shout-out to the core team working to make this vision a
-reality, including
-[psychedelicious](https://github.com/psychedelicious),
+A huge shout-out to the core team working to make this vision a reality,
+including [psychedelicious](https://github.com/psychedelicious),
 [Kyle0654](https://github.com/Kyle0654) and
-[blessedcoolant](https://github.com/blessedcoolant). [hipsterusername](https://github.com/hipsterusername)
-was the team's unofficial cheerleader and added tooltips/docs.
+[blessedcoolant](https://github.com/blessedcoolant).
+[hipsterusername](https://github.com/hipsterusername) was the team's unofficial
+cheerleader and added tooltips/docs.
diff --git a/docs/features/WEBUIHOTKEYS.md b/docs/features/WEBUIHOTKEYS.md
index 020449e0b9..1f0af08dd6 100644
--- a/docs/features/WEBUIHOTKEYS.md
+++ b/docs/features/WEBUIHOTKEYS.md
@@ -1,58 +1,62 @@
-# **WebUI Hotkey List**
+---
+title: WebUI Hotkey List
+---
+
+# :material-keyboard: **WebUI Hotkey List**
 
 ## General
 
-| Setting      | Hotkey                 |
-| ------------ | ---------------------- |
-| a            | Set All Parameters     |
-| s            | Set Seed               |
-| u            | Upscale                |
-| r            | Restoration            |
-| i            | Show Metadata          |
-| Ddl          | Delete Image           |
-| alt + a      | Focus prompt input     |
-| shift + i    | Send To Image to Image |
-| ctrl + enter | Start processing       |
-| shift + x    | cancel Processing      |
-| shift + d    | Toggle Dark Mode       |
-| `            | Toggle console         |
+| Setting           | Hotkey                 |
+| ----------------- | ---------------------- |
+| ++a++             | Set All Parameters     |
+| ++s++             | Set Seed               |
+| ++u++             | Upscale                |
+| ++r++             | Restoration            |
+| ++i++             | Show Metadata          |
+| ++d++ ++d++ ++l++ | Delete Image           |
+| ++alt+a++         | Focus prompt input     |
+| ++shift+i++       | Send To Image to Image |
+| ++ctrl+enter++    | Start processing       |
+| ++shift+x++       | cancel Processing      |
+| ++shift+d++       | Toggle Dark Mode       |
+| ++"`"++           | Toggle console         |
 
 ## Tabs
 
 | Setting | Hotkey                    |
 | ------- | ------------------------- |
-| 1       | Go to Text To Image Tab   |
-| 2       | Go to Image to Image Tab  |
-| 3       | Go to Inpainting Tab      |
-| 4       | Go to Outpainting Tab     |
-| 5       | Go to Nodes Tab           |
-| 6       | Go to Post Processing Tab |
+| ++1++   | Go to Text To Image Tab   |
+| ++2++   | Go to Image to Image Tab  |
+| ++3++   | Go to Inpainting Tab      |
+| ++4++   | Go to Outpainting Tab     |
+| ++5++   | Go to Nodes Tab           |
+| ++6++   | Go to Post Processing Tab |
 
 ## Gallery
 
-| Setting      | Hotkey                          |
-| ------------ | ------------------------------- |
-| g            | Toggle Gallery                  |
-| left arrow   | Go to previous image in gallery |
-| right arrow  | Go to next image in gallery     |
-| shift + p    | Pin gallery                     |
-| shift + up   | Increase gallery image size     |
-| shift + down | Decrease gallery image size     |
-| shift + r    | Reset image gallery size        |
+| Setting        | Hotkey                          |
+| -------------- | ------------------------------- |
+| ++g++          | Toggle Gallery                  |
+| ++left++       | Go to previous image in gallery |
+| ++right++      | Go to next image in gallery     |
+| ++shift+p++    | Pin gallery                     |
+| ++shift+up++   | Increase gallery image size     |
+| ++shift+down++ | Decrease gallery image size     |
+| ++shift+r++    | Reset image gallery size        |
 
 ## Inpainting
 
-| Setting                    | Hotkey                |
-| -------------------------- | --------------------- |
-| [                          | Decrease brush size   |
-| ]                          | Increase brush size   |
-| alt + [                    | Decrease mask opacity |
-| alt + ]                    | Increase mask opacity |
-| b                          | Select brush          |
-| e                          | Select eraser         |
-| ctrl + z                   | Undo brush stroke     |
-| ctrl + shift + z, ctrl + y | Redo brush stroke     |
-| h                          | Hide mask             |
-| shift + m                  | Invert mask           |
-| shift + c                  | Clear mask            |
-| shift + j                  | Expand canvas         |
+| Setting                      | Hotkey                |
+| ---------------------------- | --------------------- |
+| ++"["++                      | Decrease brush size   |
+| ++"]"++                      | Increase brush size   |
+| ++alt+"["++                  | Decrease mask opacity |
+| ++alt+"]"++                  | Increase mask opacity |
+| ++b++                        | Select brush          |
+| ++e++                        | Select eraser         |
+| ++ctrl+z++                   | Undo brush stroke     |
+| ++ctrl+shift+z++, ++ctrl+y++ | Redo brush stroke     |
+| ++h++                        | Hide mask             |
+| ++shift+m++                  | Invert mask           |
+| ++shift+c++                  | Clear mask            |
+| ++shift+j++                  | Expand canvas         |
diff --git a/docs/help/TROUBLESHOOT.md b/docs/help/TROUBLESHOOT.md
index dc2a9a8e41..adea24e4e8 100644
--- a/docs/help/TROUBLESHOOT.md
+++ b/docs/help/TROUBLESHOOT.md
@@ -1,27 +1,25 @@
 ---
 title: F.A.Q.
-hide:
-  - toc
 ---
 
 # :material-frequently-asked-questions: F.A.Q.
 
 ## **Frequently-Asked-Questions**
 
-Here are a few common installation problems and their solutions. Often these are caused by
-incomplete installations or crashes during the install process.
+Here are a few common installation problems and their solutions. Often these are
+caused by incomplete installations or crashes during the install process.
 
 ---
 
-### **QUESTION**
+### During `conda env create`, conda hangs indefinitely
 
-During `conda env create`, conda hangs indefinitely.
+If it is because of the last PIP step (usually stuck in the Git Clone step, you
+can check the detailed log by this method):
 
-If it is because of the last PIP step (usually stuck in the Git Clone step, you can check the detailed log by this method):
 ```bash
 export PIP_LOG="/tmp/pip_log.txt"
 touch ${PIP_LOG}
-tail -f ${PIP_LOG} & 
+tail -f ${PIP_LOG} &
 conda env create -f environment-mac.yaml --debug --verbose
 killall tail
 rm ${PIP_LOG}
@@ -29,21 +27,20 @@ rm ${PIP_LOG}
 
 **SOLUTION**
 
-Conda sometimes gets stuck  at the last PIP step, in which several git repositories are
-cloned and built.
+Conda sometimes gets stuck at the last PIP step, in which several git
+repositories are cloned and built.
 
-Enter the stable-diffusion directory and completely remove the `src`
-directory and all its contents.  The safest way to do this is to enter
-the stable-diffusion directory and give the command `git clean -f`. If
-this still doesn't fix the problem, try "conda clean -all" and then
-restart at the `conda env create` step.
+Enter the stable-diffusion directory and completely remove the `src` directory
+and all its contents. The safest way to do this is to enter the stable-diffusion
+directory and give the command `git clean -f`. If this still doesn't fix the
+problem, try "conda clean -all" and then restart at the `conda env create` step.
 
 To further understand the problem to checking the install lot using this method:
 
 ```bash
 export PIP_LOG="/tmp/pip_log.txt"
 touch ${PIP_LOG}
-tail -f ${PIP_LOG} & 
+tail -f ${PIP_LOG} &
 conda env create -f environment-mac.yaml --debug --verbose
 killall tail
 rm ${PIP_LOG}
@@ -51,43 +48,44 @@ rm ${PIP_LOG}
 
 ---
 
-### **QUESTION**
+### `invoke.py` crashes with the complaint that it can't find `ldm.simplet2i.py`
 
-`invoke.py` crashes with the complaint that it can't find `ldm.simplet2i.py`. Or it complains that
-function is being passed incorrect parameters.
+Or it complains that function is being passed incorrect parameters.
 
-### **SOLUTION**
+**SOLUTION**
 
-Reinstall the stable diffusion modules. Enter the `stable-diffusion` directory and give the command
-`pip install -e .`
+Reinstall the stable diffusion modules. Enter the `stable-diffusion` directory
+and give the command `pip install -e .`
 
 ---
 
-### **QUESTION**
+### Missing modules
 
-`invoke.py` dies, complaining of various missing modules, none of which starts with `ldm`.
+`invoke.py` dies, complaining of various missing modules, none of which starts
+with `ldm`.
 
-### **SOLUTION**
+**SOLUTION**
 
-From within the `InvokeAI` directory, run `conda env update` This is also frequently the solution to
-complaints about an unknown function in a module.
+From within the `InvokeAI` directory, run `conda env update` This is also
+frequently the solution to complaints about an unknown function in a module.
 
 ---
 
-### **QUESTION**
+### How can I try new features
 
-There's a feature or bugfix in the Stable Diffusion GitHub that you want to try out.
+There's a feature or bugfix in the Stable Diffusion GitHub that you want to try
+out.
 
-### **SOLUTION**
+**SOLUTIONS**
 
 #### **Main Branch**
 
-If the fix/feature is on the `main` branch, enter the stable-diffusion directory and do a
-`git pull`.
+If the fix/feature is on the `main` branch, enter the stable-diffusion directory
+and do a `git pull`.
 
-Usually this will be sufficient, but if you start to see errors about
-missing or incorrect modules, use the command `pip install -e .`
-and/or `conda env update` (These commands won't break anything.)
+Usually this will be sufficient, but if you start to see errors about missing or
+incorrect modules, use the command `pip install -e .` and/or `conda env update`
+(These commands won't break anything.)
 
 `pip install -e .` and/or `conda env update -f environment.yaml`
 
@@ -95,33 +93,36 @@ and/or `conda env update` (These commands won't break anything.)
 
 #### **Sub Branch**
 
-If the feature/fix is on a branch (e.g. "_foo-bugfix_"), the recipe is similar, but do a
-`git pull <name of branch>`.
+If the feature/fix is on a branch (e.g. "_foo-bugfix_"), the recipe is similar,
+but do a `git pull <name of branch>`.
 
 #### **Not Committed**
 
-If the feature/fix is in a pull request that has not yet been made part of the main branch or a
-feature/bugfix branch, then from the page for the desired pull request, look for the line at the top
-that reads "_xxxx wants to merge xx commits into lstein:main from YYYYYY_". Copy the URL in YYYY. It
-should have the format
+If the feature/fix is in a pull request that has not yet been made part of the
+main branch or a feature/bugfix branch, then from the page for the desired pull
+request, look for the line at the top that reads "_xxxx wants to merge xx
+commits into lstein:main from YYYYYY_". Copy the URL in YYYY. It should have the
+format
 
 `https://github.com/<name of contributor>/stable-diffusion/tree/<name of branch>`
 
 Then **go to the directory above stable-diffusion** and rename the directory to
-"_stable-diffusion.lstein_", "_stable-diffusion.old_", or anything else. You can then git clone the
-branch that contains the pull request:
+"_stable-diffusion.lstein_", "_stable-diffusion.old_", or anything else. You can
+then git clone the branch that contains the pull request:
 
 `git clone https://github.com/<name of contributor>/stable-diffusion/tree/<name of branch>`
 
-You will need to go through the install procedure again, but it should be fast because all the
-dependencies are already loaded.
+You will need to go through the install procedure again, but it should be fast
+because all the dependencies are already loaded.
 
 ---
 
-### **QUESTION**
+### CUDA out of memory
 
-Image generation crashed with CUDA out of memory error after successful sampling. 
+Image generation crashed with CUDA out of memory error after successful
+sampling.
 
-### **SOLUTION**
+**SOLUTION**
 
-Try to run script with option `--free_gpu_mem` This will free memory before image decoding step.
+Try to run script with option `--free_gpu_mem` This will free memory before
+image decoding step.
diff --git a/docs/index.md b/docs/index.md
index 88266ab399..d9dee0c30d 100644
--- a/docs/index.md
+++ b/docs/index.md
@@ -14,43 +14,61 @@ title: Home
 
 # ^^**InvokeAI: A Stable Diffusion Toolkit**^^ :tools: <br> <small>Formerly known as lstein/stable-diffusion</small>
 
-![project logo](assets/logo.png)
+[![project logo](assets/logo.png)](https://github.com/invoke-ai/InvokeAI)
 
 [![discord badge]][discord link]
 
-[![latest release badge]][latest release link] [![github stars badge]][github stars link] [![github forks badge]][github forks link]
+[![latest release badge]][latest release link]
+[![github stars badge]][github stars link]
+[![github forks badge]][github forks link]
 
-[![CI checks on main badge]][CI checks on main link] [![CI checks on dev badge]][CI checks on dev link] [![latest commit to dev badge]][latest commit to dev link]
+[![CI checks on main badge]][ci checks on main link]
+[![CI checks on dev badge]][ci checks on dev link]
+[![latest commit to dev badge]][latest commit to dev link]
 
-[![github open issues badge]][github open issues link] [![github open prs badge]][github open prs link]
+[![github open issues badge]][github open issues link]
+[![github open prs badge]][github open prs link]
 
-[CI checks on dev badge]: https://flat.badgen.net/github/checks/invoke-ai/InvokeAI/development?label=CI%20status%20on%20dev&cache=900&icon=github
-[CI checks on dev link]: https://github.com/invoke-ai/InvokeAI/actions?query=branch%3Adevelopment
-[CI checks on main badge]: https://flat.badgen.net/github/checks/invoke-ai/InvokeAI/main?label=CI%20status%20on%20main&cache=900&icon=github
-[CI checks on main link]: https://github.com/invoke-ai/InvokeAI/actions/workflows/test-invoke-conda.yml
+[ci checks on dev badge]:
+  https://flat.badgen.net/github/checks/invoke-ai/InvokeAI/development?label=CI%20status%20on%20dev&cache=900&icon=github
+[ci checks on dev link]:
+  https://github.com/invoke-ai/InvokeAI/actions?query=branch%3Adevelopment
+[ci checks on main badge]:
+  https://flat.badgen.net/github/checks/invoke-ai/InvokeAI/main?label=CI%20status%20on%20main&cache=900&icon=github
+[ci checks on main link]:
+  https://github.com/invoke-ai/InvokeAI/actions/workflows/test-invoke-conda.yml
 [discord badge]: https://flat.badgen.net/discord/members/ZmtBAhwWhy?icon=discord
 [discord link]: https://discord.gg/ZmtBAhwWhy
-[github forks badge]: https://flat.badgen.net/github/forks/invoke-ai/InvokeAI?icon=github
-[github forks link]: https://useful-forks.github.io/?repo=lstein%2Fstable-diffusion
-[github open issues badge]: https://flat.badgen.net/github/open-issues/invoke-ai/InvokeAI?icon=github
-[github open issues link]: https://github.com/invoke-ai/InvokeAI/issues?q=is%3Aissue+is%3Aopen
-[github open prs badge]: https://flat.badgen.net/github/open-prs/invoke-ai/InvokeAI?icon=github
-[github open prs link]: https://github.com/invoke-ai/InvokeAI/pulls?q=is%3Apr+is%3Aopen
-[github stars badge]: https://flat.badgen.net/github/stars/invoke-ai/InvokeAI?icon=github
+[github forks badge]:
+  https://flat.badgen.net/github/forks/invoke-ai/InvokeAI?icon=github
+[github forks link]:
+  https://useful-forks.github.io/?repo=lstein%2Fstable-diffusion
+[github open issues badge]:
+  https://flat.badgen.net/github/open-issues/invoke-ai/InvokeAI?icon=github
+[github open issues link]:
+  https://github.com/invoke-ai/InvokeAI/issues?q=is%3Aissue+is%3Aopen
+[github open prs badge]:
+  https://flat.badgen.net/github/open-prs/invoke-ai/InvokeAI?icon=github
+[github open prs link]:
+  https://github.com/invoke-ai/InvokeAI/pulls?q=is%3Apr+is%3Aopen
+[github stars badge]:
+  https://flat.badgen.net/github/stars/invoke-ai/InvokeAI?icon=github
 [github stars link]: https://github.com/invoke-ai/InvokeAI/stargazers
-[latest commit to dev badge]: https://flat.badgen.net/github/last-commit/invoke-ai/InvokeAI/development?icon=github&color=yellow&label=last%20dev%20commit&cache=900
-[latest commit to dev link]: https://github.com/invoke-ai/InvokeAI/commits/development
-[latest release badge]: https://flat.badgen.net/github/release/invoke-ai/InvokeAI/development?icon=github
+[latest commit to dev badge]:
+  https://flat.badgen.net/github/last-commit/invoke-ai/InvokeAI/development?icon=github&color=yellow&label=last%20dev%20commit&cache=900
+[latest commit to dev link]:
+  https://github.com/invoke-ai/InvokeAI/commits/development
+[latest release badge]:
+  https://flat.badgen.net/github/release/invoke-ai/InvokeAI/development?icon=github
 [latest release link]: https://github.com/invoke-ai/InvokeAI/releases
 
 </div>
 
 <a href="https://github.com/invoke-ai/InvokeAI">InvokeAI</a> is an
 implementation of Stable Diffusion, the open source text-to-image and
-image-to-image generator. It provides a streamlined process with
-various new features and options to aid the image generation
-process. It runs on Windows, Mac and Linux machines, and runs on GPU
-cards with as little as 4 GB or RAM.
+image-to-image generator. It provides a streamlined process with various new
+features and options to aid the image generation process. It runs on Windows,
+Mac and Linux machines, and runs on GPU cards with as little as 4 GB or RAM.
 
 **Quick links**: [<a href="https://discord.gg/ZmtBAhwWhy">Discord Server</a>] [<a href="https://github.com/invoke-ai/InvokeAI/">Code and Downloads</a>] [<a href="https://github.com/invoke-ai/InvokeAI/issues">Bug Reports</a>] [<a href="https://github.com/invoke-ai/InvokeAI/discussions">Discussion, Ideas & Q&A</a>]
 
@@ -62,8 +80,8 @@ cards with as little as 4 GB or RAM.
 
 ## :octicons-package-dependencies-24: Installation
 
-This fork is supported across multiple platforms. You can find individual installation instructions
-below.
+This fork is supported across multiple platforms. You can find individual
+installation instructions below.
 
 - :fontawesome-brands-linux: [Linux](installation/INSTALL_LINUX.md)
 - :fontawesome-brands-windows: [Windows](installation/INSTALL_WINDOWS.md)
@@ -76,6 +94,7 @@ below.
 You wil need one of the following:
 
 - :simple-nvidia: An NVIDIA-based graphics card with 4 GB or more VRAM memory.
+- :simple-amd: An AMD-based graphics card with 4 GB or more VRAM memory (Linux only)
 - :fontawesome-brands-apple: An Apple computer with an M1 chip.
 
 ### :fontawesome-solid-memory: Memory
@@ -84,7 +103,8 @@ You wil need one of the following:
 
 ### :fontawesome-regular-hard-drive: Disk
 
-- At least 12 GB of free disk space for the machine learning model, Python, and all its dependencies.
+- At least 12 GB of free disk space for the machine learning model, Python, and
+  all its dependencies.
 
 !!! info
 
@@ -93,8 +113,8 @@ You wil need one of the following:
 
     Similarly, specify full-precision mode on Apple M1 hardware.
 
-    Precision is auto configured based on the device. If however you encounter errors like 
-    `expected type Float but found Half` or `not implemented for Half` you can try starting 
+    Precision is auto configured based on the device. If however you encounter errors like
+    `expected type Float but found Half` or `not implemented for Half` you can try starting
     `invoke.py` with the `--precision=float32` flag:
 
     ```bash
@@ -103,73 +123,127 @@ You wil need one of the following:
 
 ## :octicons-log-16: Latest Changes
 
+### v2.1.0 <small>(2 November 2022)</small>
+
+- [Inpainting](https://invoke-ai.github.io/InvokeAI/features/INPAINTING/)
+  support in the WebGUI
+- Greatly improved navigation and user experience in the
+  [WebGUI](https://invoke-ai.github.io/InvokeAI/features/WEB/)
+- The prompt syntax has been enhanced with
+  [prompt weighting, cross-attention and prompt merging](https://invoke-ai.github.io/InvokeAI/features/PROMPTS/).
+- You can now load
+  [multiple models and switch among them quickly](https://docs.google.com/presentation/d/1WywGA1rny7bpFh7CLSdTr4nNpVKdlUeT0Bj0jCsILyU/edit?usp=sharing)
+  without leaving the CLI.
+- The installation process (via `scripts/preload_models.py`) now lets you select
+  among several popular
+  [Stable Diffusion models](https://invoke-ai.github.io/InvokeAI/installation/INSTALLING_MODELS/)
+  and downloads and installs them on your behalf. Among other models, this
+  script will install the current Stable Diffusion 1.5 model as well as a
+  StabilityAI variable autoencoder (VAE) which improves face generation.
+- Tired of struggling with photoeditors to get the masked region of for
+  inpainting just right? Let the AI make the mask for you using
+  [text masking](https://docs.google.com/presentation/d/1pWoY510hCVjz0M6X9CBbTznZgW2W5BYNKrmZm7B45q8/edit#slide=id.p).
+  This feature allows you to specify the part of the image to paint over using
+  just English-language phrases.
+- Tired of seeing the head of your subjects cropped off? Uncrop them in the CLI
+  with the
+  [outcrop feature](https://invoke-ai.github.io/InvokeAI/features/OUTPAINTING/#outcrop).
+- Tired of seeing your subject's bodies duplicated or mangled when generating
+  larger-dimension images? Check out the `--hires` option in the CLI, or select
+  the corresponding toggle in the WebGUI.
+- We now support textual inversion and fine-tune .bin styles and subjects from
+  the Hugging Face archive of
+  [SD Concepts](https://huggingface.co/sd-concepts-library). Load the .bin file
+  using the `--embedding_path` option. (The next version will support merging
+  and loading of multiple simultaneous models).
+- ...
+
 ### v2.0.1 <small>(13 October 2022)</small>
 
-- fix noisy images at high step count when using k* samplers
-- dream.py script now calls invoke.py module directly rather than
-  via a new python process (which could break the environment)
+- fix noisy images at high step count when using k\* samplers
+- dream.py script now calls invoke.py module directly rather than via a new
+  python process (which could break the environment)
 
 ### v2.0.0 <small>(9 October 2022)</small>
 
-- `dream.py` script renamed `invoke.py`. A `dream.py` script wrapper remains
-  for backward compatibility.
+- `dream.py` script renamed `invoke.py`. A `dream.py` script wrapper remains for
+  backward compatibility.
 - Completely new WebGUI - launch with `python3 scripts/invoke.py --web`
-- Support for <a href="https://invoke-ai.github.io/InvokeAI/features/INPAINTING/">inpainting</a> and <a href="https://invoke-ai.github.io/InvokeAI/features/OUTPAINTING/">outpainting</a>
-- img2img runs on all k* samplers
-- Support for <a href="https://invoke-ai.github.io/InvokeAI/features/PROMPTS/#negative-and-unconditioned-prompts">negative prompts</a>
+- Support for
+  <a href="https://invoke-ai.github.io/InvokeAI/features/INPAINTING/">inpainting</a>
+  and
+  <a href="https://invoke-ai.github.io/InvokeAI/features/OUTPAINTING/">outpainting</a>
+- img2img runs on all k\* samplers
+- Support for
+  <a href="https://invoke-ai.github.io/InvokeAI/features/PROMPTS/#negative-and-unconditioned-prompts">negative
+  prompts</a>
 - Support for CodeFormer face reconstruction
 - Support for Textual Inversion on Macintoshes
-- Support in both WebGUI and CLI for <a href="https://invoke-ai.github.io/InvokeAI/features/POSTPROCESS/">post-processing of previously-generated images</a>
-  using facial reconstruction, ESRGAN upscaling, outcropping (similar to DALL-E infinite canvas),
-  and "embiggen" upscaling. See the `!fix` command.
-- New `--hires` option on `invoke>` line allows <a href="https://invoke-ai.github.io/InvokeAI/features/CLI/#txt2img">larger images to be created without duplicating elements</a>, at the cost of some performance.
-- New `--perlin` and `--threshold` options allow you to add and control variation
-  during image generation (see <a href="https://github.com/invoke-ai/InvokeAI/blob/main/docs/features/OTHER.md#thresholding-and-perlin-noise-initialization-options">Thresholding and Perlin Noise Initialization</a>
-- Extensive metadata now written into PNG files, allowing reliable regeneration of images
-  and tweaking of previous settings.
-- Command-line completion in `invoke.py` now works on Windows, Linux and Mac platforms.
-- Improved <a href="https://invoke-ai.github.io/InvokeAI/features/CLI/">command-line completion behavior</a>.
-  New commands added:
+- Support in both WebGUI and CLI for
+  <a href="https://invoke-ai.github.io/InvokeAI/features/POSTPROCESS/">post-processing
+  of previously-generated images</a> using facial reconstruction, ESRGAN
+  upscaling, outcropping (similar to DALL-E infinite canvas), and "embiggen"
+  upscaling. See the `!fix` command.
+- New `--hires` option on `invoke>` line allows
+  <a href="https://invoke-ai.github.io/InvokeAI/features/CLI/#txt2img">larger
+  images to be created without duplicating elements</a>, at the cost of some
+  performance.
+- New `--perlin` and `--threshold` options allow you to add and control
+  variation during image generation (see
+  <a href="https://github.com/invoke-ai/InvokeAI/blob/main/docs/features/OTHER.md#thresholding-and-perlin-noise-initialization-options">Thresholding
+  and Perlin Noise Initialization</a>
+- Extensive metadata now written into PNG files, allowing reliable regeneration
+  of images and tweaking of previous settings.
+- Command-line completion in `invoke.py` now works on Windows, Linux and Mac
+  platforms.
+- Improved
+  <a href="https://invoke-ai.github.io/InvokeAI/features/CLI/">command-line
+  completion behavior</a>. New commands added:
   - List command-line history with `!history`
   - Search command-line history with `!search`
   - Clear history with `!clear`
 - Deprecated `--full_precision` / `-F`. Simply omit it and `invoke.py` will auto
-  configure. To switch away from auto use the new flag like `--precision=float32`.
+  configure. To switch away from auto use the new flag like
+  `--precision=float32`.
 
-For older changelogs, please visit the **[CHANGELOG](CHANGELOG.md#v114-11-september-2022)**.
+For older changelogs, please visit the
+**[CHANGELOG](CHANGELOG/#v114-11-september-2022)**.
 
 ## :material-target: Troubleshooting
 
-Please check out our **[:material-frequently-asked-questions: Q&A](help/TROUBLESHOOT.md)** to get solutions for common installation
-problems and other issues.
+Please check out our
+**[:material-frequently-asked-questions: Q&A](help/TROUBLESHOOT.md)** to get
+solutions for common installation problems and other issues.
 
 ## :octicons-repo-push-24: Contributing
 
-Anyone who wishes to contribute to this project, whether documentation, features, bug fixes, code
-cleanup, testing, or code reviews, is very much encouraged to do so. If you are unfamiliar with how
-to contribute to GitHub projects, here is a
+Anyone who wishes to contribute to this project, whether documentation,
+features, bug fixes, code cleanup, testing, or code reviews, is very much
+encouraged to do so. If you are unfamiliar with how to contribute to GitHub
+projects, here is a
 [Getting Started Guide](https://opensource.com/article/19/7/create-pull-request-github).
 
-A full set of contribution guidelines, along with templates, are in progress, but for now the most
-important thing is to **make your pull request against the "development" branch**, and not against
-"main". This will help keep public breakage to a minimum and will allow you to propose more radical
-changes.
+A full set of contribution guidelines, along with templates, are in progress,
+but for now the most important thing is to **make your pull request against the
+"development" branch**, and not against "main". This will help keep public
+breakage to a minimum and will allow you to propose more radical changes.
 
 ## :octicons-person-24: Contributors
 
 This fork is a combined effort of various people from across the world.
-[Check out the list of all these amazing people](other/CONTRIBUTORS.md). We thank them for their
-time, hard work and effort.
+[Check out the list of all these amazing people](other/CONTRIBUTORS.md). We
+thank them for their time, hard work and effort.
 
 ## :octicons-question-24: Support
 
-For support, please use this repository's GitHub Issues tracking service. Feel free to send me an
-email if you use and like the script.
+For support, please use this repository's GitHub Issues tracking service. Feel
+free to send me an email if you use and like the script.
 
 Original portions of the software are Copyright (c) 2020
 [Lincoln D. Stein](https://github.com/lstein)
 
 ## :octicons-book-24: Further Reading
 
-Please see the original README for more information on this software and underlying algorithm,
-located in the file [README-CompViz.md](other/README-CompViz.md).
+Please see the original README for more information on this software and
+underlying algorithm, located in the file
+[README-CompViz.md](other/README-CompViz.md).
diff --git a/docs/installation/INSTALLING_MODELS.md b/docs/installation/INSTALLING_MODELS.md
index b5d659b0d1..c3b30eb25f 100644
--- a/docs/installation/INSTALLING_MODELS.md
+++ b/docs/installation/INSTALLING_MODELS.md
@@ -6,224 +6,223 @@ title: Installing Models
 
 ## Model Weight Files
 
-The model weight files ('*.ckpt') are the Stable Diffusion "secret
-sauce". They are the product of training the AI on millions of
-captioned images gathered from multiple sources.
+The model weight files ('\*.ckpt') are the Stable Diffusion "secret sauce". They
+are the product of training the AI on millions of captioned images gathered from
+multiple sources.
 
-Originally there was only a single Stable Diffusion weights file,
-which many people named `model.ckpt`. Now there are dozens or more
-that have been "fine tuned" to provide particulary styles, genres, or
-other features. InvokeAI allows you to install and run multiple model
-weight files and switch between them quickly in the command-line and
-web interfaces.
+Originally there was only a single Stable Diffusion weights file, which many
+people named `model.ckpt`. Now there are dozens or more that have been "fine
+tuned" to provide particulary styles, genres, or other features. InvokeAI allows
+you to install and run multiple model weight files and switch between them
+quickly in the command-line and web interfaces.
 
-This manual will guide you through installing and configuring model
-weight files.
+This manual will guide you through installing and configuring model weight
+files.
 
 ## Base Models
 
-InvokeAI comes with support for a good initial set of models listed in
-the model configuration file `configs/models.yaml`. They are:
+InvokeAI comes with support for a good initial set of models listed in the model
+configuration file `configs/models.yaml`. They are:
 
-| Model                   | Weight File                   |   Description                    | DOWNLOAD FROM            |
-| ----------------------  | ----------------------------- |--------------------------------- | ----------------|
-| stable-diffusion-1.5    | v1-5-pruned-emaonly.ckpt      | Most recent version of base Stable Diffusion model| https://huggingface.co/runwayml/stable-diffusion-v1-5 |
-| stable-diffusion-1.4    | sd-v1-4.ckpt                  | Previous version of base Stable Diffusion model | https://huggingface.co/CompVis/stable-diffusion-v-1-4-original |
-| inpainting-1.5          | sd-v1-5-inpainting.ckpt       | Stable Diffusion 1.5 model specialized for inpainting | https://huggingface.co/runwayml/stable-diffusion-inpainting |
-| waifu-diffusion-1.3     | model-epoch09-float32.ckpt    | Stable Diffusion 1.4 trained to produce anime images | https://huggingface.co/hakurei/waifu-diffusion-v1-3 |
-| <all models>            | vae-ft-mse-840000-ema-pruned.ckpt   | A fine-tune file add-on file that improves face generation | https://huggingface.co/stabilityai/sd-vae-ft-mse-original/ |
+| Model                | Weight File                       | Description                                                | DOWNLOAD FROM                                                  |
+| -------------------- | --------------------------------- | ---------------------------------------------------------- | -------------------------------------------------------------- |
+| stable-diffusion-1.5 | v1-5-pruned-emaonly.ckpt          | Most recent version of base Stable Diffusion model         | https://huggingface.co/runwayml/stable-diffusion-v1-5          |
+| stable-diffusion-1.4 | sd-v1-4.ckpt                      | Previous version of base Stable Diffusion model            | https://huggingface.co/CompVis/stable-diffusion-v-1-4-original |
+| inpainting-1.5       | sd-v1-5-inpainting.ckpt           | Stable Diffusion 1.5 model specialized for inpainting      | https://huggingface.co/runwayml/stable-diffusion-inpainting    |
+| waifu-diffusion-1.3  | model-epoch09-float32.ckpt        | Stable Diffusion 1.4 trained to produce anime images       | https://huggingface.co/hakurei/waifu-diffusion-v1-3            |
+| `<all models>`       | vae-ft-mse-840000-ema-pruned.ckpt | A fine-tune file add-on file that improves face generation | https://huggingface.co/stabilityai/sd-vae-ft-mse-original/     |
 
-
-Note that these files are covered by an "Ethical AI" license which
-forbids certain uses. You will need to create an account on the
-Hugging Face website and accept the license terms before you can
-access the files.
+Note that these files are covered by an "Ethical AI" license which forbids
+certain uses. You will need to create an account on the Hugging Face website and
+accept the license terms before you can access the files.
 
 The predefined configuration file for InvokeAI (located at
-`configs/models.yaml`) provides entries for each of these weights
-files. `stable-diffusion-1.5` is the default model used, and we
-strongly recommend that you install this weights file if nothing else.
+`configs/models.yaml`) provides entries for each of these weights files.
+`stable-diffusion-1.5` is the default model used, and we strongly recommend that
+you install this weights file if nothing else.
 
 ## Community-Contributed Models
 
-There are too many to list here and more are being contributed every
-day. Hugging Face maintains a [fast-growing
-repository](https://huggingface.co/sd-concepts-library) of fine-tune
-(".bin") models that can be imported into InvokeAI by passing the
+There are too many to list here and more are being contributed every day.
+Hugging Face maintains a
+[fast-growing repository](https://huggingface.co/sd-concepts-library) of
+fine-tune (".bin") models that can be imported into InvokeAI by passing the
 `--embedding_path` option to the `invoke.py` command.
 
-[This page](https://rentry.org/sdmodels) hosts a large list of
-official and unofficial Stable Diffusion models and where they can be
-obtained.
+[This page](https://rentry.org/sdmodels) hosts a large list of official and
+unofficial Stable Diffusion models and where they can be obtained.
 
 ## Installation
 
 There are three ways to install weights files:
 
-1. During InvokeAI installation, the `preload_models.py` script can
-download them for you.
+1. During InvokeAI installation, the `preload_models.py` script can download
+   them for you.
 
-2. You can use the command-line interface (CLI) to import, configure
-and modify new models files.
+2. You can use the command-line interface (CLI) to import, configure and modify
+   new models files.
 
-3. You can download the files manually and add the appropriate entries
-to `models.yaml`.
+3. You can download the files manually and add the appropriate entries to
+   `models.yaml`.
 
 ### Installation via `preload_models.py`
 
-This is the most automatic way. Run `scripts/preload_models.py` from
-the console.  It will ask you to select which models to download and
-lead you through the steps of setting up a Hugging Face account if you
-haven't done so already.
+This is the most automatic way. Run `scripts/preload_models.py` from the
+console. It will ask you to select which models to download and lead you through
+the steps of setting up a Hugging Face account if you haven't done so already.
 
-To start, from within the InvokeAI directory run the command `python
-scripts/preload_models.py` (Linux/MacOS) or `python
-scripts\preload_models.py` (Windows):
+To start, run `python scripts/preload_models.py` from within the InvokeAI:
+directory
 
-```
-Loading Python libraries...
+!!! example ""
 
-** INTRODUCTION **
-Welcome to InvokeAI. This script will help download the Stable Diffusion weight files
-and other large models that are needed for text to image generation. At any point you may interrupt
-this program and resume later.
+    ```text
+    Loading Python libraries...
 
-** WEIGHT SELECTION **
-Would you like to download the Stable Diffusion model weights now? [y] 
+    ** INTRODUCTION **
+    Welcome to InvokeAI. This script will help download the Stable Diffusion weight files
+    and other large models that are needed for text to image generation. At any point you may interrupt
+    this program and resume later.
 
-Choose the weight file(s) you wish to download. Before downloading you 
-will be given the option to view and change your selections.
+    ** WEIGHT SELECTION **
+    Would you like to download the Stable Diffusion model weights now? [y]
 
-[1] stable-diffusion-1.5:
-    The newest Stable Diffusion version 1.5 weight file (4.27 GB) (recommended)
-    Download? [y] 
-[2] inpainting-1.5:
-    RunwayML SD 1.5 model optimized for inpainting (4.27 GB) (recommended)
-    Download? [y] 
-[3] stable-diffusion-1.4:
-    The original Stable Diffusion version 1.4 weight file (4.27 GB) 
-    Download? [n] n
-[4] waifu-diffusion-1.3:
-    Stable Diffusion 1.4 fine tuned on anime-styled images (4.27) 
-    Download? [n] y
-[5] ft-mse-improved-autoencoder-840000:
-    StabilityAI improved autoencoder fine-tuned for human faces (recommended; 335 MB) (recommended)
-    Download? [y] y
-The following weight files will be downloaded:
-   [1] stable-diffusion-1.5*
-   [2] inpainting-1.5
-   [4] waifu-diffusion-1.3
-   [5] ft-mse-improved-autoencoder-840000
-*default
-Ok to download? [y] 
-** LICENSE AGREEMENT FOR WEIGHT FILES **
+    Choose the weight file(s) you wish to download. Before downloading you
+    will be given the option to view and change your selections.
 
-1. To download the Stable Diffusion weight files you need to read and accept the
-   CreativeML Responsible AI license. If you have not already done so, please 
-   create an account using the "Sign Up" button:
+    [1] stable-diffusion-1.5:
+        The newest Stable Diffusion version 1.5 weight file (4.27 GB) (recommended)
+        Download? [y]
+    [2] inpainting-1.5:
+        RunwayML SD 1.5 model optimized for inpainting (4.27 GB) (recommended)
+        Download? [y]
+    [3] stable-diffusion-1.4:
+        The original Stable Diffusion version 1.4 weight file (4.27 GB)
+        Download? [n] n
+    [4] waifu-diffusion-1.3:
+        Stable Diffusion 1.4 fine tuned on anime-styled images (4.27)
+        Download? [n] y
+    [5] ft-mse-improved-autoencoder-840000:
+        StabilityAI improved autoencoder fine-tuned for human faces (recommended; 335 MB) (recommended)
+        Download? [y] y
+    The following weight files will be downloaded:
+      [1] stable-diffusion-1.5*
+      [2] inpainting-1.5
+      [4] waifu-diffusion-1.3
+      [5] ft-mse-improved-autoencoder-840000
+    *default
+    Ok to download? [y]
+    ** LICENSE AGREEMENT FOR WEIGHT FILES **
 
-   https://huggingface.co 
+    1. To download the Stable Diffusion weight files you need to read and accept the
+      CreativeML Responsible AI license. If you have not already done so, please
+      create an account using the "Sign Up" button:
 
-   You will need to verify your email address as part of the HuggingFace
-   registration process.
+      https://huggingface.co
 
-2. After creating the account, login under your account and accept
-   the license terms located here:
+      You will need to verify your email address as part of the HuggingFace
+      registration process.
 
-   https://huggingface.co/CompVis/stable-diffusion-v-1-4-original
+    2. After creating the account, login under your account and accept
+      the license terms located here:
 
-Press <enter> when you are ready to continue:
-...
-```
+      https://huggingface.co/CompVis/stable-diffusion-v-1-4-original
 
-When the script is complete, you will find the downloaded weights
-files in `models/ldm/stable-diffusion-v1` and a matching configuration
-file in `configs/models.yaml`.
+    Press <enter> when you are ready to continue:
+    ...
+    ```
 
-You can run the script again to add any models you didn't select the
-first time. Note that as a safety measure the script will _never_
-remove a previously-installed weights file. You will have to do this
-manually.
+When the script is complete, you will find the downloaded weights files in
+`models/ldm/stable-diffusion-v1` and a matching configuration file in
+`configs/models.yaml`.
+
+You can run the script again to add any models you didn't select the first time.
+Note that as a safety measure the script will _never_ remove a
+previously-installed weights file. You will have to do this manually.
 
 ### Installation via the CLI
 
-You can install a new model, including any of the community-supported
-ones, via the command-line client's `!import_model` command.
+You can install a new model, including any of the community-supported ones, via
+the command-line client's `!import_model` command.
 
-1. First download the desired model weights file and place it under `models/ldm/stable-diffusion-v1/`.
-   You may rename the weights file to something more memorable if you wish. Record the path of the
-   weights file (e.g. `models/ldm/stable-diffusion-v1/arabian-nights-1.0.ckpt`)
+1.  First download the desired model weights file and place it under
+    `models/ldm/stable-diffusion-v1/`. You may rename the weights file to
+    something more memorable if you wish. Record the path of the weights file
+    (e.g. `models/ldm/stable-diffusion-v1/arabian-nights-1.0.ckpt`)
 
-2. Launch the `invoke.py` CLI with `python scripts/invoke.py`.
+2.  Launch the `invoke.py` CLI with `python scripts/invoke.py`.
 
-3. At the `invoke>` command-line, enter the command `!import_model <path to model>`.
-   For example:
+3.  At the `invoke>` command-line, enter the command
+    `!import_model <path to model>`. For example:
 
-   `invoke> !import_model models/ldm/stable-diffusion-v1/arabian-nights-1.0.ckpt`
+    `invoke> !import_model models/ldm/stable-diffusion-v1/arabian-nights-1.0.ckpt`
 
-   (Hint - the CLI supports file path autocompletion. Type a bit of the path
-   name and hit <tab> in order to get a choice of possible completions.)
+    !!! tip "the CLI supports file path autocompletion"
 
-4. Follow the wizard's instructions to complete installation as shown in the example
-   here:
+         Type a bit of the path name and hit ++tab++ in order to get a choice of
+         possible completions.
 
-```
-invoke> <b>!import_model models/ldm/stable-diffusion-v1/arabian-nights-1.0.ckpt</b>
->> Model import in process. Please enter the values needed to configure this model:
+4.  Follow the wizard's instructions to complete installation as shown in the
+    example here:
 
-Name for this model: <b>arabian-nights</b>
-Description of this model: <b>Arabian Nights Fine Tune v1.0</b>
-Configuration file for this model: <b>configs/stable-diffusion/v1-inference.yaml</b>
-Default image width: <b>512</b>
-Default image height: <b>512</b>
->> New configuration:
-arabian-nights:
-  config: configs/stable-diffusion/v1-inference.yaml
-  description: Arabian Nights Fine Tune v1.0
-  height: 512
-  weights: models/ldm/stable-diffusion-v1/arabian-nights-1.0.ckpt
-  width: 512
-OK to import [n]? <b>y</b>
->> Caching model stable-diffusion-1.4 in system RAM
->> Loading waifu-diffusion from models/ldm/stable-diffusion-v1/arabian-nights-1.0.ckpt
-   | LatentDiffusion: Running in eps-prediction mode
-   | DiffusionWrapper has 859.52 M params.
-   | Making attention of type 'vanilla' with 512 in_channels
-   | Working with z of shape (1, 4, 32, 32) = 4096 dimensions.
-   | Making attention of type 'vanilla' with 512 in_channels
-   | Using faster float16 precision
+    !!! example ""
 
-```
+        ```text
+        invoke> !import_model models/ldm/stable-diffusion-v1/arabian-nights-1.0.ckpt
+        >> Model import in process. Please enter the values needed to configure this model:
 
-If you've previously installed the fine-tune VAE file `vae-ft-mse-840000-ema-pruned.ckpt`,
-the wizard will also ask you if you want to add this VAE to the model.
+        Name for this model: arabian-nights
+        Description of this model: Arabian Nights Fine Tune v1.0
+        Configuration file for this model: configs/stable-diffusion/v1-inference.yaml
+        Default image width: 512
+        Default image height: 512
+        >> New configuration:
+        arabian-nights:
+          config: configs/stable-diffusion/v1-inference.yaml
+          description: Arabian Nights Fine Tune v1.0
+          height: 512
+          weights: models/ldm/stable-diffusion-v1/arabian-nights-1.0.ckpt
+          width: 512
+        OK to import [n]? y
+        >> Caching model stable-diffusion-1.4 in system RAM
+        >> Loading waifu-diffusion from models/ldm/stable-diffusion-v1/arabian-nights-1.0.ckpt
+          | LatentDiffusion: Running in eps-prediction mode
+          | DiffusionWrapper has 859.52 M params.
+          | Making attention of type 'vanilla' with 512 in_channels
+          | Working with z of shape (1, 4, 32, 32) = 4096 dimensions.
+          | Making attention of type 'vanilla' with 512 in_channels
+          | Using faster float16 precision
+        ```
 
-The appropriate entry for this model will be added to `configs/models.yaml` and it will
-be available to use in the CLI immediately.
+If you've previously installed the fine-tune VAE file
+`vae-ft-mse-840000-ema-pruned.ckpt`, the wizard will also ask you if you want to
+add this VAE to the model.
 
-The CLI has additional commands for switching among, viewing, editing,
-deleting the available models. These are described in [Command Line
-Client](../features/CLI.md#model-selection-and-importation), but the two most
-frequently-used are `!models` and `!switch <name of model>`. The first
-prints a table of models that InvokeAI knows about and their load
-status. The second will load the requested model and lets you switch
-back and forth quickly among loaded models.
+The appropriate entry for this model will be added to `configs/models.yaml` and
+it will be available to use in the CLI immediately.
+
+The CLI has additional commands for switching among, viewing, editing, deleting
+the available models. These are described in
+[Command Line Client](../features/CLI.md#model-selection-and-importation), but
+the two most frequently-used are `!models` and `!switch <name of model>`. The
+first prints a table of models that InvokeAI knows about and their load status.
+The second will load the requested model and lets you switch back and forth
+quickly among loaded models.
 
 ### Manually editing of `configs/models.yaml`
 
-If you are comfortable with a text editor then you may simply edit
-`models.yaml` directly.
+If you are comfortable with a text editor then you may simply edit `models.yaml`
+directly.
 
 First you need to download the desired .ckpt file and place it in
-`models/ldm/stable-diffusion-v1` as descirbed in step #1 in the
-previous section. Record the path to the weights file,
-e.g. `models/ldm/stable-diffusion-v1/arabian-nights-1.0.ckpt`
+`models/ldm/stable-diffusion-v1` as descirbed in step #1 in the previous
+section. Record the path to the weights file, e.g.
+`models/ldm/stable-diffusion-v1/arabian-nights-1.0.ckpt`
 
-Then using a **text** editor (e.g. the Windows Notepad application),
-open the file `configs/models.yaml`, and add a new stanza that follows
-this model:
+Then using a **text** editor (e.g. the Windows Notepad application), open the
+file `configs/models.yaml`, and add a new stanza that follows this model:
 
-```
+```yaml
 arabian-nights-1.0:
   description: A great fine-tune in Arabian Nights style
   weights: ./models/ldm/stable-diffusion-v1/arabian-nights-1.0.ckpt
@@ -234,34 +233,14 @@ arabian-nights-1.0:
   default: false
 ```
 
-* arabian-nights-1.0
-  - This is the name of the model that you will refer to from within the
-  CLI and the WebGUI when you need to load and use the model.
-
-* description
-  - Any description that you want to add to the model to remind you what
-    it is.
-
-* weights
-  - Relative path to the .ckpt weights file for this model.
-
-* config
-  - This is the confusingly-named configuration file for the model itself.
-  Use `./configs/stable-diffusion/v1-inference.yaml` unless the model happens
-  to need a custom configuration, in which case the place you downloaded it
-  from will tell you what to use instead. For example, the runwayML custom
-  inpainting model requires the file `configs/stable-diffusion/v1-inpainting-inference.yaml`.
-  This is already inclued in the InvokeAI distribution and is configured automatically
-  for you by the `preload_models.py` script.
-
-* vae
-  - If you want to add a VAE file to the model, then enter its path here.
-
-* width, height
-  - This is the width and height of the images used to train the model.
-  Currently they are always 512 and 512.
+| name               | description                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                       |
+| :----------------- | :-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
+| arabian-nights-1.0 | This is the name of the model that you will refer to from within the CLI and the WebGUI when you need to load and use the model.                                                                                                                                                                                                                                                                                                                                                                                                  |
+| description        | Any description that you want to add to the model to remind you what it is.                                                                                                                                                                                                                                                                                                                                                                                                                                                       |
+| weights            | Relative path to the .ckpt weights file for this model.                                                                                                                                                                                                                                                                                                                                                                                                                                                                           |
+| config             | This is the confusingly-named configuration file for the model itself. Use `./configs/stable-diffusion/v1-inference.yaml` unless the model happens to need a custom configuration, in which case the place you downloaded it from will tell you what to use instead. For example, the runwayML custom inpainting model requires the file `configs/stable-diffusion/v1-inpainting-inference.yaml`. This is already inclued in the InvokeAI distribution and is configured automatically for you by the `preload_models.py` script. |
+| vae                | If you want to add a VAE file to the model, then enter its path here.                                                                                                                                                                                                                                                                                                                                                                                                                                                             |
+| width, height      | This is the width and height of the images used to train the model. Currently they are always 512 and 512.                                                                                                                                                                                                                                                                                                                                                                                                                        |
 
 Save the `models.yaml` and relaunch InvokeAI. The new model should now be
 available for your use.
-
-
diff --git a/docs/installation/INSTALL_DOCKER.md b/docs/installation/INSTALL_DOCKER.md
index 50c3d89c81..9f168ac2a1 100644
--- a/docs/installation/INSTALL_DOCKER.md
+++ b/docs/installation/INSTALL_DOCKER.md
@@ -4,26 +4,30 @@ title: Docker
 
 # :fontawesome-brands-docker: Docker
 
-## Before you begin
+!!! warning "For end users"
 
-- For end users: Install Stable Diffusion locally using the instructions for
-  your OS.
-- For developers: For container-related development tasks or for enabling easy
-  deployment to other environments (on-premises or cloud), follow these
-  instructions. For general use, install locally to leverage your machine's GPU.
+    We highly recommend to Install InvokeAI locally using [these instructions](index.md)"
+
+!!! tip "For developers"
+
+    For container-related development tasks or for enabling easy
+    deployment to other environments (on-premises or cloud), follow these
+    instructions.
+
+    For general use, install locally to leverage your machine's GPU.
 
 ## Why containers?
 
-They provide a flexible, reliable way to build and deploy Stable Diffusion.
-You'll also use a Docker volume to store the largest model files and image
-outputs as a first step in decoupling storage and compute. Future enhancements
-can do this for other assets. See [Processes](https://12factor.net/processes)
-under the Twelve-Factor App methodology for details on why running applications
-in such a stateless fashion is important.
+They provide a flexible, reliable way to build and deploy InvokeAI. You'll also
+use a Docker volume to store the largest model files and image outputs as a
+first step in decoupling storage and compute. Future enhancements can do this
+for other assets. See [Processes](https://12factor.net/processes) under the
+Twelve-Factor App methodology for details on why running applications in such a
+stateless fashion is important.
 
 You can specify the target platform when building the image and running the
-container. You'll also need to specify the Stable Diffusion requirements file
-that matches the container's OS and the architecture it will run on.
+container. You'll also need to specify the InvokeAI requirements file that
+matches the container's OS and the architecture it will run on.
 
 Developers on Apple silicon (M1/M2): You
 [can't access your GPU cores from Docker containers](https://github.com/pytorch/pytorch/issues/81224)
@@ -38,16 +42,19 @@ another environment with NVIDIA GPUs on-premises or in the cloud.
 
 #### Install [Docker](https://github.com/santisbon/guides#docker)
 
-On the Docker Desktop app, go to Preferences, Resources, Advanced. Increase the
-CPUs and Memory to avoid this
+On the [Docker Desktop app](https://docs.docker.com/get-docker/), go to
+Preferences, Resources, Advanced. Increase the CPUs and Memory to avoid this
 [Issue](https://github.com/invoke-ai/InvokeAI/issues/342). You may need to
 increase Swap and Disk image size too.
 
 #### Get a Huggingface-Token
 
-Go to [Hugging Face](https://huggingface.co/settings/tokens), create a token and
-temporary place it somewhere like a open texteditor window (but dont save it!,
-only keep it open, we need it in the next step)
+Besides the Docker Agent you will need an Account on
+[huggingface.co](https://huggingface.co/join).
+
+After you succesfully registered your account, go to
+[huggingface.co/settings/tokens](https://huggingface.co/settings/tokens), create
+a token and copy it, since you will need in for the next step.
 
 ### Setup
 
@@ -65,13 +72,14 @@ created in the last step.
 
 Some Suggestions of variables you may want to change besides the Token:
 
-| Environment-Variable                                                | Description                                                              |
-| ------------------------------------------------------------------- | ------------------------------------------------------------------------ |
-| `HUGGINGFACE_TOKEN="hg_aewirhghlawrgkjbarug2"`                      | This is the only required variable, without you can't get the checkpoint |
-| `ARCH=aarch64`                                                      | if you are using a ARM based CPU                                         |
-| `INVOKEAI_TAG=yourname/invokeai:latest`                             | the Container Repository / Tag which will be used                        |
-| `INVOKEAI_CONDA_ENV_FILE=environment-linux-aarch64.yml`             | since environment.yml wouldn't work with aarch                           |
-| `INVOKEAI_GIT="-b branchname https://github.com/username/reponame"` | if you want to use your own fork                                         |
+| Environment-Variable      | Default value                 | Description                                                                  |
+| ------------------------- | ----------------------------- | ---------------------------------------------------------------------------- |
+| `HUGGINGFACE_TOKEN`       | No default, but **required**! | This is the only **required** variable, without you can't get the checkpoint |
+| `ARCH`                    | x86_64                        | if you are using a ARM based CPU                                             |
+| `INVOKEAI_TAG`            | invokeai-x86_64               | the Container Repository / Tag which will be used                            |
+| `INVOKEAI_CONDA_ENV_FILE` | environment-lin-cuda.yml      | since environment.yml wouldn't work with aarch                               |
+| `INVOKEAI_GIT`            | invoke-ai/InvokeAI            | the repository to use                                                        |
+| `INVOKEAI_BRANCH`         | main                          | the branch to checkout                                                       |
 
 #### Build the Image
 
@@ -79,25 +87,41 @@ I provided a build script, which is located in `docker-build/build.sh` but still
 needs to be executed from the Repository root.
 
 ```bash
-docker-build/build.sh
+./docker-build/build.sh
 ```
 
 The build Script not only builds the container, but also creates the docker
-volume if not existing yet, or if empty it will just download the models. When
-it is done you can run the container via the run script
+volume if not existing yet, or if empty it will just download the models.
+
+#### Run the Container
+
+After the build process is done, you can run the container via the provided
+`docker-build/run.sh` script
 
 ```bash
-docker-build/run.sh
+./docker-build/run.sh
 ```
 
-When used without arguments, the container will start the website and provide
+When used without arguments, the container will start the webserver and provide
 you the link to open it. But if you want to use some other parameters you can
 also do so.
 
+!!! example ""
+
+    ```bash
+    ./docker-build/run.sh --from_file tests/validate_pr_prompt.txt
+    ```
+
+    The output folder is located on the volume which is also used to store the model.
+
+    Find out more about available CLI-Parameters at [features/CLI.md](../features/CLI.md/#arguments)
+
+---
+
 !!! warning "Deprecated"
 
-    From here on it is the rest of the previous Docker-Docs, which will still
-    provide usefull informations for one or the other.
+    From here on you will find the the previous Docker-Docs, which will still
+    provide some usefull informations.
 
 ## Usage (time to have fun)
 
diff --git a/docs/installation/INSTALL_INVOKE.md b/docs/installation/INSTALL_INVOKE.md
new file mode 100644
index 0000000000..37a35fbe1c
--- /dev/null
+++ b/docs/installation/INSTALL_INVOKE.md
@@ -0,0 +1,55 @@
+---
+title: InvokeAI Installer
+---
+
+The InvokeAI installer is a shell script that will install InvokeAI onto a stock
+computer running recent versions of Linux, MacOSX or Windows. It will leave you
+with a version that runs a stable version of InvokeAI. When a new version of
+InvokeAI is released, you will download and reinstall the new version.
+
+If you wish to tinker with unreleased versions of InvokeAI that introduce
+potentially unstable new features, you should consider using the
+[source installer](INSTALL_SOURCE.md) or one of the
+[manual install](INSTALL_MANUAL.md) methods.
+
+!!! todo
+
+    Before you begin, make sure that you meet
+    the[hardware requirements](/#hardware-requirements) and has the
+    appropriate GPU drivers installed. In particular, if you are a Linux user with
+    an AMD GPU installed, you may need to install the
+    [ROCm-driver](https://rocmdocs.amd.com/en/latest/Installation_Guide/Installation-Guide.html).
+
+Installation requires roughly 18G of free disk space to load the libraries and
+recommended model weights files.
+
+## Steps to Install
+
+1. Download the
+   [latest release](https://github.com/invoke-ai/InvokeAI/releases/latest) of
+   InvokeAI's installer for your platform
+
+2. Place the downloaded package someplace where you have plenty of HDD space,
+   and have full permissions (i.e. `~/` on Lin/Mac; your home folder on Windows)
+
+3. Extract the 'InvokeAI' folder from the downloaded package
+
+4. Open the extracted 'InvokeAI' folder
+
+5. Double-click 'install.bat' (Windows), or 'install.sh' (Lin/Mac) (or run from
+   a terminal)
+
+6. Follow the prompts
+
+7. After installation, please run the 'invoke.bat' file (on Windows) or
+   'invoke.sh' file (on Linux/Mac) to start InvokeAI.
+
+## Troubleshooting
+
+If you run into problems during or after installation, the InvokeAI team is
+available to help you. Either create an
+[Issue](https://github.com/invoke-ai/InvokeAI/issues) at our GitHub site, or
+make a request for help on the "bugs-and-support" channel of our
+[Discord server](https://discord.gg/ZmtBAhwWhy). We are a 100% volunteer
+organization, but typically somebody will be available to help you within 24
+hours, and often much sooner.
diff --git a/docs/installation/INSTALL_JUPYTER.md b/docs/installation/INSTALL_JUPYTER.md
new file mode 100644
index 0000000000..8807423be5
--- /dev/null
+++ b/docs/installation/INSTALL_JUPYTER.md
@@ -0,0 +1,27 @@
+---
+title: Running InvokeAI on Google Colab using a Jupyter Notebook
+---
+
+# THIS NEEDS TO BE FLESHED OUT
+
+## Introduction
+
+We have a [Jupyter
+notebook](https://github.com/invoke-ai/InvokeAI/blob/main/notebooks/Stable-Diffusion-local-Windows.ipynb)
+with cell-by-cell installation steps. It will download the code in
+this repo as one of the steps, so instead of cloning this repo, simply
+download the notebook from the link above and load it up in VSCode
+(with the appropriate extensions installed)/Jupyter/JupyterLab and
+start running the cells one-by-one.
+
+!!! Note "you will need NVIDIA drivers, Python 3.10, and Git installed beforehand"
+
+## Walkthrough
+
+## Updating to newer versions
+
+### Updating the stable version
+
+### Updating to the development version
+
+## Troubleshooting
diff --git a/docs/installation/INSTALL_MANUAL.md b/docs/installation/INSTALL_MANUAL.md
new file mode 100644
index 0000000000..fd53d32131
--- /dev/null
+++ b/docs/installation/INSTALL_MANUAL.md
@@ -0,0 +1,416 @@
+---
+title: Manual Installation
+---
+
+<figure markdown>
+# :fontawesome-brands-linux: Linux | :fontawesome-brands-apple: macOS | :fontawesome-brands-windows: Windows
+</figure>
+
+!!! warning "This is for advanced Users"
+
+    who are already expirienced with using conda or pip
+
+## Introduction
+
+You have two choices for manual installation, the [first one](#Conda_method)
+based on the Anaconda3 package manager (`conda`), and
+[a second one](#PIP_method) which uses basic Python virtual environment (`venv`)
+commands and the PIP package manager. Both methods require you to enter commands
+on the terminal, also known as the "console".
+
+On Windows systems you are encouraged to install and use the
+[Powershell](https://learn.microsoft.com/en-us/powershell/scripting/install/installing-powershell-on-windows?view=powershell-7.3),
+which provides compatibility with Linux and Mac shells and nice features such as
+command-line completion.
+
+### Conda method
+
+1.  Check that your system meets the
+    [hardware requirements](index.md#Hardware_Requirements) and has the
+    appropriate GPU drivers installed. In particular, if you are a Linux user
+    with an AMD GPU installed, you may need to install the
+    [ROCm driver](https://rocmdocs.amd.com/en/latest/Installation_Guide/Installation-Guide.html).
+
+    InvokeAI does not yet support Windows machines with AMD GPUs due to the lack
+    of ROCm driver support on this platform.
+
+    To confirm that the appropriate drivers are installed, run `nvidia-smi` on
+    NVIDIA/CUDA systems, and `rocm-smi` on AMD systems. These should return
+    information about the installed video card.
+
+    Macintosh users with MPS acceleration, or anybody with a CPU-only system,
+    can skip this step.
+
+2.  You will need to install Anaconda3 and Git if they are not already
+    available. Use your operating system's preferred package manager, or
+    download the installers manually. You can find them here:
+
+    - [Anaconda3](https://www.anaconda.com/)
+    - [git](https://git-scm.com/downloads)
+
+3.  Clone the [InvokeAI](https://github.com/invoke-ai/InvokeAI) source code from
+    GitHub:
+
+    ```bash
+    git clone https://github.com/invoke-ai/InvokeAI.git
+    ```
+
+    This will create InvokeAI folder where you will follow the rest of the
+    steps.
+
+4.  Enter the newly-created InvokeAI folder:
+
+    ```bash
+    cd InvokeAI
+    ```
+
+    From this step forward make sure that you are working in the InvokeAI
+    directory!
+
+5.  Select the appropriate environment file:
+
+    We have created a series of environment files suited for different operating
+    systems and GPU hardware. They are located in the
+    `environments-and-requirements` directory:
+
+    <figure markdown>
+
+    |         filename         |               OS               |
+    | :----------------------: | :----------------------------: |
+    | environment-lin-amd.yml  |  Linux with an AMD (ROCm) GPU  |
+    | environment-lin-cuda.yml | Linux with an NVIDIA CUDA GPU  |
+    | environment-mac.yml    |   Macintosh            |
+    | environment-win-cuda.yml | Windows with an NVIDA CUDA GPU |
+
+    </figure>
+
+    Choose the appropriate environment file for your system and link or copy it
+    to `environment.yml` in InvokeAI's top-level directory. To do so, run
+    following command from the repository-root:
+
+    !!! Example ""
+
+        === "Macintosh and Linux"
+
+            !!! todo "Replace `xxx` and `yyy` with the appropriate OS and GPU codes as seen in the table above"
+
+            ```bash
+            ln -sf environments-and-requirements/environment-xxx-yyy.yml environment.yml
+            ```
+
+            When this is done, confirm that a file `environment.yml` has been linked in
+            the InvokeAI root directory and that it points to the correct file in the
+            `environments-and-requirements`.
+
+            ```bash
+            ls -la
+            ```
+
+        === "Windows"
+
+            !!! todo " Since it requires admin privileges to create links, we will use the copy command to create your `environment.yml`"
+
+            ```cmd
+            copy environments-and-requirements\environment-win-cuda.yml environment.yml
+            ```
+
+            Afterwards verify that the file `environment.yml` has been created, either via the
+            explorer or by using the command `dir` from the terminal
+
+            ```cmd
+            dir
+            ```
+
+    !!! warning "Do not try to run conda on directly on the subdirectory environments file. This won't work. Instead, copy or link it to the top-level directory as shown."    
+    
+6.  Create the conda environment:
+
+    ```bash
+    conda env update
+    ```
+
+    This will create a new environment named `invokeai` and install all InvokeAI
+    dependencies into it. If something goes wrong you should take a look at
+    [troubleshooting](#troubleshooting).
+
+7.  Activate the `invokeai` environment:
+
+    In order to use the newly created environment you will first need to
+    activate it
+
+    ```bash
+    conda activate invokeai
+    ```
+
+    Your command-line prompt should change to indicate that `invokeai` is active
+    by prepending `(invokeai)`.
+
+8.  Pre-Load the model weights files:
+
+    !!! tip
+
+        If you have already downloaded the weights file(s) for another Stable
+        Diffusion distribution, you may skip this step (by selecting "skip" when
+        prompted) and configure InvokeAI to use the previously-downloaded files. The
+        process for this is described in [here](INSTALLING_MODELS.md).
+
+    ```bash
+    python scripts/preload_models.py
+    ```
+
+    The script `preload_models.py` will interactively guide you through the
+    process of downloading and installing the weights files needed for InvokeAI.
+    Note that the main Stable Diffusion weights file is protected by a license
+    agreement that you have to agree to. The script will list the steps you need
+    to take to create an account on the site that hosts the weights files,
+    accept the agreement, and provide an access token that allows InvokeAI to
+    legally download and install the weights files.
+
+    If you get an error message about a module not being installed, check that
+    the `invokeai` environment is active and if not, repeat step 5.
+
+9.  Run the command-line- or the web- interface:
+
+    !!! example ""
+
+        !!! warning "Make sure that the conda environment is activated, which should create `(invokeai)` in front of your prompt!"
+
+        === "CLI"
+
+            ```bash
+            python scripts/invoke.py
+            ```
+
+        === "local Webserver"
+
+            ```bash
+            python scripts/invoke.py --web
+            ```
+
+        === "Public Webserver"
+
+            ```bash
+            python scripts/invoke.py --web --host 0.0.0.0
+            ```
+
+        If you choose the run the web interface, point your browser at
+        http://localhost:9090 in order to load the GUI.
+
+10. Render away!
+
+    Browse the [features](../features/CLI.md) section to learn about all the things you
+    can do with InvokeAI.
+
+    Note that some GPUs are slow to warm up. In particular, when using an AMD
+    card with the ROCm driver, you may have to wait for over a minute the first
+    time you try to generate an image. Fortunately, after the warm up period
+    rendering will be fast.
+
+11. Subsequently, to relaunch the script, be sure to run "conda activate
+    invokeai", enter the `InvokeAI` directory, and then launch the invoke
+    script. If you forget to activate the 'invokeai' environment, the script
+    will fail with multiple `ModuleNotFound` errors.
+
+## Updating to newer versions of the script
+
+This distribution is changing rapidly. If you used the `git clone` method
+(step 5) to download the InvokeAI directory, then to update to the latest and
+greatest version, launch the Anaconda window, enter `InvokeAI` and type:
+
+```bash
+git pull
+conda env update
+python scripts/preload_models.py --no-interactive  #optional
+```
+
+This will bring your local copy into sync with the remote one. The last step may
+be needed to take advantage of new features or released models. The
+`--no-interactive` flag will prevent the script from prompting you to download
+the big Stable Diffusion weights files.
+
+## pip Install
+
+To install InvokeAI with only the PIP package manager, please follow these
+steps:
+
+1.  Make sure you are using Python 3.9 or higher. The rest of the install
+    procedure depends on this:
+
+    ```bash
+    python -V
+    ```
+
+2.  Install the `virtualenv` tool if you don't have it already:
+
+    ```bash
+    pip install virtualenv
+    ```
+
+3.  From within the InvokeAI top-level directory, create and activate a virtual
+    environment named `invokeai`:
+
+    ```bash
+    virtualenv invokeai
+    source invokeai/bin/activate
+    ```
+
+4.  Pick the correct `requirements*.txt` file for your hardware and operating
+    system.
+
+    We have created a series of environment files suited for different operating
+    systems and GPU hardware. They are located in the
+    `environments-and-requirements` directory:
+
+    <figure markdown>
+
+    |              filename               |                               OS                                |
+    | :---------------------------------: | :-------------------------------------------------------------: |
+    |      requirements-lin-amd.txt       |                  Linux with an AMD (ROCm) GPU                   |
+    |     requirements-lin-arm64.txt      |                 Linux running on arm64 systems                  |
+    |      requirements-lin-cuda.txt      |                 Linux with an NVIDIA (CUDA) GPU                 |
+    |    requirements-mac-mps-cpu.txt     |                Macintoshes with MPS acceleration                |
+    | requirements-lin-win-colab-cuda.txt | Windows with an NVIDA (CUDA) GPU<br>(supports Google Colab too) |
+
+    </figure>
+
+    Select the appropriate requirements file, and make a link to it from
+    `requirements.txt` in the top-level InvokeAI directory. The command to do
+    this from the top-level directory is:
+
+    !!! example ""
+
+        === "Macintosh and Linux"
+
+            !!! info "Replace `xxx` and `yyy` with the appropriate OS and GPU codes."
+
+            ```bash
+            ln -sf environments-and-requirements/requirements-xxx-yyy.txt requirements.txt
+            ```
+
+        === "Windows"
+
+            !!! info "on Windows, admin privileges are required to make links, so we use the copy command instead"
+
+            ```cmd
+            copy environments-and-requirements\requirements-lin-win-colab-cuda.txt requirements.txt
+            ```
+
+        !!! warning
+
+            Please do not link or copy `environments-and-requirements/requirements-base.txt`.
+            This is a base requirements file that does not have the platform-specific
+            libraries. Also, be sure to link or copy the platform-specific file to
+	    a top-level file named `requirements.txt` as shown here. Running pip on
+	    a requirements file in a subdirectory will not work as expected.
+
+    When this is done, confirm that a file named `requirements.txt` has been
+    created in the InvokeAI root directory and that it points to the correct
+    file in `environments-and-requirements`.
+
+5.  Run PIP
+
+    Be sure that the `invokeai` environment is active before doing this:
+
+    ```bash
+    pip install --prefer-binary -r requirements.txt
+    ```
+
+---
+
+## Troubleshooting
+
+Here are some common issues and their suggested solutions.
+
+### Conda
+
+#### Conda fails before completing `conda update`
+
+The usual source of these errors is a package incompatibility. While we have
+tried to minimize these, over time packages get updated and sometimes introduce
+incompatibilities.
+
+We suggest that you search
+[Issues](https://github.com/invoke-ai/InvokeAI/issues) or the "bugs-and-support"
+channel of the [InvokeAI Discord](https://discord.gg/ZmtBAhwWhy).
+
+You may also try to install the broken packages manually using PIP. To do this,
+activate the `invokeai` environment, and run `pip install` with the name and
+version of the package that is causing the incompatibility. For example:
+
+```bash
+pip install test-tube==0.7.5
+```
+
+You can keep doing this until all requirements are satisfied and the `invoke.py`
+script runs without errors. Please report to
+[Issues](https://github.com/invoke-ai/InvokeAI/issues) what you were able to do
+to work around the problem so that others can benefit from your investigation.
+
+#### `preload_models.py` or `invoke.py` crashes at an early stage
+
+This is usually due to an incomplete or corrupted Conda install. Make sure you
+have linked to the correct environment file and run `conda update` again.
+
+If the problem persists, a more extreme measure is to clear Conda's caches and
+remove the `invokeai` environment:
+
+```bash
+conda deactivate
+conda env remove -n invokeai
+conda clean -a
+conda update
+```
+
+This removes all cached library files, including ones that may have been
+corrupted somehow. (This is not supposed to happen, but does anyway).
+
+#### `invoke.py` crashes at a later stage
+
+If the CLI or web site had been working ok, but something unexpected happens
+later on during the session, you've encountered a code bug that is probably
+unrelated to an install issue. Please search
+[Issues](https://github.com/invoke-ai/InvokeAI/issues), file a bug report, or
+ask for help on [Discord](https://discord.gg/ZmtBAhwWhy)
+
+#### My renders are running very slowly
+
+You may have installed the wrong torch (machine learning) package, and the
+system is running on CPU rather than the GPU. To check, look at the log messages
+that appear when `invoke.py` is first starting up. One of the earlier lines
+should say `Using device type cuda`. On AMD systems, it will also say "cuda",
+and on Macintoshes, it should say "mps". If instead the message says it is
+running on "cpu", then you may need to install the correct torch library.
+
+You may be able to fix this by installing a different torch library. Here are
+the magic incantations for Conda and PIP.
+
+!!! todo "For CUDA systems"
+
+    - conda
+
+      ```bash
+      conda install pytorch torchvision torchaudio pytorch-cuda=11.6 -c pytorch -c nvidia
+      ```
+
+    - pip
+
+      ```bash
+      pip3 install torch torchvision torchaudio --extra-index-url https://download.pytorch.org/whl/cu116
+      ```
+
+!!! todo "For AMD systems"
+
+    - conda
+
+      ```bash
+      conda activate invokeai
+      pip3 install torch torchvision torchaudio --extra-index-url https://download.pytorch.org/whl/rocm5.2/
+      ```
+
+    - pip
+
+      ```bash
+      pip3 install torch torchvision torchaudio --extra-index-url https://download.pytorch.org/whl/rocm5.2/
+      ```
+
+More information and troubleshooting tips can be found at https://pytorch.org.
diff --git a/docs/installation/INSTALL_PCP.md b/docs/installation/INSTALL_PCP.md
new file mode 100644
index 0000000000..e321237e1c
--- /dev/null
+++ b/docs/installation/INSTALL_PCP.md
@@ -0,0 +1,17 @@
+---
+title: Installing InvokeAI with the Pre-Compiled PIP Installer
+---
+
+# THIS NEEDS TO BE FLESHED OUT
+
+## Introduction
+
+## Walkthrough
+
+## Updating to newer versions
+
+### Updating the stable version
+
+### Updating to the development version
+
+## Troubleshooting
\ No newline at end of file
diff --git a/docs/installation/INSTALL_SOURCE.md b/docs/installation/INSTALL_SOURCE.md
new file mode 100644
index 0000000000..e86fccde8b
--- /dev/null
+++ b/docs/installation/INSTALL_SOURCE.md
@@ -0,0 +1,156 @@
+---
+title: Source Installer
+---
+
+# The InvokeAI Source Installer
+
+## Introduction
+
+The source installer is a shell script that attempts to automate every step
+needed to install and run InvokeAI on a stock computer running recent versions
+of Linux, MacOS or Windows. It will leave you with a version that runs a stable
+version of InvokeAI with the option to upgrade to experimental versions later.
+It is not as foolproof as the [InvokeAI installer](INSTALL_INVOKE.md)
+
+Before you begin, make sure that you meet the
+[hardware requirements](index.md#Hardware_Requirements) and has the appropriate
+GPU drivers installed. In particular, if you are a Linux user with an AMD GPU
+installed, you may need to install the
+[ROCm driver](https://rocmdocs.amd.com/en/latest/Installation_Guide/Installation-Guide.html).
+
+Installation requires roughly 18G of free disk space to load the libraries and
+recommended model weights files.
+
+## Walk through
+
+Though there are multiple steps, there really is only one click involved to kick
+off the process.
+
+1.  The source installer is distributed in ZIP files. Go to the
+    [latest release](https://github.com/invoke-ai/InvokeAI/releases/latest), and
+    look for a series of files named:
+
+    - invokeAI-src-installer-mac.zip
+    - invokeAI-src-installer-windows.zip
+    - invokeAI-src-installer-linux.zip
+
+    Download the one that is appropriate for your operating system.
+
+2.  Unpack the zip file into a directory that has at least 18G of free space. Do
+    _not_ unpack into a directory that has an earlier version of InvokeAI.
+
+    This will create a new directory named "InvokeAI". This example shows how
+    this would look using the `unzip` command-line tool, but you may use any
+    graphical or command-line Zip extractor:
+
+    ```cmd
+    C:\Documents\Linco> unzip invokeAI-windows.zip
+    Archive:  C: \Linco\Downloads\invokeAI-linux.zip
+    creating: invokeAI\
+    inflating: invokeAI\install.bat
+    inflating: invokeAI\readme.txt
+    ```
+
+3.  If you are using a desktop GUI, double-click the installer file. It will be
+    named `install.bat` on Windows systems and `install.sh` on Linux and
+    Macintosh systems.
+
+4.  Alternatively, form the command line, run the shell script or .bat file:
+
+    ```cmd
+    C:\Documents\Linco> cd invokeAI
+    C:\Documents\Linco\invokeAI> install.bat
+    ```
+
+5.  Sit back and let the install script work. It will install various binary
+    requirements including Conda, Git and Python, then download the current
+    InvokeAI code and install it along with its dependencies.
+
+6.  After installation completes, the installer will launch a script called
+    `preload_models.py`, which will guide you through the first-time process of
+    selecting one or more Stable Diffusion model weights files, downloading and
+    configuring them.
+
+    Note that the main Stable Diffusion weights file is protected by a license
+    agreement that you must agree to in order to use. The script will list the
+    steps you need to take to create an account on the official site that hosts
+    the weights files, accept the agreement, and provide an access token that
+    allows InvokeAI to legally download and install the weights files.
+
+    If you have already downloaded the weights file(s) for another Stable
+    Diffusion distribution, you may skip this step (by selecting "skip" when
+    prompted) and configure InvokeAI to use the previously-downloaded files. The
+    process for this is described in [Installing Models](INSTALLING_MODELS.md).
+
+7.  The script will now exit and you'll be ready to generate some images. The
+    invokeAI directory will contain numerous files. Look for a shell script
+    named `invoke.sh` (Linux/Mac) or `invoke.bat` (Windows). Launch the script
+    by double-clicking it or typing its name at the command-line:
+
+    ```cmd
+    C:\Documents\Linco> cd invokeAI
+    C:\Documents\Linco\invokeAI> invoke.bat
+    ```
+
+The `invoke.bat` (`invoke.sh`) script will give you the choice of starting (1)
+the command-line interface, or (2) the web GUI. If you start the latter, you can
+load the user interface by pointing your browser at http://localhost:9090.
+
+The `invoke` script also offers you a third option labeled "open the developer
+console". If you choose this option, you will be dropped into a command-line
+interface in which you can run python commands directly, access developer tools,
+and launch InvokeAI with customized options. To do the latter, you would launch
+the script `scripts/invoke.py` as shown in this example:
+
+```cmd
+python scripts/invoke.py --web --max_load_models=3 \
+    --model=waifu-1.3 --steps=30 --outdir=C:/Documents/AIPhotos
+```
+
+These options are described in detail in the
+[Command-Line Interface](../features/CLI.md) documentation.
+
+## Updating to newer versions
+
+This section describes how to update InvokeAI to new versions of the software.
+
+### Updating the stable version
+
+This distribution is changing rapidly, and we add new features on a daily basis.
+To update to the latest released version (recommended), run the `update.sh`
+(Linux/Mac) or `update.bat` (Windows) scripts. This will fetch the latest
+release and re-run the `preload_models` script to download any updated models
+files that may be needed. You can also use this to add additional models that
+you did not select at installation time.
+
+### Updating to the development version
+
+There may be times that there is a feature in the `development` branch of
+InvokeAI that you'd like to take advantage of. Or perhaps there is a branch that
+corrects an annoying bug. To do this, you will use the developer's console.
+
+From within the invokeAI directory, run the command `invoke.sh` (Linux/Mac) or
+`invoke.bat` (Windows) and selection option (3) to open the developers console.
+Then run the following command to get the `development branch`:
+
+```bash
+git checkout development
+git pull
+conda env update
+```
+
+You can now close the developer console and run `invoke` as before. If you get
+complaints about missing models, then you may need to do the additional step of
+running `preload_models.py`. This happens relatively infrequently. To do this,
+simply open up the developer's console again and type
+`python scripts/preload_models.py`.
+
+## Troubleshooting
+
+If you run into problems during or after installation, the InvokeAI team is
+available to help you. Either create an
+[Issue](https://github.com/invoke-ai/InvokeAI/issues) at our GitHub site, or
+make a request for help on the "bugs-and-support" channel of our
+[Discord server](https://discord.gg/ZmtBAhwWhy). We are a 100% volunteer
+organization, but typically somebody will be available to help you within 24
+hours, and often much sooner.
diff --git a/docs/installation/index.md b/docs/installation/index.md
new file mode 100644
index 0000000000..2ede7061de
--- /dev/null
+++ b/docs/installation/index.md
@@ -0,0 +1,59 @@
+---
+title: Overview
+---
+
+We offer several ways to install InvokeAI, each one suited to your
+experience and preferences.
+
+1. [InvokeAI installer](INSTALL_INVOKE.md)
+
+    This is a installer script that installs InvokeAI and all the
+    third party libraries it depends on. When a new version of
+    InvokeAI is released, you will download and reinstall the new
+    version.
+
+    This installer is designed for people who want the system to "just
+    work", don't have an interest in tinkering with it, and do not
+    care about upgrading to unreleased experimental features.
+
+    *Note that this script has difficulty on some Macintosh machines
+    that have previously been used for Python development due to
+    conflicting development tools versions. Mac developers may wish
+    to try method (2) or one of the manual methods instead.
+
+2. [Source code installer](INSTALL_SOURCE.md)
+
+    This is a script that will install InvokeAI and all its essential
+    third party libraries. In contrast to the previous installer, it
+    includes access to a "developer console" which will allow you to
+    access experimental features on the development branch.
+
+    This method is recommended for individuals who are wish to stay
+    on the cutting edge of InvokeAI development and are not afraid
+    of occasional breakage.
+
+3. [Manual Installation](INSTALL_MANUAL.md)
+
+    In this method you will manually run the commands needed to install
+    InvokeAI and its dependencies. We offer two recipes: one suited to
+    those who prefer the `conda` tool, and one suited to those who prefer
+    `pip` and Python virtual environments.
+
+    This method is recommended for users who have previously used `conda`
+    or `pip` in the past, developers, and anyone who wishes to remain on
+    the cutting edge of future InvokeAI development and is willing to put
+    up with occasional glitches and breakage.
+
+4. [Docker Installation](INSTALL_DOCKER.md)
+
+    We also offer a method for creating Docker containers containing
+    InvokeAI and its dependencies. This method is recommended for
+    individuals with experience with Docker containers and understand
+    the pluses and minuses of a container-based install.
+
+5. [Jupyter Notebooks Installation](INSTALL_JUPYTER.md)
+
+    This method is suitable for running InvokeAI on a Google Colab
+    account. It is recommended for individuals who have previously
+    worked on the Colab and are comfortable with the Jupyter notebook
+    environment.
diff --git a/docs/installation/INSTALL_LINUX.md b/docs/installation/older_docs_to_be_removed/INSTALL_LINUX.md
similarity index 83%
rename from docs/installation/INSTALL_LINUX.md
rename to docs/installation/older_docs_to_be_removed/INSTALL_LINUX.md
index 0bbfd8cda0..684bccb153 100644
--- a/docs/installation/INSTALL_LINUX.md
+++ b/docs/installation/older_docs_to_be_removed/INSTALL_LINUX.md
@@ -42,14 +42,25 @@ title: Manual Installation, Linux
     ```
 
 5.  Use anaconda to copy necessary python packages, create a new python
-    environment named `invokeai` and activate the environment.
+    environment named `invokeai` and then activate the environment.
 
-    ```bash
-    (base) rm -rf src      # (this is a precaution in case there is already a src directory)
-    (base) ~/InvokeAI$ conda env create
-    (base) ~/InvokeAI$ conda activate invokeai
-    (invokeai) ~/InvokeAI$
-    ```
+    !!! todo "For systems with a CUDA (Nvidia) card:"
+
+       ```bash
+       (base) rm -rf src      # (this is a precaution in case there is already a src directory)
+       (base) ~/InvokeAI$ conda env create -f environment-cuda.yml
+       (base) ~/InvokeAI$ conda activate invokeai
+       (invokeai) ~/InvokeAI$
+       ```
+
+    !!! todo "For systems with an AMD card (using ROCm driver):"
+
+       ```bash
+       (base) rm -rf src      # (this is a precaution in case there is already a src directory)
+       (base) ~/InvokeAI$ conda env create -f environment-AMD.yml
+       (base) ~/InvokeAI$ conda activate invokeai
+       (invokeai) ~/InvokeAI$
+       ```
 
     After these steps, your command prompt will be prefixed by `(invokeai)` as
     shown above.
diff --git a/docs/installation/INSTALL_MAC.md b/docs/installation/older_docs_to_be_removed/INSTALL_MAC.md
similarity index 100%
rename from docs/installation/INSTALL_MAC.md
rename to docs/installation/older_docs_to_be_removed/INSTALL_MAC.md
diff --git a/docs/installation/INSTALL_WINDOWS.md b/docs/installation/older_docs_to_be_removed/INSTALL_WINDOWS.md
similarity index 82%
rename from docs/installation/INSTALL_WINDOWS.md
rename to docs/installation/older_docs_to_be_removed/INSTALL_WINDOWS.md
index bab02cd261..22d7a5dee2 100644
--- a/docs/installation/INSTALL_WINDOWS.md
+++ b/docs/installation/older_docs_to_be_removed/INSTALL_WINDOWS.md
@@ -13,22 +13,9 @@ one of the steps, so instead of cloning this repo, simply download the notebook
 from the link above and load it up in VSCode (with the appropriate extensions
 installed)/Jupyter/JupyterLab and start running the cells one-by-one.
 
-Note that you will need NVIDIA drivers, Python 3.10, and Git installed
-beforehand - simplified
-[step-by-step instructions](https://github.com/invoke-ai/InvokeAI/wiki/Easy-peasy-Windows-install)
-are available in the wiki (you'll only need steps 1, 2, & 3 ).
+Note that you will need NVIDIA drivers, Python 3.10, and Git installed beforehand.
 
-## **Manual Install**
-
-### **pip**
-
-See
-[Easy-peasy Windows install](https://github.com/invoke-ai/InvokeAI/wiki/Easy-peasy-Windows-install)
-in the wiki
-
----
-
-### **Conda**
+## **Manual Install with Conda**
 
 1. Install Anaconda3 (miniconda3 version) from [here](https://docs.anaconda.com/anaconda/install/windows/)
 
@@ -52,23 +39,29 @@ in the wiki
     cd InvokeAI
     ```
 
-6. Run the following two commands:
+6. Run the following commands:
 
-    ```batch title="step 6a"
-    conda env create
-    ```
+    !!! todo "For systems with a CUDA (Nvidia) card:"
 
-    ```batch title="step 6b"
-    conda activate invokeai
-    ```
+       ```bash
+       rmdir src      # (this is a precaution in case there is already a src directory)
+       conda env create -f environment-cuda.yml
+       conda activate invokeai
+       (invokeai)>
+       ```
+
+    !!! todo "For systems with an AMD card (using ROCm driver):"
+
+       ```bash
+       rmdir src      # (this is a precaution in case there is already a src directory)
+       conda env create -f environment-AMD.yml
+       conda activate invokeai
+       (invokeai)>
+       ```
 
     This will install all python requirements and activate the "invokeai" environment
     which sets PATH and other environment variables properly.
 
-    Note that the long form of the first command is `conda env create -f environment.yml`. If the
-    environment file isn't specified, conda will default to `environment.yml`. You will need
-    to provide the `-f` option if you wish to load a different environment file at any point.
-
 7. Load the big stable diffusion weights files and a couple of smaller machine-learning models:
 
     ```bash
diff --git a/docs/other/CONTRIBUTORS.md b/docs/other/CONTRIBUTORS.md
index e016685405..247c1c70a0 100644
--- a/docs/other/CONTRIBUTORS.md
+++ b/docs/other/CONTRIBUTORS.md
@@ -9,11 +9,11 @@ experience in this fork.
 
 We thank them for all of their time and hard work.
 
-## **Original Author:**
+## **Original Author**
 
 - [Lincoln D. Stein](mailto:lincoln.stein@gmail.com)
 
-## **Contributions by:**
+## **Contributions by**
 
 - [Sean McLellan](https://github.com/Oceanswave)
 - [Kevin Gibbons](https://github.com/bakkot)
@@ -62,7 +62,7 @@ We thank them for all of their time and hard work.
 - [psychedelicious](https://github.com/psychedelicious)
 - [damian0815](https://github.com/damian0815)
 
-## **Original CompVis Authors:**
+## **Original CompVis Authors**
 
 - [Robin Rombach](https://github.com/rromb)
 - [Patrick von Platen](https://github.com/patrickvonplaten)
diff --git a/environment-mac.yml b/environment-mac.yml
deleted file mode 100644
index e0db02c3b9..0000000000
--- a/environment-mac.yml
+++ /dev/null
@@ -1,65 +0,0 @@
-name: invokeai
-channels:
-  - pytorch
-  - conda-forge
-dependencies:
-  - python=3.9.13
-  - pip=22.2.2
-
-  - pytorch=1.12.1
-  - torchvision=0.13.1
-
-  # I suggest to keep the other deps sorted for convenience.
-  # To determine what the latest versions should be, run:
-  #
-  # ```shell
-  # sed -E 's/invokeai/invokeai-updated/;20,99s/- ([^=]+)==.+/- \1/' environment-mac.yml > environment-mac-updated.yml
-  # CONDA_SUBDIR=osx-arm64 conda env create -f environment-mac-updated.yml && conda list -n invokeai-updated | awk ' {print "  - " $1 "==" $2;} '
-  # ```
-
-  - albumentations=1.2.1
-  - coloredlogs=15.0.1
-  - diffusers=0.6.0
-  - einops=0.4.1
-  - grpcio=1.46.4
-  - humanfriendly=10.0
-  - imageio=2.21.2
-  - imageio-ffmpeg=0.4.7
-  - imgaug=0.4.0
-  - kornia=0.6.7
-  - mpmath=1.2.1
-  - nomkl # arm64 has only 1.0 while x64 needs 3.0
-  - numpy=1.23.4
-  - omegaconf=2.1.1
-  - openh264=2.3.0
-  - onnx=1.12.0
-  - onnxruntime=1.12.1
-  - pudb=2022.1
-  - pytorch-lightning=1.7.7
-  - scipy=1.9.3
-  - streamlit=1.12.2
-  - sympy=1.10.1
-  - tensorboard=2.10.0
-  - torchmetrics=0.10.1
-  - py-opencv=4.6.0
-  - flask=2.1.3
-  - flask-socketio=5.3.0
-  - flask-cors=3.0.10
-  - eventlet=0.33.1
-  - protobuf=3.20.1
-  - send2trash=1.8.0
-  - transformers=4.23.1
-  - torch-fidelity=0.3.0
-  - pip:
-      - getpass_asterisk
-      - dependency_injector==4.40.0
-      - realesrgan==0.2.5.0
-      - test-tube==0.7.5
-      - -e git+https://github.com/CompVis/taming-transformers.git@master#egg=taming-transformers
-      - -e git+https://github.com/openai/CLIP.git@main#egg=clip
-      - -e git+https://github.com/Birch-san/k-diffusion.git@mps#egg=k_diffusion
-      - -e git+https://github.com/TencentARC/GFPGAN.git#egg=gfpgan
-      - -e git+https://github.com/invoke-ai/clipseg.git@models-rename#egg=clipseg
-      - -e .
-variables:
-  PYTORCH_ENABLE_MPS_FALLBACK: 1
diff --git a/environment-linux-aarch64.yml b/environments-and-requirements/environment-lin-aarch64.yml
similarity index 59%
rename from environment-linux-aarch64.yml
rename to environments-and-requirements/environment-lin-aarch64.yml
index 6e9d2cbf06..6ec83b1b0a 100644
--- a/environment-linux-aarch64.yml
+++ b/environments-and-requirements/environment-lin-aarch64.yml
@@ -3,43 +3,43 @@ channels:
   - pytorch
   - conda-forge
 dependencies:
-  - python>=3.9
-  - pip>=20.3
+  - albumentations=0.4.3
   - cudatoolkit
-  - pytorch
-  - torchvision
-  - numpy=1.19
-  - imageio=2.9.0
-  - opencv=4.6.0
-  - getpass_asterisk
-  - pillow=8.*
+  - einops=0.3.0
+  - eventlet
+  - flask-socketio=5.3.0
   - flask=2.1.*
   - flask_cors=3.0.10
-  - flask-socketio=5.3.0
-  - send2trash=1.8.0
-  - eventlet
-  - albumentations=0.4.3
-  - pudb=2019.2
   - imageio-ffmpeg=0.4.2
-  - pytorch-lightning=1.7.7
-  - streamlit
-  - einops=0.3.0
+  - imageio=2.9.0
   - kornia=0.6
-  - torchmetrics=0.7.0
-  - transformers=4.21.3
-  - torch-fidelity=0.3.0
+  - numpy=1.19
+  - opencv=4.6.0
+  - pillow=8.*
+  - pip>=22.2.2
+  - pudb=2019.2
+  - python=3.9.*
+  - pytorch
+  - pytorch-lightning=1.7.7
+  - send2trash=1.8.0
+  - streamlit
   - tokenizers>=0.11.1,!=0.11.3,<0.13
+  - torch-fidelity=0.3.0
+  - torchmetrics=0.7.0
+  - torchvision
+  - transformers=4.21.3
   - pip:
-      - omegaconf==2.1.1
-      - realesrgan==0.2.5.0
-      - test-tube>=0.7.5
-      - pyreadline3
       - dependency_injector==4.40.0
-      - -e git+https://github.com/openai/CLIP.git@main#egg=clip
-      - -e git+https://github.com/CompVis/taming-transformers.git@master#egg=taming-transformers
-      - -e git+https://github.com/Birch-san/k-diffusion.git@mps#egg=k_diffusion
-      - -e git+https://github.com/TencentARC/GFPGAN.git#egg=gfpgan
-      - -e git+https://github.com/invoke-ai/clipseg.git@models-rename#egg=clipseg
+      - getpass_asterisk
+      - gfpgan
+      - omegaconf==2.1.1
+      - pyreadline3
+      - realesrgan
+      - taming-transformers-rom1504
+      - test-tube>=0.7.5
+      - git+https://github.com/openai/CLIP.git@main#egg=clip
+      - git+https://github.com/Birch-san/k-diffusion.git@mps#egg=k_diffusion
+      - git+https://github.com/invoke-ai/clipseg.git@relaxed-python-requirement#egg=clipseg
       - -e .
 variables:
   PYTORCH_ENABLE_MPS_FALLBACK: 1
diff --git a/environments-and-requirements/environment-lin-amd.yml b/environments-and-requirements/environment-lin-amd.yml
new file mode 100644
index 0000000000..53118330ec
--- /dev/null
+++ b/environments-and-requirements/environment-lin-amd.yml
@@ -0,0 +1,45 @@
+name: invokeai
+channels:
+  - pytorch
+  - conda-forge
+  - defaults
+dependencies:
+  - python>=3.9
+  - pip=22.2.2
+  - numpy=1.23.3
+  - pip:
+    - --extra-index-url  https://download.pytorch.org/whl/rocm5.2/
+    - albumentations==0.4.3
+    - dependency_injector==4.40.0
+    - diffusers==0.6.0
+    - einops==0.3.0
+    - eventlet
+    - flask==2.1.3
+    - flask_cors==3.0.10
+    - flask_socketio==5.3.0
+    - getpass_asterisk
+    - gfpgan
+    - imageio-ffmpeg==0.4.2
+    - imageio==2.9.0
+    - kornia==0.6.0
+    - omegaconf==2.2.3
+    - opencv-python==4.5.5.64
+    - pillow==9.2.0
+    - pudb==2019.2
+    - pyreadline3
+    - pytorch-lightning==1.7.7
+    - realesrgan
+    - send2trash==1.8.0
+    - streamlit==1.12.0
+    - taming-transformers-rom1504
+    - test-tube>=0.7.5
+    - torch
+    - torch-fidelity==0.3.0
+    - torchaudio
+    - torchmetrics==0.7.0
+    - torchvision
+    - transformers==4.21.3
+    - git+https://github.com/openai/CLIP.git@main#egg=clip
+    - git+https://github.com/Birch-san/k-diffusion.git@mps#egg=k_diffusion
+    - git+https://github.com/invoke-ai/clipseg.git@relaxed-python-requirement#egg=clipseg
+    - -e .
diff --git a/environment.yml b/environments-and-requirements/environment-lin-cuda.yml
similarity index 64%
rename from environment.yml
rename to environments-and-requirements/environment-lin-cuda.yml
index fc648e8262..a75711cfe8 100644
--- a/environment.yml
+++ b/environments-and-requirements/environment-lin-cuda.yml
@@ -13,33 +13,33 @@ dependencies:
   - cudatoolkit=11.6
   - pip:
     - albumentations==0.4.3
-    - opencv-python==4.5.5.64
-    - pudb==2019.2
-    - imageio==2.9.0
-    - imageio-ffmpeg==0.4.2
-    - pytorch-lightning==1.7.7
-    - omegaconf==2.2.3
-    - test-tube>=0.7.5
-    - streamlit==1.12.0
-    - send2trash==1.8.0
-    - pillow==9.2.0
-    - einops==0.3.0
-    - pyreadline3
-    - torch-fidelity==0.3.0
-    - transformers==4.21.3
-    - diffusers==0.6.0
-    - torchmetrics==0.7.0
-    - flask==2.1.3
-    - flask_socketio==5.3.0
-    - flask_cors==3.0.10
     - dependency_injector==4.40.0
+    - diffusers==0.6.0
+    - einops==0.3.0
     - eventlet
+    - flask==2.1.3
+    - flask_cors==3.0.10
+    - flask_socketio==5.3.0
     - getpass_asterisk
+    - gfpgan
+    - imageio-ffmpeg==0.4.2
+    - imageio==2.9.0
     - kornia==0.6.0
-    - -e git+https://github.com/openai/CLIP.git@main#egg=clip
-    - -e git+https://github.com/CompVis/taming-transformers.git@master#egg=taming-transformers
-    - -e git+https://github.com/Birch-san/k-diffusion.git@mps#egg=k_diffusion
-    - -e git+https://github.com/invoke-ai/Real-ESRGAN.git#egg=realesrgan
-    - -e git+https://github.com/invoke-ai/GFPGAN.git#egg=gfpgan
-    - -e git+https://github.com/invoke-ai/clipseg.git@models-rename#egg=clipseg
+    - omegaconf==2.2.3
+    - opencv-python==4.5.5.64
+    - pillow==9.2.0
+    - pudb==2019.2
+    - pyreadline3
+    - pytorch-lightning==1.7.7
+    - realesrgan
+    - send2trash==1.8.0
+    - streamlit==1.12.0
+    - taming-transformers-rom1504
+    - test-tube>=0.7.5
+    - torch-fidelity==0.3.0
+    - torchmetrics==0.7.0
+    - transformers==4.21.3
+    - git+https://github.com/openai/CLIP.git@main#egg=clip
+    - git+https://github.com/Birch-san/k-diffusion.git@mps#egg=k_diffusion
+    - git+https://github.com/invoke-ai/clipseg.git@relaxed-python-requirement#egg=clipseg
     - -e .
diff --git a/environments-and-requirements/environment-mac.yml b/environments-and-requirements/environment-mac.yml
new file mode 100644
index 0000000000..1ff49ec585
--- /dev/null
+++ b/environments-and-requirements/environment-mac.yml
@@ -0,0 +1,64 @@
+name: invokeai
+channels:
+  - pytorch
+  - conda-forge
+  - defaults
+dependencies:
+  - python=3.10
+  - pip>=22.2
+  - pytorch=1.12
+  - pytorch-lightning=1.7
+  - torchvision=0.13
+  - torchmetrics=0.10
+  - torch-fidelity=0.3
+
+  # I suggest to keep the other deps sorted for convenience.
+  # To determine what the latest versions should be, run:
+  #
+  # ```shell
+  # sed -E 's/invokeai/invokeai-updated/;20,99s/- ([^=]+)==.+/- \1/' environment-mac.yml > environment-mac-updated.yml
+  # CONDA_SUBDIR=osx-arm64 conda env create -f environment-mac-updated.yml && conda list -n invokeai-updated | awk ' {print "  - " $1 "==" $2;} '
+  # ```
+
+  - albumentations=1.2
+  - coloredlogs=15.0
+  - diffusers=0.6
+  - einops=0.3
+  - eventlet
+  - grpcio=1.46
+  - flask=2.1
+  - flask-socketio=5.3
+  - flask-cors=3.0
+  - humanfriendly=10.0
+  - imageio=2.21
+  - imageio-ffmpeg=0.4
+  - imgaug=0.4
+  - kornia=0.6
+  - mpmath=1.2
+  - nomkl=3
+  - numpy=1.23
+  - omegaconf=2.1
+  - openh264=2.3
+  - onnx=1.12
+  - onnxruntime=1.12
+  - pudb=2019.2
+  - protobuf=3.20
+  - py-opencv=4.6
+  - scipy=1.9
+  - streamlit=1.12
+  - sympy=1.10
+  - send2trash=1.8
+  - tensorboard=2.10
+  - transformers=4.23
+  - pip:
+      - getpass_asterisk
+      - taming-transformers-rom1504
+      - test-tube==0.7.5
+      - git+https://github.com/openai/CLIP.git@main#egg=clip
+      - git+https://github.com/invoke-ai/k-diffusion.git@mps#egg=k_diffusion
+      - git+https://github.com/invoke-ai/Real-ESRGAN.git#egg=realesrgan
+      - git+https://github.com/invoke-ai/GFPGAN.git#egg=gfpgan
+      - git+https://github.com/invoke-ai/clipseg.git@relaxed-python-requirement#egg=clipseg
+      - -e .
+variables:
+  PYTORCH_ENABLE_MPS_FALLBACK: 1
diff --git a/environments-and-requirements/environment-win-cuda.yml b/environments-and-requirements/environment-win-cuda.yml
new file mode 100644
index 0000000000..b2a919882b
--- /dev/null
+++ b/environments-and-requirements/environment-win-cuda.yml
@@ -0,0 +1,46 @@
+name: invokeai
+channels:
+  - pytorch
+  - conda-forge
+  - defaults
+dependencies:
+  - python>=3.9
+  - pip=22.2.2
+  - numpy=1.23.3
+  - torchvision=0.13.1
+  - torchaudio=0.12.1
+  - pytorch=1.12.1
+  - cudatoolkit=11.6
+  - pip:
+    - albumentations==0.4.3
+    - basicsr==1.4.1
+    - dependency_injector==4.40.0
+    - diffusers==0.6.0
+    - einops==0.3.0
+    - eventlet
+    - flask==2.1.3
+    - flask_cors==3.0.10
+    - flask_socketio==5.3.0
+    - getpass_asterisk
+    - gfpgan
+    - imageio-ffmpeg==0.4.2
+    - imageio==2.9.0
+    - kornia==0.6.0
+    - omegaconf==2.2.3
+    - opencv-python==4.5.5.64
+    - pillow==9.2.0
+    - pudb==2019.2
+    - pyreadline3
+    - pytorch-lightning==1.7.7
+    - realesrgan
+    - send2trash==1.8.0
+    - streamlit==1.12.0
+    - taming-transformers-rom1504
+    - test-tube>=0.7.5
+    - torch-fidelity==0.3.0
+    - torchmetrics==0.7.0
+    - transformers==4.21.3
+    - git+https://github.com/openai/CLIP.git@main#egg=clip
+    - git+https://github.com/Birch-san/k-diffusion.git@mps#egg=k_diffusion
+    - git+https://github.com/invoke-ai/clipseg.git@relaxed-python-requirement#egg=clipseg
+    - -e .
diff --git a/requirements.txt b/environments-and-requirements/requirements-base.txt
similarity index 67%
rename from requirements.txt
rename to environments-and-requirements/requirements-base.txt
index 939463e36e..5ae2c75f0b 100644
--- a/requirements.txt
+++ b/environments-and-requirements/requirements-base.txt
@@ -1,41 +1,36 @@
---prefer-binary
-
-albumentations
-einops
-getpass_asterisk
-huggingface-hub
-imageio-ffmpeg
-imageio
-kornia
 # pip will resolve the version which matches torch
+albumentations
+dependency_injector==4.40.0
+diffusers
+einops
+eventlet
+flask==2.1.3
+flask_cors==3.0.10
+flask_socketio==5.3.0
+flaskwebgui==0.3.7
+getpass_asterisk
+gfpgan
+huggingface-hub
+imageio
+imageio-ffmpeg
+kornia
 numpy
 omegaconf
 opencv-python
 pillow
 pip>=22
 pudb
-pytorch-lightning==1.7.7
-scikit-image>=0.19
-streamlit
 pyreadline3
-# "CompVis/taming-transformers" IS NOT INSTALLABLE
-# This is a drop-in replacement
+pytorch-lightning==1.7.7
+realesrgan
+scikit-image>=0.19
+send2trash
+streamlit
 taming-transformers-rom1504
 test-tube
 torch-fidelity
 torchmetrics
 transformers==4.21.*
-flask==2.1.3
-flask_socketio==5.3.0
-flask_cors==3.0.10
-flaskwebgui==0.3.7
-send2trash
-dependency_injector==4.40.0
-eventlet
-realesrgan
-diffusers
 git+https://github.com/openai/CLIP.git@main#egg=clip
 git+https://github.com/Birch-san/k-diffusion.git@mps#egg=k-diffusion
-git+https://github.com/invoke-ai/Real-ESRGAN.git#egg=realesrgan
-git+https://github.com/invoke-ai/GFPGAN.git#egg=gfpgan
--e git+https://github.com/invoke-ai/clipseg.git@models-rename#egg=clipseg
+git+https://github.com/invoke-ai/clipseg.git@relaxed-python-requirement#egg=clipseg
diff --git a/requirements-lin-AMD.txt b/environments-and-requirements/requirements-lin-amd.txt
similarity index 76%
rename from requirements-lin-AMD.txt
rename to environments-and-requirements/requirements-lin-amd.txt
index d12996b45a..8ca0ead88c 100644
--- a/requirements-lin-AMD.txt
+++ b/environments-and-requirements/requirements-lin-amd.txt
@@ -1,4 +1,4 @@
--r requirements.txt
+-r environments-and-requirements/requirements-base.txt
 
 # Get hardware-appropriate torch/torchvision 
 --extra-index-url https://download.pytorch.org/whl/rocm5.1.1 --trusted-host https://download.pytorch.org
diff --git a/environments-and-requirements/requirements-lin-arm64.txt b/environments-and-requirements/requirements-lin-arm64.txt
new file mode 100644
index 0000000000..304432e4c7
--- /dev/null
+++ b/environments-and-requirements/requirements-lin-arm64.txt
@@ -0,0 +1,3 @@
+--pre torch torchvision torchaudio --extra-index-url https://download.pytorch.org/whl/nightly/cpu
+-r environments-and-requirements/requirements-base.txt
+-e .
diff --git a/environments-and-requirements/requirements-lin-cuda.txt b/environments-and-requirements/requirements-lin-cuda.txt
new file mode 100644
index 0000000000..1578c60334
--- /dev/null
+++ b/environments-and-requirements/requirements-lin-cuda.txt
@@ -0,0 +1,2 @@
+-r environments-and-requirements/requirements-base.txt
+-e .
diff --git a/requirements-mac-MPS-CPU.txt b/environments-and-requirements/requirements-mac-mps-cpu.txt
similarity index 50%
rename from requirements-mac-MPS-CPU.txt
rename to environments-and-requirements/requirements-mac-mps-cpu.txt
index 190417aaa0..05c7d9a77c 100644
--- a/requirements-mac-MPS-CPU.txt
+++ b/environments-and-requirements/requirements-mac-mps-cpu.txt
@@ -1,4 +1,4 @@
--r requirements.txt
+-r environments-and-requirements/requirements-base.txt
 
 protobuf==3.19.6
 torch<1.13.0
diff --git a/requirements-lin-win-colab-CUDA.txt b/environments-and-requirements/requirements-win-colab-cuda.txt
similarity index 72%
rename from requirements-lin-win-colab-CUDA.txt
rename to environments-and-requirements/requirements-win-colab-cuda.txt
index bb6a9ae9c8..b00f1487fb 100644
--- a/requirements-lin-win-colab-CUDA.txt
+++ b/environments-and-requirements/requirements-win-colab-cuda.txt
@@ -1,7 +1,8 @@
--r requirements.txt
+-r environments-and-requirements/requirements-base.txt
 
 # Get hardware-appropriate torch/torchvision 
 --extra-index-url https://download.pytorch.org/whl/cu116 --trusted-host https://download.pytorch.org
+basicsr==1.4.1
 torch==1.12.1
 torchvision==0.13.1
 -e .
diff --git a/frontend/README.md b/frontend/README.md
index f597cc6f23..4becbb221f 100644
--- a/frontend/README.md
+++ b/frontend/README.md
@@ -5,7 +5,7 @@
 - `python scripts/dream.py --web` serves both frontend and backend at
   http://localhost:9090
 
-## Evironment
+## Environment
 
 Install [node](https://nodejs.org/en/download/) (includes npm) and optionally
 [yarn](https://yarnpkg.com/getting-started/install).
@@ -15,7 +15,7 @@ packages.
 
 ## Dev
 
-1. From `frontend/`, run `npm dev` / `yarn dev` to start the dev server.
+1. From `frontend/`, run `npm run dev` / `yarn dev` to start the dev server.
 2. Run `python scripts/dream.py --web`.
 3. Navigate to the dev server address e.g. `http://localhost:5173/`.
 
diff --git a/frontend/dist/assets/index.8eb7dfe4.js b/frontend/dist/assets/index.8eb7dfe4.js
new file mode 100644
index 0000000000..028ad32993
--- /dev/null
+++ b/frontend/dist/assets/index.8eb7dfe4.js
@@ -0,0 +1,501 @@
+function Sj(e,t){for(var n=0;n<t.length;n++){const r=t[n];if(typeof r!="string"&&!Array.isArray(r)){for(const i in r)if(i!=="default"&&!(i in e)){const o=Object.getOwnPropertyDescriptor(r,i);o&&Object.defineProperty(e,i,o.get?o:{enumerable:!0,get:()=>r[i]})}}}return Object.freeze(Object.defineProperty(e,Symbol.toStringTag,{value:"Module"}))}(function(){const t=document.createElement("link").relList;if(t&&t.supports&&t.supports("modulepreload"))return;for(const i of document.querySelectorAll('link[rel="modulepreload"]'))r(i);new MutationObserver(i=>{for(const o of i)if(o.type==="childList")for(const a of o.addedNodes)a.tagName==="LINK"&&a.rel==="modulepreload"&&r(a)}).observe(document,{childList:!0,subtree:!0});function n(i){const o={};return i.integrity&&(o.integrity=i.integrity),i.referrerpolicy&&(o.referrerPolicy=i.referrerpolicy),i.crossorigin==="use-credentials"?o.credentials="include":i.crossorigin==="anonymous"?o.credentials="omit":o.credentials="same-origin",o}function r(i){if(i.ep)return;i.ep=!0;const o=n(i);fetch(i.href,o)}})();var nu=typeof globalThis<"u"?globalThis:typeof window<"u"?window:typeof global<"u"?global:typeof self<"u"?self:{};function GC(e){return e&&e.__esModule&&Object.prototype.hasOwnProperty.call(e,"default")?e.default:e}var C={exports:{}},Gt={};/**
+ * @license React
+ * react.production.min.js
+ *
+ * Copyright (c) Facebook, Inc. and its affiliates.
+ *
+ * This source code is licensed under the MIT license found in the
+ * LICENSE file in the root directory of this source tree.
+ */var iv=Symbol.for("react.element"),wj=Symbol.for("react.portal"),Cj=Symbol.for("react.fragment"),_j=Symbol.for("react.strict_mode"),kj=Symbol.for("react.profiler"),Ej=Symbol.for("react.provider"),Pj=Symbol.for("react.context"),Tj=Symbol.for("react.forward_ref"),Lj=Symbol.for("react.suspense"),Aj=Symbol.for("react.memo"),Ij=Symbol.for("react.lazy"),ak=Symbol.iterator;function Mj(e){return e===null||typeof e!="object"?null:(e=ak&&e[ak]||e["@@iterator"],typeof e=="function"?e:null)}var mI={isMounted:function(){return!1},enqueueForceUpdate:function(){},enqueueReplaceState:function(){},enqueueSetState:function(){}},vI=Object.assign,yI={};function w0(e,t,n){this.props=e,this.context=t,this.refs=yI,this.updater=n||mI}w0.prototype.isReactComponent={};w0.prototype.setState=function(e,t){if(typeof e!="object"&&typeof e!="function"&&e!=null)throw Error("setState(...): takes an object of state variables to update or a function which returns an object of state variables.");this.updater.enqueueSetState(this,e,t,"setState")};w0.prototype.forceUpdate=function(e){this.updater.enqueueForceUpdate(this,e,"forceUpdate")};function xI(){}xI.prototype=w0.prototype;function jC(e,t,n){this.props=e,this.context=t,this.refs=yI,this.updater=n||mI}var qC=jC.prototype=new xI;qC.constructor=jC;vI(qC,w0.prototype);qC.isPureReactComponent=!0;var sk=Array.isArray,bI=Object.prototype.hasOwnProperty,KC={current:null},SI={key:!0,ref:!0,__self:!0,__source:!0};function wI(e,t,n){var r,i={},o=null,a=null;if(t!=null)for(r in t.ref!==void 0&&(a=t.ref),t.key!==void 0&&(o=""+t.key),t)bI.call(t,r)&&!SI.hasOwnProperty(r)&&(i[r]=t[r]);var s=arguments.length-2;if(s===1)i.children=n;else if(1<s){for(var l=Array(s),d=0;d<s;d++)l[d]=arguments[d+2];i.children=l}if(e&&e.defaultProps)for(r in s=e.defaultProps,s)i[r]===void 0&&(i[r]=s[r]);return{$$typeof:iv,type:e,key:o,ref:a,props:i,_owner:KC.current}}function Rj(e,t){return{$$typeof:iv,type:e.type,key:t,ref:e.ref,props:e.props,_owner:e._owner}}function ZC(e){return typeof e=="object"&&e!==null&&e.$$typeof===iv}function Oj(e){var t={"=":"=0",":":"=2"};return"$"+e.replace(/[=:]/g,function(n){return t[n]})}var lk=/\/+/g;function nb(e,t){return typeof e=="object"&&e!==null&&e.key!=null?Oj(""+e.key):t.toString(36)}function Ay(e,t,n,r,i){var o=typeof e;(o==="undefined"||o==="boolean")&&(e=null);var a=!1;if(e===null)a=!0;else switch(o){case"string":case"number":a=!0;break;case"object":switch(e.$$typeof){case iv:case wj:a=!0}}if(a)return a=e,i=i(a),e=r===""?"."+nb(a,0):r,sk(i)?(n="",e!=null&&(n=e.replace(lk,"$&/")+"/"),Ay(i,t,n,"",function(d){return d})):i!=null&&(ZC(i)&&(i=Rj(i,n+(!i.key||a&&a.key===i.key?"":(""+i.key).replace(lk,"$&/")+"/")+e)),t.push(i)),1;if(a=0,r=r===""?".":r+":",sk(e))for(var s=0;s<e.length;s++){o=e[s];var l=r+nb(o,s);a+=Ay(o,t,n,l,i)}else if(l=Mj(e),typeof l=="function")for(e=l.call(e),s=0;!(o=e.next()).done;)o=o.value,l=r+nb(o,s++),a+=Ay(o,t,n,l,i);else if(o==="object")throw t=String(e),Error("Objects are not valid as a React child (found: "+(t==="[object Object]"?"object with keys {"+Object.keys(e).join(", ")+"}":t)+"). 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+ * scheduler.production.min.js
+ *
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+ *
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l={mode:"hidden",children:r.children};return(a&1)===0&&t.child!==i?(r=t.child,r.childLanes=0,r.pendingProps=l,t.deletions=null):(r=$c(i,l),r.subtreeFlags=i.subtreeFlags&14680064),s!==null?o=$c(s,o):(o=kf(o,a,n,null),o.flags|=2),o.return=t,r.return=t,r.sibling=o,t.child=r,r=o,o=t.child,a=e.child.memoizedState,a=a===null?Tw(n):{baseLanes:a.baseLanes|n,cachePool:null,transitions:a.transitions},o.memoizedState=a,o.childLanes=e.childLanes&~n,t.memoizedState=Pw,r}return o=e.child,e=o.sibling,r=$c(o,{mode:"visible",children:r.children}),(t.mode&1)===0&&(r.lanes=n),r.return=t,r.sibling=null,e!==null&&(n=t.deletions,n===null?(t.deletions=[e],t.flags|=16):n.push(e)),t.child=r,t.memoizedState=null,r}function T9(e,t){return t=Z5({mode:"visible",children:t},e.mode,0,null),t.return=e,e.child=t}function R2(e,t,n,r){return r!==null&&p9(r),o0(t,e.child,null,n),e=T9(t,t.pendingProps.children),e.flags|=2,t.memoizedState=null,e}function mK(e,t,n,r,i,o,a){if(n)return t.flags&256?(t.flags&=-257,r=_b(Error(Re(422))),R2(e,t,a,r)):t.memoizedState!==null?(t.child=e.child,t.flags|=128,null):(o=r.fallback,i=t.mode,r=Z5({mode:"visible",children:r.children},i,0,null),o=kf(o,i,a,null),o.flags|=2,r.return=t,o.return=t,r.sibling=o,t.child=r,(t.mode&1)!==0&&o0(t,e.child,null,a),t.child.memoizedState=Tw(a),t.memoizedState=Pw,o);if((t.mode&1)===0)return R2(e,t,a,null);if(i.data==="$!"){if(r=i.nextSibling&&i.nextSibling.dataset,r)var s=r.dgst;return r=s,o=Error(Re(419)),r=_b(o,r,void 0),R2(e,t,a,r)}if(s=(a&e.childLanes)!==0,Co||s){if(r=si,r!==null){switch(a&-a){case 4:i=2;break;case 16:i=8;break;case 64:case 128:case 256:case 512:case 1024:case 2048:case 4096:case 8192:case 16384:case 32768:case 65536:case 131072:case 262144:case 524288:case 1048576:case 2097152:case 4194304:case 8388608:case 16777216:case 33554432:case 67108864:i=32;break;case 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r=t.pendingProps,i=r.revealOrder,o=r.tail;if(Qi(e,t,r.children,n),r=lr.current,(r&2)!==0)r=r&1|2,t.flags|=128;else{if(e!==null&&(e.flags&128)!==0)e:for(e=t.child;e!==null;){if(e.tag===13)e.memoizedState!==null&&rE(e,n,t);else if(e.tag===19)rE(e,n,t);else if(e.child!==null){e.child.return=e,e=e.child;continue}if(e===t)break e;for(;e.sibling===null;){if(e.return===null||e.return===t)break e;e=e.return}e.sibling.return=e.return,e=e.sibling}r&=1}if(Un(lr,r),(t.mode&1)===0)t.memoizedState=null;else switch(i){case"forwards":for(n=t.child,i=null;n!==null;)e=n.alternate,e!==null&&z3(e)===null&&(i=n),n=n.sibling;n=i,n===null?(i=t.child,t.child=null):(i=n.sibling,n.sibling=null),kb(t,!1,i,n,o);break;case"backwards":for(n=null,i=t.child,t.child=null;i!==null;){if(e=i.alternate,e!==null&&z3(e)===null){t.child=i;break}e=i.sibling,i.sibling=n,n=i,i=e}kb(t,!0,n,null,o);break;case"together":kb(t,!1,null,null,void 0);break;default:t.memoizedState=null}return t.child}function Fy(e,t){(t.mode&1)===0&&e!==null&&(e.alternate=null,t.alternate=null,t.flags|=2)}function du(e,t,n){if(e!==null&&(t.dependencies=e.dependencies),Nf|=t.lanes,(n&t.childLanes)===0)return null;if(e!==null&&t.child!==e.child)throw Error(Re(153));if(t.child!==null){for(e=t.child,n=$c(e,e.pendingProps),t.child=n,n.return=t;e.sibling!==null;)e=e.sibling,n=n.sibling=$c(e,e.pendingProps),n.return=t;n.sibling=null}return t.child}function vK(e,t,n){switch(t.tag){case 3:tR(t),i0();break;case 5:AM(t);break;case 1:ko(t.type)&&I3(t);break;case 4:b9(t,t.stateNode.containerInfo);break;case 10:var r=t.type._context,i=t.memoizedProps.value;Un(O3,r._currentValue),r._currentValue=i;break;case 13:if(r=t.memoizedState,r!==null)return r.dehydrated!==null?(Un(lr,lr.current&1),t.flags|=128,null):(n&t.child.childLanes)!==0?nR(e,t,n):(Un(lr,lr.current&1),e=du(e,t,n),e!==null?e.sibling:null);Un(lr,lr.current&1);break;case 19:if(r=(n&t.childLanes)!==0,(e.flags&128)!==0){if(r)return rR(e,t,n);t.flags|=128}if(i=t.memoizedState,i!==null&&(i.rendering=null,i.tail=null,i.lastEffect=null),Un(lr,lr.current),r)break;return null;case 22:case 23:return t.lanes=0,JM(e,t,n)}return du(e,t,n)}var iR,Lw,oR,aR;iR=function(e,t){for(var n=t.child;n!==null;){if(n.tag===5||n.tag===6)e.appendChild(n.stateNode);else if(n.tag!==4&&n.child!==null){n.child.return=n,n=n.child;continue}if(n===t)break;for(;n.sibling===null;){if(n.return===null||n.return===t)return;n=n.return}n.sibling.return=n.return,n=n.sibling}};Lw=function(){};oR=function(e,t,n,r){var i=e.memoizedProps;if(i!==r){e=t.stateNode,pf(pl.current);var o=null;switch(n){case"input":i=XS(e,i),r=XS(e,r),o=[];break;case"select":i=dr({},i,{value:void 0}),r=dr({},r,{value:void 0}),o=[];break;case"textarea":i=ew(e,i),r=ew(e,r),o=[];break;default:typeof i.onClick!="function"&&typeof r.onClick=="function"&&(e.onclick=L3)}nw(n,r);var a;n=null;for(d in i)if(!r.hasOwnProperty(d)&&i.hasOwnProperty(d)&&i[d]!=null)if(d==="style"){var s=i[d];for(a in s)s.hasOwnProperty(a)&&(n||(n={}),n[a]="")}else d!=="dangerouslySetInnerHTML"&&d!=="children"&&d!=="suppressContentEditableWarning"&&d!=="suppressHydrationWarning"&&d!=="autoFocus"&&(dm.hasOwnProperty(d)?o||(o=[]):(o=o||[]).push(d,null));for(d in r){var l=r[d];if(s=i?.[d],r.hasOwnProperty(d)&&l!==s&&(l!=null||s!=null))if(d==="style")if(s){for(a in s)!s.hasOwnProperty(a)||l&&l.hasOwnProperty(a)||(n||(n={}),n[a]="");for(a in l)l.hasOwnProperty(a)&&s[a]!==l[a]&&(n||(n={}),n[a]=l[a])}else n||(o||(o=[]),o.push(d,n)),n=l;else d==="dangerouslySetInnerHTML"?(l=l?l.__html:void 0,s=s?s.__html:void 0,l!=null&&s!==l&&(o=o||[]).push(d,l)):d==="children"?typeof l!="string"&&typeof l!="number"||(o=o||[]).push(d,""+l):d!=="suppressContentEditableWarning"&&d!=="suppressHydrationWarning"&&(dm.hasOwnProperty(d)?(l!=null&&d==="onScroll"&&jn("scroll",e),o||s===l||(o=[])):(o=o||[]).push(d,l))}n&&(o=o||[]).push("style",n);var d=o;(t.updateQueue=d)&&(t.flags|=4)}};aR=function(e,t,n,r){n!==r&&(t.flags|=4)};function tg(e,t){if(!tr)switch(e.tailMode){case"hidden":t=e.tail;for(var n=null;t!==null;)t.alternate!==null&&(n=t),t=t.sibling;n===null?e.tail=null:n.sibling=null;break;case"collapsed":n=e.tail;for(var r=null;n!==null;)n.alternate!==null&&(r=n),n=n.sibling;r===null?t||e.tail===null?e.tail=null:e.tail.sibling=null:r.sibling=null}}function Ni(e){var t=e.alternate!==null&&e.alternate.child===e.child,n=0,r=0;if(t)for(var i=e.child;i!==null;)n|=i.lanes|i.childLanes,r|=i.subtreeFlags&14680064,r|=i.flags&14680064,i.return=e,i=i.sibling;else for(i=e.child;i!==null;)n|=i.lanes|i.childLanes,r|=i.subtreeFlags,r|=i.flags,i.return=e,i=i.sibling;return e.subtreeFlags|=r,e.childLanes=n,t}function yK(e,t,n){var r=t.pendingProps;switch(h9(t),t.tag){case 2:case 16:case 15:case 0:case 11:case 7:case 8:case 12:case 9:case 14:return Ni(t),null;case 1:return ko(t.type)&&A3(),Ni(t),null;case 3:return r=t.stateNode,a0(),Kn(_o),Kn($i),w9(),r.pendingContext&&(r.context=r.pendingContext,r.pendingContext=null),(e===null||e.child===null)&&(I2(t)?t.flags|=4:e===null||e.memoizedState.isDehydrated&&(t.flags&256)===0||(t.flags|=1024,ms!==null&&(zw(ms),ms=null))),Lw(e,t),Ni(t),null;case 5:S9(t);var i=pf(Cm.current);if(n=t.type,e!==null&&t.stateNode!=null)oR(e,t,n,r,i),e.ref!==t.ref&&(t.flags|=512,t.flags|=2097152);else{if(!r){if(t.stateNode===null)throw Error(Re(166));return Ni(t),null}if(e=pf(pl.current),I2(t)){r=t.stateNode,n=t.type;var o=t.memoizedProps;switch(r[ul]=t,r[Sm]=o,e=(t.mode&1)!==0,n){case"dialog":jn("cancel",r),jn("close",r);break;case"iframe":case"object":case"embed":jn("load",r);break;case"video":case"audio":for(i=0;i<bg.length;i++)jn(bg[i],r);break;case"source":jn("error",r);break;case"img":case"image":case"link":jn("error",r),jn("load",r);break;case"details":jn("toggle",r);break;case"input":fk(r,o),jn("invalid",r);break;case"select":r._wrapperState={wasMultiple:!!o.multiple},jn("invalid",r);break;case"textarea":pk(r,o),jn("invalid",r)}nw(n,o),i=null;for(var a in o)if(o.hasOwnProperty(a)){var s=o[a];a==="children"?typeof s=="string"?r.textContent!==s&&(o.suppressHydrationWarning!==!0&&A2(r.textContent,s,e),i=["children",s]):typeof s=="number"&&r.textContent!==""+s&&(o.suppressHydrationWarning!==!0&&A2(r.textContent,s,e),i=["children",""+s]):dm.hasOwnProperty(a)&&s!=null&&a==="onScroll"&&jn("scroll",r)}switch(n){case"input":w2(r),hk(r,o,!0);break;case"textarea":w2(r),gk(r);break;case"select":case"option":break;default:typeof o.onClick=="function"&&(r.onclick=L3)}r=i,t.updateQueue=r,r!==null&&(t.flags|=4)}else{a=i.nodeType===9?i:i.ownerDocument,e==="http://www.w3.org/1999/xhtml"&&(e=RI(n)),e==="http://www.w3.org/1999/xhtml"?n==="script"?(e=a.createElement("div"),e.innerHTML="<script><\/script>",e=e.removeChild(e.firstChild)):typeof r.is=="string"?e=a.createElement(n,{is:r.is}):(e=a.createElement(n),n==="select"&&(a=e,r.multiple?a.multiple=!0:r.size&&(a.size=r.size))):e=a.createElementNS(e,n),e[ul]=t,e[Sm]=r,iR(e,t,!1,!1),t.stateNode=e;e:{switch(a=rw(n,r),n){case"dialog":jn("cancel",e),jn("close",e),i=r;break;case"iframe":case"object":case"embed":jn("load",e),i=r;break;case"video":case"audio":for(i=0;i<bg.length;i++)jn(bg[i],e);i=r;break;case"source":jn("error",e),i=r;break;case"img":case"image":case"link":jn("error",e),jn("load",e),i=r;break;case"details":jn("toggle",e),i=r;break;case"input":fk(e,r),i=XS(e,r),jn("invalid",e);break;case"option":i=r;break;case"select":e._wrapperState={wasMultiple:!!r.multiple},i=dr({},r,{value:void 0}),jn("invalid",e);break;case"textarea":pk(e,r),i=ew(e,r),jn("invalid",e);break;default:i=r}nw(n,i),s=i;for(o in s)if(s.hasOwnProperty(o)){var l=s[o];o==="style"?DI(e,l):o==="dangerouslySetInnerHTML"?(l=l?l.__html:void 0,l!=null&&OI(e,l)):o==="children"?typeof l=="string"?(n!=="textarea"||l!=="")&&fm(e,l):typeof l=="number"&&fm(e,""+l):o!=="suppressContentEditableWarning"&&o!=="suppressHydrationWarning"&&o!=="autoFocus"&&(dm.hasOwnProperty(o)?l!=null&&o==="onScroll"&&jn("scroll",e):l!=null&&QC(e,o,l,a))}switch(n){case"input":w2(e),hk(e,r,!1);break;case"textarea":w2(e),gk(e);break;case"option":r.value!=null&&e.setAttribute("value",""+Vc(r.value));break;case"select":e.multiple=!!r.multiple,o=r.value,o!=null?Mp(e,!!r.multiple,o,!1):r.defaultValue!=null&&Mp(e,!!r.multiple,r.defaultValue,!0);break;default:typeof i.onClick=="function"&&(e.onclick=L3)}switch(n){case"button":case"input":case"select":case"textarea":r=!!r.autoFocus;break e;case"img":r=!0;break e;default:r=!1}}r&&(t.flags|=4)}t.ref!==null&&(t.flags|=512,t.flags|=2097152)}return Ni(t),null;case 6:if(e&&t.stateNode!=null)aR(e,t,e.memoizedProps,r);else{if(typeof r!="string"&&t.stateNode===null)throw Error(Re(166));if(n=pf(Cm.current),pf(pl.current),I2(t)){if(r=t.stateNode,n=t.memoizedProps,r[ul]=t,(o=r.nodeValue!==n)&&(e=ta,e!==null))switch(e.tag){case 3:A2(r.nodeValue,n,(e.mode&1)!==0);break;case 5:e.memoizedProps.suppressHydrationWarning!==!0&&A2(r.nodeValue,n,(e.mode&1)!==0)}o&&(t.flags|=4)}else r=(n.nodeType===9?n:n.ownerDocument).createTextNode(r),r[ul]=t,t.stateNode=r}return Ni(t),null;case 13:if(Kn(lr),r=t.memoizedState,e===null||e.memoizedState!==null&&e.memoizedState.dehydrated!==null){if(tr&&Jo!==null&&(t.mode&1)!==0&&(t.flags&128)===0)CM(),i0(),t.flags|=98560,o=!1;else if(o=I2(t),r!==null&&r.dehydrated!==null){if(e===null){if(!o)throw Error(Re(318));if(o=t.memoizedState,o=o!==null?o.dehydrated:null,!o)throw Error(Re(317));o[ul]=t}else i0(),(t.flags&128)===0&&(t.memoizedState=null),t.flags|=4;Ni(t),o=!1}else ms!==null&&(zw(ms),ms=null),o=!0;if(!o)return t.flags&65536?t:null}return(t.flags&128)!==0?(t.lanes=n,t):(r=r!==null,r!==(e!==null&&e.memoizedState!==null)&&r&&(t.child.flags|=8192,(t.mode&1)!==0&&(e===null||(lr.current&1)!==0?Yr===0&&(Yr=3):O9())),t.updateQueue!==null&&(t.flags|=4),Ni(t),null);case 4:return a0(),Lw(e,t),e===null&&xm(t.stateNode.containerInfo),Ni(t),null;case 10:return v9(t.type._context),Ni(t),null;case 17:return ko(t.type)&&A3(),Ni(t),null;case 19:if(Kn(lr),o=t.memoizedState,o===null)return Ni(t),null;if(r=(t.flags&128)!==0,a=o.rendering,a===null)if(r)tg(o,!1);else{if(Yr!==0||e!==null&&(e.flags&128)!==0)for(e=t.child;e!==null;){if(a=z3(e),a!==null){for(t.flags|=128,tg(o,!1),r=a.updateQueue,r!==null&&(t.updateQueue=r,t.flags|=4),t.subtreeFlags=0,r=n,n=t.child;n!==null;)o=n,e=r,o.flags&=14680066,a=o.alternate,a===null?(o.childLanes=0,o.lanes=e,o.child=null,o.subtreeFlags=0,o.memoizedProps=null,o.memoizedState=null,o.updateQueue=null,o.dependencies=null,o.stateNode=null):(o.childLanes=a.childLanes,o.lanes=a.lanes,o.child=a.child,o.subtreeFlags=0,o.deletions=null,o.memoizedProps=a.memoizedProps,o.memoizedState=a.memoizedState,o.updateQueue=a.updateQueue,o.type=a.type,e=a.dependencies,o.dependencies=e===null?null:{lanes:e.lanes,firstContext:e.firstContext}),n=n.sibling;return 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e.current.memoizedState.isDehydrated&&(_f(e,t).flags|=256),e=V3(e,t),e!==2&&(t=So,So=n,t!==null&&zw(t)),e}function zw(e){So===null?So=e:So.push.apply(So,e)}function _K(e){for(var t=e;;){if(t.flags&16384){var n=t.updateQueue;if(n!==null&&(n=n.stores,n!==null))for(var r=0;r<n.length;r++){var i=n[r],o=i.getSnapshot;i=i.value;try{if(!_s(o(),i))return!1}catch{return!1}}}if(n=t.child,t.subtreeFlags&16384&&n!==null)n.return=t,t=n;else{if(t===e)break;for(;t.sibling===null;){if(t.return===null||t.return===e)return!0;t=t.return}t.sibling.return=t.return,t=t.sibling}}return!0}function _c(e,t){for(t&=~A9,t&=~K5,e.suspendedLanes|=t,e.pingedLanes&=~t,e=e.expirationTimes;0<t;){var n=31-ys(t),r=1<<n;e[n]=-1,t&=~r}}function cE(e){if((tn&6)!==0)throw Error(Re(327));zp();var t=k3(e,0);if((t&1)===0)return Eo(e,Mr()),null;var n=V3(e,t);if(e.tag!==0&&n===2){var r=lw(e);r!==0&&(t=r,n=Dw(e,r))}if(n===1)throw n=Pm,_f(e,0),_c(e,t),Eo(e,Mr()),n;if(n===6)throw Error(Re(345));return 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i=r.next,o=n.pending;if(o!==null){var a=o.next;o.next=i,r.next=a}n.pending=r}hf=null}return e}function hR(e,t){do{var n=Hr;try{if(m9(),Dy.current=B3,F3){for(var r=cr.memoizedState;r!==null;){var i=r.queue;i!==null&&(i.pending=null),r=r.next}F3=!1}if(Of=0,ai=Zr=cr=null,Hg=!1,_m=0,L9.current=null,n===null||n.return===null){Yr=1,Pm=t,Hr=null;break}e:{var o=e,a=n.return,s=n,l=t;if(t=Pi,s.flags|=32768,l!==null&&typeof l=="object"&&typeof l.then=="function"){var d=l,p=s,g=p.tag;if((p.mode&1)===0&&(g===0||g===11||g===15)){var m=p.alternate;m?(p.updateQueue=m.updateQueue,p.memoizedState=m.memoizedState,p.lanes=m.lanes):(p.updateQueue=null,p.memoizedState=null)}var y=Xk(a);if(y!==null){y.flags&=-257,Qk(y,a,s,o,t),y.mode&1&&Yk(o,d,t),t=y,l=d;var b=t.updateQueue;if(b===null){var S=new Set;S.add(l),t.updateQueue=S}else b.add(l);break e}else{if((t&1)===0){Yk(o,d,t),O9();break e}l=Error(Re(426))}}else if(tr&&s.mode&1){var 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o=n.lanes|n.childLanes;if(sq(e,o),e===si&&(Hr=si=null,Pi=0),(n.subtreeFlags&2064)===0&&(n.flags&2064)===0||N2||(N2=!0,xR(_3,function(){return zp(),null})),o=(n.flags&15990)!==0,(n.subtreeFlags&15990)!==0||o){o=Oa.transition,Oa.transition=null;var a=kn;kn=1;var s=tn;tn|=4,L9.current=null,SK(e,n),cR(n,e),qq(pw),E3=!!hw,pw=hw=null,e.current=n,wK(n),Qj(),tn=s,kn=a,Oa.transition=o}else e.current=n;if(N2&&(N2=!1,Tc=e,W3=i),o=e.pendingLanes,o===0&&(Fc=null),tq(n.stateNode),Eo(e,Mr()),t!==null)for(r=e.onRecoverableError,n=0;n<t.length;n++)i=t[n],r(i.value,{componentStack:i.stack,digest:i.digest});if(H3)throw H3=!1,e=Ow,Ow=null,e;return(W3&1)!==0&&e.tag!==0&&zp(),o=e.pendingLanes,(o&1)!==0?e===Nw?Ug++:(Ug=0,Nw=e):Ug=0,ed(),null}function zp(){if(Tc!==null){var e=ZI(W3),t=Oa.transition,n=kn;try{if(Oa.transition=null,kn=16>e?16:e,Tc===null)var r=!1;else{if(e=Tc,Tc=null,W3=0,(tn&6)!==0)throw Error(Re(331));var i=tn;for(tn|=4,Qe=e.current;Qe!==null;){var o=Qe,a=o.child;if((Qe.flags&16)!==0){var s=o.deletions;if(s!==null){for(var l=0;l<s.length;l++){var d=s[l];for(Qe=d;Qe!==null;){var p=Qe;switch(p.tag){case 0:case 11:case 15:Wg(8,p,o)}var g=p.child;if(g!==null)g.return=p,Qe=g;else for(;Qe!==null;){p=Qe;var m=p.sibling,y=p.return;if(sR(p),p===d){Qe=null;break}if(m!==null){m.return=y,Qe=m;break}Qe=y}}}var b=o.alternate;if(b!==null){var S=b.child;if(S!==null){b.child=null;do{var E=S.sibling;S.sibling=null,S=E}while(S!==null)}}Qe=o}}if((o.subtreeFlags&2064)!==0&&a!==null)a.return=o,Qe=a;else e:for(;Qe!==null;){if(o=Qe,(o.flags&2048)!==0)switch(o.tag){case 0:case 11:case 15:Wg(9,o,o.return)}var P=o.sibling;if(P!==null){P.return=o.return,Qe=P;break e}Qe=o.return}}var k=e.current;for(Qe=k;Qe!==null;){a=Qe;var L=a.child;if((a.subtreeFlags&2064)!==0&&L!==null)L.return=a,Qe=L;else e:for(a=k;Qe!==null;){if(s=Qe,(s.flags&2048)!==0)try{switch(s.tag){case 0:case 11:case 15:q5(9,s)}}catch(O){br(s,s.return,O)}if(s===a){Qe=null;break e}var I=s.sibling;if(I!==null){I.return=s.return,Qe=I;break 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t=e.memoizedState,n=0;t!==null&&(n=t.retryLane),vR(e,n)}function AK(e,t){var n=0;switch(e.tag){case 13:var r=e.stateNode,i=e.memoizedState;i!==null&&(n=i.retryLane);break;case 19:r=e.stateNode;break;default:throw Error(Re(314))}r!==null&&r.delete(t),vR(e,n)}var yR;yR=function(e,t,n){if(e!==null)if(e.memoizedProps!==t.pendingProps||_o.current)Co=!0;else{if((e.lanes&n)===0&&(t.flags&128)===0)return Co=!1,vK(e,t,n);Co=(e.flags&131072)!==0}else Co=!1,tr&&(t.flags&1048576)!==0&&SM(t,R3,t.index);switch(t.lanes=0,t.tag){case 2:var r=t.type;Fy(e,t),e=t.pendingProps;var i=r0(t,$i.current);Dp(t,n),i=_9(null,t,r,e,i,n);var o=k9();return t.flags|=1,typeof i=="object"&&i!==null&&typeof i.render=="function"&&i.$$typeof===void 0?(t.tag=1,t.memoizedState=null,t.updateQueue=null,ko(r)?(o=!0,I3(t)):o=!1,t.memoizedState=i.state!==null&&i.state!==void 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J9(e){const{focusBorderColor:t,errorBorderColor:n}=e;return{focusBorderColor:t||Ie("blue.500","blue.300")(e),errorBorderColor:n||Ie("red.500","red.300")(e)}}var Iee=au(e=>{const{theme:t}=e,{focusBorderColor:n,errorBorderColor:r}=J9(e);return{field:{border:"1px solid",borderColor:"inherit",bg:"inherit",_hover:{borderColor:Ie("gray.300","whiteAlpha.400")(e)},_readOnly:{boxShadow:"none !important",userSelect:"all"},_invalid:{borderColor:Ei(t,r),boxShadow:`0 0 0 1px ${Ei(t,r)}`},_focusVisible:{zIndex:1,borderColor:Ei(t,n),boxShadow:`0 0 0 1px ${Ei(t,n)}`}},addon:{border:"1px solid",borderColor:Ie("inherit","whiteAlpha.50")(e),bg:Ie("gray.100","whiteAlpha.300")(e)}}}),Mee=au(e=>{const{theme:t}=e,{focusBorderColor:n,errorBorderColor:r}=J9(e);return{field:{border:"2px solid",borderColor:"transparent",bg:Ie("gray.100","whiteAlpha.50")(e),_hover:{bg:Ie("gray.200","whiteAlpha.100")(e)},_readOnly:{boxShadow:"none 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0}},lte={px:"6",py:"4"},ute=Jw(e=>({overlay:nte,dialogContainer:wr(rte,e),dialog:wr(ite,e),header:ote,closeButton:ate,body:wr(ste,e),footer:lte}));function us(e){return Jw(e==="full"?{dialog:{maxW:"100vw",minH:"$100vh",my:"0",borderRadius:"0"}}:{dialog:{maxW:e}})}var cte={xs:us("xs"),sm:us("sm"),md:us("md"),lg:us("lg"),xl:us("xl"),"2xl":us("2xl"),"3xl":us("3xl"),"4xl":us("4xl"),"5xl":us("5xl"),"6xl":us("6xl"),full:us("full")},dte=tte({baseStyle:ute,sizes:cte,defaultProps:{size:"md"}}),fte={letterSpacings:{tighter:"-0.05em",tight:"-0.025em",normal:"0",wide:"0.025em",wider:"0.05em",widest:"0.1em"},lineHeights:{normal:"normal",none:1,shorter:1.25,short:1.375,base:1.5,tall:1.625,taller:"2",3:".75rem",4:"1rem",5:"1.25rem",6:"1.5rem",7:"1.75rem",8:"2rem",9:"2.25rem",10:"2.5rem"},fontWeights:{hairline:100,thin:200,light:300,normal:400,medium:500,semibold:600,bold:700,extrabold:800,black:900},fonts:{heading:'-apple-system, BlinkMacSystemFont, "Segoe UI", Helvetica, Arial, sans-serif, "Apple 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0:jE.field,textAlign:"center"},Cte={lg:{fontSize:"lg",w:12,h:12,borderRadius:"md"},md:{fontSize:"md",w:10,h:10,borderRadius:"md"},sm:{fontSize:"sm",w:8,h:8,borderRadius:"sm"},xs:{fontSize:"xs",w:6,h:6,borderRadius:"sm"}},qE,_te={outline:e=>{var t,n;return((n=wr((t=fn.variants)==null?void 0:t.outline,e))==null?void 0:n.field)??{}},flushed:e=>{var t,n;return((n=wr((t=fn.variants)==null?void 0:t.flushed,e))==null?void 0:n.field)??{}},filled:e=>{var t,n;return((n=wr((t=fn.variants)==null?void 0:t.filled,e))==null?void 0:n.field)??{}},unstyled:((qE=fn.variants)==null?void 0:qE.unstyled.field)??{}},kte={baseStyle:wte,sizes:Cte,variants:_te,defaultProps:fn.defaultProps},{defineMultiStyleConfig:Ete,definePartsStyle:Pte}=nr(jX.keys),Ob=Ao("popper-bg"),Tte=Ao("popper-arrow-bg"),Lte=Ao("popper-arrow-shadow-color"),Ate={zIndex:10},Ite=e=>{const t=Ie("white","gray.700")(e),n=Ie("gray.200","whiteAlpha.300")(e);return{[Ob.variable]:`colors.${t}`,bg:Ob.reference,[Tte.variable]:Ob.reference,[Lte.variable]:`colors.${n}`,width:"xs",border:"1px solid",borderColor:"inherit",borderRadius:"md",boxShadow:"sm",zIndex:"inherit",_focusVisible:{outline:0,boxShadow:"outline"}}},Mte={px:3,py:2,borderBottomWidth:"1px"},Rte={px:3,py:2},Ote={px:3,py:2,borderTopWidth:"1px"},Nte={position:"absolute",borderRadius:"md",top:1,insetEnd:2,padding:2},Dte=Pte(e=>({popper:Ate,content:Ite(e),header:Mte,body:Rte,footer:Ote,closeButton:Nte})),zte=Ete({baseStyle:Dte}),{defineMultiStyleConfig:Fte,definePartsStyle:wg}=nr(qX.keys),Bte=e=>{const{colorScheme:t,theme:n,isIndeterminate:r,hasStripe:i}=e,o=Ie($E(),$E("1rem","rgba(0,0,0,0.1)"))(e),a=Ie(`${t}.500`,`${t}.200`)(e),s=`linear-gradient(
+    to right,
+    transparent 0%,
+    ${Ei(n,a)} 50%,
+    transparent 100%
+  )`;return{...!r&&i&&o,...r?{bgImage:s}:{bgColor:a}}},$te={lineHeight:"1",fontSize:"0.25em",fontWeight:"bold",color:"white"},Hte=e=>({bg:Ie("gray.100","whiteAlpha.300")(e)}),Wte=e=>({transitionProperty:"common",transitionDuration:"slow",...Bte(e)}),Vte=wg(e=>({label:$te,filledTrack:Wte(e),track:Hte(e)})),Ute={xs:wg({track:{h:"1"}}),sm:wg({track:{h:"2"}}),md:wg({track:{h:"3"}}),lg:wg({track:{h:"4"}})},Gte=Fte({sizes:Ute,baseStyle:Vte,defaultProps:{size:"md",colorScheme:"blue"}}),{defineMultiStyleConfig:jte,definePartsStyle:qy}=nr(KX.keys),qte=e=>{var t;const n=(t=wr(K3.baseStyle,e))==null?void 0:t.control;return{...n,borderRadius:"full",_checked:{...n?._checked,_before:{content:'""',display:"inline-block",pos:"relative",w:"50%",h:"50%",borderRadius:"50%",bg:"currentColor"}}}},Kte=qy(e=>{var t,n,r,i;return{label:(n=(t=K3).baseStyle)==null?void 0:n.call(t,e).label,container:(i=(r=K3).baseStyle)==null?void 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t=Ie("gray.100","gray.800")(e),n=Ie("gray.400","gray.600")(e),{startColor:r=t,endColor:i=n,theme:o}=e,a=Ei(o,r),s=Ei(o,i);return{[ine.variable]:a,[one.variable]:s,opacity:.7,borderRadius:"2px",borderColor:a,background:s}},sne={baseStyle:ane},lne=e=>({borderRadius:"md",fontWeight:"semibold",_focusVisible:{boxShadow:"outline",padding:"4",position:"fixed",top:"6",insetStart:"6",bg:Ie("white","gray.700")(e)}}),une={baseStyle:lne},{defineMultiStyleConfig:cne,definePartsStyle:S4}=nr(YX.keys),Om=da("slider-thumb-size"),Nm=da("slider-track-size"),dne=e=>{const{orientation:t}=e;return{display:"inline-block",position:"relative",cursor:"pointer",_disabled:{opacity:.6,cursor:"default",pointerEvents:"none"},...X9({orientation:t,vertical:{h:"100%"},horizontal:{w:"100%"}})}},fne=e=>({...X9({orientation:e.orientation,horizontal:{h:Nm.reference},vertical:{w:Nm.reference}}),overflow:"hidden",borderRadius:"sm",bg:Ie("gray.200","whiteAlpha.200")(e),_disabled:{bg:Ie("gray.300","whiteAlpha.300")(e)}}),hne=e=>{const{orientation:t}=e;return{...X9({orientation:t,vertical:{left:"50%",transform:"translateX(-50%)",_active:{transform:"translateX(-50%) 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0:t.flushed(e).field)??{}},filled:e=>{var t;return((t=fn.variants)==null?void 0:t.filled(e).field)??{}},unstyled:((rP=fn.variants)==null?void 0:rP.unstyled.field)??{}},iP,oP,aP,sP,vre={xs:((iP=fn.sizes)==null?void 0:iP.xs.field)??{},sm:((oP=fn.sizes)==null?void 0:oP.sm.field)??{},md:((aP=fn.sizes)==null?void 0:aP.md.field)??{},lg:((sP=fn.sizes)==null?void 0:sP.lg.field)??{}},yre={baseStyle:gre,sizes:vre,variants:mre,defaultProps:{size:"md",variant:"outline"}},Db=Ao("tooltip-bg"),lP=Ao("tooltip-fg"),xre=Ao("popper-arrow-bg"),bre=e=>{const t=Ie("gray.700","gray.300")(e),n=Ie("whiteAlpha.900","gray.900")(e);return{bg:Db.reference,color:lP.reference,[Db.variable]:`colors.${t}`,[lP.variable]:`colors.${n}`,[xre.variable]:Db.reference,px:"2",py:"0.5",borderRadius:"sm",fontWeight:"medium",fontSize:"sm",boxShadow:"md",maxW:"xs",zIndex:"tooltip"}},Sre={baseStyle:bre},wre={Accordion:HQ,Alert:ZQ,Avatar:aJ,Badge:qg,Breadcrumb:mJ,Button:kJ,Checkbox:K3,CloseButton:NJ,Code:BJ,Container:HJ,Divider:jJ,Drawer:ree,Editable:cee,Form:mee,FormError:wee,FormLabel:_ee,Heading:Pee,Input:fn,Kbd:zee,Link:Bee,List:Uee,Menu:ete,Modal:dte,NumberInput:Ste,PinInput:kte,Popover:zte,Progress:Gte,Radio:Yte,Select:rne,Skeleton:sne,SkipLink:une,Slider:bne,Spinner:Cne,Stat:Ine,Switch:Fne,Table:Gne,Tabs:are,Tag:pre,Textarea:yre,Tooltip:Sre},Cre={none:0,"1px":"1px solid","2px":"2px solid","4px":"4px solid","8px":"8px 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hle(t)?yle(e,t,n,r):{target:t,transitionEnd:r}}const ble=(e,t,n,r)=>{const i=dle(e,t,r);return t=i.target,r=i.transitionEnd,xle(e,t,n,r)};function Sle(e){return window.getComputedStyle(e)}const jN={treeType:"dom",readValueFromInstance(e,t){if(hv.has(t)){const n=b8(t);return n&&n.default||0}else{const n=Sle(e),r=(AO(t)?n.getPropertyValue(t):n[t])||0;return typeof r=="string"?r.trim():r}},sortNodePosition(e,t){return e.compareDocumentPosition(t)&2?1:-1},getBaseTarget(e,t){var n;return(n=e.style)===null||n===void 0?void 0:n[t]},measureViewportBox(e,{transformPagePoint:t}){return FN(e,t)},resetTransform(e,t,n){const{transformTemplate:r}=n;t.style.transform=r?r({},""):"none",e.scheduleRender()},restoreTransform(e,t){e.style.transform=t.style.transform},removeValueFromRenderState(e,{vars:t,style:n}){delete t[e],delete n[e]},makeTargetAnimatable(e,{transition:t,transitionEnd:n,...r},{transformValues:i},o=!0){let a=Ese(r,t||{},e);if(i&&(n&&(n=i(n)),r&&(r=i(r)),a&&(a=i(a))),o){_se(e,r,a);const s=ble(e,r,a,n);n=s.transitionEnd,r=s.target}return{transition:t,transitionEnd:n,...r}},scrapeMotionValuesFromProps:l8,build(e,t,n,r,i){e.isVisible!==void 0&&(t.style.visibility=e.isVisible?"visible":"hidden"),o8(t,n,r,i.transformTemplate)},render:VO},wle=HN(jN),Cle=HN({...jN,getBaseTarget(e,t){return e[t]},readValueFromInstance(e,t){var n;return hv.has(t)?((n=b8(t))===null||n===void 0?void 0:n.default)||0:(t=UO.has(t)?t:WO(t),e.getAttribute(t))},scrapeMotionValuesFromProps:jO,build(e,t,n,r,i){s8(t,n,r,i.transformTemplate)},render:GO}),_le=(e,t)=>r8(e)?Cle(t,{enableHardwareAcceleration:!1}):wle(t,{enableHardwareAcceleration:!0});function JP(e,t){return t.max===t.min?0:e/(t.max-t.min)*100}const og={correct:(e,t)=>{if(!t.target)return e;if(typeof e=="string")if(gt.test(e))e=parseFloat(e);else return e;const n=JP(e,t.target.x),r=JP(e,t.target.y);return`${n}% ${r}%`}},eT="_$css",kle={correct:(e,{treeScale:t,projectionDelta:n})=>{const r=e,i=e.includes("var("),o=[];i&&(e=e.replace(VN,y=>(o.push(y),eT)));const a=hu.parse(e);if(a.length>5)return r;const s=hu.createTransformer(e),l=typeof a[0]!="number"?1:0,d=n.x.scale*t.x,p=n.y.scale*t.y;a[0+l]/=d,a[1+l]/=p;const g=Sr(d,p,.5);typeof a[2+l]=="number"&&(a[2+l]/=g),typeof a[3+l]=="number"&&(a[3+l]/=g);let m=s(a);if(i){let y=0;m=m.replace(eT,()=>{const b=o[y];return y++,b})}return m}};class Ele extends ne.Component{componentDidMount(){const{visualElement:t,layoutGroup:n,switchLayoutGroup:r,layoutId:i}=this.props,{projection:o}=t;Fie(Tle),o&&(n.group&&n.group.add(o),r&&r.register&&i&&r.register(o),o.root.didUpdate(),o.addEventListener("animationComplete",()=>{this.safeToRemove()}),o.setOptions({...o.options,onExitComplete:()=>this.safeToRemove()})),Xg.hasEverUpdated=!0}getSnapshotBeforeUpdate(t){const{layoutDependency:n,visualElement:r,drag:i,isPresent:o}=this.props,a=r.projection;return a&&(a.isPresent=o,i||t.layoutDependency!==n||n===void 0?a.willUpdate():this.safeToRemove(),t.isPresent!==o&&(o?a.promote():a.relegate()||ws.postRender(()=>{var s;!((s=a.getStack())===null||s===void 0)&&s.members.length||this.safeToRemove()}))),null}componentDidUpdate(){const{projection:t}=this.props.visualElement;t&&(t.root.didUpdate(),!t.currentAnimation&&t.isLead()&&this.safeToRemove())}componentWillUnmount(){const{visualElement:t,layoutGroup:n,switchLayoutGroup:r}=this.props,{projection:i}=t;i&&(i.scheduleCheckAfterUnmount(),n?.group&&n.group.remove(i),r?.deregister&&r.deregister(i))}safeToRemove(){const{safeToRemove:t}=this.props;t?.()}render(){return null}}function Ple(e){const[t,n]=x8(),r=C.exports.useContext(n8);return w(Ele,{...e,layoutGroup:r,switchLayoutGroup:C.exports.useContext(TO),isPresent:t,safeToRemove:n})}const 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ZN(e){return lT(e.x)&&lT(e.y)}function YN(e,t){return e.x.min===t.x.min&&e.x.max===t.x.max&&e.y.min===t.y.min&&e.y.max===t.y.max}function uT(e){return aa(e.x)/aa(e.y)}function Fle(e,t,n=.1){return y8(e,t)<=n}class Ble{constructor(){this.members=[]}add(t){_8(this.members,t),t.scheduleRender()}remove(t){if(k8(this.members,t),t===this.prevLead&&(this.prevLead=void 0),t===this.lead){const n=this.members[this.members.length-1];n&&this.promote(n)}}relegate(t){const n=this.members.findIndex(i=>t===i);if(n===0)return!1;let r;for(let i=n;i>=0;i--){const o=this.members[i];if(o.isPresent!==!1){r=o;break}}return r?(this.promote(r),!0):!1}promote(t,n){var r;const i=this.lead;if(t!==i&&(this.prevLead=i,this.lead=t,t.show(),i)){i.instance&&i.scheduleRender(),t.scheduleRender(),t.resumeFrom=i,n&&(t.resumeFrom.preserveOpacity=!0),i.snapshot&&(t.snapshot=i.snapshot,t.snapshot.latestValues=i.animationValues||i.latestValues,t.snapshot.isShared=!0),!((r=t.root)===null||r===void 0)&&r.isUpdating&&(t.isLayoutDirty=!0);const{crossfade:o}=t.options;o===!1&&i.hide()}}exitAnimationComplete(){this.members.forEach(t=>{var n,r,i,o,a;(r=(n=t.options).onExitComplete)===null||r===void 0||r.call(n),(a=(i=t.resumingFrom)===null||i===void 0?void 0:(o=i.options).onExitComplete)===null||a===void 0||a.call(o)})}scheduleRender(){this.members.forEach(t=>{t.instance&&t.scheduleRender(!1)})}removeLeadSnapshot(){this.lead&&this.lead.snapshot&&(this.lead.snapshot=void 0)}}const $le="translate3d(0px, 0px, 0) scale(1, 1) scale(1, 1)";function cT(e,t,n){const r=e.x.translate/t.x,i=e.y.translate/t.y;let o=`translate3d(${r}px, ${i}px, 0) `;if(o+=`scale(${1/t.x}, ${1/t.y}) `,n){const{rotate:l,rotateX:d,rotateY:p}=n;l&&(o+=`rotate(${l}deg) `),d&&(o+=`rotateX(${d}deg) `),p&&(o+=`rotateY(${p}deg) `)}const a=e.x.scale*t.x,s=e.y.scale*t.y;return o+=`scale(${a}, ${s})`,o===$le?"none":o}const Hle=(e,t)=>e.depth-t.depth;class Wle{constructor(){this.children=[],this.isDirty=!1}add(t){_8(this.children,t),this.isDirty=!0}remove(t){k8(this.children,t),this.isDirty=!0}forEach(t){this.isDirty&&this.children.sort(Hle),this.isDirty=!1,this.children.forEach(t)}}const dT=["","X","Y","Z"],fT=1e3;function XN({attachResizeListener:e,defaultParent:t,measureScroll:n,checkIsScrollRoot:r,resetTransform:i}){return class{constructor(a,s={},l=t?.()){this.children=new Set,this.options={},this.isTreeAnimating=!1,this.isAnimationBlocked=!1,this.isLayoutDirty=!1,this.updateManuallyBlocked=!1,this.updateBlockedByResize=!1,this.isUpdating=!1,this.isSVG=!1,this.needsReset=!1,this.shouldResetTransform=!1,this.treeScale={x:1,y:1},this.eventHandlers=new Map,this.potentialNodes=new Map,this.checkUpdateFailed=()=>{this.isUpdating&&(this.isUpdating=!1,this.clearAllSnapshots())},this.updateProjection=()=>{this.nodes.forEach(qle),this.nodes.forEach(Kle)},this.hasProjected=!1,this.isVisible=!0,this.animationProgress=0,this.sharedNodes=new Map,this.id=a,this.latestValues=s,this.root=l?l.root||l:this,this.path=l?[...l.path,l]:[],this.parent=l,this.depth=l?l.depth+1:0,a&&this.root.registerPotentialNode(a,this);for(let d=0;d<this.path.length;d++)this.path[d].shouldResetTransform=!0;this.root===this&&(this.nodes=new Wle)}addEventListener(a,s){return this.eventHandlers.has(a)||this.eventHandlers.set(a,new em),this.eventHandlers.get(a).add(s)}notifyListeners(a,...s){const l=this.eventHandlers.get(a);l?.notify(...s)}hasListeners(a){return this.eventHandlers.has(a)}registerPotentialNode(a,s){this.potentialNodes.set(a,s)}mount(a,s=!1){var l;if(this.instance)return;this.isSVG=a instanceof SVGElement&&a.tagName!=="svg",this.instance=a;const{layoutId:d,layout:p,visualElement:g}=this.options;if(g&&!g.getInstance()&&g.mount(a),this.root.nodes.add(this),(l=this.parent)===null||l===void 0||l.children.add(this),this.id&&this.root.potentialNodes.delete(this.id),s&&(p||d)&&(this.isLayoutDirty=!0),e){let m;const y=()=>this.root.updateBlockedByResize=!1;e(a,()=>{this.root.updateBlockedByResize=!0,m&&m(),m=LN(y,250),Xg.hasAnimatedSinceResize&&(Xg.hasAnimatedSinceResize=!1,this.nodes.forEach(pT))})}d&&this.root.registerSharedNode(d,this),this.options.animate!==!1&&g&&(d||p)&&this.addEventListener("didUpdate",({delta:m,hasLayoutChanged:y,hasRelativeTargetChanged:b,layout:S})=>{var E,P,k,L,I;if(this.isTreeAnimationBlocked()){this.target=void 0,this.relativeTarget=void 0;return}const O=(P=(E=this.options.transition)!==null&&E!==void 0?E:g.getDefaultTransition())!==null&&P!==void 0?P:Jle,{onLayoutAnimationStart:N,onLayoutAnimationComplete:D}=g.getProps(),F=!this.targetLayout||!YN(this.targetLayout,S)||b,U=!y&&b;if(((k=this.resumeFrom)===null||k===void 0?void 0:k.instance)||U||y&&(F||!this.currentAnimation)){this.resumeFrom&&(this.resumingFrom=this.resumeFrom,this.resumingFrom.resumingFrom=void 0),this.setAnimationOrigin(m,U);const W={...w8(O,"layout"),onPlay:N,onComplete:D};g.shouldReduceMotion&&(W.delay=0,W.type=!1),this.startAnimation(W)}else!y&&this.animationProgress===0&&pT(this),this.isLead()&&((I=(L=this.options).onExitComplete)===null||I===void 0||I.call(L));this.targetLayout=S})}unmount(){var a,s;this.options.layoutId&&this.willUpdate(),this.root.nodes.remove(this),(a=this.getStack())===null||a===void 0||a.remove(this),(s=this.parent)===null||s===void 0||s.children.delete(this),this.instance=void 0,zf.preRender(this.updateProjection)}blockUpdate(){this.updateManuallyBlocked=!0}unblockUpdate(){this.updateManuallyBlocked=!1}isUpdateBlocked(){return this.updateManuallyBlocked||this.updateBlockedByResize}isTreeAnimationBlocked(){var a;return this.isAnimationBlocked||((a=this.parent)===null||a===void 0?void 0:a.isTreeAnimationBlocked())||!1}startUpdate(){var a;this.isUpdateBlocked()||(this.isUpdating=!0,(a=this.nodes)===null||a===void 0||a.forEach(Zle))}willUpdate(a=!0){var s,l,d;if(this.root.isUpdateBlocked()){(l=(s=this.options).onExitComplete)===null||l===void 0||l.call(s);return}if(!this.root.isUpdating&&this.root.startUpdate(),this.isLayoutDirty)return;this.isLayoutDirty=!0;for(let y=0;y<this.path.length;y++){const b=this.path[y];b.shouldResetTransform=!0,b.updateScroll()}const{layoutId:p,layout:g}=this.options;if(p===void 0&&!g)return;const m=(d=this.options.visualElement)===null||d===void 0?void 0:d.getProps().transformTemplate;this.prevTransformTemplateValue=m?.(this.latestValues,""),this.updateSnapshot(),a&&this.notifyListeners("willUpdate")}didUpdate(){if(this.isUpdateBlocked()){this.unblockUpdate(),this.clearAllSnapshots(),this.nodes.forEach(hT);return}!this.isUpdating||(this.isUpdating=!1,this.potentialNodes.size&&(this.potentialNodes.forEach(eue),this.potentialNodes.clear()),this.nodes.forEach(jle),this.nodes.forEach(Vle),this.nodes.forEach(Ule),this.clearAllSnapshots(),Vb.update(),Vb.preRender(),Vb.render())}clearAllSnapshots(){this.nodes.forEach(Gle),this.sharedNodes.forEach(Yle)}scheduleUpdateProjection(){ws.preRender(this.updateProjection,!1,!0)}scheduleCheckAfterUnmount(){ws.postRender(()=>{this.isLayoutDirty?this.root.didUpdate():this.root.checkUpdateFailed()})}updateSnapshot(){if(this.snapshot||!this.instance)return;const a=this.measure(),s=this.removeTransform(this.removeElementScroll(a));yT(s),this.snapshot={measured:a,layout:s,latestValues:{}}}updateLayout(){var a;if(!this.instance||(this.updateScroll(),!(this.options.alwaysMeasureLayout&&this.isLead())&&!this.isLayoutDirty))return;if(this.resumeFrom&&!this.resumeFrom.instance)for(let d=0;d<this.path.length;d++)this.path[d].updateScroll();const s=this.measure();yT(s);const l=this.layout;this.layout={measured:s,actual:this.removeElementScroll(s)},this.layoutCorrected=bi(),this.isLayoutDirty=!1,this.projectionDelta=void 0,this.notifyListeners("measure",this.layout.actual),(a=this.options.visualElement)===null||a===void 0||a.notifyLayoutMeasure(this.layout.actual,l?.actual)}updateScroll(){this.options.layoutScroll&&this.instance&&(this.isScrollRoot=r(this.instance),this.scroll=n(this.instance))}resetTransform(){var a;if(!i)return;const s=this.isLayoutDirty||this.shouldResetTransform,l=this.projectionDelta&&!ZN(this.projectionDelta),d=(a=this.options.visualElement)===null||a===void 0?void 0:a.getProps().transformTemplate,p=d?.(this.latestValues,""),g=p!==this.prevTransformTemplateValue;s&&(l||rf(this.latestValues)||g)&&(i(this.instance,p),this.shouldResetTransform=!1,this.scheduleRender())}measure(){const{visualElement:a}=this.options;if(!a)return bi();const s=a.measureViewportBox(),{scroll:l}=this.root;return l&&(wc(s.x,l.x),wc(s.y,l.y)),s}removeElementScroll(a){const s=bi();cs(s,a);for(let l=0;l<this.path.length;l++){const d=this.path[l],{scroll:p,options:g,isScrollRoot:m}=d;if(d!==this.root&&p&&g.layoutScroll){if(m){cs(s,a);const{scroll:y}=this.root;y&&(wc(s.x,-y.x),wc(s.y,-y.y))}wc(s.x,p.x),wc(s.y,p.y)}}return s}applyTransform(a,s=!1){const l=bi();cs(l,a);for(let d=0;d<this.path.length;d++){const p=this.path[d];!s&&p.options.layoutScroll&&p.scroll&&p!==p.root&&Tp(l,{x:-p.scroll.x,y:-p.scroll.y}),rf(p.latestValues)&&Tp(l,p.latestValues)}return rf(this.latestValues)&&Tp(l,this.latestValues),l}removeTransform(a){var s;const l=bi();cs(l,a);for(let d=0;d<this.path.length;d++){const p=this.path[d];if(!p.instance||!rf(p.latestValues))continue;v6(p.latestValues)&&p.updateSnapshot();const g=bi(),m=p.measure();cs(g,m),sT(l,p.latestValues,(s=p.snapshot)===null||s===void 0?void 0:s.layout,g)}return rf(this.latestValues)&&sT(l,this.latestValues),l}setTargetDelta(a){this.targetDelta=a,this.root.scheduleUpdateProjection()}setOptions(a){this.options={...this.options,...a,crossfade:a.crossfade!==void 0?a.crossfade:!0}}clearMeasurements(){this.scroll=void 0,this.layout=void 0,this.snapshot=void 0,this.prevTransformTemplateValue=void 0,this.targetDelta=void 0,this.target=void 0,this.isLayoutDirty=!1}resolveTargetDelta(){var a;const{layout:s,layoutId:l}=this.options;!this.layout||!(s||l)||(!this.targetDelta&&!this.relativeTarget&&(this.relativeParent=this.getClosestProjectingParent(),this.relativeParent&&this.relativeParent.layout&&(this.relativeTarget=bi(),this.relativeTargetOrigin=bi(),nm(this.relativeTargetOrigin,this.layout.actual,this.relativeParent.layout.actual),cs(this.relativeTarget,this.relativeTargetOrigin))),!(!this.relativeTarget&&!this.targetDelta)&&(this.target||(this.target=bi(),this.targetWithTransforms=bi()),this.relativeTarget&&this.relativeTargetOrigin&&((a=this.relativeParent)===null||a===void 0?void 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s,l,d;(l=(s=this.options).scheduleRender)===null||l===void 0||l.call(s),a&&((d=this.getStack())===null||d===void 0||d.scheduleRender()),this.resumingFrom&&!this.resumingFrom.instance&&(this.resumingFrom=void 0)}setAnimationOrigin(a,s=!1){var l;const d=this.snapshot,p=d?.latestValues||{},g={...this.latestValues},m=rm();this.relativeTarget=this.relativeTargetOrigin=void 0,this.attemptToResolveRelativeTarget=!s;const y=bi(),b=d?.isShared,S=(((l=this.getStack())===null||l===void 0?void 0:l.members.length)||0)<=1,E=Boolean(b&&!S&&this.options.crossfade===!0&&!this.path.some(Qle));this.animationProgress=0,this.mixTargetDelta=P=>{var k;const L=P/1e3;gT(m.x,a.x,L),gT(m.y,a.y,L),this.setTargetDelta(m),this.relativeTarget&&this.relativeTargetOrigin&&this.layout&&((k=this.relativeParent)===null||k===void 0?void 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this.options.layoutId&&(p.pointerEvents=m===this?Yy(a.pointerEvents)||"":"none"),p}clearSnapshot(){this.resumeFrom=this.snapshot=void 0}resetTree(){this.root.nodes.forEach(a=>{var s;return(s=a.currentAnimation)===null||s===void 0?void 0:s.stop()}),this.root.nodes.forEach(hT),this.root.sharedNodes.clear()}}}function Vle(e){e.updateLayout()}function Ule(e){var t,n,r;const i=((t=e.resumeFrom)===null||t===void 0?void 0:t.snapshot)||e.snapshot;if(e.isLead()&&e.layout&&i&&e.hasListeners("didUpdate")){const{actual:o,measured:a}=e.layout,{animationType:s}=e.options;s==="size"?il(m=>{const y=i.isShared?i.measured[m]:i.layout[m],b=aa(y);y.min=o[m].min,y.max=y.min+b}):QN(s,i.layout,o)&&il(m=>{const y=i.isShared?i.measured[m]:i.layout[m],b=aa(o[m]);y.max=y.min+b});const l=rm();tm(l,o,i.layout);const d=rm();i.isShared?tm(d,e.applyTransform(a,!0),i.measured):tm(d,o,i.layout);const p=!ZN(l);let g=!1;if(!e.resumeFrom&&(e.relativeParent=e.getClosestProjectingParent(),e.relativeParent&&!e.relativeParent.resumeFrom)){const{snapshot:m,layout:y}=e.relativeParent;if(m&&y){const b=bi();nm(b,i.layout,m.layout);const S=bi();nm(S,o,y.actual),YN(b,S)||(g=!0)}}e.notifyListeners("didUpdate",{layout:o,snapshot:i,delta:d,layoutDelta:l,hasLayoutChanged:p,hasRelativeTargetChanged:g})}else e.isLead()&&((r=(n=e.options).onExitComplete)===null||r===void 0||r.call(n));e.options.transition=void 0}function Gle(e){e.clearSnapshot()}function hT(e){e.clearMeasurements()}function jle(e){const{visualElement:t}=e.options;t?.getProps().onBeforeLayoutMeasure&&t.notifyBeforeLayoutMeasure(),e.resetTransform()}function pT(e){e.finishAnimation(),e.targetDelta=e.relativeTarget=e.target=void 0}function qle(e){e.resolveTargetDelta()}function Kle(e){e.calcProjection()}function Zle(e){e.resetRotation()}function Yle(e){e.removeLeadSnapshot()}function 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n=this.props.childRef.current;if(n&&t.isPresent&&!this.props.isPresent){const r=this.props.sizeRef.current;r.height=n.offsetHeight||0,r.width=n.offsetWidth||0,r.top=n.offsetTop,r.left=n.offsetLeft}return null}componentDidUpdate(){}render(){return this.props.children}}function aue({children:e,isPresent:t}){const n=C.exports.useId(),r=C.exports.useRef(null),i=C.exports.useRef({width:0,height:0,top:0,left:0});return C.exports.useInsertionEffect(()=>{const{width:o,height:a,top:s,left:l}=i.current;if(t||!r.current||!o||!a)return;r.current.dataset.motionPopId=n;const d=document.createElement("style");return document.head.appendChild(d),d.sheet&&d.sheet.insertRule(`
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+            position: absolute !important;
+            width: ${o}px !important;
+            height: ${a}px !important;
+            top: ${s}px !important;
+            left: ${l}px !important;
+          }
+        `),()=>{document.head.removeChild(d)}},[t]),w(oue,{isPresent:t,childRef:r,sizeRef:i,children:C.exports.cloneElement(e,{ref:r})})}const Kb=({children:e,initial:t,isPresent:n,onExitComplete:r,custom:i,presenceAffectsLayout:o,mode:a})=>{const s=E4(sue),l=C.exports.useId(),d=C.exports.useMemo(()=>({id:l,initial:t,isPresent:n,custom:i,onExitComplete:p=>{s.set(p,!0);for(const g of s.values())if(!g)return;r&&r()},register:p=>(s.set(p,!1),()=>s.delete(p))}),o?void 0:[n]);return C.exports.useMemo(()=>{s.forEach((p,g)=>s.set(g,!1))},[n]),C.exports.useEffect(()=>{!n&&!s.size&&r&&r()},[n]),a==="popLayout"&&(e=w(aue,{isPresent:n,children:e})),w(P0.Provider,{value:d,children:e})};function sue(){return new Map}const fp=e=>e.key||"";function lue(e,t){e.forEach(n=>{const r=fp(n);t.set(r,n)})}function uue(e){const t=[];return C.exports.Children.forEach(e,n=>{C.exports.isValidElement(n)&&t.push(n)}),t}const bu=({children:e,custom:t,initial:n=!0,onExitComplete:r,exitBeforeEnter:i,presenceAffectsLayout:o=!0,mode:a="sync"})=>{i&&(a="wait",_N(!1,"Replace exitBeforeEnter with mode='wait'"));let[s]=iue();const l=C.exports.useContext(n8).forceRender;l&&(s=l);const d=JN(),p=uue(e);let g=p;const m=new Set,y=C.exports.useRef(g),b=C.exports.useRef(new Map).current,S=C.exports.useRef(!0);if(X3(()=>{S.current=!1,lue(p,b),y.current=g}),d8(()=>{S.current=!0,b.clear(),m.clear()}),S.current)return w(Fn,{children:g.map(L=>w(Kb,{isPresent:!0,initial:n?void 0:!1,presenceAffectsLayout:o,mode:a,children:L},fp(L)))});g=[...g];const E=y.current.map(fp),P=p.map(fp),k=E.length;for(let L=0;L<k;L++){const I=E[L];P.indexOf(I)===-1&&m.add(I)}return a==="wait"&&m.size&&(g=[]),m.forEach(L=>{if(P.indexOf(L)!==-1)return;const I=b.get(L);if(!I)return;const O=E.indexOf(L),N=()=>{b.delete(L),m.delete(L);const D=y.current.findIndex(F=>F.key===L);if(y.current.splice(D,1),!m.size){if(y.current=p,d.current===!1)return;s(),r&&r()}};g.splice(O,0,w(Kb,{isPresent:!1,onExitComplete:N,custom:t,presenceAffectsLayout:o,mode:a,children:I},fp(I)))}),g=g.map(L=>{const I=L.key;return m.has(I)?L:w(Kb,{isPresent:!0,presenceAffectsLayout:o,mode:a,children:L},fp(L))}),CN!=="production"&&a==="wait"&&g.length>1&&console.warn(`You're attempting to animate multiple children within AnimatePresence, but its mode is set to "wait". 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+  :root {
+    --chakra-vh: 100vh;
+  }
+
+  @supports (height: -webkit-fill-available) {
+    :root {
+      --chakra-vh: -webkit-fill-available;
+    }
+  }
+
+  @supports (height: -moz-fill-available) {
+    :root {
+      --chakra-vh: -moz-fill-available;
+    }
+  }
+
+  @supports (height: 100lvh) {
+    :root {
+      --chakra-vh: 100lvh;
+    }
+  }
+`,_ce=()=>w(y4,{styles:yD}),kce=()=>w(y4,{styles:`
+      html {
+        line-height: 1.5;
+        -webkit-text-size-adjust: 100%;
+        font-family: system-ui, sans-serif;
+        -webkit-font-smoothing: antialiased;
+        text-rendering: optimizeLegibility;
+        -moz-osx-font-smoothing: grayscale;
+        touch-action: manipulation;
+      }
+
+      body {
+        position: relative;
+        min-height: 100%;
+        font-feature-settings: 'kern';
+      }
+
+      *,
+      *::before,
+      *::after {
+        border-width: 0;
+        border-style: solid;
+        box-sizing: border-box;
+      }
+
+      main {
+        display: block;
+      }
+
+      hr {
+        border-top-width: 1px;
+        box-sizing: content-box;
+        height: 0;
+        overflow: visible;
+      }
+
+      pre,
+      code,
+      kbd,
+      samp {
+        font-family: SFMono-Regular,  Menlo, Monaco, Consolas, monospace;
+        font-size: 1em;
+      }
+
+      a {
+        background-color: transparent;
+        color: inherit;
+        text-decoration: inherit;
+      }
+
+      abbr[title] {
+        border-bottom: none;
+        text-decoration: underline;
+        -webkit-text-decoration: underline dotted;
+        text-decoration: underline dotted;
+      }
+
+      b,
+      strong {
+        font-weight: bold;
+      }
+
+      small {
+        font-size: 80%;
+      }
+
+      sub,
+      sup {
+        font-size: 75%;
+        line-height: 0;
+        position: relative;
+        vertical-align: baseline;
+      }
+
+      sub {
+        bottom: -0.25em;
+      }
+
+      sup {
+        top: -0.5em;
+      }
+
+      img {
+        border-style: none;
+      }
+
+      button,
+      input,
+      optgroup,
+      select,
+      textarea {
+        font-family: inherit;
+        font-size: 100%;
+        line-height: 1.15;
+        margin: 0;
+      }
+
+      button,
+      input {
+        overflow: visible;
+      }
+
+      button,
+      select {
+        text-transform: none;
+      }
+
+      button::-moz-focus-inner,
+      [type="button"]::-moz-focus-inner,
+      [type="reset"]::-moz-focus-inner,
+      [type="submit"]::-moz-focus-inner {
+        border-style: none;
+        padding: 0;
+      }
+
+      fieldset {
+        padding: 0.35em 0.75em 0.625em;
+      }
+
+      legend {
+        box-sizing: border-box;
+        color: inherit;
+        display: table;
+        max-width: 100%;
+        padding: 0;
+        white-space: normal;
+      }
+
+      progress {
+        vertical-align: baseline;
+      }
+
+      textarea {
+        overflow: auto;
+      }
+
+      [type="checkbox"],
+      [type="radio"] {
+        box-sizing: border-box;
+        padding: 0;
+      }
+
+      [type="number"]::-webkit-inner-spin-button,
+      [type="number"]::-webkit-outer-spin-button {
+        -webkit-appearance: none !important;
+      }
+
+      input[type="number"] {
+        -moz-appearance: textfield;
+      }
+
+      [type="search"] {
+        -webkit-appearance: textfield;
+        outline-offset: -2px;
+      }
+
+      [type="search"]::-webkit-search-decoration {
+        -webkit-appearance: none !important;
+      }
+
+      ::-webkit-file-upload-button {
+        -webkit-appearance: button;
+        font: inherit;
+      }
+
+      details {
+        display: block;
+      }
+
+      summary {
+        display: list-item;
+      }
+
+      template {
+        display: none;
+      }
+
+      [hidden] {
+        display: none !important;
+      }
+
+      body,
+      blockquote,
+      dl,
+      dd,
+      h1,
+      h2,
+      h3,
+      h4,
+      h5,
+      h6,
+      hr,
+      figure,
+      p,
+      pre {
+        margin: 0;
+      }
+
+      button {
+        background: transparent;
+        padding: 0;
+      }
+
+      fieldset {
+        margin: 0;
+        padding: 0;
+      }
+
+      ol,
+      ul {
+        margin: 0;
+        padding: 0;
+      }
+
+      textarea {
+        resize: vertical;
+      }
+
+      button,
+      [role="button"] {
+        cursor: pointer;
+      }
+
+      button::-moz-focus-inner {
+        border: 0 !important;
+      }
+
+      table {
+        border-collapse: collapse;
+      }
+
+      h1,
+      h2,
+      h3,
+      h4,
+      h5,
+      h6 {
+        font-size: inherit;
+        font-weight: inherit;
+      }
+
+      button,
+      input,
+      optgroup,
+      select,
+      textarea {
+        padding: 0;
+        line-height: inherit;
+        color: inherit;
+      }
+
+      img,
+      svg,
+      video,
+      canvas,
+      audio,
+      iframe,
+      embed,
+      object {
+        display: block;
+      }
+
+      img,
+      video {
+        max-width: 100%;
+        height: auto;
+      }
+
+      [data-js-focus-visible] :focus:not([data-focus-visible-added]):not([data-focus-visible-disabled]) {
+        outline: none;
+        box-shadow: none;
+      }
+
+      select::-ms-expand {
+        display: none;
+      }
+
+      ${yD}
+    `});function Lf(e,t,n,r){const i=ur(n);return C.exports.useEffect(()=>{const o=typeof e=="function"?e():e??document;if(!(!n||!o))return o.addEventListener(t,i,r),()=>{o.removeEventListener(t,i,r)}},[t,e,r,i,n]),()=>{const o=typeof e=="function"?e():e??document;o?.removeEventListener(t,i,r)}}function Ece(e){return"current"in e}var xD=()=>typeof window<"u";function Pce(){const e=navigator.userAgentData;return e?.platform??navigator.platform}var Tce=e=>xD()&&e.test(navigator.vendor),Lce=e=>xD()&&e.test(Pce()),Ace=()=>Lce(/mac|iphone|ipad|ipod/i),Ice=()=>Ace()&&Tce(/apple/i);function Mce(e){const{ref:t,elements:n,enabled:r}=e,i=()=>{var o;return((o=t.current)==null?void 0:o.ownerDocument)??document};Lf(i,"pointerdown",o=>{if(!Ice()||!r)return;const a=o.target,l=(n??[t]).some(d=>{const p=Ece(d)?d.current:d;return p?.contains(a)||p===a});i().activeElement!==a&&l&&(o.preventDefault(),a.focus())})}var Rce=wX?C.exports.useLayoutEffect:C.exports.useEffect;function AT(e,t=[]){const n=C.exports.useRef(e);return Rce(()=>{n.current=e}),C.exports.useCallback((...r)=>{var i;return(i=n.current)==null?void 0:i.call(n,...r)},t)}function Oce(e,t){const n=e!==void 0;return[n,n&&typeof e<"u"?e:t]}function Nce(e,t){const n=C.exports.useId();return C.exports.useMemo(()=>e||[t,n].filter(Boolean).join("-"),[e,t,n])}function d5(e={}){const{onClose:t,onOpen:n,isOpen:r,id:i}=e,o=AT(n),a=AT(t),[s,l]=C.exports.useState(e.defaultIsOpen||!1),[d,p]=Oce(r,s),g=Nce(i,"disclosure"),m=C.exports.useCallback(()=>{d||l(!1),a?.()},[d,a]),y=C.exports.useCallback(()=>{d||l(!0),o?.()},[d,o]),b=C.exports.useCallback(()=>{(p?m:y)()},[p,y,m]);return{isOpen:!!p,onOpen:y,onClose:m,onToggle:b,isControlled:d,getButtonProps:(S={})=>({...S,"aria-expanded":p,"aria-controls":g,onClick:CX(S.onClick,b)}),getDisclosureProps:(S={})=>({...S,hidden:!p,id:g})}}function N8(e){const t=Object.assign({},e);for(let n in t)t[n]===void 0&&delete t[n];return t}var D8=Ee(function(t,n){const{htmlSize:r,...i}=t,o=Ai("Input",i),a=hn(i),s=A8(a),l=Rr("chakra-input",t.className);return ne.createElement(be.input,{size:r,...s,__css:o.field,ref:n,className:l})});D8.displayName="Input";D8.id="Input";var[Dce,bD]=xn({name:"InputGroupStylesContext",errorMessage:`useInputGroupStyles returned is 'undefined'. 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While weights can be included in the prompt, standard CLI Commands/parameters will not work.",href:"link/to/docs/feature3.html",guideImage:"asset/path.gif"},[1]:{text:"As new invocations are generated, files from the output directory will be displayed here. Generations have additional options to configure new generations.",href:"link/to/docs/feature3.html",guideImage:"asset/path.gif"},[2]:{text:"These options will enable alternative processing modes for Invoke. Seamless tiling will work to generate repeating patterns in the output. High Resolution Optimization performs a two-step generation cycle, and should be used at higher resolutions when you desire a more coherent image/composition. ",href:"link/to/docs/feature3.html",guideImage:"asset/path.gif"},[3]:{text:"Seed values provide an initial set of noise which guide the denoising process, and can be randomized or populated with a seed from a previous invocation. The Threshold feature can be used to mitigate undesirable outcomes at higher CFG values (try between 0-10), and Perlin can be used to add Perlin noise into the denoising process - Both serve to add variation to your outputs. ",href:"link/to/docs/feature3.html",guideImage:"asset/path.gif"},[4]:{text:"Try a variation with an amount of between 0 and 1 to change the output image for the set seed - Interesting variations on the seed are found between 0.1 and 0.3.",href:"link/to/docs/feature3.html",guideImage:"asset/path.gif"},[5]:{text:"Using ESRGAN you can increase the output resolution without requiring a higher width/height in the initial generation.",href:"link/to/docs/feature1.html",guideImage:"asset/path.gif"},[6]:{text:"Using GFPGAN or Codeformer, Face Correction will attempt to identify faces in outputs, and correct any defects/abnormalities. Higher strength values will apply a stronger corrective pressure on outputs, resulting in more appealing faces. With Codeformer, a higher fidelity will attempt to preserve the original image, at the expense of face correction strength.",href:"link/to/docs/feature3.html",guideImage:"asset/path.gif"},[7]:{text:"ImageToImage allows the upload of an initial image, which InvokeAI will use to guide the generation process, along with a prompt. A lower value for this setting will more closely resemble the original image. 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lp(){}wu.defaultProps={in:!1,mountOnEnter:!1,unmountOnExit:!1,appear:!1,enter:!0,exit:!0,onEnter:lp,onEntering:lp,onEntered:lp,onExit:lp,onExiting:lp,onExited:lp};wu.UNMOUNTED=Pg;wu.EXITED=af;wu.ENTERING=sf;wu.ENTERED=hp;wu.EXITING=eC;const aye=wu;var sye=function(t,n){return t&&n&&n.split(" ").forEach(function(r){return iye(t,r)})},_S=function(t,n){return t&&n&&n.split(" ").forEach(function(r){return oye(t,r)})},V7=function(e){X8(t,e);function t(){for(var r,i=arguments.length,o=new Array(i),a=0;a<i;a++)o[a]=arguments[a];return r=e.call.apply(e,[this].concat(o))||this,r.appliedClasses={appear:{},enter:{},exit:{}},r.onEnter=function(s,l){var d=r.resolveArguments(s,l),p=d[0],g=d[1];r.removeClasses(p,"exit"),r.addClass(p,g?"appear":"enter","base"),r.props.onEnter&&r.props.onEnter(s,l)},r.onEntering=function(s,l){var d=r.resolveArguments(s,l),p=d[0],g=d[1],m=g?"appear":"enter";r.addClass(p,m,"active"),r.props.onEntering&&r.props.onEntering(s,l)},r.onEntered=function(s,l){var d=r.resolveArguments(s,l),p=d[0],g=d[1],m=g?"appear":"enter";r.removeClasses(p,m),r.addClass(p,m,"done"),r.props.onEntered&&r.props.onEntered(s,l)},r.onExit=function(s){var l=r.resolveArguments(s),d=l[0];r.removeClasses(d,"appear"),r.removeClasses(d,"enter"),r.addClass(d,"exit","base"),r.props.onExit&&r.props.onExit(s)},r.onExiting=function(s){var l=r.resolveArguments(s),d=l[0];r.addClass(d,"exit","active"),r.props.onExiting&&r.props.onExiting(s)},r.onExited=function(s){var l=r.resolveArguments(s),d=l[0];r.removeClasses(d,"exit"),r.addClass(d,"exit","done"),r.props.onExited&&r.props.onExited(s)},r.resolveArguments=function(s,l){return r.props.nodeRef?[r.props.nodeRef.current,s]:[s,l]},r.getClassNames=function(s){var l=r.props.classNames,d=typeof l=="string",p=d&&l?l+"-":"",g=d?""+p+s:l[s],m=d?g+"-active":l[s+"Active"],y=d?g+"-done":l[s+"Done"];return{baseClassName:g,activeClassName:m,doneClassName:y}},r}var n=t.prototype;return n.addClass=function(i,o,a){var s=this.getClassNames(o)[a+"ClassName"],l=this.getClassNames("enter"),d=l.doneClassName;o==="appear"&&a==="done"&&d&&(s+=" "+d),a==="active"&&i&&_B(i),s&&(this.appliedClasses[o][a]=s,sye(i,s))},n.removeClasses=function(i,o){var a=this.appliedClasses[o],s=a.base,l=a.active,d=a.done;this.appliedClasses[o]={},s&&_S(i,s),l&&_S(i,l),d&&_S(i,d)},n.render=function(){var i=this.props;i.classNames;var o=K8(i,["classNames"]);return w(aye,{...o,onEnter:this.onEnter,onEntered:this.onEntered,onEntering:this.onEntering,onExit:this.onExit,onExiting:this.onExiting,onExited:this.onExited})},t}(ne.Component);V7.defaultProps={classNames:""};V7.propTypes={};const kB=V7,EB=(e,t,n=!0)=>{C.exports.useEffect(()=>{function r(i){e.current&&!e.current.contains(i.target)&&t()}return n&&document.addEventListener("mousedown",r),()=>{n&&document.removeEventListener("mousedown",r)}},[e,n,t])},Ea=(e,t)=>Math.floor(e/t)*t,FL=(e,t)=>Math.round(e/t)*t,lye={tool:"brush",brushSize:50,maskColor:{r:255,g:90,b:90,a:.5},canvasDimensions:{width:0,height:0},boundingBoxDimensions:{width:512,height:512},boundingBoxCoordinate:{x:0,y:0},boundingBoxPreviewFill:{r:0,g:0,b:0,a:.7},shouldShowBoundingBox:!0,shouldShowBoundingBoxFill:!0,cursorPosition:null,lines:[],pastLines:[],futureLines:[],shouldShowMask:!0,shouldInvertMask:!1,shouldShowCheckboardTransparency:!1,shouldShowBrush:!0,shouldShowBrushPreview:!1,needsCache:!1,isDrawing:!1,stageScale:1,shouldUseInpaintReplace:!1,inpaintReplace:1,shouldLockBoundingBox:!0},uye=lye,PB=J4({name:"inpainting",initialState:uye,reducers:{setTool:(e,t)=>{e.tool=t.payload},toggleTool:e=>{e.tool=e.tool==="brush"?"eraser":"brush"},setBrushSize:(e,t)=>{e.brushSize=t.payload},addLine:(e,t)=>{e.pastLines.push(e.lines),e.lines.push(t.payload),e.futureLines=[]},addPointToCurrentLine:(e,t)=>{e.lines[e.lines.length-1].points.push(...t.payload)},undo:e=>{if(e.pastLines.length===0)return;const t=e.pastLines.pop();!t||(e.futureLines.unshift(e.lines),e.lines=t)},redo:e=>{if(e.futureLines.length===0)return;const t=e.futureLines.shift();!t||(e.pastLines.push(e.lines),e.lines=t)},clearMask:e=>{e.pastLines.push(e.lines),e.lines=[],e.futureLines=[],e.shouldInvertMask=!1},toggleShouldInvertMask:e=>{e.shouldInvertMask=!e.shouldInvertMask},toggleShouldShowMask:e=>{e.shouldShowMask=!e.shouldShowMask},setShouldInvertMask:(e,t)=>{e.shouldInvertMask=t.payload},setShouldShowMask:(e,t)=>{e.shouldShowMask=t.payload,t.payload||(e.shouldInvertMask=!1)},setShouldShowCheckboardTransparency:(e,t)=>{e.shouldShowCheckboardTransparency=t.payload},setShouldShowBrushPreview:(e,t)=>{e.shouldShowBrushPreview=t.payload},setShouldShowBrush:(e,t)=>{e.shouldShowBrush=t.payload},setMaskColor:(e,t)=>{e.maskColor=t.payload},setCursorPosition:(e,t)=>{e.cursorPosition=t.payload},clearImageToInpaint:e=>{e.imageToInpaint=void 0},setImageToInpaint:(e,t)=>{const{width:n,height:r}=t.payload,{width:i,height:o}=e.boundingBoxDimensions,{x:a,y:s}=e.boundingBoxCoordinate,l={x:a,y:s},d={width:i,height:o};i+a>n&&(i>n&&(d.width=Ea(n,64)),l.x=n-d.width),o+s>r&&(o>r&&(d.height=Ea(r,64)),l.y=r-d.height),e.boundingBoxDimensions=d,e.boundingBoxCoordinate=l,e.canvasDimensions={width:n,height:r},e.imageToInpaint=t.payload,e.needsCache=!0},setCanvasDimensions:(e,t)=>{e.canvasDimensions=t.payload;const{width:n,height:r}=t.payload,{width:i,height:o}=e.boundingBoxDimensions,a=Ea(Wt.clamp(i,64,n),64),s=Ea(Wt.clamp(o,64,r),64);e.boundingBoxDimensions={width:a,height:s}},setBoundingBoxDimensions:(e,t)=>{e.boundingBoxDimensions=t.payload;const{width:n,height:r}=t.payload,{x:i,y:o}=e.boundingBoxCoordinate,{width:a,height:s}=e.canvasDimensions,l=Ea(a,64),d=Ea(s,64),p=Ea(n,64),g=Ea(r,64),m=i+n-a,y=o+r-s,b=Wt.clamp(p,64,l),S=Wt.clamp(g,64,d),E=m>0?i-m:i,P=y>0?o-y:o,k=Wt.clamp(E,0,l-b),L=Wt.clamp(P,0,d-S);e.boundingBoxDimensions={width:b,height:S},e.boundingBoxCoordinate={x:k,y:L}},setBoundingBoxCoordinate:(e,t)=>{e.boundingBoxCoordinate=t.payload},setBoundingBoxPreviewFill:(e,t)=>{e.boundingBoxPreviewFill=t.payload},setNeedsCache:(e,t)=>{e.needsCache=t.payload},setStageScale:(e,t)=>{e.stageScale=t.payload,e.needsCache=!1},setShouldShowBoundingBoxFill:(e,t)=>{e.shouldShowBoundingBoxFill=t.payload},setIsDrawing:(e,t)=>{e.isDrawing=t.payload},setClearBrushHistory:e=>{e.pastLines=[],e.futureLines=[]},setShouldUseInpaintReplace:(e,t)=>{e.shouldUseInpaintReplace=t.payload},setInpaintReplace:(e,t)=>{e.inpaintReplace=t.payload},setShouldLockBoundingBox:(e,t)=>{e.shouldLockBoundingBox=t.payload},toggleShouldLockBoundingBox:e=>{e.shouldLockBoundingBox=!e.shouldLockBoundingBox},setShouldShowBoundingBox:(e,t)=>{e.shouldShowBoundingBox=t.payload}}}),{setTool:BL,setBrushSize:cye,addLine:$L,addPointToCurrentLine:HL,setShouldInvertMask:dye,setShouldShowMask:fye,setShouldShowCheckboardTransparency:$9e,setShouldShowBrushPreview:kS,setMaskColor:hye,clearMask:pye,clearImageToInpaint:U7,undo:gye,redo:mye,setCursorPosition:WL,setCanvasDimensions:H9e,setImageToInpaint:E5,setBoundingBoxDimensions:Tg,setBoundingBoxCoordinate:VL,setBoundingBoxPreviewFill:W9e,setNeedsCache:Cf,setStageScale:vye,toggleTool:yye,setShouldShowBoundingBox:xye,setShouldShowBoundingBoxFill:bye,setIsDrawing:Lg,setShouldShowBrush:V9e,setClearBrushHistory:Sye,setShouldUseInpaintReplace:wye,setInpaintReplace:Cye,setShouldLockBoundingBox:TB,toggleShouldLockBoundingBox:_ye}=PB.actions,kye=PB.reducer,LB=""+new URL("logo.13003d72.png",import.meta.url).href,Eye=jt(e=>e.options,e=>{const{shouldShowOptionsPanel:t,shouldHoldOptionsPanelOpen:n,shouldPinOptionsPanel:r,optionsPanelScrollPosition:i}=e;return{shouldShowOptionsPanel:t,shouldHoldOptionsPanelOpen:n,shouldPinOptionsPanel:r,optionsPanelScrollPosition:i}},{memoizeOptions:{resultEqualityCheck:Wt.isEqual}}),G7=e=>{const t=ct(),{shouldShowOptionsPanel:n,shouldHoldOptionsPanelOpen:r,shouldPinOptionsPanel:i}=$e(Eye),o=C.exports.useRef(null),a=C.exports.useRef(null),s=C.exports.useRef(null),{children:l}=e;Dt("o",()=>{t(MC(!n))},[n]),Dt("shift+o",()=>{m()},[i]);const d=C.exports.useCallback(()=>{i||(t(e6e(a.current?a.current.scrollTop:0)),t(MC(!1)),t(t6e(!1)))},[t,i]);EB(o,d,!i);const p=()=>{s.current=window.setTimeout(()=>d(),500)},g=()=>{s.current&&window.clearTimeout(s.current)},m=()=>{t(Jwe(!i)),t(Cf(!0))};return w(kB,{nodeRef:o,in:n||r&&!i,unmountOnExit:!0,timeout:200,classNames:"options-panel-wrapper",children:w("div",{className:"options-panel-wrapper","data-pinned":i,tabIndex:1,ref:o,onMouseEnter:i?void 0:g,onMouseOver:i?void 0:g,children:w("div",{className:"options-panel-margin",children:te("div",{className:"options-panel",ref:a,onMouseLeave:y=>{y.target!==a.current?g():!i&&p()},children:[w(Xr,{label:"Pin Options Panel",children:w("div",{className:"options-panel-pin-button","data-selected":i,onClick:m,children:i?w(SB,{}):w(wB,{})})}),!i&&te("div",{className:"invoke-ai-logo-wrapper",children:[w("img",{src:LB,alt:"invoke-ai-logo"}),te("h1",{children:["invoke ",w("strong",{children:"ai"})]})]}),l]})})})})};function Pye(){const e=$e(n=>n.options.showAdvancedOptions),t={seed:{header:w(M7,{}),feature:Ji.SEED,options:w(R7,{})},variations:{header:w(N7,{}),feature:Ji.VARIATIONS,options:w(D7,{})},face_restore:{header:w(L7,{}),feature:Ji.FACE_CORRECTION,options:w(fx,{})},upscale:{header:w(O7,{}),feature:Ji.UPSCALE,options:w(hx,{})},other:{header:w(oB,{}),feature:Ji.OTHER,options:w(aB,{})}};return te(G7,{children:[w(W7,{}),w(H7,{}),w(B7,{}),w(iB,{label:"Image To Image Strength",styleClass:"main-option-block image-to-image-strength-main-option"}),w(Lve,{}),w(z7,{}),e?w($7,{accordionInfo:t}):null]})}const AB=C.exports.createContext(null),IB=e=>{const{styleClass:t}=e,n=C.exports.useContext(AB),r=()=>{n&&n()};return w("div",{className:`image-uploader-button-outer ${t}`,onClick:r,children:te("div",{className:"image-upload-button",children:[w(G2e,{}),w(Af,{size:"lg",children:"Click or Drag and Drop"})]})})},Tye=jt(e=>e.system,e=>{const{shouldConfirmOnDelete:t,isConnected:n,isProcessing:r}=e;return{shouldConfirmOnDelete:t,isConnected:n,isProcessing:r}}),tC=C.exports.forwardRef(({image:e,children:t},n)=>{const{isOpen:r,onOpen:i,onClose:o}=d5(),a=ct(),{shouldConfirmOnDelete:s,isConnected:l,isProcessing:d}=$e(Tye),p=C.exports.useRef(null),g=b=>{b.stopPropagation(),s?i():m()},m=()=>{l&&!d&&a(Y2e(e)),o()};Dt("del",()=>{s?i():m()},[e,s]);const y=b=>a(uB(!b.target.checked));return te(Fn,{children:[C.exports.cloneElement(t,{onClick:g,ref:n}),w(Ype,{isOpen:r,leastDestructiveRef:p,onClose:o,children:w(Km,{children:te(Xpe,{children:[w(l7,{fontSize:"lg",fontWeight:"bold",children:"Delete image"}),w(g5,{children:te(Dn,{direction:"column",gap:5,children:[w(wo,{children:"Are you sure? You can't undo this action afterwards."}),w(td,{children:te(Dn,{alignItems:"center",children:[w(Gf,{mb:0,children:"Don't ask me again"}),w(f7,{checked:!s,onChange:y})]})})]})}),te(s7,{children:[w(Na,{ref:p,onClick:o,children:"Cancel"}),w(Na,{colorScheme:"red",onClick:m,ml:3,children:"Delete"})]})]})})})]})}),UL=({title:e="Popup",styleClass:t,delay:n=50,popoverOptions:r,actionButton:i,children:o})=>te(j4,{trigger:"hover",closeDelay:n,children:[w(Z4,{children:w(Pl,{children:o})}),te(K4,{className:`popover-content ${t}`,children:[w(q4,{className:"popover-arrow"}),w(Jz,{className:"popover-header",children:e}),te("div",{className:"popover-options",children:[r||null,i]})]})]});jt(e=>e.gallery,e=>e.intermediateImage,{memoizeOptions:{resultEqualityCheck:(e,t)=>e===void 0&&t===void 0||e.uuid===t.uuid}});const Lye=jt([e=>e.system,e=>e.options,e=>e.gallery,Ga],(e,t,n,r)=>{const{isProcessing:i,isConnected:o,isGFPGANAvailable:a,isESRGANAvailable:s}=e,{upscalingLevel:l,facetoolStrength:d,shouldShowImageDetails:p}=t,{intermediateImage:g}=n;return{isProcessing:i,isConnected:o,isGFPGANAvailable:a,isESRGANAvailable:s,upscalingLevel:l,facetoolStrength:d,intermediateImage:g,shouldShowImageDetails:p,activeTabName:r}},{memoizeOptions:{resultEqualityCheck:la.exports.isEqual}}),Aye=({image:e})=>{const t=ct(),{isProcessing:n,isConnected:r,isGFPGANAvailable:i,isESRGANAvailable:o,upscalingLevel:a,facetoolStrength:s,intermediateImage:l,shouldShowImageDetails:d,activeTabName:p}=$e(Lye),g=od(),m=()=>{t(nv(e)),t(Pa(1))};Dt("shift+i",()=>{e?(m(),g({title:"Sent To Image To Image",status:"success",duration:2500,isClosable:!0})):g({title:"No Image Loaded",description:"No image found to send to image to image module.",status:"error",duration:2500,isClosable:!0})},[e]);const y=()=>e.metadata&&t(Uwe(e.metadata));Dt("a",()=>{["txt2img","img2img"].includes(e?.metadata?.image?.type)?(y(),g({title:"Parameters Set",status:"success",duration:2500,isClosable:!0})):g({title:"Parameters Not Set",description:"No metadata found for this image.",status:"error",duration:2500,isClosable:!0})},[e]);const b=()=>e.metadata&&t(Iv(e.metadata.image.seed));Dt("s",()=>{e?.metadata?.image?.seed?(b(),g({title:"Seed Set",status:"success",duration:2500,isClosable:!0})):g({title:"Seed Not Set",description:"Could not find seed for this image.",status:"error",duration:2500,isClosable:!0})},[e]);const S=()=>t(K2e(e));Dt("u",()=>{o&&Boolean(!l)&&r&&!n&&a?S():g({title:"Upscaling Failed",status:"error",duration:2500,isClosable:!0})},[e,o,l,r,n,a]);const E=()=>t(Z2e(e));Dt("r",()=>{i&&Boolean(!l)&&r&&!n&&s?E():g({title:"Face Restoration Failed",status:"error",duration:2500,isClosable:!0})},[e,i,l,r,n,s]);const P=()=>t(Zwe(!d)),k=()=>{t(E5(e)),p!=="inpainting"&&t(Pa("inpainting")),g({title:"Sent to Inpainting",status:"success",duration:2500,isClosable:!0})};return Dt("i",()=>{e?P():g({title:"Failed to load metadata",status:"error",duration:2500,isClosable:!0})},[e,d]),te("div",{className:"current-image-options",children:[w(sn,{icon:w(m2e,{}),tooltip:"Send To Image To Image","aria-label":"Send To Image To Image",onClick:m}),w(sn,{icon:w(mB,{}),tooltip:"Send To Inpainting","aria-label":"Send To Inpainting",onClick:k}),w(sn,{icon:w(gB,{}),tooltip:"Use All","aria-label":"Use All",isDisabled:!["txt2img","img2img"].includes(e?.metadata?.image?.type),onClick:y}),w(sn,{icon:w($2e,{}),tooltip:"Use Seed","aria-label":"Use Seed",isDisabled:!e?.metadata?.image?.seed,onClick:b}),w(UL,{title:"Restore Faces",popoverOptions:w(fx,{}),actionButton:w(k5,{label:"Restore Faces",isDisabled:!i||Boolean(l)||!(r&&!n)||!s,onClick:E}),children:w(sn,{icon:w(l2e,{}),"aria-label":"Restore Faces"})}),w(UL,{title:"Upscale",styleClass:"upscale-popover",popoverOptions:w(hx,{}),actionButton:w(k5,{label:"Upscale Image",isDisabled:!o||Boolean(l)||!(r&&!n)||!a,onClick:S}),children:w(sn,{icon:w(f2e,{}),"aria-label":"Upscale"})}),w(sn,{icon:w(u2e,{}),tooltip:"Details","aria-label":"Details",onClick:P}),w(tC,{image:e,children:w(sn,{icon:w(s2e,{}),tooltip:"Delete Image","aria-label":"Delete Image",isDisabled:Boolean(l)||!r||n})})]})},Iye={currentImageUuid:"",shouldPinGallery:!0,shouldShowGallery:!0,galleryScrollPosition:0,galleryImageMinimumWidth:64,galleryImageObjectFit:"cover",shouldHoldGalleryOpen:!1,shouldAutoSwitchToNewImages:!0,currentCategory:"result",categories:{user:{images:[],latest_mtime:void 0,earliest_mtime:void 0,areMoreImagesAvailable:!0},result:{images:[],latest_mtime:void 0,earliest_mtime:void 0,areMoreImagesAvailable:!0}},galleryWidth:300},MB=J4({name:"gallery",initialState:Iye,reducers:{setCurrentImage:(e,t)=>{e.currentImage=t.payload,e.currentImageUuid=t.payload.uuid},removeImage:(e,t)=>{const{uuid:n,category:r}=t.payload,i=e.categories[r].images,o=i.filter(a=>a.uuid!==n);if(n===e.currentImageUuid){const a=i.findIndex(l=>l.uuid===n),s=la.exports.clamp(a,0,o.length-1);e.currentImage=o.length?o[s]:void 0,e.currentImageUuid=o.length?o[s].uuid:""}e.categories[r].images=o},addImage:(e,t)=>{const{image:n,category:r}=t.payload,{uuid:i,url:o,mtime:a}=n,s=e.categories[r];s.images.find(l=>l.url===o&&l.mtime===a)||(s.images.unshift(n),e.shouldAutoSwitchToNewImages&&(e.currentImageUuid=i,e.currentImage=n,e.currentCategory=r),e.intermediateImage=void 0,s.latest_mtime=a)},setIntermediateImage:(e,t)=>{e.intermediateImage=t.payload},clearIntermediateImage:e=>{e.intermediateImage=void 0},selectNextImage:e=>{const{currentImage:t}=e;if(!t)return;const 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w("div",{className:this.props.className||"",style:zr(zr({position:"absolute",userSelect:"none"},Zye[this.props.direction]),this.props.replaceStyles||{}),onMouseDown:this.onMouseDown,onTouchStart:this.onTouchStart,children:this.props.children})},t}(C.exports.PureComponent),Xye=globalThis&&globalThis.__extends||function(){var e=function(t,n){return e=Object.setPrototypeOf||{__proto__:[]}instanceof Array&&function(r,i){r.__proto__=i}||function(r,i){for(var o in i)Object.prototype.hasOwnProperty.call(i,o)&&(r[o]=i[o])},e(t,n)};return function(t,n){e(t,n);function r(){this.constructor=t}t.prototype=n===null?Object.create(n):(r.prototype=n.prototype,new r)}}(),ol=globalThis&&globalThis.__assign||function(){return ol=Object.assign||function(e){for(var t,n=1,r=arguments.length;n<r;n++){t=arguments[n];for(var i in t)Object.prototype.hasOwnProperty.call(t,i)&&(e[i]=t[i])}return e},ol.apply(this,arguments)},Qye={width:"auto",height:"auto"},uy=function(e,t,n){return Math.max(Math.min(e,n),t)},KL=function(e,t){return Math.round(e/t)*t},up=function(e,t){return new RegExp(e,"i").test(t)},cy=function(e){return Boolean(e.touches&&e.touches.length)},Jye=function(e){return Boolean((e.clientX||e.clientX===0)&&(e.clientY||e.clientY===0))},ZL=function(e,t,n){n===void 0&&(n=0);var r=t.reduce(function(o,a,s){return Math.abs(a-e)<Math.abs(t[o]-e)?s:o},0),i=Math.abs(t[r]-e);return n===0||i<n?t[r]:e},ES=function(e){return e=e.toString(),e==="auto"||e.endsWith("px")||e.endsWith("%")||e.endsWith("vh")||e.endsWith("vw")||e.endsWith("vmax")||e.endsWith("vmin")?e:e+"px"},dy=function(e,t,n,r){if(e&&typeof e=="string"){if(e.endsWith("px"))return Number(e.replace("px",""));if(e.endsWith("%")){var i=Number(e.replace("%",""))/100;return t*i}if(e.endsWith("vw")){var i=Number(e.replace("vw",""))/100;return n*i}if(e.endsWith("vh")){var i=Number(e.replace("vh",""))/100;return r*i}}return e},e3e=function(e,t,n,r,i,o,a){return 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null;var o=r.window.document.createElement("div");return o.style.width="100%",o.style.height="100%",o.style.position="absolute",o.style.transform="scale(0, 0)",o.style.left="0",o.style.flex="0 0 100%",o.classList?o.classList.add(YL):o.className+=YL,i.appendChild(o),o},r.removeBase=function(i){var o=r.parentNode;!o||o.removeChild(i)},r.ref=function(i){i&&(r.resizable=i)},r.state={isResizing:!1,width:typeof(r.propsSize&&r.propsSize.width)>"u"?"auto":r.propsSize&&r.propsSize.width,height:typeof(r.propsSize&&r.propsSize.height)>"u"?"auto":r.propsSize&&r.propsSize.height,direction:"right",original:{x:0,y:0,width:0,height:0},backgroundStyle:{height:"100%",width:"100%",backgroundColor:"rgba(0,0,0,0)",cursor:"auto",opacity:0,position:"fixed",zIndex:9999,top:"0",left:"0",bottom:"0",right:"0"},flexBasis:void 0},r.onResizeStart=r.onResizeStart.bind(r),r.onMouseMove=r.onMouseMove.bind(r),r.onMouseUp=r.onMouseUp.bind(r),r}return Object.defineProperty(t.prototype,"parentNode",{get:function(){return 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n=this.window.getComputedStyle(this.resizable);this.setState({width:this.state.width||this.size.width,height:this.state.height||this.size.height,flexBasis:n.flexBasis!=="auto"?n.flexBasis:void 0})}},t.prototype.componentWillUnmount=function(){this.window&&this.unbindEvents()},t.prototype.createSizeForCssProperty=function(n,r){var i=this.propsSize&&this.propsSize[r];return this.state[r]==="auto"&&this.state.original[r]===n&&(typeof i>"u"||i==="auto")?"auto":n},t.prototype.calculateNewMaxFromBoundary=function(n,r){var i=this.props.boundsByDirection,o=this.state.direction,a=i&&up("left",o),s=i&&up("top",o),l,d;if(this.props.bounds==="parent"){var p=this.parentNode;p&&(l=a?this.resizableRight-this.parentLeft:p.offsetWidth+(this.parentLeft-this.resizableLeft),d=s?this.resizableBottom-this.parentTop:p.offsetHeight+(this.parentTop-this.resizableTop))}else this.props.bounds==="window"?this.window&&(l=a?this.resizableRight:this.window.innerWidth-this.resizableLeft,d=s?this.resizableBottom:this.window.innerHeight-this.resizableTop):this.props.bounds&&(l=a?this.resizableRight-this.targetLeft:this.props.bounds.offsetWidth+(this.targetLeft-this.resizableLeft),d=s?this.resizableBottom-this.targetTop:this.props.bounds.offsetHeight+(this.targetTop-this.resizableTop));return l&&Number.isFinite(l)&&(n=n&&n<l?n:l),d&&Number.isFinite(d)&&(r=r&&r<d?r:d),{maxWidth:n,maxHeight:r}},t.prototype.calculateNewSizeFromDirection=function(n,r){var i=this.props.scale||1,o=this.props.resizeRatio||1,a=this.state,s=a.direction,l=a.original,d=this.props,p=d.lockAspectRatio,g=d.lockAspectRatioExtraHeight,m=d.lockAspectRatioExtraWidth,y=l.width,b=l.height,S=g||0,E=m||0;return up("right",s)&&(y=l.width+(n-l.x)*o/i,p&&(b=(y-E)/this.ratio+S)),up("left",s)&&(y=l.width-(n-l.x)*o/i,p&&(b=(y-E)/this.ratio+S)),up("bottom",s)&&(b=l.height+(r-l.y)*o/i,p&&(y=(b-S)*this.ratio+E)),up("top",s)&&(b=l.height-(r-l.y)*o/i,p&&(y=(b-S)*this.ratio+E)),{newWidth:y,newHeight:b}},t.prototype.calculateNewSizeFromAspectRatio=function(n,r,i,o){var a=this.props,s=a.lockAspectRatio,l=a.lockAspectRatioExtraHeight,d=a.lockAspectRatioExtraWidth,p=typeof o.width>"u"?10:o.width,g=typeof i.width>"u"||i.width<0?n:i.width,m=typeof o.height>"u"?10:o.height,y=typeof i.height>"u"||i.height<0?r:i.height,b=l||0,S=d||0;if(s){var E=(m-b)*this.ratio+S,P=(y-b)*this.ratio+S,k=(p-S)/this.ratio+b,L=(g-S)/this.ratio+b,I=Math.max(p,E),O=Math.min(g,P),N=Math.max(m,k),D=Math.min(y,L);n=uy(n,I,O),r=uy(r,N,D)}else n=uy(n,p,g),r=uy(r,m,y);return{newWidth:n,newHeight:r}},t.prototype.setBoundingClientRect=function(){if(this.props.bounds==="parent"){var n=this.parentNode;if(n){var r=n.getBoundingClientRect();this.parentLeft=r.left,this.parentTop=r.top}}if(this.props.bounds&&typeof this.props.bounds!="string"){var i=this.props.bounds.getBoundingClientRect();this.targetLeft=i.left,this.targetTop=i.top}if(this.resizable){var o=this.resizable.getBoundingClientRect(),a=o.left,s=o.top,l=o.right,d=o.bottom;this.resizableLeft=a,this.resizableRight=l,this.resizableTop=s,this.resizableBottom=d}},t.prototype.onResizeStart=function(n,r){if(!(!this.resizable||!this.window)){var i=0,o=0;if(n.nativeEvent&&Jye(n.nativeEvent)?(i=n.nativeEvent.clientX,o=n.nativeEvent.clientY):n.nativeEvent&&cy(n.nativeEvent)&&(i=n.nativeEvent.touches[0].clientX,o=n.nativeEvent.touches[0].clientY),this.props.onResizeStart&&this.resizable){var a=this.props.onResizeStart(n,r,this.resizable);if(a===!1)return}this.props.size&&(typeof this.props.size.height<"u"&&this.props.size.height!==this.state.height&&this.setState({height:this.props.size.height}),typeof this.props.size.width<"u"&&this.props.size.width!==this.state.width&&this.setState({width:this.props.size.width})),this.ratio=typeof this.props.lockAspectRatio=="number"?this.props.lockAspectRatio:this.size.width/this.size.height;var s,l=this.window.getComputedStyle(this.resizable);if(l.flexBasis!=="auto"){var d=this.parentNode;if(d){var p=this.window.getComputedStyle(d).flexDirection;this.flexDir=p.startsWith("row")?"row":"column",s=l.flexBasis}}this.setBoundingClientRect(),this.bindEvents();var g={original:{x:i,y:o,width:this.size.width,height:this.size.height},isResizing:!0,backgroundStyle:ol(ol({},this.state.backgroundStyle),{cursor:this.window.getComputedStyle(n.target).cursor||"auto"}),direction:r,flexBasis:s};this.setState(g)}},t.prototype.onMouseMove=function(n){var r=this;if(!(!this.state.isResizing||!this.resizable||!this.window)){if(this.window.TouchEvent&&cy(n))try{n.preventDefault(),n.stopPropagation()}catch{}var i=this.props,o=i.maxWidth,a=i.maxHeight,s=i.minWidth,l=i.minHeight,d=cy(n)?n.touches[0].clientX:n.clientX,p=cy(n)?n.touches[0].clientY:n.clientY,g=this.state,m=g.direction,y=g.original,b=g.width,S=g.height,E=this.getParentSize(),P=e3e(E,this.window.innerWidth,this.window.innerHeight,o,a,s,l);o=P.maxWidth,a=P.maxHeight,s=P.minWidth,l=P.minHeight;var k=this.calculateNewSizeFromDirection(d,p),L=k.newHeight,I=k.newWidth,O=this.calculateNewMaxFromBoundary(o,a);this.props.snap&&this.props.snap.x&&(I=ZL(I,this.props.snap.x,this.props.snapGap)),this.props.snap&&this.props.snap.y&&(L=ZL(L,this.props.snap.y,this.props.snapGap));var N=this.calculateNewSizeFromAspectRatio(I,L,{width:O.maxWidth,height:O.maxHeight},{width:s,height:l});if(I=N.newWidth,L=N.newHeight,this.props.grid){var D=KL(I,this.props.grid[0]),F=KL(L,this.props.grid[1]),U=this.props.snapGap||0;I=U===0||Math.abs(D-I)<=U?D:I,L=U===0||Math.abs(F-L)<=U?F:L}var W={width:I-y.width,height:L-y.height};if(b&&typeof b=="string"){if(b.endsWith("%")){var q=I/E.width*100;I=q+"%"}else if(b.endsWith("vw")){var pe=I/this.window.innerWidth*100;I=pe+"vw"}else if(b.endsWith("vh")){var fe=I/this.window.innerHeight*100;I=fe+"vh"}}if(S&&typeof S=="string"){if(S.endsWith("%")){var q=L/E.height*100;L=q+"%"}else if(S.endsWith("vw")){var pe=L/this.window.innerWidth*100;L=pe+"vw"}else if(S.endsWith("vh")){var fe=L/this.window.innerHeight*100;L=fe+"vh"}}var ye={width:this.createSizeForCssProperty(I,"width"),height:this.createSizeForCssProperty(L,"height")};this.flexDir==="row"?ye.flexBasis=ye.width:this.flexDir==="column"&&(ye.flexBasis=ye.height),El.exports.flushSync(function(){r.setState(ye)}),this.props.onResize&&this.props.onResize(n,m,this.resizable,W)}},t.prototype.onMouseUp=function(n){var r=this.state,i=r.isResizing,o=r.direction,a=r.original;if(!(!i||!this.resizable)){var s={width:this.size.width-a.width,height:this.size.height-a.height};this.props.onResizeStop&&this.props.onResizeStop(n,o,this.resizable,s),this.props.size&&this.setState(this.props.size),this.unbindEvents(),this.setState({isResizing:!1,backgroundStyle:ol(ol({},this.state.backgroundStyle),{cursor:"auto"})})}},t.prototype.updateSize=function(n){this.setState({width:n.width,height:n.height})},t.prototype.renderResizer=function(){var n=this,r=this.props,i=r.enable,o=r.handleStyles,a=r.handleClasses,s=r.handleWrapperStyle,l=r.handleWrapperClass,d=r.handleComponent;if(!i)return null;var p=Object.keys(i).map(function(g){return i[g]!==!1?w(Yye,{direction:g,onResizeStart:n.onResizeStart,replaceStyles:o&&o[g],className:a&&a[g],children:d&&d[g]?d[g]:null},g):null});return w("div",{className:l,style:s,children:p})},t.prototype.render=function(){var n=this,r=Object.keys(this.props).reduce(function(a,s){return t3e.indexOf(s)!==-1||(a[s]=n.props[s]),a},{}),i=ol(ol(ol({position:"relative",userSelect:this.state.isResizing?"none":"auto"},this.props.style),this.sizeStyle),{maxWidth:this.props.maxWidth,maxHeight:this.props.maxHeight,minWidth:this.props.minWidth,minHeight:this.props.minHeight,boxSizing:"border-box",flexShrink:0});this.state.flexBasis&&(i.flexBasis=this.state.flexBasis);var o=this.props.as||"div";return te(o,{...ol({ref:this.ref,style:i,className:this.props.className},r),children:[this.state.isResizing&&w("div",{style:this.state.backgroundStyle}),this.props.children,this.renderResizer()]})},t.defaultProps={as:"div",onResizeStart:function(){},onResize:function(){},onResizeStop:function(){},enable:{top:!0,right:!0,bottom:!0,left:!0,topRight:!0,bottomRight:!0,bottomLeft:!0,topLeft:!0},style:{},grid:[1,1],lockAspectRatio:!1,lockAspectRatioExtraWidth:0,lockAspectRatioExtraHeight:0,scale:1,resizeRatio:1,snapGap:0},t}(C.exports.PureComponent);function Gn(e,t,{checkForDefaultPrevented:n=!0}={}){return function(i){if(e?.(i),n===!1||!i.defaultPrevented)return t?.(i)}}function Pv(e,t=[]){let n=[];function r(o,a){const s=C.exports.createContext(a),l=n.length;n=[...n,a];function d(g){const{scope:m,children:y,...b}=g,S=m?.[e][l]||s,E=C.exports.useMemo(()=>b,Object.values(b));return C.exports.createElement(S.Provider,{value:E},y)}function p(g,m){const y=m?.[e][l]||s,b=C.exports.useContext(y);if(b)return b;if(a!==void 0)return a;throw new Error(`\`${g}\` must be used within \`${o}\``)}return d.displayName=o+"Provider",[d,p]}const i=()=>{const o=n.map(a=>C.exports.createContext(a));return function(s){const l=s?.[e]||o;return C.exports.useMemo(()=>({[`__scope${e}`]:{...s,[e]:l}}),[s,l])}};return i.scopeName=e,[r,n3e(i,...t)]}function n3e(...e){const t=e[0];if(e.length===1)return t;const n=()=>{const r=e.map(i=>({useScope:i(),scopeName:i.scopeName}));return function(o){const a=r.reduce((s,{useScope:l,scopeName:d})=>{const 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n=t||{},r=n.pixelRatio||Je.pixelRatio||hxe();this.pixelRatio=r,this._canvas=se.createCanvasElement(),this._canvas.style.padding="0",this._canvas.style.margin="0",this._canvas.style.border="0",this._canvas.style.background="transparent",this._canvas.style.position="absolute",this._canvas.style.top="0",this._canvas.style.left="0"}getContext(){return this.context}getPixelRatio(){return this.pixelRatio}setPixelRatio(t){var n=this.pixelRatio;this.pixelRatio=t,this.setSize(this.getWidth()/n,this.getHeight()/n)}setWidth(t){this.width=this._canvas.width=t*this.pixelRatio,this._canvas.style.width=t+"px";var n=this.pixelRatio,r=this.getContext()._context;r.scale(n,n)}setHeight(t){this.height=this._canvas.height=t*this.pixelRatio,this._canvas.style.height=t+"px";var n=this.pixelRatio,r=this.getContext()._context;r.scale(n,n)}getWidth(){return this.width}getHeight(){return this.height}setSize(t,n){this.setWidth(t||0),this.setHeight(n||0)}toDataURL(t,n){try{return this._canvas.toDataURL(t,n)}catch{try{return this._canvas.toDataURL()}catch(i){return se.error("Unable to get data URL. 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"+g.message+" This post my help you https://konvajs.org/docs/posts/Tainted_Canvas.html.")}this._filterUpToDate=!0}return i}return r}on(t,n){if(this._cache&&this._cache.delete(my),arguments.length===3)return this._delegate.apply(this,arguments);var r=t.split(c3),i=r.length,o,a,s,l,d;for(o=0;o<i;o++)a=r[o],s=a.split("."),l=s[0],d=s[1]||"",this.eventListeners[l]||(this.eventListeners[l]=[]),this.eventListeners[l].push({name:d,handler:n});return this}off(t,n){var r=(t||"").split(c3),i=r.length,o,a,s,l,d,p;if(this._cache&&this._cache.delete(my),!t)for(a in this.eventListeners)this._off(a);for(o=0;o<i;o++)if(s=r[o],l=s.split("."),d=l[0],p=l[1],d)this.eventListeners[d]&&this._off(d,p,n);else for(a in this.eventListeners)this._off(a,p,n);return this}dispatchEvent(t){var n={target:this,type:t.type,evt:t};return this.fire(t.type,n),this}addEventListener(t,n){return this.on(t,function(r){n.call(this,r.evt)}),this}removeEventListener(t){return this.off(t),this}_delegate(t,n,r){var i=this;this.on(t,function(o){for(var a=o.target.findAncestors(n,!0,i),s=0;s<a.length;s++)o=se.cloneObject(o),o.currentTarget=a[s],r.call(a[s],o)})}remove(){return this.isDragging()&&this.stopDrag(),an._dragElements.delete(this._id),this._remove(),this}_clearCaches(){this._clearSelfAndDescendantCache(Jl),this._clearSelfAndDescendantCache(u3),this._clearSelfAndDescendantCache(vA),this._clearSelfAndDescendantCache(wA),this._clearSelfAndDescendantCache(gC),this._clearSelfAndDescendantCache(pC)}_remove(){this._clearCaches();var t=this.getParent();t&&t.children&&(t.children.splice(this.index,1),t._setChildrenIndices(),this.parent=null)}destroy(){return this.remove(),this}getAttr(t){var n="get"+se._capitalize(t);return se._isFunction(this[n])?this[n]():this.attrs[t]}getAncestors(){for(var t=this.getParent(),n=[];t;)n.push(t),t=t.getParent();return n}getAttrs(){return this.attrs||{}}setAttrs(t){return this._batchTransformChanges(()=>{var n,r;if(!t)return this;for(n in t)n!==gxe&&(r=bA+se._capitalize(n),se._isFunction(this[r])?this[r](t[n]):this._setAttr(n,t[n]))}),this}isListening(){return this._getCache(pC,this._isListening)}_isListening(t){if(!this.listening())return!1;const r=this.getParent();return r&&r!==t&&this!==t?r._isListening(t):!0}isVisible(){return this._getCache(gC,this._isVisible)}_isVisible(t){if(!this.visible())return!1;const r=this.getParent();return r&&r!==t&&this!==t?r._isVisible(t):!0}shouldDrawHit(t,n=!1){if(t)return this._isVisible(t)&&this._isListening(t);var r=this.getLayer(),i=!1;an._dragElements.forEach(a=>{a.dragStatus==="dragging"&&(a.node.nodeType==="Stage"||a.node.getLayer()===r)&&(i=!0)});var o=!n&&!Je.hitOnDragEnabled&&i;return this.isListening()&&this.isVisible()&&!o}show(){return this.visible(!0),this}hide(){return this.visible(!1),this}getZIndex(){return this.index||0}getAbsoluteZIndex(){var t=this.getDepth(),n=this,r=0,i,o,a,s;function l(d){for(i=[],o=d.length,a=0;a<o;a++)s=d[a],r++,s.nodeType!==SA&&(i=i.concat(s.getChildren().slice())),s._id===n._id&&(a=o);i.length>0&&i[0].getDepth()<=t&&l(i)}return n.nodeType!==vxe&&l(n.getStage().getChildren()),r}getDepth(){for(var t=0,n=this.parent;n;)t++,n=n.parent;return t}_batchTransformChanges(t){this._batchingTransformChange=!0,t(),this._batchingTransformChange=!1,this._needClearTransformCache&&(this._clearCache(xc),this._clearSelfAndDescendantCache(Jl)),this._needClearTransformCache=!1}setPosition(t){return this._batchTransformChanges(()=>{this.x(t.x),this.y(t.y)}),this}getPosition(){return{x:this.x(),y:this.y()}}getRelativePointerPosition(){if(!this.getStage())return null;var t=this.getStage().getPointerPosition();if(!t)return null;var n=this.getAbsoluteTransform().copy();return n.invert(),n.point(t)}getAbsolutePosition(t){let n=!1,r=this.parent;for(;r;){if(r.isCached()){n=!0;break}r=r.parent}n&&!t&&(t=!0);var i=this.getAbsoluteTransform(t).getMatrix(),o=new Qo,a=this.offset();return o.m=i.slice(),o.translate(a.x,a.y),o.getTranslation()}setAbsolutePosition(t){var n=this._clearTransform();this.attrs.x=n.x,this.attrs.y=n.y,delete n.x,delete n.y,this._clearCache(xc);var r=this._getAbsoluteTransform().copy();return r.invert(),r.translate(t.x,t.y),t={x:this.attrs.x+r.getTranslation().x,y:this.attrs.y+r.getTranslation().y},this._setTransform(n),this.setPosition({x:t.x,y:t.y}),this._clearCache(xc),this._clearSelfAndDescendantCache(Jl),this}_setTransform(t){var n;for(n in t)this.attrs[n]=t[n]}_clearTransform(){var t={x:this.x(),y:this.y(),rotation:this.rotation(),scaleX:this.scaleX(),scaleY:this.scaleY(),offsetX:this.offsetX(),offsetY:this.offsetY(),skewX:this.skewX(),skewY:this.skewY()};return this.attrs.x=0,this.attrs.y=0,this.attrs.rotation=0,this.attrs.scaleX=1,this.attrs.scaleY=1,this.attrs.offsetX=0,this.attrs.offsetY=0,this.attrs.skewX=0,this.attrs.skewY=0,t}move(t){var n=t.x,r=t.y,i=this.x(),o=this.y();return n!==void 0&&(i+=n),r!==void 0&&(o+=r),this.setPosition({x:i,y:o}),this}_eachAncestorReverse(t,n){var r=[],i=this.getParent(),o,a;if(!(n&&n._id===this._id)){for(r.unshift(this);i&&(!n||i._id!==n._id);)r.unshift(i),i=i.parent;for(o=r.length,a=0;a<o;a++)t(r[a])}}rotate(t){return this.rotation(this.rotation()+t),this}moveToTop(){if(!this.parent)return se.warn("Node has no parent. moveToTop function is ignored."),!1;var t=this.index,n=this.parent.getChildren().length;return t<n-1?(this.parent.children.splice(t,1),this.parent.children.push(this),this.parent._setChildrenIndices(),!0):!1}moveUp(){if(!this.parent)return se.warn("Node has no parent. moveUp function is ignored."),!1;var t=this.index,n=this.parent.getChildren().length;return t<n-1?(this.parent.children.splice(t,1),this.parent.children.splice(t+1,0,this),this.parent._setChildrenIndices(),!0):!1}moveDown(){if(!this.parent)return se.warn("Node has no parent. moveDown function is ignored."),!1;var t=this.index;return t>0?(this.parent.children.splice(t,1),this.parent.children.splice(t-1,0,this),this.parent._setChildrenIndices(),!0):!1}moveToBottom(){if(!this.parent)return se.warn("Node has no parent. moveToBottom function is ignored."),!1;var t=this.index;return t>0?(this.parent.children.splice(t,1),this.parent.children.unshift(this),this.parent._setChildrenIndices(),!0):!1}setZIndex(t){if(!this.parent)return se.warn("Node has no parent. zIndex parameter is ignored."),this;(t<0||t>=this.parent.children.length)&&se.warn("Unexpected value "+t+" for zIndex property. zIndex is just index of a node in children of its parent. Expected value is from 0 to "+(this.parent.children.length-1)+".");var n=this.index;return this.parent.children.splice(n,1),this.parent.children.splice(t,0,this),this.parent._setChildrenIndices(),this}getAbsoluteOpacity(){return this._getCache(u3,this._getAbsoluteOpacity)}_getAbsoluteOpacity(){var t=this.opacity(),n=this.getParent();return n&&!n._isUnderCache&&(t*=n.getAbsoluteOpacity()),t}moveTo(t){return this.getParent()!==t&&(this._remove(),t.add(this)),this}toObject(){var t={},n=this.getAttrs(),r,i,o,a,s;t.attrs={};for(r in n)i=n[r],s=se.isObject(i)&&!se._isPlainObject(i)&&!se._isArray(i),!s&&(o=typeof this[r]=="function"&&this[r],delete n[r],a=o?o.call(this):null,n[r]=i,a!==i&&(t.attrs[r]=i));return t.className=this.getClassName(),se._prepareToStringify(t)}toJSON(){return JSON.stringify(this.toObject())}getParent(){return this.parent}findAncestors(t,n,r){var i=[];n&&this._isMatch(t)&&i.push(this);for(var o=this.parent;o;){if(o===r)return i;o._isMatch(t)&&i.push(o),o=o.parent}return i}isAncestorOf(t){return!1}findAncestor(t,n,r){return this.findAncestors(t,n,r)[0]}_isMatch(t){if(!t)return!1;if(typeof t=="function")return t(this);var n=t.replace(/ /g,"").split(","),r=n.length,i,o;for(i=0;i<r;i++)if(o=n[i],se.isValidSelector(o)||(se.warn('Selector "'+o+'" is invalid. Allowed selectors examples are "#foo", ".bar" or "Group".'),se.warn('If you have a custom shape with such className, please change it to start with upper letter like "Triangle".'),se.warn("Konva is awesome, right?")),o.charAt(0)==="#"){if(this.id()===o.slice(1))return!0}else if(o.charAt(0)==="."){if(this.hasName(o.slice(1)))return!0}else if(this.className===o||this.nodeType===o)return!0;return!1}getLayer(){var t=this.getParent();return t?t.getLayer():null}getStage(){return this._getCache(wA,this._getStage)}_getStage(){var t=this.getParent();if(t)return t.getStage()}fire(t,n={},r){return n.target=n.target||this,r?this._fireAndBubble(t,n):this._fire(t,n),this}getAbsoluteTransform(t){return t?this._getAbsoluteTransform(t):this._getCache(Jl,this._getAbsoluteTransform)}_getAbsoluteTransform(t){var n;if(t)return n=new Qo,this._eachAncestorReverse(function(i){var o=i.transformsEnabled();o==="all"?n.multiply(i.getTransform()):o==="position"&&n.translate(i.x()-i.offsetX(),i.y()-i.offsetY())},t),n;n=this._cache.get(Jl)||new Qo,this.parent?this.parent.getAbsoluteTransform().copyInto(n):n.reset();var r=this.transformsEnabled();if(r==="all")n.multiply(this.getTransform());else if(r==="position"){const i=this.attrs.x||0,o=this.attrs.y||0,a=this.attrs.offsetX||0,s=this.attrs.offsetY||0;n.translate(i-a,o-s)}return n.dirty=!1,n}getAbsoluteScale(t){for(var n=this;n;)n._isUnderCache&&(t=n),n=n.getParent();const i=this.getAbsoluteTransform(t).decompose();return{x:i.scaleX,y:i.scaleY}}getAbsoluteRotation(){return this.getAbsoluteTransform().decompose().rotation}getTransform(){return this._getCache(xc,this._getTransform)}_getTransform(){var t,n,r=this._cache.get(xc)||new Qo;r.reset();var i=this.x(),o=this.y(),a=Je.getAngle(this.rotation()),s=(t=this.attrs.scaleX)!==null&&t!==void 0?t:1,l=(n=this.attrs.scaleY)!==null&&n!==void 0?n:1,d=this.attrs.skewX||0,p=this.attrs.skewY||0,g=this.attrs.offsetX||0,m=this.attrs.offsetY||0;return(i!==0||o!==0)&&r.translate(i,o),a!==0&&r.rotate(a),(d!==0||p!==0)&&r.skew(d,p),(s!==1||l!==1)&&r.scale(s,l),(g!==0||m!==0)&&r.translate(-1*g,-1*m),r.dirty=!1,r}clone(t){var n=se.cloneObject(this.attrs),r,i,o,a,s;for(r in t)n[r]=t[r];var l=new this.constructor(n);for(r in this.eventListeners)for(i=this.eventListeners[r],o=i.length,a=0;a<o;a++)s=i[a],s.name.indexOf(mxe)<0&&(l.eventListeners[r]||(l.eventListeners[r]=[]),l.eventListeners[r].push(s));return l}_toKonvaCanvas(t){t=t||{};var n=this.getClientRect(),r=this.getStage(),i=t.x!==void 0?t.x:Math.floor(n.x),o=t.y!==void 0?t.y:Math.floor(n.y),a=t.pixelRatio||1,s=new t0({width:t.width||Math.ceil(n.width)||(r?r.width():0),height:t.height||Math.ceil(n.height)||(r?r.height():0),pixelRatio:a}),l=s.getContext();return t.imageSmoothingEnabled===!1&&(l._context.imageSmoothingEnabled=!1),l.save(),(i||o)&&l.translate(-1*i,-1*o),this.drawScene(s),l.restore(),s}toCanvas(t){return this._toKonvaCanvas(t)._canvas}toDataURL(t){t=t||{};var n=t.mimeType||null,r=t.quality||null,i=this._toKonvaCanvas(t).toDataURL(n,r);return t.callback&&t.callback(i),i}toImage(t){return new Promise((n,r)=>{try{const i=t?.callback;i&&delete t.callback,se._urlToImage(this.toDataURL(t),function(o){n(o),i?.(o)})}catch(i){r(i)}})}toBlob(t){return new Promise((n,r)=>{try{const i=t?.callback;i&&delete t.callback,this.toCanvas(t).toBlob(o=>{n(o),i?.(o)})}catch(i){r(i)}})}setSize(t){return this.width(t.width),this.height(t.height),this}getSize(){return{width:this.width(),height:this.height()}}getClassName(){return this.className||this.nodeType}getType(){return this.nodeType}getDragDistance(){return this.attrs.dragDistance!==void 0?this.attrs.dragDistance:this.parent?this.parent.getDragDistance():Je.dragDistance}_off(t,n,r){var i=this.eventListeners[t],o,a,s;for(o=0;o<i.length;o++)if(a=i[o].name,s=i[o].handler,(a!=="konva"||n==="konva")&&(!n||a===n)&&(!r||r===s)){if(i.splice(o,1),i.length===0){delete this.eventListeners[t];break}o--}}_fireChangeEvent(t,n,r){this._fire(t+pxe,{oldVal:n,newVal:r})}addName(t){if(!this.hasName(t)){var n=this.name(),r=n?n+" "+t:t;this.name(r)}return this}hasName(t){if(!t)return!1;const n=this.name();if(!n)return!1;var r=(n||"").split(/\s/g);return r.indexOf(t)!==-1}removeName(t){var n=(this.name()||"").split(/\s/g),r=n.indexOf(t);return r!==-1&&(n.splice(r,1),this.name(n.join(" "))),this}setAttr(t,n){var r=this[bA+se._capitalize(t)];return se._isFunction(r)?r.call(this,n):this._setAttr(t,n),this}_requestDraw(){if(Je.autoDrawEnabled){const t=this.getLayer()||this.getStage();t?.batchDraw()}}_setAttr(t,n){var r=this.attrs[t];r===n&&!se.isObject(n)||(n==null?delete this.attrs[t]:this.attrs[t]=n,this._shouldFireChangeEvents&&this._fireChangeEvent(t,r,n),this._requestDraw())}_setComponentAttr(t,n,r){var i;r!==void 0&&(i=this.attrs[t],i||(this.attrs[t]=this.getAttr(t)),this.attrs[t][n]=r,this._fireChangeEvent(t,i,r))}_fireAndBubble(t,n,r){n&&this.nodeType===SA&&(n.target=this);var i=(t===yA||t===xA)&&(r&&(this===r||this.isAncestorOf&&this.isAncestorOf(r))||this.nodeType==="Stage"&&!r);if(!i){this._fire(t,n);var o=(t===yA||t===xA)&&r&&r.isAncestorOf&&r.isAncestorOf(this)&&!r.isAncestorOf(this.parent);(n&&!n.cancelBubble||!n)&&this.parent&&this.parent.isListening()&&!o&&(r&&r.parent?this._fireAndBubble.call(this.parent,t,n,r):this._fireAndBubble.call(this.parent,t,n))}}_getProtoListeners(t){let n=this._cache.get(my);if(!n){n={};let i=Object.getPrototypeOf(this);for(;i;){if(!i.eventListeners){i=Object.getPrototypeOf(i);continue}for(var r in i.eventListeners){const o=i.eventListeners[r],a=n[r]||[];n[r]=o.concat(a)}i=Object.getPrototypeOf(i)}this._cache.set(my,n)}return n[t]}_fire(t,n){n=n||{},n.currentTarget=this,n.type=t;const r=this._getProtoListeners(t);if(r)for(var i=0;i<r.length;i++)r[i].handler.call(this,n);const o=this.eventListeners[t];if(o)for(var i=0;i<o.length;i++)o[i].handler.call(this,n)}draw(){return this.drawScene(),this.drawHit(),this}_createDragElement(t){var n=t?t.pointerId:void 0,r=this.getStage(),i=this.getAbsolutePosition(),o=r._getPointerById(n)||r._changedPointerPositions[0]||i;an._dragElements.set(this._id,{node:this,startPointerPos:o,offset:{x:o.x-i.x,y:o.y-i.y},dragStatus:"ready",pointerId:n})}startDrag(t,n=!0){an._dragElements.has(this._id)||this._createDragElement(t);const r=an._dragElements.get(this._id);r.dragStatus="dragging",this.fire("dragstart",{type:"dragstart",target:this,evt:t&&t.evt},n)}_setDragPosition(t,n){const r=this.getStage()._getPointerById(n.pointerId);if(!!r){var i={x:r.x-n.offset.x,y:r.y-n.offset.y},o=this.dragBoundFunc();if(o!==void 0){const a=o.call(this,i,t);a?i=a:se.warn("dragBoundFunc did not return any value. That is unexpected behavior. You must return new absolute position from dragBoundFunc.")}(!this._lastPos||this._lastPos.x!==i.x||this._lastPos.y!==i.y)&&(this.setAbsolutePosition(i),this._requestDraw()),this._lastPos=i}}stopDrag(t){const n=an._dragElements.get(this._id);n&&(n.dragStatus="stopped"),an._endDragBefore(t),an._endDragAfter(t)}setDraggable(t){this._setAttr("draggable",t),this._dragChange()}isDragging(){const t=an._dragElements.get(this._id);return t?t.dragStatus==="dragging":!1}_listenDrag(){this._dragCleanup(),this.on("mousedown.konva touchstart.konva",function(t){var n=t.evt.button!==void 0,r=!n||Je.dragButtons.indexOf(t.evt.button)>=0;if(!!r&&!this.isDragging()){var i=!1;an._dragElements.forEach(o=>{this.isAncestorOf(o.node)&&(i=!0)}),i||this._createDragElement(t)}})}_dragChange(){if(this.attrs.draggable)this._listenDrag();else{this._dragCleanup();var t=this.getStage();if(!t)return;const n=an._dragElements.get(this._id),r=n&&n.dragStatus==="dragging",i=n&&n.dragStatus==="ready";r?this.stopDrag():i&&an._dragElements.delete(this._id)}}_dragCleanup(){this.off("mousedown.konva"),this.off("touchstart.konva")}isClientRectOnScreen(t={x:0,y:0}){const n=this.getStage();if(!n)return!1;const r={x:-t.x,y:-t.y,width:n.width()+2*t.x,height:n.height()+2*t.y};return se.haveIntersection(r,this.getClientRect())}static create(t,n){return se._isString(t)&&(t=JSON.parse(t)),this._createNode(t,n)}static _createNode(t,n){var r=ze.prototype.getClassName.call(t),i=t.children,o,a,s;n&&(t.attrs.container=n),Je[r]||(se.warn('Can not find a node with class name "'+r+'". Fallback to "Shape".'),r="Shape");const l=Je[r];if(o=new l(t.attrs),i)for(a=i.length,s=0;s<a;s++)o.add(ze._createNode(i[s]));return o}}ze.prototype.nodeType="Node";ze.prototype._attrsAffectingSize=[];ze.prototype.eventListeners={};ze.prototype.on.call(ze.prototype,yxe,function(){if(this._batchingTransformChange){this._needClearTransformCache=!0;return}this._clearCache(xc),this._clearSelfAndDescendantCache(Jl)});ze.prototype.on.call(ze.prototype,"visibleChange.konva",function(){this._clearSelfAndDescendantCache(gC)});ze.prototype.on.call(ze.prototype,"listeningChange.konva",function(){this._clearSelfAndDescendantCache(pC)});ze.prototype.on.call(ze.prototype,"opacityChange.konva",function(){this._clearSelfAndDescendantCache(u3)});const Bn=j.addGetterSetter;Bn(ze,"zIndex");Bn(ze,"absolutePosition");Bn(ze,"position");Bn(ze,"x",0,De());Bn(ze,"y",0,De());Bn(ze,"globalCompositeOperation","source-over",B0());Bn(ze,"opacity",1,De());Bn(ze,"name","",B0());Bn(ze,"id","",B0());Bn(ze,"rotation",0,De());j.addComponentsGetterSetter(ze,"scale",["x","y"]);Bn(ze,"scaleX",1,De());Bn(ze,"scaleY",1,De());j.addComponentsGetterSetter(ze,"skew",["x","y"]);Bn(ze,"skewX",0,De());Bn(ze,"skewY",0,De());j.addComponentsGetterSetter(ze,"offset",["x","y"]);Bn(ze,"offsetX",0,De());Bn(ze,"offsetY",0,De());Bn(ze,"dragDistance",null,De());Bn(ze,"width",0,De());Bn(ze,"height",0,De());Bn(ze,"listening",!0,ks());Bn(ze,"preventDefault",!0,ks());Bn(ze,"filters",null,function(e){return this._filterUpToDate=!1,e});Bn(ze,"visible",!0,ks());Bn(ze,"transformsEnabled","all",B0());Bn(ze,"size");Bn(ze,"dragBoundFunc");Bn(ze,"draggable",!1,ks());j.backCompat(ze,{rotateDeg:"rotate",setRotationDeg:"setRotation",getRotationDeg:"getRotation"});class ia extends ze{constructor(){super(...arguments),this.children=[]}getChildren(t){if(!t)return this.children||[];const n=this.children||[];var r=[];return n.forEach(function(i){t(i)&&r.push(i)}),r}hasChildren(){return this.getChildren().length>0}removeChildren(){return this.getChildren().forEach(t=>{t.parent=null,t.index=0,t.remove()}),this.children=[],this._requestDraw(),this}destroyChildren(){return this.getChildren().forEach(t=>{t.parent=null,t.index=0,t.destroy()}),this.children=[],this._requestDraw(),this}add(...t){if(arguments.length>1){for(var n=0;n<arguments.length;n++)this.add(arguments[n]);return this}var r=t[0];return r.getParent()?(r.moveTo(this),this):(this._validateAdd(r),r.index=this.getChildren().length,r.parent=this,r._clearCaches(),this.getChildren().push(r),this._fire("add",{child:r}),this._requestDraw(),this)}destroy(){return this.hasChildren()&&this.destroyChildren(),super.destroy(),this}find(t){return this._generalFind(t,!1)}findOne(t){var n=this._generalFind(t,!0);return n.length>0?n[0]:void 0}_generalFind(t,n){var r=[];return this._descendants(i=>{const o=i._isMatch(t);return o&&r.push(i),!!(o&&n)}),r}_descendants(t){let n=!1;const r=this.getChildren();for(const i of r){if(n=t(i),n)return!0;if(!!i.hasChildren()&&(n=i._descendants(t),n))return!0}return!1}toObject(){var t=ze.prototype.toObject.call(this);return t.children=[],this.getChildren().forEach(n=>{t.children.push(n.toObject())}),t}isAncestorOf(t){for(var n=t.getParent();n;){if(n._id===this._id)return!0;n=n.getParent()}return!1}clone(t){var n=ze.prototype.clone.call(this,t);return this.getChildren().forEach(function(r){n.add(r.clone())}),n}getAllIntersections(t){var n=[];return this.find("Shape").forEach(function(r){r.isVisible()&&r.intersects(t)&&n.push(r)}),n}_clearSelfAndDescendantCache(t){var n;super._clearSelfAndDescendantCache(t),!this.isCached()&&((n=this.children)===null||n===void 0||n.forEach(function(r){r._clearSelfAndDescendantCache(t)}))}_setChildrenIndices(){var t;(t=this.children)===null||t===void 0||t.forEach(function(n,r){n.index=r}),this._requestDraw()}drawScene(t,n){var r=this.getLayer(),i=t||r&&r.getCanvas(),o=i&&i.getContext(),a=this._getCanvasCache(),s=a&&a.scene,l=i&&i.isCache;if(!this.isVisible()&&!l)return this;if(s){o.save();var d=this.getAbsoluteTransform(n).getMatrix();o.transform(d[0],d[1],d[2],d[3],d[4],d[5]),this._drawCachedSceneCanvas(o),o.restore()}else this._drawChildren("drawScene",i,n);return this}drawHit(t,n){if(!this.shouldDrawHit(n))return this;var r=this.getLayer(),i=t||r&&r.hitCanvas,o=i&&i.getContext(),a=this._getCanvasCache(),s=a&&a.hit;if(s){o.save();var l=this.getAbsoluteTransform(n).getMatrix();o.transform(l[0],l[1],l[2],l[3],l[4],l[5]),this._drawCachedHitCanvas(o),o.restore()}else this._drawChildren("drawHit",i,n);return this}_drawChildren(t,n,r){var i,o=n&&n.getContext(),a=this.clipWidth(),s=this.clipHeight(),l=this.clipFunc(),d=a&&s||l;const p=r===this;if(d){o.save();var g=this.getAbsoluteTransform(r),m=g.getMatrix();if(o.transform(m[0],m[1],m[2],m[3],m[4],m[5]),o.beginPath(),l)l.call(this,o,this);else{var y=this.clipX(),b=this.clipY();o.rect(y,b,a,s)}o.clip(),m=g.copy().invert().getMatrix(),o.transform(m[0],m[1],m[2],m[3],m[4],m[5])}var S=!p&&this.globalCompositeOperation()!=="source-over"&&t==="drawScene";S&&(o.save(),o._applyGlobalCompositeOperation(this)),(i=this.children)===null||i===void 0||i.forEach(function(E){E[t](n,r)}),S&&o.restore(),d&&o.restore()}getClientRect(t){var n;t=t||{};var r=t.skipTransform,i=t.relativeTo,o,a,s,l,d={x:1/0,y:1/0,width:0,height:0},p=this;(n=this.children)===null||n===void 0||n.forEach(function(S){if(!!S.visible()){var E=S.getClientRect({relativeTo:p,skipShadow:t.skipShadow,skipStroke:t.skipStroke});E.width===0&&E.height===0||(o===void 0?(o=E.x,a=E.y,s=E.x+E.width,l=E.y+E.height):(o=Math.min(o,E.x),a=Math.min(a,E.y),s=Math.max(s,E.x+E.width),l=Math.max(l,E.y+E.height)))}});for(var g=this.find("Shape"),m=!1,y=0;y<g.length;y++){var b=g[y];if(b._isVisible(this)){m=!0;break}}return m&&o!==void 0?d={x:o,y:a,width:s-o,height:l-a}:d={x:0,y:0,width:0,height:0},r?d:this._transformedRect(d,i)}}j.addComponentsGetterSetter(ia,"clip",["x","y","width","height"]);j.addGetterSetter(ia,"clipX",void 0,De());j.addGetterSetter(ia,"clipY",void 0,De());j.addGetterSetter(ia,"clipWidth",void 0,De());j.addGetterSetter(ia,"clipHeight",void 0,De());j.addGetterSetter(ia,"clipFunc");const tv=new Map,b$=Je._global.PointerEvent!==void 0;function RS(e){return tv.get(e)}function n_(e){return{evt:e,pointerId:e.pointerId}}function S$(e,t){return tv.get(e)===t}function w$(e,t){sm(e),t.getStage()&&(tv.set(e,t),b$&&t._fire("gotpointercapture",n_(new PointerEvent("gotpointercapture"))))}function sm(e,t){const n=tv.get(e);if(!n)return;const r=n.getStage();r&&r.content,tv.delete(e),b$&&n._fire("lostpointercapture",n_(new PointerEvent("lostpointercapture")))}var bxe="Stage",Sxe="string",CA="px",wxe="mouseout",C$="mouseleave",_$="mouseover",k$="mouseenter",E$="mousemove",P$="mousedown",T$="mouseup",Ag="pointermove",Ig="pointerdown",Ap="pointerup",Mg="pointercancel",Cxe="lostpointercapture",vy="pointerout",yy="pointerleave",xy="pointerover",by="pointerenter",mC="contextmenu",L$="touchstart",A$="touchend",I$="touchmove",M$="touchcancel",vC="wheel",_xe=5,kxe=[[k$,"_pointerenter"],[P$,"_pointerdown"],[E$,"_pointermove"],[T$,"_pointerup"],[C$,"_pointerleave"],[L$,"_pointerdown"],[I$,"_pointermove"],[A$,"_pointerup"],[M$,"_pointercancel"],[_$,"_pointerover"],[vC,"_wheel"],[mC,"_contextmenu"],[Ig,"_pointerdown"],[Ag,"_pointermove"],[Ap,"_pointerup"],[Mg,"_pointercancel"],[Cxe,"_lostpointercapture"]];const OS={mouse:{[vy]:wxe,[yy]:C$,[xy]:_$,[by]:k$,[Ag]:E$,[Ig]:P$,[Ap]:T$,[Mg]:"mousecancel",pointerclick:"click",pointerdblclick:"dblclick"},touch:{[vy]:"touchout",[yy]:"touchleave",[xy]:"touchover",[by]:"touchenter",[Ag]:I$,[Ig]:L$,[Ap]:A$,[Mg]:M$,pointerclick:"tap",pointerdblclick:"dbltap"},pointer:{[vy]:vy,[yy]:yy,[xy]:xy,[by]:by,[Ag]:Ag,[Ig]:Ig,[Ap]:Ap,[Mg]:Mg,pointerclick:"pointerclick",pointerdblclick:"pointerdblclick"}},Rg=e=>e.indexOf("pointer")>=0?"pointer":e.indexOf("touch")>=0?"touch":"mouse",cp=e=>{const t=Rg(e);if(t==="pointer")return Je.pointerEventsEnabled&&OS.pointer;if(t==="touch")return OS.touch;if(t==="mouse")return OS.mouse};function _A(e={}){return(e.clipFunc||e.clipWidth||e.clipHeight)&&se.warn("Stage does not support clipping. Please use clip for Layers or Groups."),e}const Exe="Pointer position is missing and not registered by the stage. Looks like it is outside of the stage container. You can set it manually from event: stage.setPointersPositions(event);",d3=[];class wx extends ia{constructor(t){super(_A(t)),this._pointerPositions=[],this._changedPointerPositions=[],this._buildDOM(),this._bindContentEvents(),d3.push(this),this.on("widthChange.konva heightChange.konva",this._resizeDOM),this.on("visibleChange.konva",this._checkVisibility),this.on("clipWidthChange.konva clipHeightChange.konva clipFuncChange.konva",()=>{_A(this.attrs)}),this._checkVisibility()}_validateAdd(t){const n=t.getType()==="Layer",r=t.getType()==="FastLayer";n||r||se.throw("You may only add layers to the stage.")}_checkVisibility(){if(!this.content)return;const t=this.visible()?"":"none";this.content.style.display=t}setContainer(t){if(typeof t===Sxe){if(t.charAt(0)==="."){var n=t.slice(1);t=document.getElementsByClassName(n)[0]}else{var r;t.charAt(0)!=="#"?r=t:r=t.slice(1),t=document.getElementById(r)}if(!t)throw"Can not find container in document with id "+r}return this._setAttr("container",t),this.content&&(this.content.parentElement&&this.content.parentElement.removeChild(this.content),t.appendChild(this.content)),this}shouldDrawHit(){return!0}clear(){var t=this.children,n=t.length,r;for(r=0;r<n;r++)t[r].clear();return this}clone(t){return t||(t={}),t.container=typeof document<"u"&&document.createElement("div"),ia.prototype.clone.call(this,t)}destroy(){super.destroy();var t=this.content;t&&se._isInDocument(t)&&this.container().removeChild(t);var n=d3.indexOf(this);return n>-1&&d3.splice(n,1),this}getPointerPosition(){const t=this._pointerPositions[0]||this._changedPointerPositions[0];return t?{x:t.x,y:t.y}:(se.warn(Exe),null)}_getPointerById(t){return this._pointerPositions.find(n=>n.id===t)}getPointersPositions(){return this._pointerPositions}getStage(){return this}getContent(){return this.content}_toKonvaCanvas(t){t=t||{},t.x=t.x||0,t.y=t.y||0,t.width=t.width||this.width(),t.height=t.height||this.height();var n=new t0({width:t.width,height:t.height,pixelRatio:t.pixelRatio||1}),r=n.getContext()._context,i=this.children;return(t.x||t.y)&&r.translate(-1*t.x,-1*t.y),i.forEach(function(o){if(!!o.isVisible()){var a=o._toKonvaCanvas(t);r.drawImage(a._canvas,t.x,t.y,a.getWidth()/a.getPixelRatio(),a.getHeight()/a.getPixelRatio())}}),n}getIntersection(t){if(!t)return null;var n=this.children,r=n.length,i=r-1,o;for(o=i;o>=0;o--){const a=n[o].getIntersection(t);if(a)return a}return null}_resizeDOM(){var t=this.width(),n=this.height();this.content&&(this.content.style.width=t+CA,this.content.style.height=n+CA),this.bufferCanvas.setSize(t,n),this.bufferHitCanvas.setSize(t,n),this.children.forEach(r=>{r.setSize({width:t,height:n}),r.draw()})}add(t,...n){if(arguments.length>1){for(var r=0;r<arguments.length;r++)this.add(arguments[r]);return this}super.add(t);var i=this.children.length;return i>_xe&&se.warn("The stage has "+i+" layers. Recommended maximum number of layers is 3-5. Adding more layers into the stage may drop the performance. Rethink your tree structure, you can use Konva.Group."),t.setSize({width:this.width(),height:this.height()}),t.draw(),Je.isBrowser&&this.content.appendChild(t.canvas._canvas),this}getParent(){return null}getLayer(){return null}hasPointerCapture(t){return S$(t,this)}setPointerCapture(t){w$(t,this)}releaseCapture(t){sm(t)}getLayers(){return this.children}_bindContentEvents(){!Je.isBrowser||kxe.forEach(([t,n])=>{this.content.addEventListener(t,r=>{this[n](r)},{passive:!1})})}_pointerenter(t){this.setPointersPositions(t);const n=cp(t.type);this._fire(n.pointerenter,{evt:t,target:this,currentTarget:this})}_pointerover(t){this.setPointersPositions(t);const n=cp(t.type);this._fire(n.pointerover,{evt:t,target:this,currentTarget:this})}_getTargetShape(t){let n=this[t+"targetShape"];return n&&!n.getStage()&&(n=null),n}_pointerleave(t){const n=cp(t.type),r=Rg(t.type);if(!!n){this.setPointersPositions(t);var i=this._getTargetShape(r),o=!an.isDragging||Je.hitOnDragEnabled;i&&o?(i._fireAndBubble(n.pointerout,{evt:t}),i._fireAndBubble(n.pointerleave,{evt:t}),this._fire(n.pointerleave,{evt:t,target:this,currentTarget:this}),this[r+"targetShape"]=null):o&&(this._fire(n.pointerleave,{evt:t,target:this,currentTarget:this}),this._fire(n.pointerout,{evt:t,target:this,currentTarget:this})),this.pointerPos=void 0,this._pointerPositions=[]}}_pointerdown(t){const n=cp(t.type),r=Rg(t.type);if(!!n){this.setPointersPositions(t);var i=!1;this._changedPointerPositions.forEach(o=>{var a=this.getIntersection(o);if(an.justDragged=!1,Je["_"+r+"ListenClick"]=!0,!(a&&a.isListening()))return;Je.capturePointerEventsEnabled&&a.setPointerCapture(o.id),this[r+"ClickStartShape"]=a,a._fireAndBubble(n.pointerdown,{evt:t,pointerId:o.id}),i=!0;const l=t.type.indexOf("touch")>=0;a.preventDefault()&&t.cancelable&&l&&t.preventDefault()}),i||this._fire(n.pointerdown,{evt:t,target:this,currentTarget:this,pointerId:this._pointerPositions[0].id})}}_pointermove(t){const n=cp(t.type),r=Rg(t.type);if(!n)return;an.isDragging&&an.node.preventDefault()&&t.cancelable&&t.preventDefault(),this.setPointersPositions(t);var i=!an.isDragging||Je.hitOnDragEnabled;if(!i)return;var o={};let a=!1;var s=this._getTargetShape(r);this._changedPointerPositions.forEach(l=>{const d=RS(l.id)||this.getIntersection(l),p=l.id,g={evt:t,pointerId:p};var m=s!==d;if(m&&s&&(s._fireAndBubble(n.pointerout,Object.assign({},g),d),s._fireAndBubble(n.pointerleave,Object.assign({},g),d)),d){if(o[d._id])return;o[d._id]=!0}d&&d.isListening()?(a=!0,m&&(d._fireAndBubble(n.pointerover,Object.assign({},g),s),d._fireAndBubble(n.pointerenter,Object.assign({},g),s),this[r+"targetShape"]=d),d._fireAndBubble(n.pointermove,Object.assign({},g))):s&&(this._fire(n.pointerover,{evt:t,target:this,currentTarget:this,pointerId:p}),this[r+"targetShape"]=null)}),a||this._fire(n.pointermove,{evt:t,target:this,currentTarget:this,pointerId:this._changedPointerPositions[0].id})}_pointerup(t){const n=cp(t.type),r=Rg(t.type);if(!n)return;this.setPointersPositions(t);const i=this[r+"ClickStartShape"],o=this[r+"ClickEndShape"];var a={};let s=!1;this._changedPointerPositions.forEach(l=>{const d=RS(l.id)||this.getIntersection(l);if(d){if(d.releaseCapture(l.id),a[d._id])return;a[d._id]=!0}const p=l.id,g={evt:t,pointerId:p};let m=!1;Je["_"+r+"InDblClickWindow"]?(m=!0,clearTimeout(this[r+"DblTimeout"])):an.justDragged||(Je["_"+r+"InDblClickWindow"]=!0,clearTimeout(this[r+"DblTimeout"])),this[r+"DblTimeout"]=setTimeout(function(){Je["_"+r+"InDblClickWindow"]=!1},Je.dblClickWindow),d&&d.isListening()?(s=!0,this[r+"ClickEndShape"]=d,d._fireAndBubble(n.pointerup,Object.assign({},g)),Je["_"+r+"ListenClick"]&&i&&i===d&&(d._fireAndBubble(n.pointerclick,Object.assign({},g)),m&&o&&o===d&&d._fireAndBubble(n.pointerdblclick,Object.assign({},g)))):(this[r+"ClickEndShape"]=null,Je["_"+r+"ListenClick"]&&this._fire(n.pointerclick,{evt:t,target:this,currentTarget:this,pointerId:p}),m&&this._fire(n.pointerdblclick,{evt:t,target:this,currentTarget:this,pointerId:p}))}),s||this._fire(n.pointerup,{evt:t,target:this,currentTarget:this,pointerId:this._changedPointerPositions[0].id}),Je["_"+r+"ListenClick"]=!1,t.cancelable&&r!=="touch"&&t.preventDefault()}_contextmenu(t){this.setPointersPositions(t);var n=this.getIntersection(this.getPointerPosition());n&&n.isListening()?n._fireAndBubble(mC,{evt:t}):this._fire(mC,{evt:t,target:this,currentTarget:this})}_wheel(t){this.setPointersPositions(t);var n=this.getIntersection(this.getPointerPosition());n&&n.isListening()?n._fireAndBubble(vC,{evt:t}):this._fire(vC,{evt:t,target:this,currentTarget:this})}_pointercancel(t){this.setPointersPositions(t);const n=RS(t.pointerId)||this.getIntersection(this.getPointerPosition());n&&n._fireAndBubble(Ap,n_(t)),sm(t.pointerId)}_lostpointercapture(t){sm(t.pointerId)}setPointersPositions(t){var n=this._getContentPosition(),r=null,i=null;t=t||window.event,t.touches!==void 0?(this._pointerPositions=[],this._changedPointerPositions=[],Array.prototype.forEach.call(t.touches,o=>{this._pointerPositions.push({id:o.identifier,x:(o.clientX-n.left)/n.scaleX,y:(o.clientY-n.top)/n.scaleY})}),Array.prototype.forEach.call(t.changedTouches||t.touches,o=>{this._changedPointerPositions.push({id:o.identifier,x:(o.clientX-n.left)/n.scaleX,y:(o.clientY-n.top)/n.scaleY})})):(r=(t.clientX-n.left)/n.scaleX,i=(t.clientY-n.top)/n.scaleY,this.pointerPos={x:r,y:i},this._pointerPositions=[{x:r,y:i,id:se._getFirstPointerId(t)}],this._changedPointerPositions=[{x:r,y:i,id:se._getFirstPointerId(t)}])}_setPointerPosition(t){se.warn('Method _setPointerPosition is deprecated. Use "stage.setPointersPositions(event)" instead.'),this.setPointersPositions(t)}_getContentPosition(){if(!this.content||!this.content.getBoundingClientRect)return{top:0,left:0,scaleX:1,scaleY:1};var t=this.content.getBoundingClientRect();return{top:t.top,left:t.left,scaleX:t.width/this.content.clientWidth||1,scaleY:t.height/this.content.clientHeight||1}}_buildDOM(){if(this.bufferCanvas=new t0({width:this.width(),height:this.height()}),this.bufferHitCanvas=new t_({pixelRatio:1,width:this.width(),height:this.height()}),!!Je.isBrowser){var t=this.container();if(!t)throw"Stage has no container. A container is required.";t.innerHTML="",this.content=document.createElement("div"),this.content.style.position="relative",this.content.style.userSelect="none",this.content.className="konvajs-content",this.content.setAttribute("role","presentation"),t.appendChild(this.content),this._resizeDOM()}}cache(){return se.warn("Cache function is not allowed for stage. You may use cache only for layers, groups and shapes."),this}clearCache(){return this}batchDraw(){return this.getChildren().forEach(function(t){t.batchDraw()}),this}}wx.prototype.nodeType=bxe;hr(wx);j.addGetterSetter(wx,"container");var R$="hasShadow",O$="shadowRGBA",N$="patternImage",D$="linearGradient",z$="radialGradient";let Sy;function NS(){return Sy||(Sy=se.createCanvasElement().getContext("2d"),Sy)}const lm={};function Pxe(e){e.fill()}function Txe(e){e.stroke()}function Lxe(e){e.fill()}function Axe(e){e.stroke()}function Ixe(){this._clearCache(R$)}function Mxe(){this._clearCache(O$)}function Rxe(){this._clearCache(N$)}function Oxe(){this._clearCache(D$)}function Nxe(){this._clearCache(z$)}class Ae extends ze{constructor(t){super(t);let n;for(;n=se.getRandomColor(),!(n&&!(n in lm)););this.colorKey=n,lm[n]=this}getContext(){return se.warn("shape.getContext() method is deprecated. Please do not use it."),this.getLayer().getContext()}getCanvas(){return se.warn("shape.getCanvas() method is deprecated. Please do not use it."),this.getLayer().getCanvas()}getSceneFunc(){return this.attrs.sceneFunc||this._sceneFunc}getHitFunc(){return this.attrs.hitFunc||this._hitFunc}hasShadow(){return this._getCache(R$,this._hasShadow)}_hasShadow(){return this.shadowEnabled()&&this.shadowOpacity()!==0&&!!(this.shadowColor()||this.shadowBlur()||this.shadowOffsetX()||this.shadowOffsetY())}_getFillPattern(){return this._getCache(N$,this.__getFillPattern)}__getFillPattern(){if(this.fillPatternImage()){var t=NS();const n=t.createPattern(this.fillPatternImage(),this.fillPatternRepeat()||"repeat");if(n&&n.setTransform){const r=new Qo;r.translate(this.fillPatternX(),this.fillPatternY()),r.rotate(Je.getAngle(this.fillPatternRotation())),r.scale(this.fillPatternScaleX(),this.fillPatternScaleY()),r.translate(-1*this.fillPatternOffsetX(),-1*this.fillPatternOffsetY());const i=r.getMatrix(),o=typeof DOMMatrix>"u"?{a:i[0],b:i[1],c:i[2],d:i[3],e:i[4],f:i[5]}:new DOMMatrix(i);n.setTransform(o)}return n}}_getLinearGradient(){return this._getCache(D$,this.__getLinearGradient)}__getLinearGradient(){var t=this.fillLinearGradientColorStops();if(t){for(var n=NS(),r=this.fillLinearGradientStartPoint(),i=this.fillLinearGradientEndPoint(),o=n.createLinearGradient(r.x,r.y,i.x,i.y),a=0;a<t.length;a+=2)o.addColorStop(t[a],t[a+1]);return o}}_getRadialGradient(){return this._getCache(z$,this.__getRadialGradient)}__getRadialGradient(){var t=this.fillRadialGradientColorStops();if(t){for(var n=NS(),r=this.fillRadialGradientStartPoint(),i=this.fillRadialGradientEndPoint(),o=n.createRadialGradient(r.x,r.y,this.fillRadialGradientStartRadius(),i.x,i.y,this.fillRadialGradientEndRadius()),a=0;a<t.length;a+=2)o.addColorStop(t[a],t[a+1]);return o}}getShadowRGBA(){return this._getCache(O$,this._getShadowRGBA)}_getShadowRGBA(){if(!!this.hasShadow()){var t=se.colorToRGBA(this.shadowColor());if(t)return"rgba("+t.r+","+t.g+","+t.b+","+t.a*(this.shadowOpacity()||1)+")"}}hasFill(){return this._calculate("hasFill",["fillEnabled","fill","fillPatternImage","fillLinearGradientColorStops","fillRadialGradientColorStops"],()=>this.fillEnabled()&&!!(this.fill()||this.fillPatternImage()||this.fillLinearGradientColorStops()||this.fillRadialGradientColorStops()))}hasStroke(){return this._calculate("hasStroke",["strokeEnabled","strokeWidth","stroke","strokeLinearGradientColorStops"],()=>this.strokeEnabled()&&this.strokeWidth()&&!!(this.stroke()||this.strokeLinearGradientColorStops()))}hasHitStroke(){const t=this.hitStrokeWidth();return t==="auto"?this.hasStroke():this.strokeEnabled()&&!!t}intersects(t){var n=this.getStage(),r=n.bufferHitCanvas,i;return r.getContext().clear(),this.drawHit(r,null,!0),i=r.context.getImageData(Math.round(t.x),Math.round(t.y),1,1).data,i[3]>0}destroy(){return ze.prototype.destroy.call(this),delete lm[this.colorKey],delete this.colorKey,this}_useBufferCanvas(t){var n;if(!this.getStage()||!((n=this.attrs.perfectDrawEnabled)!==null&&n!==void 0?n:!0))return!1;const i=t||this.hasFill(),o=this.hasStroke(),a=this.getAbsoluteOpacity()!==1;if(i&&o&&a)return!0;const s=this.hasShadow(),l=this.shadowForStrokeEnabled();return!!(i&&o&&s&&l)}setStrokeHitEnabled(t){se.warn("strokeHitEnabled property is deprecated. Please use hitStrokeWidth instead."),t?this.hitStrokeWidth("auto"):this.hitStrokeWidth(0)}getStrokeHitEnabled(){return this.hitStrokeWidth()!==0}getSelfRect(){var t=this.size();return{x:this._centroid?-t.width/2:0,y:this._centroid?-t.height/2:0,width:t.width,height:t.height}}getClientRect(t={}){const n=t.skipTransform,r=t.relativeTo,i=this.getSelfRect(),a=!t.skipStroke&&this.hasStroke()&&this.strokeWidth()||0,s=i.width+a,l=i.height+a,d=!t.skipShadow&&this.hasShadow(),p=d?this.shadowOffsetX():0,g=d?this.shadowOffsetY():0,m=s+Math.abs(p),y=l+Math.abs(g),b=d&&this.shadowBlur()||0,S=m+b*2,E=y+b*2,P={width:S,height:E,x:-(a/2+b)+Math.min(p,0)+i.x,y:-(a/2+b)+Math.min(g,0)+i.y};return n?P:this._transformedRect(P,r)}drawScene(t,n){var r=this.getLayer(),i=t||r.getCanvas(),o=i.getContext(),a=this._getCanvasCache(),s=this.getSceneFunc(),l=this.hasShadow(),d,p,g,m=i.isCache,y=n===this;if(!this.isVisible()&&!y)return this;if(a){o.save();var b=this.getAbsoluteTransform(n).getMatrix();return o.transform(b[0],b[1],b[2],b[3],b[4],b[5]),this._drawCachedSceneCanvas(o),o.restore(),this}if(!s)return this;if(o.save(),this._useBufferCanvas()&&!m){d=this.getStage(),p=d.bufferCanvas,g=p.getContext(),g.clear(),g.save(),g._applyLineJoin(this);var S=this.getAbsoluteTransform(n).getMatrix();g.transform(S[0],S[1],S[2],S[3],S[4],S[5]),s.call(this,g,this),g.restore();var E=p.pixelRatio;l&&o._applyShadow(this),o._applyOpacity(this),o._applyGlobalCompositeOperation(this),o.drawImage(p._canvas,0,0,p.width/E,p.height/E)}else{if(o._applyLineJoin(this),!y){var S=this.getAbsoluteTransform(n).getMatrix();o.transform(S[0],S[1],S[2],S[3],S[4],S[5]),o._applyOpacity(this),o._applyGlobalCompositeOperation(this)}l&&o._applyShadow(this),s.call(this,o,this)}return o.restore(),this}drawHit(t,n,r=!1){if(!this.shouldDrawHit(n,r))return this;var i=this.getLayer(),o=t||i.hitCanvas,a=o&&o.getContext(),s=this.hitFunc()||this.sceneFunc(),l=this._getCanvasCache(),d=l&&l.hit;if(this.colorKey||se.warn("Looks like your canvas has a destroyed shape in it. Do not reuse shape after you destroyed it. If you want to reuse shape you should call remove() instead of destroy()"),d){a.save();var p=this.getAbsoluteTransform(n).getMatrix();return a.transform(p[0],p[1],p[2],p[3],p[4],p[5]),this._drawCachedHitCanvas(a),a.restore(),this}if(!s)return this;if(a.save(),a._applyLineJoin(this),!(this===n)){var m=this.getAbsoluteTransform(n).getMatrix();a.transform(m[0],m[1],m[2],m[3],m[4],m[5])}return s.call(this,a,this),a.restore(),this}drawHitFromCache(t=0){var n=this._getCanvasCache(),r=this._getCachedSceneCanvas(),i=n.hit,o=i.getContext(),a=i.getWidth(),s=i.getHeight(),l,d,p,g,m,y;o.clear(),o.drawImage(r._canvas,0,0,a,s);try{for(l=o.getImageData(0,0,a,s),d=l.data,p=d.length,g=se._hexToRgb(this.colorKey),m=0;m<p;m+=4)y=d[m+3],y>t?(d[m]=g.r,d[m+1]=g.g,d[m+2]=g.b,d[m+3]=255):d[m+3]=0;o.putImageData(l,0,0)}catch(b){se.error("Unable to draw hit graph from cached scene canvas. "+b.message)}return this}hasPointerCapture(t){return S$(t,this)}setPointerCapture(t){w$(t,this)}releaseCapture(t){sm(t)}}Ae.prototype._fillFunc=Pxe;Ae.prototype._strokeFunc=Txe;Ae.prototype._fillFuncHit=Lxe;Ae.prototype._strokeFuncHit=Axe;Ae.prototype._centroid=!1;Ae.prototype.nodeType="Shape";hr(Ae);Ae.prototype.eventListeners={};Ae.prototype.on.call(Ae.prototype,"shadowColorChange.konva shadowBlurChange.konva shadowOffsetChange.konva shadowOpacityChange.konva shadowEnabledChange.konva",Ixe);Ae.prototype.on.call(Ae.prototype,"shadowColorChange.konva shadowOpacityChange.konva shadowEnabledChange.konva",Mxe);Ae.prototype.on.call(Ae.prototype,"fillPriorityChange.konva fillPatternImageChange.konva fillPatternRepeatChange.konva fillPatternScaleXChange.konva fillPatternScaleYChange.konva fillPatternOffsetXChange.konva fillPatternOffsetYChange.konva fillPatternXChange.konva fillPatternYChange.konva fillPatternRotationChange.konva",Rxe);Ae.prototype.on.call(Ae.prototype,"fillPriorityChange.konva fillLinearGradientColorStopsChange.konva fillLinearGradientStartPointXChange.konva fillLinearGradientStartPointYChange.konva fillLinearGradientEndPointXChange.konva fillLinearGradientEndPointYChange.konva",Oxe);Ae.prototype.on.call(Ae.prototype,"fillPriorityChange.konva fillRadialGradientColorStopsChange.konva fillRadialGradientStartPointXChange.konva fillRadialGradientStartPointYChange.konva fillRadialGradientEndPointXChange.konva fillRadialGradientEndPointYChange.konva fillRadialGradientStartRadiusChange.konva fillRadialGradientEndRadiusChange.konva",Nxe);j.addGetterSetter(Ae,"stroke",void 0,x$());j.addGetterSetter(Ae,"strokeWidth",2,De());j.addGetterSetter(Ae,"fillAfterStrokeEnabled",!1);j.addGetterSetter(Ae,"hitStrokeWidth","auto",e_());j.addGetterSetter(Ae,"strokeHitEnabled",!0,ks());j.addGetterSetter(Ae,"perfectDrawEnabled",!0,ks());j.addGetterSetter(Ae,"shadowForStrokeEnabled",!0,ks());j.addGetterSetter(Ae,"lineJoin");j.addGetterSetter(Ae,"lineCap");j.addGetterSetter(Ae,"sceneFunc");j.addGetterSetter(Ae,"hitFunc");j.addGetterSetter(Ae,"dash");j.addGetterSetter(Ae,"dashOffset",0,De());j.addGetterSetter(Ae,"shadowColor",void 0,B0());j.addGetterSetter(Ae,"shadowBlur",0,De());j.addGetterSetter(Ae,"shadowOpacity",1,De());j.addComponentsGetterSetter(Ae,"shadowOffset",["x","y"]);j.addGetterSetter(Ae,"shadowOffsetX",0,De());j.addGetterSetter(Ae,"shadowOffsetY",0,De());j.addGetterSetter(Ae,"fillPatternImage");j.addGetterSetter(Ae,"fill",void 0,x$());j.addGetterSetter(Ae,"fillPatternX",0,De());j.addGetterSetter(Ae,"fillPatternY",0,De());j.addGetterSetter(Ae,"fillLinearGradientColorStops");j.addGetterSetter(Ae,"strokeLinearGradientColorStops");j.addGetterSetter(Ae,"fillRadialGradientStartRadius",0);j.addGetterSetter(Ae,"fillRadialGradientEndRadius",0);j.addGetterSetter(Ae,"fillRadialGradientColorStops");j.addGetterSetter(Ae,"fillPatternRepeat","repeat");j.addGetterSetter(Ae,"fillEnabled",!0);j.addGetterSetter(Ae,"strokeEnabled",!0);j.addGetterSetter(Ae,"shadowEnabled",!0);j.addGetterSetter(Ae,"dashEnabled",!0);j.addGetterSetter(Ae,"strokeScaleEnabled",!0);j.addGetterSetter(Ae,"fillPriority","color");j.addComponentsGetterSetter(Ae,"fillPatternOffset",["x","y"]);j.addGetterSetter(Ae,"fillPatternOffsetX",0,De());j.addGetterSetter(Ae,"fillPatternOffsetY",0,De());j.addComponentsGetterSetter(Ae,"fillPatternScale",["x","y"]);j.addGetterSetter(Ae,"fillPatternScaleX",1,De());j.addGetterSetter(Ae,"fillPatternScaleY",1,De());j.addComponentsGetterSetter(Ae,"fillLinearGradientStartPoint",["x","y"]);j.addComponentsGetterSetter(Ae,"strokeLinearGradientStartPoint",["x","y"]);j.addGetterSetter(Ae,"fillLinearGradientStartPointX",0);j.addGetterSetter(Ae,"strokeLinearGradientStartPointX",0);j.addGetterSetter(Ae,"fillLinearGradientStartPointY",0);j.addGetterSetter(Ae,"strokeLinearGradientStartPointY",0);j.addComponentsGetterSetter(Ae,"fillLinearGradientEndPoint",["x","y"]);j.addComponentsGetterSetter(Ae,"strokeLinearGradientEndPoint",["x","y"]);j.addGetterSetter(Ae,"fillLinearGradientEndPointX",0);j.addGetterSetter(Ae,"strokeLinearGradientEndPointX",0);j.addGetterSetter(Ae,"fillLinearGradientEndPointY",0);j.addGetterSetter(Ae,"strokeLinearGradientEndPointY",0);j.addComponentsGetterSetter(Ae,"fillRadialGradientStartPoint",["x","y"]);j.addGetterSetter(Ae,"fillRadialGradientStartPointX",0);j.addGetterSetter(Ae,"fillRadialGradientStartPointY",0);j.addComponentsGetterSetter(Ae,"fillRadialGradientEndPoint",["x","y"]);j.addGetterSetter(Ae,"fillRadialGradientEndPointX",0);j.addGetterSetter(Ae,"fillRadialGradientEndPointY",0);j.addGetterSetter(Ae,"fillPatternRotation",0);j.backCompat(Ae,{dashArray:"dash",getDashArray:"getDash",setDashArray:"getDash",drawFunc:"sceneFunc",getDrawFunc:"getSceneFunc",setDrawFunc:"setSceneFunc",drawHitFunc:"hitFunc",getDrawHitFunc:"getHitFunc",setDrawHitFunc:"setHitFunc"});var Dxe="#",zxe="beforeDraw",Fxe="draw",F$=[{x:0,y:0},{x:-1,y:-1},{x:1,y:-1},{x:1,y:1},{x:-1,y:1}],Bxe=F$.length;class Xf extends ia{constructor(t){super(t),this.canvas=new t0,this.hitCanvas=new t_({pixelRatio:1}),this._waitingForDraw=!1,this.on("visibleChange.konva",this._checkVisibility),this._checkVisibility(),this.on("imageSmoothingEnabledChange.konva",this._setSmoothEnabled),this._setSmoothEnabled()}createPNGStream(){return this.canvas._canvas.createPNGStream()}getCanvas(){return this.canvas}getNativeCanvasElement(){return this.canvas._canvas}getHitCanvas(){return this.hitCanvas}getContext(){return this.getCanvas().getContext()}clear(t){return this.getContext().clear(t),this.getHitCanvas().getContext().clear(t),this}setZIndex(t){super.setZIndex(t);var n=this.getStage();return n&&n.content&&(n.content.removeChild(this.getNativeCanvasElement()),t<n.children.length-1?n.content.insertBefore(this.getNativeCanvasElement(),n.children[t+1].getCanvas()._canvas):n.content.appendChild(this.getNativeCanvasElement())),this}moveToTop(){ze.prototype.moveToTop.call(this);var t=this.getStage();return t&&t.content&&(t.content.removeChild(this.getNativeCanvasElement()),t.content.appendChild(this.getNativeCanvasElement())),!0}moveUp(){var t=ze.prototype.moveUp.call(this);if(!t)return!1;var n=this.getStage();return!n||!n.content?!1:(n.content.removeChild(this.getNativeCanvasElement()),this.index<n.children.length-1?n.content.insertBefore(this.getNativeCanvasElement(),n.children[this.index+1].getCanvas()._canvas):n.content.appendChild(this.getNativeCanvasElement()),!0)}moveDown(){if(ze.prototype.moveDown.call(this)){var t=this.getStage();if(t){var n=t.children;t.content&&(t.content.removeChild(this.getNativeCanvasElement()),t.content.insertBefore(this.getNativeCanvasElement(),n[this.index+1].getCanvas()._canvas))}return!0}return!1}moveToBottom(){if(ze.prototype.moveToBottom.call(this)){var t=this.getStage();if(t){var n=t.children;t.content&&(t.content.removeChild(this.getNativeCanvasElement()),t.content.insertBefore(this.getNativeCanvasElement(),n[1].getCanvas()._canvas))}return!0}return!1}getLayer(){return this}remove(){var t=this.getNativeCanvasElement();return ze.prototype.remove.call(this),t&&t.parentNode&&se._isInDocument(t)&&t.parentNode.removeChild(t),this}getStage(){return this.parent}setSize({width:t,height:n}){return this.canvas.setSize(t,n),this.hitCanvas.setSize(t,n),this._setSmoothEnabled(),this}_validateAdd(t){var n=t.getType();n!=="Group"&&n!=="Shape"&&se.throw("You may only add groups and shapes to a layer.")}_toKonvaCanvas(t){return t=t||{},t.width=t.width||this.getWidth(),t.height=t.height||this.getHeight(),t.x=t.x!==void 0?t.x:this.x(),t.y=t.y!==void 0?t.y:this.y(),ze.prototype._toKonvaCanvas.call(this,t)}_checkVisibility(){this.visible()?this.canvas._canvas.style.display="block":this.canvas._canvas.style.display="none"}_setSmoothEnabled(){this.getContext()._context.imageSmoothingEnabled=this.imageSmoothingEnabled()}getWidth(){if(this.parent)return this.parent.width()}setWidth(){se.warn('Can not change width of layer. Use "stage.width(value)" function instead.')}getHeight(){if(this.parent)return this.parent.height()}setHeight(){se.warn('Can not change height of layer. Use "stage.height(value)" function instead.')}batchDraw(){return this._waitingForDraw||(this._waitingForDraw=!0,se.requestAnimFrame(()=>{this.draw(),this._waitingForDraw=!1})),this}getIntersection(t){if(!this.isListening()||!this.isVisible())return null;for(var n=1,r=!1;;){for(let i=0;i<Bxe;i++){const o=F$[i],a=this._getIntersection({x:t.x+o.x*n,y:t.y+o.y*n}),s=a.shape;if(s)return s;if(r=!!a.antialiased,!a.antialiased)break}if(r)n+=1;else return null}}_getIntersection(t){const n=this.hitCanvas.pixelRatio,r=this.hitCanvas.context.getImageData(Math.round(t.x*n),Math.round(t.y*n),1,1).data,i=r[3];if(i===255){const o=se._rgbToHex(r[0],r[1],r[2]),a=lm[Dxe+o];return a?{shape:a}:{antialiased:!0}}else if(i>0)return{antialiased:!0};return{}}drawScene(t,n){var r=this.getLayer(),i=t||r&&r.getCanvas();return this._fire(zxe,{node:this}),this.clearBeforeDraw()&&i.getContext().clear(),ia.prototype.drawScene.call(this,i,n),this._fire(Fxe,{node:this}),this}drawHit(t,n){var r=this.getLayer(),i=t||r&&r.hitCanvas;return r&&r.clearBeforeDraw()&&r.getHitCanvas().getContext().clear(),ia.prototype.drawHit.call(this,i,n),this}enableHitGraph(){return this.hitGraphEnabled(!0),this}disableHitGraph(){return this.hitGraphEnabled(!1),this}setHitGraphEnabled(t){se.warn("hitGraphEnabled method is deprecated. Please use layer.listening() instead."),this.listening(t)}getHitGraphEnabled(t){return se.warn("hitGraphEnabled method is deprecated. Please use layer.listening() instead."),this.listening()}toggleHitCanvas(){if(!(!this.parent||!this.parent.content)){var t=this.parent,n=!!this.hitCanvas._canvas.parentNode;n?t.content.removeChild(this.hitCanvas._canvas):t.content.appendChild(this.hitCanvas._canvas)}}}Xf.prototype.nodeType="Layer";hr(Xf);j.addGetterSetter(Xf,"imageSmoothingEnabled",!0);j.addGetterSetter(Xf,"clearBeforeDraw",!0);j.addGetterSetter(Xf,"hitGraphEnabled",!0,ks());class r_ extends Xf{constructor(t){super(t),this.listening(!1),se.warn('Konva.Fast layer is deprecated. Please use "new Konva.Layer({ listening: false })" instead.')}}r_.prototype.nodeType="FastLayer";hr(r_);class b0 extends ia{_validateAdd(t){var n=t.getType();n!=="Group"&&n!=="Shape"&&se.throw("You may only add groups and shapes to groups.")}}b0.prototype.nodeType="Group";hr(b0);var DS=function(){return e0.performance&&e0.performance.now?function(){return e0.performance.now()}:function(){return new Date().getTime()}}();class Ia{constructor(t,n){this.id=Ia.animIdCounter++,this.frame={time:0,timeDiff:0,lastTime:DS(),frameRate:0},this.func=t,this.setLayers(n)}setLayers(t){var n=[];return t?t.length>0?n=t:n=[t]:n=[],this.layers=n,this}getLayers(){return this.layers}addLayer(t){var n=this.layers,r=n.length,i;for(i=0;i<r;i++)if(n[i]._id===t._id)return!1;return this.layers.push(t),!0}isRunning(){var t=Ia,n=t.animations,r=n.length,i;for(i=0;i<r;i++)if(n[i].id===this.id)return!0;return!1}start(){return this.stop(),this.frame.timeDiff=0,this.frame.lastTime=DS(),Ia._addAnimation(this),this}stop(){return Ia._removeAnimation(this),this}_updateFrameObject(t){this.frame.timeDiff=t-this.frame.lastTime,this.frame.lastTime=t,this.frame.time+=this.frame.timeDiff,this.frame.frameRate=1e3/this.frame.timeDiff}static _addAnimation(t){this.animations.push(t),this._handleAnimation()}static _removeAnimation(t){var n=t.id,r=this.animations,i=r.length,o;for(o=0;o<i;o++)if(r[o].id===n){this.animations.splice(o,1);break}}static _runFrames(){var t={},n=this.animations,r,i,o,a,s,l,d,p,g;for(a=0;a<n.length;a++)if(r=n[a],i=r.layers,o=r.func,r._updateFrameObject(DS()),l=i.length,o?g=o.call(r,r.frame)!==!1:g=!0,!!g)for(s=0;s<l;s++)d=i[s],d._id!==void 0&&(t[d._id]=d);for(p in t)!t.hasOwnProperty(p)||t[p].batchDraw()}static _animationLoop(){var t=Ia;t.animations.length?(t._runFrames(),se.requestAnimFrame(t._animationLoop)):t.animRunning=!1}static _handleAnimation(){this.animRunning||(this.animRunning=!0,se.requestAnimFrame(this._animationLoop))}}Ia.animations=[];Ia.animIdCounter=0;Ia.animRunning=!1;var $xe={node:1,duration:1,easing:1,onFinish:1,yoyo:1},Hxe=1,kA=2,EA=3,Wxe=0,PA=["fill","stroke","shadowColor"];class Vxe{constructor(t,n,r,i,o,a,s){this.prop=t,this.propFunc=n,this.begin=i,this._pos=i,this.duration=a,this._change=0,this.prevPos=0,this.yoyo=s,this._time=0,this._position=0,this._startTime=0,this._finish=0,this.func=r,this._change=o-this.begin,this.pause()}fire(t){var n=this[t];n&&n()}setTime(t){t>this.duration?this.yoyo?(this._time=this.duration,this.reverse()):this.finish():t<0?this.yoyo?(this._time=0,this.play()):this.reset():(this._time=t,this.update())}getTime(){return this._time}setPosition(t){this.prevPos=this._pos,this.propFunc(t),this._pos=t}getPosition(t){return t===void 0&&(t=this._time),this.func(t,this.begin,this._change,this.duration)}play(){this.state=kA,this._startTime=this.getTimer()-this._time,this.onEnterFrame(),this.fire("onPlay")}reverse(){this.state=EA,this._time=this.duration-this._time,this._startTime=this.getTimer()-this._time,this.onEnterFrame(),this.fire("onReverse")}seek(t){this.pause(),this._time=t,this.update(),this.fire("onSeek")}reset(){this.pause(),this._time=0,this.update(),this.fire("onReset")}finish(){this.pause(),this._time=this.duration,this.update(),this.fire("onFinish")}update(){this.setPosition(this.getPosition(this._time)),this.fire("onUpdate")}onEnterFrame(){var t=this.getTimer()-this._startTime;this.state===kA?this.setTime(t):this.state===EA&&this.setTime(this.duration-t)}pause(){this.state=Hxe,this.fire("onPause")}getTimer(){return new Date().getTime()}}class Ir{constructor(t){var n=this,r=t.node,i=r._id,o,a=t.easing||um.Linear,s=!!t.yoyo,l;typeof t.duration>"u"?o=.3:t.duration===0?o=.001:o=t.duration,this.node=r,this._id=Wxe++;var d=r.getLayer()||(r instanceof Je.Stage?r.getLayers():null);d||se.error("Tween constructor have `node` that is not in a layer. Please add node into layer first."),this.anim=new Ia(function(){n.tween.onEnterFrame()},d),this.tween=new Vxe(l,function(p){n._tweenFunc(p)},a,0,1,o*1e3,s),this._addListeners(),Ir.attrs[i]||(Ir.attrs[i]={}),Ir.attrs[i][this._id]||(Ir.attrs[i][this._id]={}),Ir.tweens[i]||(Ir.tweens[i]={});for(l in t)$xe[l]===void 0&&this._addAttr(l,t[l]);this.reset(),this.onFinish=t.onFinish,this.onReset=t.onReset,this.onUpdate=t.onUpdate}_addAttr(t,n){var r=this.node,i=r._id,o,a,s,l,d,p,g,m;if(s=Ir.tweens[i][t],s&&delete Ir.attrs[i][s][t],o=r.getAttr(t),se._isArray(n))if(a=[],d=Math.max(n.length,o.length),t==="points"&&n.length!==o.length&&(n.length>o.length?(g=o,o=se._prepareArrayForTween(o,n,r.closed())):(p=n,n=se._prepareArrayForTween(n,o,r.closed()))),t.indexOf("fill")===0)for(l=0;l<d;l++)if(l%2===0)a.push(n[l]-o[l]);else{var y=se.colorToRGBA(o[l]);m=se.colorToRGBA(n[l]),o[l]=y,a.push({r:m.r-y.r,g:m.g-y.g,b:m.b-y.b,a:m.a-y.a})}else for(l=0;l<d;l++)a.push(n[l]-o[l]);else PA.indexOf(t)!==-1?(o=se.colorToRGBA(o),m=se.colorToRGBA(n),a={r:m.r-o.r,g:m.g-o.g,b:m.b-o.b,a:m.a-o.a}):a=n-o;Ir.attrs[i][this._id][t]={start:o,diff:a,end:n,trueEnd:p,trueStart:g},Ir.tweens[i][t]=this._id}_tweenFunc(t){var n=this.node,r=Ir.attrs[n._id][this._id],i,o,a,s,l,d,p,g;for(i in r){if(o=r[i],a=o.start,s=o.diff,g=o.end,se._isArray(a))if(l=[],p=Math.max(a.length,g.length),i.indexOf("fill")===0)for(d=0;d<p;d++)d%2===0?l.push((a[d]||0)+s[d]*t):l.push("rgba("+Math.round(a[d].r+s[d].r*t)+","+Math.round(a[d].g+s[d].g*t)+","+Math.round(a[d].b+s[d].b*t)+","+(a[d].a+s[d].a*t)+")");else for(d=0;d<p;d++)l.push((a[d]||0)+s[d]*t);else PA.indexOf(i)!==-1?l="rgba("+Math.round(a.r+s.r*t)+","+Math.round(a.g+s.g*t)+","+Math.round(a.b+s.b*t)+","+(a.a+s.a*t)+")":l=a+s*t;n.setAttr(i,l)}}_addListeners(){this.tween.onPlay=()=>{this.anim.start()},this.tween.onReverse=()=>{this.anim.start()},this.tween.onPause=()=>{this.anim.stop()},this.tween.onFinish=()=>{var t=this.node,n=Ir.attrs[t._id][this._id];n.points&&n.points.trueEnd&&t.setAttr("points",n.points.trueEnd),this.onFinish&&this.onFinish.call(this)},this.tween.onReset=()=>{var t=this.node,n=Ir.attrs[t._id][this._id];n.points&&n.points.trueStart&&t.points(n.points.trueStart),this.onReset&&this.onReset()},this.tween.onUpdate=()=>{this.onUpdate&&this.onUpdate.call(this)}}play(){return this.tween.play(),this}reverse(){return this.tween.reverse(),this}reset(){return this.tween.reset(),this}seek(t){return this.tween.seek(t*1e3),this}pause(){return this.tween.pause(),this}finish(){return this.tween.finish(),this}destroy(){var t=this.node._id,n=this._id,r=Ir.tweens[t],i;this.pause();for(i in r)delete Ir.tweens[t][i];delete Ir.attrs[t][n]}}Ir.attrs={};Ir.tweens={};ze.prototype.to=function(e){var t=e.onFinish;e.node=this,e.onFinish=function(){this.destroy(),t&&t()};var n=new Ir(e);n.play()};const um={BackEaseIn(e,t,n,r){var i=1.70158;return n*(e/=r)*e*((i+1)*e-i)+t},BackEaseOut(e,t,n,r){var i=1.70158;return n*((e=e/r-1)*e*((i+1)*e+i)+1)+t},BackEaseInOut(e,t,n,r){var i=1.70158;return(e/=r/2)<1?n/2*(e*e*(((i*=1.525)+1)*e-i))+t:n/2*((e-=2)*e*(((i*=1.525)+1)*e+i)+2)+t},ElasticEaseIn(e,t,n,r,i,o){var a=0;return e===0?t:(e/=r)===1?t+n:(o||(o=r*.3),!i||i<Math.abs(n)?(i=n,a=o/4):a=o/(2*Math.PI)*Math.asin(n/i),-(i*Math.pow(2,10*(e-=1))*Math.sin((e*r-a)*(2*Math.PI)/o))+t)},ElasticEaseOut(e,t,n,r,i,o){var a=0;return e===0?t:(e/=r)===1?t+n:(o||(o=r*.3),!i||i<Math.abs(n)?(i=n,a=o/4):a=o/(2*Math.PI)*Math.asin(n/i),i*Math.pow(2,-10*e)*Math.sin((e*r-a)*(2*Math.PI)/o)+n+t)},ElasticEaseInOut(e,t,n,r,i,o){var a=0;return e===0?t:(e/=r/2)===2?t+n:(o||(o=r*(.3*1.5)),!i||i<Math.abs(n)?(i=n,a=o/4):a=o/(2*Math.PI)*Math.asin(n/i),e<1?-.5*(i*Math.pow(2,10*(e-=1))*Math.sin((e*r-a)*(2*Math.PI)/o))+t:i*Math.pow(2,-10*(e-=1))*Math.sin((e*r-a)*(2*Math.PI)/o)*.5+n+t)},BounceEaseOut(e,t,n,r){return(e/=r)<1/2.75?n*(7.5625*e*e)+t:e<2/2.75?n*(7.5625*(e-=1.5/2.75)*e+.75)+t:e<2.5/2.75?n*(7.5625*(e-=2.25/2.75)*e+.9375)+t:n*(7.5625*(e-=2.625/2.75)*e+.984375)+t},BounceEaseIn(e,t,n,r){return n-um.BounceEaseOut(r-e,0,n,r)+t},BounceEaseInOut(e,t,n,r){return e<r/2?um.BounceEaseIn(e*2,0,n,r)*.5+t:um.BounceEaseOut(e*2-r,0,n,r)*.5+n*.5+t},EaseIn(e,t,n,r){return n*(e/=r)*e+t},EaseOut(e,t,n,r){return-n*(e/=r)*(e-2)+t},EaseInOut(e,t,n,r){return(e/=r/2)<1?n/2*e*e+t:-n/2*(--e*(e-2)-1)+t},StrongEaseIn(e,t,n,r){return n*(e/=r)*e*e*e*e+t},StrongEaseOut(e,t,n,r){return n*((e=e/r-1)*e*e*e*e+1)+t},StrongEaseInOut(e,t,n,r){return(e/=r/2)<1?n/2*e*e*e*e*e+t:n/2*((e-=2)*e*e*e*e+2)+t},Linear(e,t,n,r){return n*e/r+t}},$f=se._assign(Je,{Util:se,Transform:Qo,Node:ze,Container:ia,Stage:wx,stages:d3,Layer:Xf,FastLayer:r_,Group:b0,DD:an,Shape:Ae,shapes:lm,Animation:Ia,Tween:Ir,Easings:um,Context:bx,Canvas:Sx});class _u extends Ae{_sceneFunc(t){var n=Je.getAngle(this.angle()),r=this.clockwise();t.beginPath(),t.arc(0,0,this.outerRadius(),0,n,r),t.arc(0,0,this.innerRadius(),n,0,!r),t.closePath(),t.fillStrokeShape(this)}getWidth(){return this.outerRadius()*2}getHeight(){return this.outerRadius()*2}setWidth(t){this.outerRadius(t/2)}setHeight(t){this.outerRadius(t/2)}getSelfRect(){const t=this.innerRadius(),n=this.outerRadius(),r=this.clockwise(),i=Je.getAngle(r?360-this.angle():this.angle()),o=Math.cos(Math.min(i,Math.PI)),a=1,s=Math.sin(Math.min(Math.max(Math.PI,i),3*Math.PI/2)),l=Math.sin(Math.min(i,Math.PI/2)),d=o*(o>0?t:n),p=a*n,g=s*(s>0?t:n),m=l*(l>0?n:t);return{x:d,y:r?-1*m:g,width:p-d,height:m-g}}}_u.prototype._centroid=!0;_u.prototype.className="Arc";_u.prototype._attrsAffectingSize=["innerRadius","outerRadius"];hr(_u);j.addGetterSetter(_u,"innerRadius",0,De());j.addGetterSetter(_u,"outerRadius",0,De());j.addGetterSetter(_u,"angle",0,De());j.addGetterSetter(_u,"clockwise",!1,ks());function yC(e,t,n,r,i,o,a){var s=Math.sqrt(Math.pow(n-e,2)+Math.pow(r-t,2)),l=Math.sqrt(Math.pow(i-n,2)+Math.pow(o-r,2)),d=a*s/(s+l),p=a*l/(s+l),g=n-d*(i-e),m=r-d*(o-t),y=n+p*(i-e),b=r+p*(o-t);return[g,m,y,b]}function TA(e,t){var n=e.length,r=[],i,o;for(i=2;i<n-2;i+=2)o=yC(e[i-2],e[i-1],e[i],e[i+1],e[i+2],e[i+3],t),!isNaN(o[0])&&(r.push(o[0]),r.push(o[1]),r.push(e[i]),r.push(e[i+1]),r.push(o[2]),r.push(o[3]));return r}class ku extends Ae{constructor(t){super(t),this.on("pointsChange.konva tensionChange.konva closedChange.konva bezierChange.konva",function(){this._clearCache("tensionPoints")})}_sceneFunc(t){var n=this.points(),r=n.length,i=this.tension(),o=this.closed(),a=this.bezier(),s,l,d;if(!!r){if(t.beginPath(),t.moveTo(n[0],n[1]),i!==0&&r>4){for(s=this.getTensionPoints(),l=s.length,d=o?0:4,o||t.quadraticCurveTo(s[0],s[1],s[2],s[3]);d<l-2;)t.bezierCurveTo(s[d++],s[d++],s[d++],s[d++],s[d++],s[d++]);o||t.quadraticCurveTo(s[l-2],s[l-1],n[r-2],n[r-1])}else if(a)for(d=2;d<r;)t.bezierCurveTo(n[d++],n[d++],n[d++],n[d++],n[d++],n[d++]);else for(d=2;d<r;d+=2)t.lineTo(n[d],n[d+1]);o?(t.closePath(),t.fillStrokeShape(this)):t.strokeShape(this)}}getTensionPoints(){return this._getCache("tensionPoints",this._getTensionPoints)}_getTensionPoints(){return this.closed()?this._getTensionPointsClosed():TA(this.points(),this.tension())}_getTensionPointsClosed(){var t=this.points(),n=t.length,r=this.tension(),i=yC(t[n-2],t[n-1],t[0],t[1],t[2],t[3],r),o=yC(t[n-4],t[n-3],t[n-2],t[n-1],t[0],t[1],r),a=TA(t,r),s=[i[2],i[3]].concat(a).concat([o[0],o[1],t[n-2],t[n-1],o[2],o[3],i[0],i[1],t[0],t[1]]);return s}getWidth(){return this.getSelfRect().width}getHeight(){return this.getSelfRect().height}getSelfRect(){var t=this.points();if(t.length<4)return{x:t[0]||0,y:t[1]||0,width:0,height:0};this.tension()!==0?t=[t[0],t[1],...this._getTensionPoints(),t[t.length-2],t[t.length-1]]:t=this.points();for(var n=t[0],r=t[0],i=t[1],o=t[1],a,s,l=0;l<t.length/2;l++)a=t[l*2],s=t[l*2+1],n=Math.min(n,a),r=Math.max(r,a),i=Math.min(i,s),o=Math.max(o,s);return{x:n,y:i,width:r-n,height:o-i}}}ku.prototype.className="Line";ku.prototype._attrsAffectingSize=["points","bezier","tension"];hr(ku);j.addGetterSetter(ku,"closed",!1);j.addGetterSetter(ku,"bezier",!1);j.addGetterSetter(ku,"tension",0,De());j.addGetterSetter(ku,"points",[],J4e());class In extends Ae{constructor(t){super(t),this.dataArray=[],this.pathLength=0,this.dataArray=In.parsePathData(this.data()),this.pathLength=0;for(var n=0;n<this.dataArray.length;++n)this.pathLength+=this.dataArray[n].pathLength;this.on("dataChange.konva",function(){this.dataArray=In.parsePathData(this.data()),this.pathLength=0;for(var r=0;r<this.dataArray.length;++r)this.pathLength+=this.dataArray[r].pathLength})}_sceneFunc(t){var n=this.dataArray;t.beginPath();for(var r=!1,i=0;i<n.length;i++){var o=n[i].command,a=n[i].points;switch(o){case"L":t.lineTo(a[0],a[1]);break;case"M":t.moveTo(a[0],a[1]);break;case"C":t.bezierCurveTo(a[0],a[1],a[2],a[3],a[4],a[5]);break;case"Q":t.quadraticCurveTo(a[0],a[1],a[2],a[3]);break;case"A":var s=a[0],l=a[1],d=a[2],p=a[3],g=a[4],m=a[5],y=a[6],b=a[7],S=d>p?d:p,E=d>p?1:d/p,P=d>p?p/d:1;t.translate(s,l),t.rotate(y),t.scale(E,P),t.arc(0,0,S,g,g+m,1-b),t.scale(1/E,1/P),t.rotate(-y),t.translate(-s,-l);break;case"z":r=!0,t.closePath();break}}!r&&!this.hasFill()?t.strokeShape(this):t.fillStrokeShape(this)}getSelfRect(){var t=[];this.dataArray.forEach(function(d){if(d.command==="A"){var p=d.points[4],g=d.points[5],m=d.points[4]+g,y=Math.PI/180;if(Math.abs(p-m)<y&&(y=Math.abs(p-m)),g<0)for(let b=p-y;b>m;b-=y){const S=In.getPointOnEllipticalArc(d.points[0],d.points[1],d.points[2],d.points[3],b,0);t.push(S.x,S.y)}else for(let b=p+y;b<m;b+=y){const S=In.getPointOnEllipticalArc(d.points[0],d.points[1],d.points[2],d.points[3],b,0);t.push(S.x,S.y)}}else if(d.command==="C")for(let b=0;b<=1;b+=.01){const S=In.getPointOnCubicBezier(b,d.start.x,d.start.y,d.points[0],d.points[1],d.points[2],d.points[3],d.points[4],d.points[5]);t.push(S.x,S.y)}else t=t.concat(d.points)});for(var n=t[0],r=t[0],i=t[1],o=t[1],a,s,l=0;l<t.length/2;l++)a=t[l*2],s=t[l*2+1],isNaN(a)||(n=Math.min(n,a),r=Math.max(r,a)),isNaN(s)||(i=Math.min(i,s),o=Math.max(o,s));return{x:n,y:i,width:r-n,height:o-i}}getLength(){return this.pathLength}getPointAtLength(t){var n,r=0,i=this.dataArray.length;if(!i)return null;for(;r<i&&t>this.dataArray[r].pathLength;)t-=this.dataArray[r].pathLength,++r;if(r===i)return n=this.dataArray[r-1].points.slice(-2),{x:n[0],y:n[1]};if(t<.01)return n=this.dataArray[r].points.slice(0,2),{x:n[0],y:n[1]};var o=this.dataArray[r],a=o.points;switch(o.command){case"L":return In.getPointOnLine(t,o.start.x,o.start.y,a[0],a[1]);case"C":return In.getPointOnCubicBezier(t/o.pathLength,o.start.x,o.start.y,a[0],a[1],a[2],a[3],a[4],a[5]);case"Q":return In.getPointOnQuadraticBezier(t/o.pathLength,o.start.x,o.start.y,a[0],a[1],a[2],a[3]);case"A":var s=a[0],l=a[1],d=a[2],p=a[3],g=a[4],m=a[5],y=a[6];return g+=m*t/o.pathLength,In.getPointOnEllipticalArc(s,l,d,p,g,y)}return null}static getLineLength(t,n,r,i){return Math.sqrt((r-t)*(r-t)+(i-n)*(i-n))}static getPointOnLine(t,n,r,i,o,a,s){a===void 0&&(a=n),s===void 0&&(s=r);var l=(o-r)/(i-n+1e-8),d=Math.sqrt(t*t/(1+l*l));i<n&&(d*=-1);var p=l*d,g;if(i===n)g={x:a,y:s+p};else if((s-r)/(a-n+1e-8)===l)g={x:a+d,y:s+p};else{var m,y,b=this.getLineLength(n,r,i,o),S=(a-n)*(i-n)+(s-r)*(o-r);S=S/(b*b),m=n+S*(i-n),y=r+S*(o-r);var E=this.getLineLength(a,s,m,y),P=Math.sqrt(t*t-E*E);d=Math.sqrt(P*P/(1+l*l)),i<n&&(d*=-1),p=l*d,g={x:m+d,y:y+p}}return g}static getPointOnCubicBezier(t,n,r,i,o,a,s,l,d){function p(E){return E*E*E}function g(E){return 3*E*E*(1-E)}function m(E){return 3*E*(1-E)*(1-E)}function y(E){return(1-E)*(1-E)*(1-E)}var b=l*p(t)+a*g(t)+i*m(t)+n*y(t),S=d*p(t)+s*g(t)+o*m(t)+r*y(t);return{x:b,y:S}}static getPointOnQuadraticBezier(t,n,r,i,o,a,s){function l(y){return y*y}function d(y){return 2*y*(1-y)}function p(y){return(1-y)*(1-y)}var g=a*l(t)+i*d(t)+n*p(t),m=s*l(t)+o*d(t)+r*p(t);return{x:g,y:m}}static getPointOnEllipticalArc(t,n,r,i,o,a){var s=Math.cos(a),l=Math.sin(a),d={x:r*Math.cos(o),y:i*Math.sin(o)};return{x:t+(d.x*s-d.y*l),y:n+(d.x*l+d.y*s)}}static parsePathData(t){if(!t)return[];var n=t,r=["m","M","l","L","v","V","h","H","z","Z","c","C","q","Q","t","T","s","S","a","A"];n=n.replace(new RegExp(" ","g"),",");for(var i=0;i<r.length;i++)n=n.replace(new RegExp(r[i],"g"),"|"+r[i]);var o=n.split("|"),a=[],s=[],l=0,d=0,p=/([-+]?((\d+\.\d+)|((\d+)|(\.\d+)))(?:e[-+]?\d+)?)/gi,g;for(i=1;i<o.length;i++){var m=o[i],y=m.charAt(0);for(m=m.slice(1),s.length=0;g=p.exec(m);)s.push(g[0]);for(var b=[],S=0,E=s.length;S<E;S++){if(s[S]==="00"){b.push(0,0);continue}var P=parseFloat(s[S]);isNaN(P)?b.push(0):b.push(P)}for(;b.length>0&&!isNaN(b[0]);){var k=null,L=[],I=l,O=d,N,D,F,U,W,q,pe,fe,ye,Pe;switch(y){case"l":l+=b.shift(),d+=b.shift(),k="L",L.push(l,d);break;case"L":l=b.shift(),d=b.shift(),L.push(l,d);break;case"m":var xe=b.shift(),Y=b.shift();if(l+=xe,d+=Y,k="M",a.length>2&&a[a.length-1].command==="z"){for(var V=a.length-2;V>=0;V--)if(a[V].command==="M"){l=a[V].points[0]+xe,d=a[V].points[1]+Y;break}}L.push(l,d),y="l";break;case"M":l=b.shift(),d=b.shift(),k="M",L.push(l,d),y="L";break;case"h":l+=b.shift(),k="L",L.push(l,d);break;case"H":l=b.shift(),k="L",L.push(l,d);break;case"v":d+=b.shift(),k="L",L.push(l,d);break;case"V":d=b.shift(),k="L",L.push(l,d);break;case"C":L.push(b.shift(),b.shift(),b.shift(),b.shift()),l=b.shift(),d=b.shift(),L.push(l,d);break;case"c":L.push(l+b.shift(),d+b.shift(),l+b.shift(),d+b.shift()),l+=b.shift(),d+=b.shift(),k="C",L.push(l,d);break;case"S":D=l,F=d,N=a[a.length-1],N.command==="C"&&(D=l+(l-N.points[2]),F=d+(d-N.points[3])),L.push(D,F,b.shift(),b.shift()),l=b.shift(),d=b.shift(),k="C",L.push(l,d);break;case"s":D=l,F=d,N=a[a.length-1],N.command==="C"&&(D=l+(l-N.points[2]),F=d+(d-N.points[3])),L.push(D,F,l+b.shift(),d+b.shift()),l+=b.shift(),d+=b.shift(),k="C",L.push(l,d);break;case"Q":L.push(b.shift(),b.shift()),l=b.shift(),d=b.shift(),L.push(l,d);break;case"q":L.push(l+b.shift(),d+b.shift()),l+=b.shift(),d+=b.shift(),k="Q",L.push(l,d);break;case"T":D=l,F=d,N=a[a.length-1],N.command==="Q"&&(D=l+(l-N.points[0]),F=d+(d-N.points[1])),l=b.shift(),d=b.shift(),k="Q",L.push(D,F,l,d);break;case"t":D=l,F=d,N=a[a.length-1],N.command==="Q"&&(D=l+(l-N.points[0]),F=d+(d-N.points[1])),l+=b.shift(),d+=b.shift(),k="Q",L.push(D,F,l,d);break;case"A":U=b.shift(),W=b.shift(),q=b.shift(),pe=b.shift(),fe=b.shift(),ye=l,Pe=d,l=b.shift(),d=b.shift(),k="A",L=this.convertEndpointToCenterParameterization(ye,Pe,l,d,pe,fe,U,W,q);break;case"a":U=b.shift(),W=b.shift(),q=b.shift(),pe=b.shift(),fe=b.shift(),ye=l,Pe=d,l+=b.shift(),d+=b.shift(),k="A",L=this.convertEndpointToCenterParameterization(ye,Pe,l,d,pe,fe,U,W,q);break}a.push({command:k||y,points:L,start:{x:I,y:O},pathLength:this.calcLength(I,O,k||y,L)})}(y==="z"||y==="Z")&&a.push({command:"z",points:[],start:void 0,pathLength:0})}return a}static calcLength(t,n,r,i){var o,a,s,l,d=In;switch(r){case"L":return d.getLineLength(t,n,i[0],i[1]);case"C":for(o=0,a=d.getPointOnCubicBezier(0,t,n,i[0],i[1],i[2],i[3],i[4],i[5]),l=.01;l<=1;l+=.01)s=d.getPointOnCubicBezier(l,t,n,i[0],i[1],i[2],i[3],i[4],i[5]),o+=d.getLineLength(a.x,a.y,s.x,s.y),a=s;return o;case"Q":for(o=0,a=d.getPointOnQuadraticBezier(0,t,n,i[0],i[1],i[2],i[3]),l=.01;l<=1;l+=.01)s=d.getPointOnQuadraticBezier(l,t,n,i[0],i[1],i[2],i[3]),o+=d.getLineLength(a.x,a.y,s.x,s.y),a=s;return o;case"A":o=0;var p=i[4],g=i[5],m=i[4]+g,y=Math.PI/180;if(Math.abs(p-m)<y&&(y=Math.abs(p-m)),a=d.getPointOnEllipticalArc(i[0],i[1],i[2],i[3],p,0),g<0)for(l=p-y;l>m;l-=y)s=d.getPointOnEllipticalArc(i[0],i[1],i[2],i[3],l,0),o+=d.getLineLength(a.x,a.y,s.x,s.y),a=s;else for(l=p+y;l<m;l+=y)s=d.getPointOnEllipticalArc(i[0],i[1],i[2],i[3],l,0),o+=d.getLineLength(a.x,a.y,s.x,s.y),a=s;return s=d.getPointOnEllipticalArc(i[0],i[1],i[2],i[3],m,0),o+=d.getLineLength(a.x,a.y,s.x,s.y),o}return 0}static convertEndpointToCenterParameterization(t,n,r,i,o,a,s,l,d){var p=d*(Math.PI/180),g=Math.cos(p)*(t-r)/2+Math.sin(p)*(n-i)/2,m=-1*Math.sin(p)*(t-r)/2+Math.cos(p)*(n-i)/2,y=g*g/(s*s)+m*m/(l*l);y>1&&(s*=Math.sqrt(y),l*=Math.sqrt(y));var b=Math.sqrt((s*s*(l*l)-s*s*(m*m)-l*l*(g*g))/(s*s*(m*m)+l*l*(g*g)));o===a&&(b*=-1),isNaN(b)&&(b=0);var S=b*s*m/l,E=b*-l*g/s,P=(t+r)/2+Math.cos(p)*S-Math.sin(p)*E,k=(n+i)/2+Math.sin(p)*S+Math.cos(p)*E,L=function(W){return Math.sqrt(W[0]*W[0]+W[1]*W[1])},I=function(W,q){return(W[0]*q[0]+W[1]*q[1])/(L(W)*L(q))},O=function(W,q){return(W[0]*q[1]<W[1]*q[0]?-1:1)*Math.acos(I(W,q))},N=O([1,0],[(g-S)/s,(m-E)/l]),D=[(g-S)/s,(m-E)/l],F=[(-1*g-S)/s,(-1*m-E)/l],U=O(D,F);return I(D,F)<=-1&&(U=Math.PI),I(D,F)>=1&&(U=0),a===0&&U>0&&(U=U-2*Math.PI),a===1&&U<0&&(U=U+2*Math.PI),[P,k,s,l,N,U,p,a]}}In.prototype.className="Path";In.prototype._attrsAffectingSize=["data"];hr(In);j.addGetterSetter(In,"data");class Qf extends ku{_sceneFunc(t){super._sceneFunc(t);var n=Math.PI*2,r=this.points(),i=r,o=this.tension()!==0&&r.length>4;o&&(i=this.getTensionPoints());var a=this.pointerLength(),s=r.length,l,d;if(o){const m=[i[i.length-4],i[i.length-3],i[i.length-2],i[i.length-1],r[s-2],r[s-1]],y=In.calcLength(i[i.length-4],i[i.length-3],"C",m),b=In.getPointOnQuadraticBezier(Math.min(1,1-a/y),m[0],m[1],m[2],m[3],m[4],m[5]);l=r[s-2]-b.x,d=r[s-1]-b.y}else l=r[s-2]-r[s-4],d=r[s-1]-r[s-3];var p=(Math.atan2(d,l)+n)%n,g=this.pointerWidth();this.pointerAtEnding()&&(t.save(),t.beginPath(),t.translate(r[s-2],r[s-1]),t.rotate(p),t.moveTo(0,0),t.lineTo(-a,g/2),t.lineTo(-a,-g/2),t.closePath(),t.restore(),this.__fillStroke(t)),this.pointerAtBeginning()&&(t.save(),t.beginPath(),t.translate(r[0],r[1]),o?(l=(i[0]+i[2])/2-r[0],d=(i[1]+i[3])/2-r[1]):(l=r[2]-r[0],d=r[3]-r[1]),t.rotate((Math.atan2(-d,-l)+n)%n),t.moveTo(0,0),t.lineTo(-a,g/2),t.lineTo(-a,-g/2),t.closePath(),t.restore(),this.__fillStroke(t))}__fillStroke(t){var n=this.dashEnabled();n&&(this.attrs.dashEnabled=!1,t.setLineDash([])),t.fillStrokeShape(this),n&&(this.attrs.dashEnabled=!0)}getSelfRect(){const t=super.getSelfRect(),n=this.pointerWidth()/2;return{x:t.x-n,y:t.y-n,width:t.width+n*2,height:t.height+n*2}}}Qf.prototype.className="Arrow";hr(Qf);j.addGetterSetter(Qf,"pointerLength",10,De());j.addGetterSetter(Qf,"pointerWidth",10,De());j.addGetterSetter(Qf,"pointerAtBeginning",!1);j.addGetterSetter(Qf,"pointerAtEnding",!0);class $0 extends Ae{_sceneFunc(t){t.beginPath(),t.arc(0,0,this.attrs.radius||0,0,Math.PI*2,!1),t.closePath(),t.fillStrokeShape(this)}getWidth(){return this.radius()*2}getHeight(){return this.radius()*2}setWidth(t){this.radius()!==t/2&&this.radius(t/2)}setHeight(t){this.radius()!==t/2&&this.radius(t/2)}}$0.prototype._centroid=!0;$0.prototype.className="Circle";$0.prototype._attrsAffectingSize=["radius"];hr($0);j.addGetterSetter($0,"radius",0,De());class ld extends Ae{_sceneFunc(t){var n=this.radiusX(),r=this.radiusY();t.beginPath(),t.save(),n!==r&&t.scale(1,r/n),t.arc(0,0,n,0,Math.PI*2,!1),t.restore(),t.closePath(),t.fillStrokeShape(this)}getWidth(){return this.radiusX()*2}getHeight(){return this.radiusY()*2}setWidth(t){this.radiusX(t/2)}setHeight(t){this.radiusY(t/2)}}ld.prototype.className="Ellipse";ld.prototype._centroid=!0;ld.prototype._attrsAffectingSize=["radiusX","radiusY"];hr(ld);j.addComponentsGetterSetter(ld,"radius",["x","y"]);j.addGetterSetter(ld,"radiusX",0,De());j.addGetterSetter(ld,"radiusY",0,De());class Es extends Ae{constructor(t){super(t),this.on("imageChange.konva",()=>{this._setImageLoad()}),this._setImageLoad()}_setImageLoad(){const t=this.image();t&&t.complete||t&&t.readyState===4||t&&t.addEventListener&&t.addEventListener("load",()=>{this._requestDraw()})}_useBufferCanvas(){return super._useBufferCanvas(!0)}_sceneFunc(t){const n=this.getWidth(),r=this.getHeight(),i=this.attrs.image;let o;if(i){const a=this.attrs.cropWidth,s=this.attrs.cropHeight;a&&s?o=[i,this.cropX(),this.cropY(),a,s,0,0,n,r]:o=[i,0,0,n,r]}(this.hasFill()||this.hasStroke())&&(t.beginPath(),t.rect(0,0,n,r),t.closePath(),t.fillStrokeShape(this)),i&&t.drawImage.apply(t,o)}_hitFunc(t){var n=this.width(),r=this.height();t.beginPath(),t.rect(0,0,n,r),t.closePath(),t.fillStrokeShape(this)}getWidth(){var t,n;return(t=this.attrs.width)!==null&&t!==void 0?t:(n=this.image())===null||n===void 0?void 0:n.width}getHeight(){var t,n;return(t=this.attrs.height)!==null&&t!==void 0?t:(n=this.image())===null||n===void 0?void 0:n.height}static fromURL(t,n,r=null){var i=se.createImageElement();i.onload=function(){var o=new Es({image:i});n(o)},i.onerror=r,i.crossOrigin="Anonymous",i.src=t}}Es.prototype.className="Image";hr(Es);j.addGetterSetter(Es,"image");j.addComponentsGetterSetter(Es,"crop",["x","y","width","height"]);j.addGetterSetter(Es,"cropX",0,De());j.addGetterSetter(Es,"cropY",0,De());j.addGetterSetter(Es,"cropWidth",0,De());j.addGetterSetter(Es,"cropHeight",0,De());var B$=["fontFamily","fontSize","fontStyle","padding","lineHeight","text","width","height","pointerDirection","pointerWidth","pointerHeight"],Uxe="Change.konva",Gxe="none",xC="up",bC="right",SC="down",wC="left",jxe=B$.length;class i_ extends b0{constructor(t){super(t),this.on("add.konva",function(n){this._addListeners(n.child),this._sync()})}getText(){return this.find("Text")[0]}getTag(){return this.find("Tag")[0]}_addListeners(t){var n=this,r,i=function(){n._sync()};for(r=0;r<jxe;r++)t.on(B$[r]+Uxe,i)}getWidth(){return this.getText().width()}getHeight(){return this.getText().height()}_sync(){var t=this.getText(),n=this.getTag(),r,i,o,a,s,l,d;if(t&&n){switch(r=t.width(),i=t.height(),o=n.pointerDirection(),a=n.pointerWidth(),d=n.pointerHeight(),s=0,l=0,o){case xC:s=r/2,l=-1*d;break;case bC:s=r+a,l=i/2;break;case SC:s=r/2,l=i+d;break;case wC:s=-1*a,l=i/2;break}n.setAttrs({x:-1*s,y:-1*l,width:r,height:i}),t.setAttrs({x:-1*s,y:-1*l})}}}i_.prototype.className="Label";hr(i_);class Jf extends Ae{_sceneFunc(t){var n=this.width(),r=this.height(),i=this.pointerDirection(),o=this.pointerWidth(),a=this.pointerHeight(),s=this.cornerRadius();let l=0,d=0,p=0,g=0;typeof s=="number"?l=d=p=g=Math.min(s,n/2,r/2):(l=Math.min(s[0]||0,n/2,r/2),d=Math.min(s[1]||0,n/2,r/2),g=Math.min(s[2]||0,n/2,r/2),p=Math.min(s[3]||0,n/2,r/2)),t.beginPath(),t.moveTo(l,0),i===xC&&(t.lineTo((n-o)/2,0),t.lineTo(n/2,-1*a),t.lineTo((n+o)/2,0)),t.lineTo(n-d,0),t.arc(n-d,d,d,Math.PI*3/2,0,!1),i===bC&&(t.lineTo(n,(r-a)/2),t.lineTo(n+o,r/2),t.lineTo(n,(r+a)/2)),t.lineTo(n,r-g),t.arc(n-g,r-g,g,0,Math.PI/2,!1),i===SC&&(t.lineTo((n+o)/2,r),t.lineTo(n/2,r+a),t.lineTo((n-o)/2,r)),t.lineTo(p,r),t.arc(p,r-p,p,Math.PI/2,Math.PI,!1),i===wC&&(t.lineTo(0,(r+a)/2),t.lineTo(-1*o,r/2),t.lineTo(0,(r-a)/2)),t.lineTo(0,l),t.arc(l,l,l,Math.PI,Math.PI*3/2,!1),t.closePath(),t.fillStrokeShape(this)}getSelfRect(){var t=0,n=0,r=this.pointerWidth(),i=this.pointerHeight(),o=this.pointerDirection(),a=this.width(),s=this.height();return o===xC?(n-=i,s+=i):o===SC?s+=i:o===wC?(t-=r*1.5,a+=r):o===bC&&(a+=r*1.5),{x:t,y:n,width:a,height:s}}}Jf.prototype.className="Tag";hr(Jf);j.addGetterSetter(Jf,"pointerDirection",Gxe);j.addGetterSetter(Jf,"pointerWidth",0,De());j.addGetterSetter(Jf,"pointerHeight",0,De());j.addGetterSetter(Jf,"cornerRadius",0,y$(4));class Lv extends Ae{_sceneFunc(t){var n=this.cornerRadius(),r=this.width(),i=this.height();if(t.beginPath(),!n)t.rect(0,0,r,i);else{let o=0,a=0,s=0,l=0;typeof n=="number"?o=a=s=l=Math.min(n,r/2,i/2):(o=Math.min(n[0]||0,r/2,i/2),a=Math.min(n[1]||0,r/2,i/2),l=Math.min(n[2]||0,r/2,i/2),s=Math.min(n[3]||0,r/2,i/2)),t.moveTo(o,0),t.lineTo(r-a,0),t.arc(r-a,a,a,Math.PI*3/2,0,!1),t.lineTo(r,i-l),t.arc(r-l,i-l,l,0,Math.PI/2,!1),t.lineTo(s,i),t.arc(s,i-s,s,Math.PI/2,Math.PI,!1),t.lineTo(0,o),t.arc(o,o,o,Math.PI,Math.PI*3/2,!1)}t.closePath(),t.fillStrokeShape(this)}}Lv.prototype.className="Rect";hr(Lv);j.addGetterSetter(Lv,"cornerRadius",0,y$(4));class eh extends Ae{_sceneFunc(t){const n=this._getPoints();t.beginPath(),t.moveTo(n[0].x,n[0].y);for(var r=1;r<n.length;r++)t.lineTo(n[r].x,n[r].y);t.closePath(),t.fillStrokeShape(this)}_getPoints(){const t=this.attrs.sides,n=this.attrs.radius||0,r=[];for(var i=0;i<t;i++)r.push({x:n*Math.sin(i*2*Math.PI/t),y:-1*n*Math.cos(i*2*Math.PI/t)});return r}getSelfRect(){const t=this._getPoints();var n=t[0].x,r=t[0].y,i=t[0].x,o=t[0].y;return t.forEach(a=>{n=Math.min(n,a.x),r=Math.max(r,a.x),i=Math.min(i,a.y),o=Math.max(o,a.y)}),{x:n,y:i,width:r-n,height:o-i}}getWidth(){return this.radius()*2}getHeight(){return this.radius()*2}setWidth(t){this.radius(t/2)}setHeight(t){this.radius(t/2)}}eh.prototype.className="RegularPolygon";eh.prototype._centroid=!0;eh.prototype._attrsAffectingSize=["radius"];hr(eh);j.addGetterSetter(eh,"radius",0,De());j.addGetterSetter(eh,"sides",0,De());var LA=Math.PI*2;class th extends Ae{_sceneFunc(t){t.beginPath(),t.arc(0,0,this.innerRadius(),0,LA,!1),t.moveTo(this.outerRadius(),0),t.arc(0,0,this.outerRadius(),LA,0,!0),t.closePath(),t.fillStrokeShape(this)}getWidth(){return this.outerRadius()*2}getHeight(){return this.outerRadius()*2}setWidth(t){this.outerRadius(t/2)}setHeight(t){this.outerRadius(t/2)}}th.prototype.className="Ring";th.prototype._centroid=!0;th.prototype._attrsAffectingSize=["innerRadius","outerRadius"];hr(th);j.addGetterSetter(th,"innerRadius",0,De());j.addGetterSetter(th,"outerRadius",0,De());class Ll extends Ae{constructor(t){super(t),this._updated=!0,this.anim=new Ia(()=>{var n=this._updated;return this._updated=!1,n}),this.on("animationChange.konva",function(){this.frameIndex(0)}),this.on("frameIndexChange.konva",function(){this._updated=!0}),this.on("frameRateChange.konva",function(){!this.anim.isRunning()||(clearInterval(this.interval),this._setInterval())})}_sceneFunc(t){var n=this.animation(),r=this.frameIndex(),i=r*4,o=this.animations()[n],a=this.frameOffsets(),s=o[i+0],l=o[i+1],d=o[i+2],p=o[i+3],g=this.image();if((this.hasFill()||this.hasStroke())&&(t.beginPath(),t.rect(0,0,d,p),t.closePath(),t.fillStrokeShape(this)),g)if(a){var m=a[n],y=r*2;t.drawImage(g,s,l,d,p,m[y+0],m[y+1],d,p)}else t.drawImage(g,s,l,d,p,0,0,d,p)}_hitFunc(t){var n=this.animation(),r=this.frameIndex(),i=r*4,o=this.animations()[n],a=this.frameOffsets(),s=o[i+2],l=o[i+3];if(t.beginPath(),a){var d=a[n],p=r*2;t.rect(d[p+0],d[p+1],s,l)}else t.rect(0,0,s,l);t.closePath(),t.fillShape(this)}_useBufferCanvas(){return super._useBufferCanvas(!0)}_setInterval(){var t=this;this.interval=setInterval(function(){t._updateIndex()},1e3/this.frameRate())}start(){if(!this.isRunning()){var t=this.getLayer();this.anim.setLayers(t),this._setInterval(),this.anim.start()}}stop(){this.anim.stop(),clearInterval(this.interval)}isRunning(){return this.anim.isRunning()}_updateIndex(){var t=this.frameIndex(),n=this.animation(),r=this.animations(),i=r[n],o=i.length/4;t<o-1?this.frameIndex(t+1):this.frameIndex(0)}}Ll.prototype.className="Sprite";hr(Ll);j.addGetterSetter(Ll,"animation");j.addGetterSetter(Ll,"animations");j.addGetterSetter(Ll,"frameOffsets");j.addGetterSetter(Ll,"image");j.addGetterSetter(Ll,"frameIndex",0,De());j.addGetterSetter(Ll,"frameRate",17,De());j.backCompat(Ll,{index:"frameIndex",getIndex:"getFrameIndex",setIndex:"setFrameIndex"});class ud extends Ae{_sceneFunc(t){var n=this.innerRadius(),r=this.outerRadius(),i=this.numPoints();t.beginPath(),t.moveTo(0,0-r);for(var o=1;o<i*2;o++){var a=o%2===0?r:n,s=a*Math.sin(o*Math.PI/i),l=-1*a*Math.cos(o*Math.PI/i);t.lineTo(s,l)}t.closePath(),t.fillStrokeShape(this)}getWidth(){return this.outerRadius()*2}getHeight(){return this.outerRadius()*2}setWidth(t){this.outerRadius(t/2)}setHeight(t){this.outerRadius(t/2)}}ud.prototype.className="Star";ud.prototype._centroid=!0;ud.prototype._attrsAffectingSize=["innerRadius","outerRadius"];hr(ud);j.addGetterSetter(ud,"numPoints",5,De());j.addGetterSetter(ud,"innerRadius",0,De());j.addGetterSetter(ud,"outerRadius",0,De());function $$(e){return Array.from(e)}var dp="auto",qxe="center",fg="justify",Kxe="Change.konva",Zxe="2d",AA="-",H$="left",Yxe="text",Xxe="Text",Qxe="top",Jxe="bottom",IA="middle",W$="normal",ebe="px ",wy=" ",tbe="right",nbe="word",rbe="char",MA="none",zS="\u2026",V$=["fontFamily","fontSize","fontStyle","fontVariant","padding","align","verticalAlign","lineHeight","text","width","height","wrap","ellipsis","letterSpacing"],ibe=V$.length;function obe(e){return e.split(",").map(t=>{t=t.trim();const n=t.indexOf(" ")>=0,r=t.indexOf('"')>=0||t.indexOf("'")>=0;return n&&!r&&(t=`"${t}"`),t}).join(", ")}var Cy;function FS(){return Cy||(Cy=se.createCanvasElement().getContext(Zxe),Cy)}function abe(e){e.fillText(this._partialText,this._partialTextX,this._partialTextY)}function sbe(e){e.strokeText(this._partialText,this._partialTextX,this._partialTextY)}function lbe(e){return e=e||{},!e.fillLinearGradientColorStops&&!e.fillRadialGradientColorStops&&!e.fillPatternImage&&(e.fill=e.fill||"black"),e}class fr extends Ae{constructor(t){super(lbe(t)),this._partialTextX=0,this._partialTextY=0;for(var n=0;n<ibe;n++)this.on(V$[n]+Kxe,this._setTextData);this._setTextData()}_sceneFunc(t){var n=this.textArr,r=n.length;if(!!this.text()){var i=this.padding(),o=this.fontSize(),a=this.lineHeight()*o,s=this.verticalAlign(),l=0,d=this.align(),p=this.getWidth(),g=this.letterSpacing(),m=this.fill(),y=this.textDecoration(),b=y.indexOf("underline")!==-1,S=y.indexOf("line-through")!==-1,E,P=0,P=a/2,k=0,L=0;for(t.setAttr("font",this._getContextFont()),t.setAttr("textBaseline",IA),t.setAttr("textAlign",H$),s===IA?l=(this.getHeight()-r*a-i*2)/2:s===Jxe&&(l=this.getHeight()-r*a-i*2),t.translate(i,l+i),E=0;E<r;E++){var k=0,L=0,I=n[E],O=I.text,N=I.width,D=I.lastInParagraph,F,U,W;if(t.save(),d===tbe?k+=p-N-i*2:d===qxe&&(k+=(p-N-i*2)/2),b&&(t.save(),t.beginPath(),t.moveTo(k,P+L+Math.round(o/2)),F=O.split(" ").length-1,U=F===0,W=d===fg&&D&&!U?p-i*2:N,t.lineTo(k+Math.round(W),P+L+Math.round(o/2)),t.lineWidth=o/15,t.strokeStyle=m,t.stroke(),t.restore()),S&&(t.save(),t.beginPath(),t.moveTo(k,P+L),F=O.split(" ").length-1,U=F===0,W=d===fg&&D&&!U?p-i*2:N,t.lineTo(k+Math.round(W),P+L),t.lineWidth=o/15,t.strokeStyle=m,t.stroke(),t.restore()),g!==0||d===fg){F=O.split(" ").length-1;for(var q=$$(O),pe=0;pe<q.length;pe++){var fe=q[pe];fe===" "&&!D&&d===fg&&(k+=(p-i*2-N)/F),this._partialTextX=k,this._partialTextY=P+L,this._partialText=fe,t.fillStrokeShape(this),k+=this.measureSize(fe).width+g}}else this._partialTextX=k,this._partialTextY=P+L,this._partialText=O,t.fillStrokeShape(this);t.restore(),r>1&&(P+=a)}}}_hitFunc(t){var n=this.getWidth(),r=this.getHeight();t.beginPath(),t.rect(0,0,n,r),t.closePath(),t.fillStrokeShape(this)}setText(t){var n=se._isString(t)?t:t==null?"":t+"";return this._setAttr(Yxe,n),this}getWidth(){var t=this.attrs.width===dp||this.attrs.width===void 0;return t?this.getTextWidth()+this.padding()*2:this.attrs.width}getHeight(){var t=this.attrs.height===dp||this.attrs.height===void 0;return t?this.fontSize()*this.textArr.length*this.lineHeight()+this.padding()*2:this.attrs.height}getTextWidth(){return this.textWidth}getTextHeight(){return se.warn("text.getTextHeight() method is deprecated. Use text.height() - for full height and text.fontSize() - for one line height."),this.textHeight}measureSize(t){var n=FS(),r=this.fontSize(),i;return n.save(),n.font=this._getContextFont(),i=n.measureText(t),n.restore(),{width:i.width,height:r}}_getContextFont(){return this.fontStyle()+wy+this.fontVariant()+wy+(this.fontSize()+ebe)+obe(this.fontFamily())}_addTextLine(t){this.align()===fg&&(t=t.trim());var n=this._getTextWidth(t);return this.textArr.push({text:t,width:n,lastInParagraph:!1})}_getTextWidth(t){var n=this.letterSpacing(),r=t.length;return FS().measureText(t).width+(r?n*(r-1):0)}_setTextData(){var t=this.text().split(`
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pe=t.dataArray,fe=S+1;fe<pe.length;fe++){if(pe[fe].pathLength>0)return S=fe,pe[fe];pe[fe].command==="M"&&(m={x:pe[fe].points[0],y:pe[fe].points[1]})}return{}},k=function(pe){var fe=t._getTextSize(pe).width+r;pe===" "&&i==="justify"&&(fe+=(s-a)/g);var ye=0,Pe=0;for(y=void 0;Math.abs(fe-ye)/fe>.01&&Pe<20;){Pe++;for(var xe=ye;b===void 0;)b=P(),b&&xe+b.pathLength<fe&&(xe+=b.pathLength,b=void 0);if(Object.keys(b).length===0||m===void 0)return;var Y=!1;switch(b.command){case"L":In.getLineLength(m.x,m.y,b.points[0],b.points[1])>fe?y=In.getPointOnLine(fe,m.x,m.y,b.points[0],b.points[1],m.x,m.y):b=void 0;break;case"A":var 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q$="tr-konva",cbe=["resizeEnabledChange","rotateAnchorOffsetChange","rotateEnabledChange","enabledAnchorsChange","anchorSizeChange","borderEnabledChange","borderStrokeChange","borderStrokeWidthChange","borderDashChange","anchorStrokeChange","anchorStrokeWidthChange","anchorFillChange","anchorCornerRadiusChange","ignoreStrokeChange"].map(e=>e+`.${q$}`).join(" "),RA="nodesRect",dbe=["widthChange","heightChange","scaleXChange","scaleYChange","skewXChange","skewYChange","rotationChange","offsetXChange","offsetYChange","transformsEnabledChange","strokeWidthChange"],fbe={"top-left":-45,"top-center":0,"top-right":45,"middle-right":-90,"middle-left":90,"bottom-left":-135,"bottom-center":180,"bottom-right":135};const hbe="ontouchstart"in Je._global;function pbe(e,t){if(e==="rotater")return"crosshair";t+=se.degToRad(fbe[e]||0);var n=(se.radToDeg(t)%360+360)%360;return se._inRange(n,315+22.5,360)||se._inRange(n,0,22.5)?"ns-resize":se._inRange(n,45-22.5,45+22.5)?"nesw-resize":se._inRange(n,90-22.5,90+22.5)?"ew-resize":se._inRange(n,135-22.5,135+22.5)?"nwse-resize":se._inRange(n,180-22.5,180+22.5)?"ns-resize":se._inRange(n,225-22.5,225+22.5)?"nesw-resize":se._inRange(n,270-22.5,270+22.5)?"ew-resize":se._inRange(n,315-22.5,315+22.5)?"nwse-resize":(se.error("Transformer has unknown angle for cursor detection: "+n),"pointer")}var O5=["top-left","top-center","top-right","middle-right","middle-left","bottom-left","bottom-center","bottom-right"],OA=1e8;function gbe(e){return{x:e.x+e.width/2*Math.cos(e.rotation)+e.height/2*Math.sin(-e.rotation),y:e.y+e.height/2*Math.cos(e.rotation)+e.width/2*Math.sin(e.rotation)}}function K$(e,t,n){const r=n.x+(e.x-n.x)*Math.cos(t)-(e.y-n.y)*Math.sin(t),i=n.y+(e.x-n.x)*Math.sin(t)+(e.y-n.y)*Math.cos(t);return Object.assign(Object.assign({},e),{rotation:e.rotation+t,x:r,y:i})}function mbe(e,t){const n=gbe(e);return K$(e,t,n)}function vbe(e,t,n){let r=t;for(let i=0;i<e.length;i++){const o=Je.getAngle(e[i]),a=Math.abs(o-t)%(Math.PI*2);Math.min(a,Math.PI*2-a)<n&&(r=o)}return r}class bn extends b0{constructor(t){super(t),this._transforming=!1,this._createElements(),this._handleMouseMove=this._handleMouseMove.bind(this),this._handleMouseUp=this._handleMouseUp.bind(this),this.update=this.update.bind(this),this.on(cbe,this.update),this.getNode()&&this.update()}attachTo(t){return this.setNode(t),this}setNode(t){return se.warn("tr.setNode(shape), tr.node(shape) and tr.attachTo(shape) methods are deprecated. Please use tr.nodes(nodesArray) instead."),this.setNodes([t])}getNode(){return this._nodes&&this._nodes[0]}_getEventNamespace(){return q$+this._id}setNodes(t=[]){this._nodes&&this._nodes.length&&this.detach(),this._nodes=t,t.length===1&&this.useSingleNodeRotation()?this.rotation(t[0].getAbsoluteRotation()):this.rotation(0),this._nodes.forEach(r=>{const i=()=>{this.nodes().length===1&&this.useSingleNodeRotation()&&this.rotation(this.nodes()[0].getAbsoluteRotation()),this._resetTransformCache(),!this._transforming&&!this.isDragging()&&this.update()},o=r._attrsAffectingSize.map(a=>a+"Change."+this._getEventNamespace()).join(" ");r.on(o,i),r.on(dbe.map(a=>a+`.${this._getEventNamespace()}`).join(" "),i),r.on(`absoluteTransformChange.${this._getEventNamespace()}`,i),this._proxyDrag(r)}),this._resetTransformCache();var n=!!this.findOne(".top-left");return n&&this.update(),this}_proxyDrag(t){let n;t.on(`dragstart.${this._getEventNamespace()}`,r=>{n=t.getAbsolutePosition(),!this.isDragging()&&t!==this.findOne(".back")&&this.startDrag(r,!1)}),t.on(`dragmove.${this._getEventNamespace()}`,r=>{if(!n)return;const i=t.getAbsolutePosition(),o=i.x-n.x,a=i.y-n.y;this.nodes().forEach(s=>{if(s===t||s.isDragging())return;const l=s.getAbsolutePosition();s.setAbsolutePosition({x:l.x+o,y:l.y+a}),s.startDrag(r)}),n=null})}getNodes(){return this._nodes||[]}getActiveAnchor(){return this._movingAnchorName}detach(){this._nodes&&this._nodes.forEach(t=>{t.off("."+this._getEventNamespace())}),this._nodes=[],this._resetTransformCache()}_resetTransformCache(){this._clearCache(RA),this._clearCache("transform"),this._clearSelfAndDescendantCache("absoluteTransform")}_getNodeRect(){return this._getCache(RA,this.__getNodeRect)}__getNodeShape(t,n=this.rotation(),r){var i=t.getClientRect({skipTransform:!0,skipShadow:!0,skipStroke:this.ignoreStroke()}),o=t.getAbsoluteScale(r),a=t.getAbsolutePosition(r),s=i.x*o.x-t.offsetX()*o.x,l=i.y*o.y-t.offsetY()*o.y;const d=(Je.getAngle(t.getAbsoluteRotation())+Math.PI*2)%(Math.PI*2),p={x:a.x+s*Math.cos(d)+l*Math.sin(-d),y:a.y+l*Math.cos(d)+s*Math.sin(d),width:i.width*o.x,height:i.height*o.y,rotation:d};return K$(p,-Je.getAngle(n),{x:0,y:0})}__getNodeRect(){var t=this.getNode();if(!t)return{x:-OA,y:-OA,width:0,height:0,rotation:0};const n=[];this.nodes().map(d=>{const p=d.getClientRect({skipTransform:!0,skipShadow:!0,skipStroke:this.ignoreStroke()});var g=[{x:p.x,y:p.y},{x:p.x+p.width,y:p.y},{x:p.x+p.width,y:p.y+p.height},{x:p.x,y:p.y+p.height}],m=d.getAbsoluteTransform();g.forEach(function(y){var b=m.point(y);n.push(b)})});const r=new Qo;r.rotate(-Je.getAngle(this.rotation()));var i,o,a,s;n.forEach(function(d){var p=r.point(d);i===void 0&&(i=a=p.x,o=s=p.y),i=Math.min(i,p.x),o=Math.min(o,p.y),a=Math.max(a,p.x),s=Math.max(s,p.y)}),r.invert();const l=r.point({x:i,y:o});return{x:l.x,y:l.y,width:a-i,height:s-o,rotation:Je.getAngle(this.rotation())}}getX(){return this._getNodeRect().x}getY(){return this._getNodeRect().y}getWidth(){return this._getNodeRect().width}getHeight(){return this._getNodeRect().height}_createElements(){this._createBack(),O5.forEach(function(t){this._createAnchor(t)}.bind(this)),this._createAnchor("rotater")}_createAnchor(t){var n=new Lv({stroke:"rgb(0, 161, 255)",fill:"white",strokeWidth:1,name:t+" _anchor",dragDistance:0,draggable:!0,hitStrokeWidth:hbe?10:"auto"}),r=this;n.on("mousedown touchstart",function(i){r._handleMouseDown(i)}),n.on("dragstart",i=>{n.stopDrag(),i.cancelBubble=!0}),n.on("dragend",i=>{i.cancelBubble=!0}),n.on("mouseenter",()=>{var i=Je.getAngle(this.rotation()),o=pbe(t,i);n.getStage().content&&(n.getStage().content.style.cursor=o),this._cursorChange=!0}),n.on("mouseout",()=>{n.getStage().content&&(n.getStage().content.style.cursor=""),this._cursorChange=!1}),this.add(n)}_createBack(){var t=new Ae({name:"back",width:0,height:0,draggable:!0,sceneFunc(n){var r=this.getParent(),i=r.padding();n.beginPath(),n.rect(-i,-i,this.width()+i*2,this.height()+i*2),n.moveTo(this.width()/2,-i),r.rotateEnabled()&&n.lineTo(this.width()/2,-r.rotateAnchorOffset()*se._sign(this.height())-i),n.fillStrokeShape(this)},hitFunc:(n,r)=>{if(!!this.shouldOverdrawWholeArea()){var i=this.padding();n.beginPath(),n.rect(-i,-i,r.width()+i*2,r.height()+i*2),n.fillStrokeShape(r)}}});this.add(t),this._proxyDrag(t),t.on("dragstart",n=>{n.cancelBubble=!0}),t.on("dragmove",n=>{n.cancelBubble=!0}),t.on("dragend",n=>{n.cancelBubble=!0}),this.on("dragmove",n=>{this.update()})}_handleMouseDown(t){this._movingAnchorName=t.target.name().split(" ")[0];var n=this._getNodeRect(),r=n.width,i=n.height,o=Math.sqrt(Math.pow(r,2)+Math.pow(i,2));this.sin=Math.abs(i/o),this.cos=Math.abs(r/o),typeof window<"u"&&(window.addEventListener("mousemove",this._handleMouseMove),window.addEventListener("touchmove",this._handleMouseMove),window.addEventListener("mouseup",this._handleMouseUp,!0),window.addEventListener("touchend",this._handleMouseUp,!0)),this._transforming=!0;var a=t.target.getAbsolutePosition(),s=t.target.getStage().getPointerPosition();this._anchorDragOffset={x:s.x-a.x,y:s.y-a.y},this._fire("transformstart",{evt:t.evt,target:this.getNode()}),this._nodes.forEach(l=>{l._fire("transformstart",{evt:t.evt,target:l})})}_handleMouseMove(t){var n,r,i,o=this.findOne("."+this._movingAnchorName),a=o.getStage();a.setPointersPositions(t);const s=a.getPointerPosition();let l={x:s.x-this._anchorDragOffset.x,y:s.y-this._anchorDragOffset.y};const d=o.getAbsolutePosition();this.anchorDragBoundFunc()&&(l=this.anchorDragBoundFunc()(d,l,t)),o.setAbsolutePosition(l);const p=o.getAbsolutePosition();if(!(d.x===p.x&&d.y===p.y)){if(this._movingAnchorName==="rotater"){var g=this._getNodeRect();n=o.x()-g.width/2,r=-o.y()+g.height/2;let pe=Math.atan2(-r,n)+Math.PI/2;g.height<0&&(pe-=Math.PI);var m=Je.getAngle(this.rotation());const fe=m+pe,ye=Je.getAngle(this.rotationSnapTolerance()),xe=vbe(this.rotationSnaps(),fe,ye)-g.rotation,Y=mbe(g,xe);this._fitNodesInto(Y,t);return}var y=this.keepRatio()||t.shiftKey,k=this.centeredScaling()||t.altKey;if(this._movingAnchorName==="top-left"){if(y){var b=k?{x:this.width()/2,y:this.height()/2}:{x:this.findOne(".bottom-right").x(),y:this.findOne(".bottom-right").y()};i=Math.sqrt(Math.pow(b.x-o.x(),2)+Math.pow(b.y-o.y(),2));var S=this.findOne(".top-left").x()>b.x?-1:1,E=this.findOne(".top-left").y()>b.y?-1:1;n=i*this.cos*S,r=i*this.sin*E,this.findOne(".top-left").x(b.x-n),this.findOne(".top-left").y(b.y-r)}}else if(this._movingAnchorName==="top-center")this.findOne(".top-left").y(o.y());else if(this._movingAnchorName==="top-right"){if(y){var b=k?{x:this.width()/2,y:this.height()/2}:{x:this.findOne(".bottom-left").x(),y:this.findOne(".bottom-left").y()};i=Math.sqrt(Math.pow(o.x()-b.x,2)+Math.pow(b.y-o.y(),2));var S=this.findOne(".top-right").x()<b.x?-1:1,E=this.findOne(".top-right").y()>b.y?-1:1;n=i*this.cos*S,r=i*this.sin*E,this.findOne(".top-right").x(b.x+n),this.findOne(".top-right").y(b.y-r)}var P=o.position();this.findOne(".top-left").y(P.y),this.findOne(".bottom-right").x(P.x)}else if(this._movingAnchorName==="middle-left")this.findOne(".top-left").x(o.x());else if(this._movingAnchorName==="middle-right")this.findOne(".bottom-right").x(o.x());else if(this._movingAnchorName==="bottom-left"){if(y){var b=k?{x:this.width()/2,y:this.height()/2}:{x:this.findOne(".top-right").x(),y:this.findOne(".top-right").y()};i=Math.sqrt(Math.pow(b.x-o.x(),2)+Math.pow(o.y()-b.y,2));var S=b.x<o.x()?-1:1,E=o.y()<b.y?-1:1;n=i*this.cos*S,r=i*this.sin*E,o.x(b.x-n),o.y(b.y+r)}P=o.position(),this.findOne(".top-left").x(P.x),this.findOne(".bottom-right").y(P.y)}else if(this._movingAnchorName==="bottom-center")this.findOne(".bottom-right").y(o.y());else if(this._movingAnchorName==="bottom-right"){if(y){var b=k?{x:this.width()/2,y:this.height()/2}:{x:this.findOne(".top-left").x(),y:this.findOne(".top-left").y()};i=Math.sqrt(Math.pow(o.x()-b.x,2)+Math.pow(o.y()-b.y,2));var S=this.findOne(".bottom-right").x()<b.x?-1:1,E=this.findOne(".bottom-right").y()<b.y?-1:1;n=i*this.cos*S,r=i*this.sin*E,this.findOne(".bottom-right").x(b.x+n),this.findOne(".bottom-right").y(b.y+r)}}else console.error(new Error("Wrong position argument of selection resizer: "+this._movingAnchorName));var k=this.centeredScaling()||t.altKey;if(k){var L=this.findOne(".top-left"),I=this.findOne(".bottom-right"),O=L.x(),N=L.y(),D=this.getWidth()-I.x(),F=this.getHeight()-I.y();I.move({x:-O,y:-N}),L.move({x:D,y:F})}var U=this.findOne(".top-left").getAbsolutePosition();n=U.x,r=U.y;var W=this.findOne(".bottom-right").x()-this.findOne(".top-left").x(),q=this.findOne(".bottom-right").y()-this.findOne(".top-left").y();this._fitNodesInto({x:n,y:r,width:W,height:q,rotation:Je.getAngle(this.rotation())},t)}}_handleMouseUp(t){this._removeEvents(t)}getAbsoluteTransform(){return this.getTransform()}_removeEvents(t){if(this._transforming){this._transforming=!1,typeof window<"u"&&(window.removeEventListener("mousemove",this._handleMouseMove),window.removeEventListener("touchmove",this._handleMouseMove),window.removeEventListener("mouseup",this._handleMouseUp,!0),window.removeEventListener("touchend",this._handleMouseUp,!0));var n=this.getNode();this._fire("transformend",{evt:t,target:n}),n&&this._nodes.forEach(r=>{r._fire("transformend",{evt:t,target:r})}),this._movingAnchorName=null}}_fitNodesInto(t,n){var r=this._getNodeRect();const i=1;if(se._inRange(t.width,-this.padding()*2-i,i)){this.update();return}if(se._inRange(t.height,-this.padding()*2-i,i)){this.update();return}const o=this.flipEnabled();var a=new Qo;if(a.rotate(Je.getAngle(this.rotation())),this._movingAnchorName&&t.width<0&&this._movingAnchorName.indexOf("left")>=0){const g=a.point({x:-this.padding()*2,y:0});if(t.x+=g.x,t.y+=g.y,t.width+=this.padding()*2,this._movingAnchorName=this._movingAnchorName.replace("left","right"),this._anchorDragOffset.x-=g.x,this._anchorDragOffset.y-=g.y,!o){this.update();return}}else if(this._movingAnchorName&&t.width<0&&this._movingAnchorName.indexOf("right")>=0){const g=a.point({x:this.padding()*2,y:0});if(this._movingAnchorName=this._movingAnchorName.replace("right","left"),this._anchorDragOffset.x-=g.x,this._anchorDragOffset.y-=g.y,t.width+=this.padding()*2,!o){this.update();return}}if(this._movingAnchorName&&t.height<0&&this._movingAnchorName.indexOf("top")>=0){const g=a.point({x:0,y:-this.padding()*2});if(t.x+=g.x,t.y+=g.y,this._movingAnchorName=this._movingAnchorName.replace("top","bottom"),this._anchorDragOffset.x-=g.x,this._anchorDragOffset.y-=g.y,t.height+=this.padding()*2,!o){this.update();return}}else if(this._movingAnchorName&&t.height<0&&this._movingAnchorName.indexOf("bottom")>=0){const g=a.point({x:0,y:this.padding()*2});if(this._movingAnchorName=this._movingAnchorName.replace("bottom","top"),this._anchorDragOffset.x-=g.x,this._anchorDragOffset.y-=g.y,t.height+=this.padding()*2,!o){this.update();return}}if(this.boundBoxFunc()){const g=this.boundBoxFunc()(r,t);g?t=g:se.warn("boundBoxFunc returned falsy. You should return new bound rect from it!")}const s=1e7,l=new Qo;l.translate(r.x,r.y),l.rotate(r.rotation),l.scale(r.width/s,r.height/s);const d=new Qo;d.translate(t.x,t.y),d.rotate(t.rotation),d.scale(t.width/s,t.height/s);const p=d.multiply(l.invert());this._nodes.forEach(g=>{var m;const y=g.getParent().getAbsoluteTransform(),b=g.getTransform().copy();b.translate(g.offsetX(),g.offsetY());const S=new Qo;S.multiply(y.copy().invert()).multiply(p).multiply(y).multiply(b);const E=S.decompose();g.setAttrs(E),this._fire("transform",{evt:n,target:g}),g._fire("transform",{evt:n,target:g}),(m=g.getLayer())===null||m===void 0||m.batchDraw()}),this.rotation(se._getRotation(t.rotation)),this._resetTransformCache(),this.update(),this.getLayer().batchDraw()}forceUpdate(){this._resetTransformCache(),this.update()}_batchChangeChild(t,n){this.findOne(t).setAttrs(n)}update(){var t,n=this._getNodeRect();this.rotation(se._getRotation(n.rotation));var r=n.width,i=n.height,o=this.enabledAnchors(),a=this.resizeEnabled(),s=this.padding(),l=this.anchorSize();this.find("._anchor").forEach(d=>{d.setAttrs({width:l,height:l,offsetX:l/2,offsetY:l/2,stroke:this.anchorStroke(),strokeWidth:this.anchorStrokeWidth(),fill:this.anchorFill(),cornerRadius:this.anchorCornerRadius()})}),this._batchChangeChild(".top-left",{x:0,y:0,offsetX:l/2+s,offsetY:l/2+s,visible:a&&o.indexOf("top-left")>=0}),this._batchChangeChild(".top-center",{x:r/2,y:0,offsetY:l/2+s,visible:a&&o.indexOf("top-center")>=0}),this._batchChangeChild(".top-right",{x:r,y:0,offsetX:l/2-s,offsetY:l/2+s,visible:a&&o.indexOf("top-right")>=0}),this._batchChangeChild(".middle-left",{x:0,y:i/2,offsetX:l/2+s,visible:a&&o.indexOf("middle-left")>=0}),this._batchChangeChild(".middle-right",{x:r,y:i/2,offsetX:l/2-s,visible:a&&o.indexOf("middle-right")>=0}),this._batchChangeChild(".bottom-left",{x:0,y:i,offsetX:l/2+s,offsetY:l/2-s,visible:a&&o.indexOf("bottom-left")>=0}),this._batchChangeChild(".bottom-center",{x:r/2,y:i,offsetY:l/2-s,visible:a&&o.indexOf("bottom-center")>=0}),this._batchChangeChild(".bottom-right",{x:r,y:i,offsetX:l/2-s,offsetY:l/2-s,visible:a&&o.indexOf("bottom-right")>=0}),this._batchChangeChild(".rotater",{x:r/2,y:-this.rotateAnchorOffset()*se._sign(i)-s,visible:this.rotateEnabled()}),this._batchChangeChild(".back",{width:r,height:i,visible:this.borderEnabled(),stroke:this.borderStroke(),strokeWidth:this.borderStrokeWidth(),dash:this.borderDash(),x:0,y:0}),(t=this.getLayer())===null||t===void 0||t.batchDraw()}isTransforming(){return this._transforming}stopTransform(){if(this._transforming){this._removeEvents();var t=this.findOne("."+this._movingAnchorName);t&&t.stopDrag()}}destroy(){return this.getStage()&&this._cursorChange&&this.getStage().content&&(this.getStage().content.style.cursor=""),b0.prototype.destroy.call(this),this.detach(),this._removeEvents(),this}toObject(){return ze.prototype.toObject.call(this)}getClientRect(){return this.nodes().length>0?super.getClientRect():{x:0,y:0,width:0,height:0}}}function ybe(e){return e instanceof Array||se.warn("enabledAnchors value should be an array"),e instanceof Array&&e.forEach(function(t){O5.indexOf(t)===-1&&se.warn("Unknown anchor name: "+t+". Available names are: "+O5.join(", "))}),e||[]}bn.prototype.className="Transformer";hr(bn);j.addGetterSetter(bn,"enabledAnchors",O5,ybe);j.addGetterSetter(bn,"flipEnabled",!0,ks());j.addGetterSetter(bn,"resizeEnabled",!0);j.addGetterSetter(bn,"anchorSize",10,De());j.addGetterSetter(bn,"rotateEnabled",!0);j.addGetterSetter(bn,"rotationSnaps",[]);j.addGetterSetter(bn,"rotateAnchorOffset",50,De());j.addGetterSetter(bn,"rotationSnapTolerance",5,De());j.addGetterSetter(bn,"borderEnabled",!0);j.addGetterSetter(bn,"anchorStroke","rgb(0, 161, 255)");j.addGetterSetter(bn,"anchorStrokeWidth",1,De());j.addGetterSetter(bn,"anchorFill","white");j.addGetterSetter(bn,"anchorCornerRadius",0,De());j.addGetterSetter(bn,"borderStroke","rgb(0, 161, 255)");j.addGetterSetter(bn,"borderStrokeWidth",1,De());j.addGetterSetter(bn,"borderDash");j.addGetterSetter(bn,"keepRatio",!0);j.addGetterSetter(bn,"centeredScaling",!1);j.addGetterSetter(bn,"ignoreStroke",!1);j.addGetterSetter(bn,"padding",0,De());j.addGetterSetter(bn,"node");j.addGetterSetter(bn,"nodes");j.addGetterSetter(bn,"boundBoxFunc");j.addGetterSetter(bn,"anchorDragBoundFunc");j.addGetterSetter(bn,"shouldOverdrawWholeArea",!1);j.addGetterSetter(bn,"useSingleNodeRotation",!0);j.backCompat(bn,{lineEnabled:"borderEnabled",rotateHandlerOffset:"rotateAnchorOffset",enabledHandlers:"enabledAnchors"});class Eu extends Ae{_sceneFunc(t){t.beginPath(),t.arc(0,0,this.radius(),0,Je.getAngle(this.angle()),this.clockwise()),t.lineTo(0,0),t.closePath(),t.fillStrokeShape(this)}getWidth(){return this.radius()*2}getHeight(){return this.radius()*2}setWidth(t){this.radius(t/2)}setHeight(t){this.radius(t/2)}}Eu.prototype.className="Wedge";Eu.prototype._centroid=!0;Eu.prototype._attrsAffectingSize=["radius"];hr(Eu);j.addGetterSetter(Eu,"radius",0,De());j.addGetterSetter(Eu,"angle",0,De());j.addGetterSetter(Eu,"clockwise",!1);j.backCompat(Eu,{angleDeg:"angle",getAngleDeg:"getAngle",setAngleDeg:"setAngle"});function NA(){this.r=0,this.g=0,this.b=0,this.a=0,this.next=null}var 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Sbe(e,t){var n=e.data,r=e.width,i=e.height,o,a,s,l,d,p,g,m,y,b,S,E,P,k,L,I,O,N,D,F,U,W,q,pe,fe=t+t+1,ye=r-1,Pe=i-1,xe=t+1,Y=xe*(xe+1)/2,V=new NA,ae=null,oe=V,X=null,me=null,ve=xbe[t],Se=bbe[t];for(s=1;s<fe;s++)oe=oe.next=new 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f=c.stateNode;f.pendingContext?hd(c,f.pendingContext,f.pendingContext!==f.context):f.context&&hd(c,f.context,!1),de(c,f.containerInfo)}function n2(c,f,v,_,T){return zu(),s1(T),f.flags|=256,pi(c,f,v,_),f.child}var C1={dehydrated:null,treeContext:null,retryLane:0};function _1(c){return{baseLanes:c,cachePool:null,transitions:null}}function r2(c,f,v){var _=f.pendingProps,T=Pt.current,M=!1,$=(f.flags&128)!==0,ee;if((ee=$)||(ee=c!==null&&c.memoizedState===null?!1:(T&2)!==0),ee?(M=!0,f.flags&=-129):(c===null||c.memoizedState!==null)&&(T|=1),gn(Pt,T&1),c===null)return bd(f),c=f.memoizedState,c!==null&&(c=c.dehydrated,c!==null)?((f.mode&1)===0?f.lanes=1:fd(c)?f.lanes=8:f.lanes=1073741824,null):($=_.children,c=_.fallback,M?(_=f.mode,M=f.child,$={mode:"hidden",children:$},(_&1)===0&&M!==null?(M.childLanes=0,M.pendingProps=$):M=Kh($,_,0,null),c=Zs(c,_,v,null),M.return=f,c.return=f,M.sibling=c,f.child=M,f.child.memoizedState=_1(v),f.memoizedState=C1,c):$o(f,$));if(T=c.memoizedState,T!==null&&(ee=T.dehydrated,ee!==null))return Ax(c,f,$,_,ee,T,v);if(M){M=_.fallback,$=f.mode,T=c.child,ee=T.sibling;var 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f.flags&256?(f.flags&=-257,_=b1(Error(a(422))),Ch(c,f,$,_)):f.memoizedState!==null?(f.child=c.child,f.flags|=128,null):(M=_.fallback,T=f.mode,_=Kh({mode:"visible",children:_.children},T,0,null),M=Zs(M,T,$,null),M.flags|=2,_.return=f,M.return=f,_.sibling=M,f.child=_,(f.mode&1)!==0&&Hu(f,c.child,null,$),f.child.memoizedState=_1($),f.memoizedState=C1,M);if((f.mode&1)===0)return Ch(c,f,$,null);if(fd(T))return _=G0(T).digest,M=Error(a(419)),_=b1(M,_,void 0),Ch(c,f,$,_);if(v=($&c.childLanes)!==0,Ri||v){if(_=Nr,_!==null){switch($&-$){case 4:T=2;break;case 16:T=8;break;case 64:case 128:case 256:case 512:case 1024:case 2048:case 4096:case 8192:case 16384:case 32768:case 65536:case 131072:case 262144:case 524288:case 1048576:case 2097152:case 4194304:case 8388608:case 16777216:case 33554432:case 67108864:T=32;break;case 536870912:T=268435456;break;default:T=0}T=(T&(_.suspendedLanes|$))!==0?0:T,T!==0&&T!==M.retryLane&&(M.retryLane=T,zo(c,T),go(_,c,T,-1))}return 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a2(c,f){if(c!==null&&c.child===f.child)return!0;if((f.flags&16)!==0)return!1;for(c=f.child;c!==null;){if((c.flags&12854)!==0||(c.subtreeFlags&12854)!==0)return!1;c=c.sibling}return!0}var Fd,Bd,_h,kh;if(it)Fd=function(c,f){for(var v=f.child;v!==null;){if(v.tag===5||v.tag===6)me(c,v.stateNode);else if(v.tag!==4&&v.child!==null){v.child.return=v,v=v.child;continue}if(v===f)break;for(;v.sibling===null;){if(v.return===null||v.return===f)return;v=v.return}v.sibling.return=v.return,v=v.sibling}},Bd=function(){},_h=function(c,f,v,_,T){if(c=c.memoizedProps,c!==_){var M=f.stateNode,$=ge(fo.current);v=Se(M,v,c,_,T,$),(f.updateQueue=v)&&mr(f)}},kh=function(c,f,v,_){v!==_&&mr(f)};else if(At){Fd=function(c,f,v,_){for(var T=f.child;T!==null;){if(T.tag===5){var M=T.stateNode;v&&_&&(M=ih(M,T.type,T.memoizedProps,T)),me(c,M)}else if(T.tag===6)M=T.stateNode,v&&_&&(M=oh(M,T.memoizedProps,T)),me(c,M);else if(T.tag!==4){if(T.tag===22&&T.memoizedState!==null)M=T.child,M!==null&&(M.return=T),Fd(c,T,!0,!0);else if(T.child!==null){T.child.return=T,T=T.child;continue}}if(T===f)break;for(;T.sibling===null;){if(T.return===null||T.return===f)return;T=T.return}T.sibling.return=T.return,T=T.sibling}};var E1=function(c,f,v,_){for(var T=f.child;T!==null;){if(T.tag===5){var M=T.stateNode;v&&_&&(M=ih(M,T.type,T.memoizedProps,T)),rh(c,M)}else if(T.tag===6)M=T.stateNode,v&&_&&(M=oh(M,T.memoizedProps,T)),rh(c,M);else if(T.tag!==4){if(T.tag===22&&T.memoizedState!==null)M=T.child,M!==null&&(M.return=T),E1(c,T,!0,!0);else if(T.child!==null){T.child.return=T,T=T.child;continue}}if(T===f)break;for(;T.sibling===null;){if(T.return===null||T.return===f)return;T=T.return}T.sibling.return=T.return,T=T.sibling}};Bd=function(c,f){var v=f.stateNode;if(!a2(c,f)){c=v.containerInfo;var _=pa(c);E1(_,f,!1,!1),v.pendingChildren=_,mr(f),V0(c,_)}},_h=function(c,f,v,_,T){var M=c.stateNode,$=c.memoizedProps;if((c=a2(c,f))&&$===_)f.stateNode=M;else{var ee=f.stateNode,le=ge(fo.current),Oe=null;$!==_&&(Oe=Se(ee,v,$,_,T,le)),c&&Oe===null?f.stateNode=M:(M=W0(M,Oe,v,$,_,f,c,ee),ve(M,v,_,T,le)&&mr(f),f.stateNode=M,c?mr(f):Fd(M,f,!1,!1))}},kh=function(c,f,v,_){v!==_?(c=ge(Q.current),v=ge(fo.current),f.stateNode=je(_,c,v,f),mr(f)):f.stateNode=c.stateNode}}else Bd=function(){},_h=function(){},kh=function(){};function tc(c,f){if(!Rn)switch(c.tailMode){case"hidden":f=c.tail;for(var v=null;f!==null;)f.alternate!==null&&(v=f),f=f.sibling;v===null?c.tail=null:v.sibling=null;break;case"collapsed":v=c.tail;for(var _=null;v!==null;)v.alternate!==null&&(_=v),v=v.sibling;_===null?f||c.tail===null?c.tail=null:c.tail.sibling=null:_.sibling=null}}function ei(c){var f=c.alternate!==null&&c.alternate.child===c.child,v=0,_=0;if(f)for(var T=c.child;T!==null;)v|=T.lanes|T.childLanes,_|=T.subtreeFlags&14680064,_|=T.flags&14680064,T.return=c,T=T.sibling;else for(T=c.child;T!==null;)v|=T.lanes|T.childLanes,_|=T.subtreeFlags,_|=T.flags,T.return=c,T=T.sibling;return c.subtreeFlags|=_,c.childLanes=v,f}function Mx(c,f,v){var _=f.pendingProps;switch(a1(f),f.tag){case 2:case 16:case 15:case 0:case 11:case 7:case 8:case 12:case 9:case 14:return ei(f),null;case 1:return Ur(f.type)&&Ka(),ei(f),null;case 3:return v=f.stateNode,Ve(),Cn(Vr),Cn(Cr),d1(),v.pendingContext&&(v.context=v.pendingContext,v.pendingContext=null),(c===null||c.child===null)&&(Du(f)?mr(f):c===null||c.memoizedState.isDehydrated&&(f.flags&256)===0||(f.flags|=1024,Ii!==null&&(Uh(Ii),Ii=null))),Bd(c,f),ei(f),null;case 5:Yt(f),v=ge(Q.current);var T=f.type;if(c!==null&&f.stateNode!=null)_h(c,f,T,_,v),c.ref!==f.ref&&(f.flags|=512,f.flags|=2097152);else{if(!_){if(f.stateNode===null)throw Error(a(166));return ei(f),null}if(c=ge(fo.current),Du(f)){if(!wt)throw Error(a(175));c=Z0(f.stateNode,f.type,f.memoizedProps,v,c,f,!co),f.updateQueue=c,c!==null&&mr(f)}else{var 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zu(),(f.flags&128)===0&&(f.memoizedState=null),f.flags|=4;ei(f),T=!1}else Ii!==null&&(Uh(Ii),Ii=null),T=!0;if(!T)return f.flags&65536?f:null}return(f.flags&128)!==0?(f.lanes=v,f):(v=_!==null,v!==(c!==null&&c.memoizedState!==null)&&v&&(f.child.flags|=8192,(f.mode&1)!==0&&(c===null||(Pt.current&1)!==0?yr===0&&(yr=3):Ud())),f.updateQueue!==null&&(f.flags|=4),ei(f),null);case 4:return Ve(),Bd(c,f),c===null&&ot(f.stateNode.containerInfo),ei(f),null;case 10:return _d(f.type._context),ei(f),null;case 17:return Ur(f.type)&&Ka(),ei(f),null;case 19:if(Cn(Pt),T=f.memoizedState,T===null)return 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+ *
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Error{constructor(t,n,r){super(t),this.description=n,this.context=r,this.type="TransportError"}}class _H extends Wr{constructor(t){super(),this.writable=!1,Ex(this,t),this.opts=t,this.query=t.query,this.readyState="",this.socket=t.socket}onError(t,n,r){return super.emitReserved("error",new x6e(t,n,r)),this}open(){return(this.readyState==="closed"||this.readyState==="")&&(this.readyState="opening",this.doOpen()),this}close(){return(this.readyState==="opening"||this.readyState==="open")&&(this.doClose(),this.onClose()),this}send(t){this.readyState==="open"&&this.write(t)}onOpen(){this.readyState="open",this.writable=!0,super.emitReserved("open")}onData(t){const n=xH(t,this.socket.binaryType);this.onPacket(n)}onPacket(t){super.emitReserved("packet",t)}onClose(t){this.readyState="closed",super.emitReserved("close",t)}}const kH="0123456789ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz-_".split(""),RC=64,b6e={};let KA=0,Ey=0,ZA;function YA(e){let t="";do t=kH[e%RC]+t,e=Math.floor(e/RC);while(e>0);return t}function EH(){const e=YA(+new Date);return e!==ZA?(KA=0,ZA=e):e+"."+YA(KA++)}for(;Ey<RC;Ey++)b6e[kH[Ey]]=Ey;function PH(e){let t="";for(let n in e)e.hasOwnProperty(n)&&(t.length&&(t+="&"),t+=encodeURIComponent(n)+"="+encodeURIComponent(e[n]));return t}function S6e(e){let t={},n=e.split("&");for(let r=0,i=n.length;r<i;r++){let o=n[r].split("=");t[decodeURIComponent(o[0])]=decodeURIComponent(o[1])}return t}let TH=!1;try{TH=typeof XMLHttpRequest<"u"&&"withCredentials"in new XMLHttpRequest}catch{}const w6e=TH;function LH(e){const t=e.xdomain;try{if(typeof XMLHttpRequest<"u"&&(!t||w6e))return new XMLHttpRequest}catch{}if(!t)try{return new Mc[["Active"].concat("Object").join("X")]("Microsoft.XMLHTTP")}catch{}}function C6e(){}const _6e=function(){return new LH({xdomain:!1}).responseType!=null}();class k6e extends _H{constructor(t){if(super(t),this.polling=!1,typeof location<"u"){const r=location.protocol==="https:";let i=location.port;i||(i=r?"443":"80"),this.xd=typeof location<"u"&&t.hostname!==location.hostname||i!==t.port,this.xs=t.secure!==r}const n=t&&t.forceBase64;this.supportsBinary=_6e&&!n}get name(){return"polling"}doOpen(){this.poll()}pause(t){this.readyState="pausing";const n=()=>{this.readyState="paused",t()};if(this.polling||!this.writable){let r=0;this.polling&&(r++,this.once("pollComplete",function(){--r||n()})),this.writable||(r++,this.once("drain",function(){--r||n()}))}else n()}poll(){this.polling=!0,this.doPoll(),this.emitReserved("poll")}onData(t){const n=r=>{if(this.readyState==="opening"&&r.type==="open"&&this.onOpen(),r.type==="close")return this.onClose({description:"transport closed by the server"}),!1;this.onPacket(r)};f6e(t,this.socket.binaryType).forEach(n),this.readyState!=="closed"&&(this.polling=!1,this.emitReserved("pollComplete"),this.readyState==="open"&&this.poll())}doClose(){const t=()=>{this.write([{type:"close"}])};this.readyState==="open"?t():this.once("open",t)}write(t){this.writable=!1,d6e(t,n=>{this.doWrite(n,()=>{this.writable=!0,this.emitReserved("drain")})})}uri(){let t=this.query||{};const n=this.opts.secure?"https":"http";let r="";this.opts.timestampRequests!==!1&&(t[this.opts.timestampParam]=EH()),!this.supportsBinary&&!t.sid&&(t.b64=1),this.opts.port&&(n==="https"&&Number(this.opts.port)!==443||n==="http"&&Number(this.opts.port)!==80)&&(r=":"+this.opts.port);const i=PH(t),o=this.opts.hostname.indexOf(":")!==-1;return n+"://"+(o?"["+this.opts.hostname+"]":this.opts.hostname)+r+this.opts.path+(i.length?"?"+i:"")}request(t={}){return Object.assign(t,{xd:this.xd,xs:this.xs},this.opts),new Sl(this.uri(),t)}doWrite(t,n){const r=this.request({method:"POST",data:t});r.on("success",n),r.on("error",(i,o)=>{this.onError("xhr post error",i,o)})}doPoll(){const t=this.request();t.on("data",this.onData.bind(this)),t.on("error",(n,r)=>{this.onError("xhr poll 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n&&(n.withCredentials=this.opts.withCredentials),this.opts.requestTimeout&&(n.timeout=this.opts.requestTimeout),n.onreadystatechange=()=>{n.readyState===4&&(n.status===200||n.status===1223?this.onLoad():this.setTimeoutFn(()=>{this.onError(typeof n.status=="number"?n.status:0)},0))},n.send(this.data)}catch(r){this.setTimeoutFn(()=>{this.onError(r)},0);return}typeof document<"u"&&(this.index=Sl.requestsCount++,Sl.requests[this.index]=this)}onError(t){this.emitReserved("error",t,this.xhr),this.cleanup(!0)}cleanup(t){if(!(typeof this.xhr>"u"||this.xhr===null)){if(this.xhr.onreadystatechange=C6e,t)try{this.xhr.abort()}catch{}typeof document<"u"&&delete Sl.requests[this.index],this.xhr=null}}onLoad(){const t=this.xhr.responseText;t!==null&&(this.emitReserved("data",t),this.emitReserved("success"),this.cleanup())}abort(){this.cleanup()}}Sl.requestsCount=0;Sl.requests={};if(typeof document<"u"){if(typeof attachEvent=="function")attachEvent("onunload",XA);else if(typeof addEventListener=="function"){const e="onpagehide"in Mc?"pagehide":"unload";addEventListener(e,XA,!1)}}function XA(){for(let e in Sl.requests)Sl.requests.hasOwnProperty(e)&&Sl.requests[e].abort()}const AH=(()=>typeof Promise=="function"&&typeof Promise.resolve=="function"?t=>Promise.resolve().then(t):(t,n)=>n(t,0))(),Py=Mc.WebSocket||Mc.MozWebSocket,QA=!0,E6e="arraybuffer",JA=typeof navigator<"u"&&typeof navigator.product=="string"&&navigator.product.toLowerCase()==="reactnative";class P6e extends _H{constructor(t){super(t),this.supportsBinary=!t.forceBase64}get name(){return"websocket"}doOpen(){if(!this.check())return;const t=this.uri(),n=this.opts.protocols,r=JA?{}:CH(this.opts,"agent","perMessageDeflate","pfx","key","passphrase","cert","ca","ciphers","rejectUnauthorized","localAddress","protocolVersion","origin","maxPayload","family","checkServerIdentity");this.opts.extraHeaders&&(r.headers=this.opts.extraHeaders);try{this.ws=QA&&!JA?n?new Py(t,n):new Py(t):new Py(t,n,r)}catch(i){return this.emitReserved("error",i)}this.ws.binaryType=this.socket.binaryType||E6e,this.addEventListeners()}addEventListeners(){this.ws.onopen=()=>{this.opts.autoUnref&&this.ws._socket.unref(),this.onOpen()},this.ws.onclose=t=>this.onClose({description:"websocket connection closed",context:t}),this.ws.onmessage=t=>this.onData(t.data),this.ws.onerror=t=>this.onError("websocket error",t)}write(t){this.writable=!1;for(let n=0;n<t.length;n++){const r=t[n],i=n===t.length-1;yH(r,this.supportsBinary,o=>{const a={};try{QA&&this.ws.send(o)}catch{}i&&AH(()=>{this.writable=!0,this.emitReserved("drain")},this.setTimeoutFn)})}}doClose(){typeof this.ws<"u"&&(this.ws.close(),this.ws=null)}uri(){let t=this.query||{};const n=this.opts.secure?"wss":"ws";let r="";this.opts.port&&(n==="wss"&&Number(this.opts.port)!==443||n==="ws"&&Number(this.opts.port)!==80)&&(r=":"+this.opts.port),this.opts.timestampRequests&&(t[this.opts.timestampParam]=EH()),this.supportsBinary||(t.b64=1);const i=PH(t),o=this.opts.hostname.indexOf(":")!==-1;return n+"://"+(o?"["+this.opts.hostname+"]":this.opts.hostname)+r+this.opts.path+(i.length?"?"+i:"")}check(){return!!Py}}const T6e={websocket:P6e,polling:k6e},L6e=/^(?:(?![^:@]+:[^:@\/]*@)(http|https|ws|wss):\/\/)?((?:(([^:@]*)(?::([^:@]*))?)?@)?((?:[a-f0-9]{0,4}:){2,7}[a-f0-9]{0,4}|[^:\/?#]*)(?::(\d*))?)(((\/(?:[^?#](?![^?#\/]*\.[^?#\/.]+(?:[?#]|$)))*\/?)?([^?#\/]*))(?:\?([^#]*))?(?:#(.*))?)/,A6e=["source","protocol","authority","userInfo","user","password","host","port","relative","path","directory","file","query","anchor"];function OC(e){const t=e,n=e.indexOf("["),r=e.indexOf("]");n!=-1&&r!=-1&&(e=e.substring(0,n)+e.substring(n,r).replace(/:/g,";")+e.substring(r,e.length));let i=L6e.exec(e||""),o={},a=14;for(;a--;)o[A6e[a]]=i[a]||"";return n!=-1&&r!=-1&&(o.source=t,o.host=o.host.substring(1,o.host.length-1).replace(/;/g,":"),o.authority=o.authority.replace("[","").replace("]","").replace(/;/g,":"),o.ipv6uri=!0),o.pathNames=I6e(o,o.path),o.queryKey=M6e(o,o.query),o}function I6e(e,t){const n=/\/{2,9}/g,r=t.replace(n,"/").split("/");return(t.slice(0,1)=="/"||t.length===0)&&r.splice(0,1),t.slice(-1)=="/"&&r.splice(r.length-1,1),r}function M6e(e,t){const n={};return t.replace(/(?:^|&)([^&=]*)=?([^&]*)/g,function(r,i,o){i&&(n[i]=o)}),n}class Ec extends Wr{constructor(t,n={}){super(),t&&typeof t=="object"&&(n=t,t=null),t?(t=OC(t),n.hostname=t.host,n.secure=t.protocol==="https"||t.protocol==="wss",n.port=t.port,t.query&&(n.query=t.query)):n.host&&(n.hostname=OC(n.host).host),Ex(this,n),this.secure=n.secure!=null?n.secure:typeof location<"u"&&location.protocol==="https:",n.hostname&&!n.port&&(n.port=this.secure?"443":"80"),this.hostname=n.hostname||(typeof location<"u"?location.hostname:"localhost"),this.port=n.port||(typeof location<"u"&&location.port?location.port:this.secure?"443":"80"),this.transports=n.transports||["polling","websocket"],this.readyState="",this.writeBuffer=[],this.prevBufferLen=0,this.opts=Object.assign({path:"/engine.io",agent:!1,withCredentials:!1,upgrade:!0,timestampParam:"t",rememberUpgrade:!1,rejectUnauthorized:!0,perMessageDeflate:{threshold:1024},transportOptions:{},closeOnBeforeunload:!0},n),this.opts.path=this.opts.path.replace(/\/$/,"")+"/",typeof this.opts.query=="string"&&(this.opts.query=S6e(this.opts.query)),this.id=null,this.upgrades=null,this.pingInterval=null,this.pingTimeout=null,this.pingTimeoutTimer=null,typeof addEventListener=="function"&&(this.opts.closeOnBeforeunload&&(this.beforeunloadEventListener=()=>{this.transport&&(this.transport.removeAllListeners(),this.transport.close())},addEventListener("beforeunload",this.beforeunloadEventListener,!1)),this.hostname!=="localhost"&&(this.offlineEventListener=()=>{this.onClose("transport close",{description:"network connection lost"})},addEventListener("offline",this.offlineEventListener,!1))),this.open()}createTransport(t){const n=Object.assign({},this.opts.query);n.EIO=wH,n.transport=t,this.id&&(n.sid=this.id);const r=Object.assign({},this.opts.transportOptions[t],this.opts,{query:n,socket:this,hostname:this.hostname,secure:this.secure,port:this.port});return new T6e[t](r)}open(){let t;if(this.opts.rememberUpgrade&&Ec.priorWebsocketSuccess&&this.transports.indexOf("websocket")!==-1)t="websocket";else if(this.transports.length===0){this.setTimeoutFn(()=>{this.emitReserved("error","No transports available")},0);return}else t=this.transports[0];this.readyState="opening";try{t=this.createTransport(t)}catch{this.transports.shift(),this.open();return}t.open(),this.setTransport(t)}setTransport(t){this.transport&&this.transport.removeAllListeners(),this.transport=t,t.on("drain",this.onDrain.bind(this)).on("packet",this.onPacket.bind(this)).on("error",this.onError.bind(this)).on("close",n=>this.onClose("transport close",n))}probe(t){let n=this.createTransport(t),r=!1;Ec.priorWebsocketSuccess=!1;const i=()=>{r||(n.send([{type:"ping",data:"probe"}]),n.once("packet",g=>{if(!r)if(g.type==="pong"&&g.data==="probe"){if(this.upgrading=!0,this.emitReserved("upgrading",n),!n)return;Ec.priorWebsocketSuccess=n.name==="websocket",this.transport.pause(()=>{r||this.readyState!=="closed"&&(p(),this.setTransport(n),n.send([{type:"upgrade"}]),this.emitReserved("upgrade",n),n=null,this.upgrading=!1,this.flush())})}else{const m=new Error("probe error");m.transport=n.name,this.emitReserved("upgradeError",m)}}))};function o(){r||(r=!0,p(),n.close(),n=null)}const a=g=>{const m=new Error("probe error: "+g);m.transport=n.name,o(),this.emitReserved("upgradeError",m)};function s(){a("transport closed")}function l(){a("socket closed")}function d(g){n&&g.name!==n.name&&o()}const p=()=>{n.removeListener("open",i),n.removeListener("error",a),n.removeListener("close",s),this.off("close",l),this.off("upgrading",d)};n.once("open",i),n.once("error",a),n.once("close",s),this.once("close",l),this.once("upgrading",d),n.open()}onOpen(){if(this.readyState="open",Ec.priorWebsocketSuccess=this.transport.name==="websocket",this.emitReserved("open"),this.flush(),this.readyState==="open"&&this.opts.upgrade&&this.transport.pause){let t=0;const n=this.upgrades.length;for(;t<n;t++)this.probe(this.upgrades[t])}}onPacket(t){if(this.readyState==="opening"||this.readyState==="open"||this.readyState==="closing")switch(this.emitReserved("packet",t),this.emitReserved("heartbeat"),t.type){case"open":this.onHandshake(JSON.parse(t.data));break;case"ping":this.resetPingTimeout(),this.sendPacket("pong"),this.emitReserved("ping"),this.emitReserved("pong");break;case"error":const n=new Error("server error");n.code=t.data,this.onError(n);break;case"message":this.emitReserved("data",t.data),this.emitReserved("message",t.data);break}}onHandshake(t){this.emitReserved("handshake",t),this.id=t.sid,this.transport.query.sid=t.sid,this.upgrades=this.filterUpgrades(t.upgrades),this.pingInterval=t.pingInterval,this.pingTimeout=t.pingTimeout,this.maxPayload=t.maxPayload,this.onOpen(),this.readyState!=="closed"&&this.resetPingTimeout()}resetPingTimeout(){this.clearTimeoutFn(this.pingTimeoutTimer),this.pingTimeoutTimer=this.setTimeoutFn(()=>{this.onClose("ping timeout")},this.pingInterval+this.pingTimeout),this.opts.autoUnref&&this.pingTimeoutTimer.unref()}onDrain(){this.writeBuffer.splice(0,this.prevBufferLen),this.prevBufferLen=0,this.writeBuffer.length===0?this.emitReserved("drain"):this.flush()}flush(){if(this.readyState!=="closed"&&this.transport.writable&&!this.upgrading&&this.writeBuffer.length){const t=this.getWritablePackets();this.transport.send(t),this.prevBufferLen=t.length,this.emitReserved("flush")}}getWritablePackets(){if(!(this.maxPayload&&this.transport.name==="polling"&&this.writeBuffer.length>1))return this.writeBuffer;let n=1;for(let r=0;r<this.writeBuffer.length;r++){const i=this.writeBuffer[r].data;if(i&&(n+=v6e(i)),r>0&&n>this.maxPayload)return this.writeBuffer.slice(0,r);n+=2}return this.writeBuffer}write(t,n,r){return this.sendPacket("message",t,n,r),this}send(t,n,r){return this.sendPacket("message",t,n,r),this}sendPacket(t,n,r,i){if(typeof n=="function"&&(i=n,n=void 0),typeof r=="function"&&(i=r,r=null),this.readyState==="closing"||this.readyState==="closed")return;r=r||{},r.compress=r.compress!==!1;const o={type:t,data:n,options:r};this.emitReserved("packetCreate",o),this.writeBuffer.push(o),i&&this.once("flush",i),this.flush()}close(){const t=()=>{this.onClose("forced close"),this.transport.close()},n=()=>{this.off("upgrade",n),this.off("upgradeError",n),t()},r=()=>{this.once("upgrade",n),this.once("upgradeError",n)};return(this.readyState==="opening"||this.readyState==="open")&&(this.readyState="closing",this.writeBuffer.length?this.once("drain",()=>{this.upgrading?r():t()}):this.upgrading?r():t()),this}onError(t){Ec.priorWebsocketSuccess=!1,this.emitReserved("error",t),this.onClose("transport error",t)}onClose(t,n){(this.readyState==="opening"||this.readyState==="open"||this.readyState==="closing")&&(this.clearTimeoutFn(this.pingTimeoutTimer),this.transport.removeAllListeners("close"),this.transport.close(),this.transport.removeAllListeners(),typeof removeEventListener=="function"&&(removeEventListener("beforeunload",this.beforeunloadEventListener,!1),removeEventListener("offline",this.offlineEventListener,!1)),this.readyState="closed",this.id=null,this.emitReserved("close",t,n),this.writeBuffer=[],this.prevBufferLen=0)}filterUpgrades(t){const n=[];let r=0;const i=t.length;for(;r<i;r++)~this.transports.indexOf(t[r])&&n.push(t[r]);return n}}Ec.protocol=wH;function R6e(e,t="",n){let r=e;n=n||typeof location<"u"&&location,e==null&&(e=n.protocol+"//"+n.host),typeof e=="string"&&(e.charAt(0)==="/"&&(e.charAt(1)==="/"?e=n.protocol+e:e=n.host+e),/^(https?|wss?):\/\//.test(e)||(typeof n<"u"?e=n.protocol+"//"+e:e="https://"+e),r=OC(e)),r.port||(/^(http|ws)$/.test(r.protocol)?r.port="80":/^(http|ws)s$/.test(r.protocol)&&(r.port="443")),r.path=r.path||"/";const o=r.host.indexOf(":")!==-1?"["+r.host+"]":r.host;return r.id=r.protocol+"://"+o+":"+r.port+t,r.href=r.protocol+"://"+o+(n&&n.port===r.port?"":":"+r.port),r}const O6e=typeof ArrayBuffer=="function",N6e=e=>typeof ArrayBuffer.isView=="function"?ArrayBuffer.isView(e):e.buffer instanceof ArrayBuffer,IH=Object.prototype.toString,D6e=typeof Blob=="function"||typeof Blob<"u"&&IH.call(Blob)==="[object BlobConstructor]",z6e=typeof File=="function"||typeof File<"u"&&IH.call(File)==="[object FileConstructor]";function f_(e){return O6e&&(e instanceof ArrayBuffer||N6e(e))||D6e&&e instanceof Blob||z6e&&e instanceof File}function m3(e,t){if(!e||typeof e!="object")return!1;if(Array.isArray(e)){for(let n=0,r=e.length;n<r;n++)if(m3(e[n]))return!0;return!1}if(f_(e))return!0;if(e.toJSON&&typeof e.toJSON=="function"&&arguments.length===1)return m3(e.toJSON(),!0);for(const n in e)if(Object.prototype.hasOwnProperty.call(e,n)&&m3(e[n]))return!0;return!1}function F6e(e){const t=[],n=e.data,r=e;return r.data=NC(n,t),r.attachments=t.length,{packet:r,buffers:t}}function NC(e,t){if(!e)return e;if(f_(e)){const n={_placeholder:!0,num:t.length};return t.push(e),n}else if(Array.isArray(e)){const n=new Array(e.length);for(let r=0;r<e.length;r++)n[r]=NC(e[r],t);return n}else if(typeof e=="object"&&!(e instanceof Date)){const n={};for(const r in e)Object.prototype.hasOwnProperty.call(e,r)&&(n[r]=NC(e[r],t));return n}return e}function B6e(e,t){return e.data=DC(e.data,t),e.attachments=void 0,e}function DC(e,t){if(!e)return e;if(e&&e._placeholder===!0){if(typeof e.num=="number"&&e.num>=0&&e.num<t.length)return t[e.num];throw new Error("illegal attachments")}else if(Array.isArray(e))for(let n=0;n<e.length;n++)e[n]=DC(e[n],t);else if(typeof e=="object")for(const n in e)Object.prototype.hasOwnProperty.call(e,n)&&(e[n]=DC(e[n],t));return e}const $6e=5;var Jt;(function(e){e[e.CONNECT=0]="CONNECT",e[e.DISCONNECT=1]="DISCONNECT",e[e.EVENT=2]="EVENT",e[e.ACK=3]="ACK",e[e.CONNECT_ERROR=4]="CONNECT_ERROR",e[e.BINARY_EVENT=5]="BINARY_EVENT",e[e.BINARY_ACK=6]="BINARY_ACK"})(Jt||(Jt={}));class H6e{constructor(t){this.replacer=t}encode(t){return(t.type===Jt.EVENT||t.type===Jt.ACK)&&m3(t)?(t.type=t.type===Jt.EVENT?Jt.BINARY_EVENT:Jt.BINARY_ACK,this.encodeAsBinary(t)):[this.encodeAsString(t)]}encodeAsString(t){let n=""+t.type;return(t.type===Jt.BINARY_EVENT||t.type===Jt.BINARY_ACK)&&(n+=t.attachments+"-"),t.nsp&&t.nsp!=="/"&&(n+=t.nsp+","),t.id!=null&&(n+=t.id),t.data!=null&&(n+=JSON.stringify(t.data,this.replacer)),n}encodeAsBinary(t){const n=F6e(t),r=this.encodeAsString(n.packet),i=n.buffers;return i.unshift(r),i}}class h_ extends Wr{constructor(t){super(),this.reviver=t}add(t){let n;if(typeof t=="string"){if(this.reconstructor)throw new Error("got plaintext data when reconstructing a packet");n=this.decodeString(t),n.type===Jt.BINARY_EVENT||n.type===Jt.BINARY_ACK?(this.reconstructor=new W6e(n),n.attachments===0&&super.emitReserved("decoded",n)):super.emitReserved("decoded",n)}else if(f_(t)||t.base64)if(this.reconstructor)n=this.reconstructor.takeBinaryData(t),n&&(this.reconstructor=null,super.emitReserved("decoded",n));else throw new Error("got binary data when not reconstructing a packet");else throw new Error("Unknown type: "+t)}decodeString(t){let n=0;const r={type:Number(t.charAt(0))};if(Jt[r.type]===void 0)throw new Error("unknown packet type "+r.type);if(r.type===Jt.BINARY_EVENT||r.type===Jt.BINARY_ACK){const o=n+1;for(;t.charAt(++n)!=="-"&&n!=t.length;);const a=t.substring(o,n);if(a!=Number(a)||t.charAt(n)!=="-")throw new Error("Illegal attachments");r.attachments=Number(a)}if(t.charAt(n+1)==="/"){const o=n+1;for(;++n&&!(t.charAt(n)===","||n===t.length););r.nsp=t.substring(o,n)}else r.nsp="/";const i=t.charAt(n+1);if(i!==""&&Number(i)==i){const o=n+1;for(;++n;){const a=t.charAt(n);if(a==null||Number(a)!=a){--n;break}if(n===t.length)break}r.id=Number(t.substring(o,n+1))}if(t.charAt(++n)){const o=this.tryParse(t.substr(n));if(h_.isPayloadValid(r.type,o))r.data=o;else throw new Error("invalid payload")}return r}tryParse(t){try{return JSON.parse(t,this.reviver)}catch{return!1}}static isPayloadValid(t,n){switch(t){case Jt.CONNECT:return typeof n=="object";case Jt.DISCONNECT:return n===void 0;case Jt.CONNECT_ERROR:return typeof n=="string"||typeof n=="object";case Jt.EVENT:case Jt.BINARY_EVENT:return Array.isArray(n)&&n.length>0;case Jt.ACK:case Jt.BINARY_ACK:return Array.isArray(n)}}destroy(){this.reconstructor&&this.reconstructor.finishedReconstruction()}}class W6e{constructor(t){this.packet=t,this.buffers=[],this.reconPack=t}takeBinaryData(t){if(this.buffers.push(t),this.buffers.length===this.reconPack.attachments){const n=B6e(this.reconPack,this.buffers);return this.finishedReconstruction(),n}return null}finishedReconstruction(){this.reconPack=null,this.buffers=[]}}const V6e=Object.freeze(Object.defineProperty({__proto__:null,protocol:$6e,get PacketType(){return Jt},Encoder:H6e,Decoder:h_},Symbol.toStringTag,{value:"Module"}));function gs(e,t,n){return e.on(t,n),function(){e.off(t,n)}}const U6e=Object.freeze({connect:1,connect_error:1,disconnect:1,disconnecting:1,newListener:1,removeListener:1});class MH extends Wr{constructor(t,n,r){super(),this.connected=!1,this.receiveBuffer=[],this.sendBuffer=[],this.ids=0,this.acks={},this.flags={},this.io=t,this.nsp=n,r&&r.auth&&(this.auth=r.auth),this.io._autoConnect&&this.open()}get disconnected(){return!this.connected}subEvents(){if(this.subs)return;const t=this.io;this.subs=[gs(t,"open",this.onopen.bind(this)),gs(t,"packet",this.onpacket.bind(this)),gs(t,"error",this.onerror.bind(this)),gs(t,"close",this.onclose.bind(this))]}get active(){return!!this.subs}connect(){return this.connected?this:(this.subEvents(),this.io._reconnecting||this.io.open(),this.io._readyState==="open"&&this.onopen(),this)}open(){return this.connect()}send(...t){return t.unshift("message"),this.emit.apply(this,t),this}emit(t,...n){if(U6e.hasOwnProperty(t))throw new Error('"'+t.toString()+'" is a reserved event name');n.unshift(t);const r={type:Jt.EVENT,data:n};if(r.options={},r.options.compress=this.flags.compress!==!1,typeof n[n.length-1]=="function"){const a=this.ids++,s=n.pop();this._registerAckCallback(a,s),r.id=a}const i=this.io.engine&&this.io.engine.transport&&this.io.engine.transport.writable;return this.flags.volatile&&(!i||!this.connected)||(this.connected?(this.notifyOutgoingListeners(r),this.packet(r)):this.sendBuffer.push(r)),this.flags={},this}_registerAckCallback(t,n){const r=this.flags.timeout;if(r===void 0){this.acks[t]=n;return}const i=this.io.setTimeoutFn(()=>{delete this.acks[t];for(let o=0;o<this.sendBuffer.length;o++)this.sendBuffer[o].id===t&&this.sendBuffer.splice(o,1);n.call(this,new Error("operation has timed out"))},r);this.acks[t]=(...o)=>{this.io.clearTimeoutFn(i),n.apply(this,[null,...o])}}packet(t){t.nsp=this.nsp,this.io._packet(t)}onopen(){typeof this.auth=="function"?this.auth(t=>{this.packet({type:Jt.CONNECT,data:t})}):this.packet({type:Jt.CONNECT,data:this.auth})}onerror(t){this.connected||this.emitReserved("connect_error",t)}onclose(t,n){this.connected=!1,delete this.id,this.emitReserved("disconnect",t,n)}onpacket(t){if(t.nsp===this.nsp)switch(t.type){case Jt.CONNECT:if(t.data&&t.data.sid){const i=t.data.sid;this.onconnect(i)}else this.emitReserved("connect_error",new Error("It seems you are trying to reach a Socket.IO server in v2.x with a v3.x client, but they are not compatible (more information here: https://socket.io/docs/v3/migrating-from-2-x-to-3-0/)"));break;case Jt.EVENT:case Jt.BINARY_EVENT:this.onevent(t);break;case Jt.ACK:case Jt.BINARY_ACK:this.onack(t);break;case Jt.DISCONNECT:this.ondisconnect();break;case Jt.CONNECT_ERROR:this.destroy();const r=new Error(t.data.message);r.data=t.data.data,this.emitReserved("connect_error",r);break}}onevent(t){const n=t.data||[];t.id!=null&&n.push(this.ack(t.id)),this.connected?this.emitEvent(n):this.receiveBuffer.push(Object.freeze(n))}emitEvent(t){if(this._anyListeners&&this._anyListeners.length){const n=this._anyListeners.slice();for(const r of n)r.apply(this,t)}super.emit.apply(this,t)}ack(t){const n=this;let r=!1;return function(...i){r||(r=!0,n.packet({type:Jt.ACK,id:t,data:i}))}}onack(t){const n=this.acks[t.id];typeof n=="function"&&(n.apply(this,t.data),delete this.acks[t.id])}onconnect(t){this.id=t,this.connected=!0,this.emitBuffered(),this.emitReserved("connect")}emitBuffered(){this.receiveBuffer.forEach(t=>this.emitEvent(t)),this.receiveBuffer=[],this.sendBuffer.forEach(t=>{this.notifyOutgoingListeners(t),this.packet(t)}),this.sendBuffer=[]}ondisconnect(){this.destroy(),this.onclose("io server disconnect")}destroy(){this.subs&&(this.subs.forEach(t=>t()),this.subs=void 0),this.io._destroy(this)}disconnect(){return this.connected&&this.packet({type:Jt.DISCONNECT}),this.destroy(),this.connected&&this.onclose("io client disconnect"),this}close(){return this.disconnect()}compress(t){return this.flags.compress=t,this}get volatile(){return this.flags.volatile=!0,this}timeout(t){return this.flags.timeout=t,this}onAny(t){return this._anyListeners=this._anyListeners||[],this._anyListeners.push(t),this}prependAny(t){return this._anyListeners=this._anyListeners||[],this._anyListeners.unshift(t),this}offAny(t){if(!this._anyListeners)return this;if(t){const n=this._anyListeners;for(let r=0;r<n.length;r++)if(t===n[r])return n.splice(r,1),this}else this._anyListeners=[];return this}listenersAny(){return this._anyListeners||[]}onAnyOutgoing(t){return this._anyOutgoingListeners=this._anyOutgoingListeners||[],this._anyOutgoingListeners.push(t),this}prependAnyOutgoing(t){return this._anyOutgoingListeners=this._anyOutgoingListeners||[],this._anyOutgoingListeners.unshift(t),this}offAnyOutgoing(t){if(!this._anyOutgoingListeners)return this;if(t){const n=this._anyOutgoingListeners;for(let r=0;r<n.length;r++)if(t===n[r])return n.splice(r,1),this}else this._anyOutgoingListeners=[];return this}listenersAnyOutgoing(){return this._anyOutgoingListeners||[]}notifyOutgoingListeners(t){if(this._anyOutgoingListeners&&this._anyOutgoingListeners.length){const n=this._anyOutgoingListeners.slice();for(const r of n)r.apply(this,t.data)}}}function H0(e){e=e||{},this.ms=e.min||100,this.max=e.max||1e4,this.factor=e.factor||2,this.jitter=e.jitter>0&&e.jitter<=1?e.jitter:0,this.attempts=0}H0.prototype.duration=function(){var e=this.ms*Math.pow(this.factor,this.attempts++);if(this.jitter){var t=Math.random(),n=Math.floor(t*this.jitter*e);e=(Math.floor(t*10)&1)==0?e-n:e+n}return Math.min(e,this.max)|0};H0.prototype.reset=function(){this.attempts=0};H0.prototype.setMin=function(e){this.ms=e};H0.prototype.setMax=function(e){this.max=e};H0.prototype.setJitter=function(e){this.jitter=e};class zC extends Wr{constructor(t,n){var r;super(),this.nsps={},this.subs=[],t&&typeof t=="object"&&(n=t,t=void 0),n=n||{},n.path=n.path||"/socket.io",this.opts=n,Ex(this,n),this.reconnection(n.reconnection!==!1),this.reconnectionAttempts(n.reconnectionAttempts||1/0),this.reconnectionDelay(n.reconnectionDelay||1e3),this.reconnectionDelayMax(n.reconnectionDelayMax||5e3),this.randomizationFactor((r=n.randomizationFactor)!==null&&r!==void 0?r:.5),this.backoff=new H0({min:this.reconnectionDelay(),max:this.reconnectionDelayMax(),jitter:this.randomizationFactor()}),this.timeout(n.timeout==null?2e4:n.timeout),this._readyState="closed",this.uri=t;const i=n.parser||V6e;this.encoder=new i.Encoder,this.decoder=new i.Decoder,this._autoConnect=n.autoConnect!==!1,this._autoConnect&&this.open()}reconnection(t){return arguments.length?(this._reconnection=!!t,this):this._reconnection}reconnectionAttempts(t){return t===void 0?this._reconnectionAttempts:(this._reconnectionAttempts=t,this)}reconnectionDelay(t){var n;return t===void 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a=this._timeout;a===0&&i();const s=this.setTimeoutFn(()=>{i(),n.close(),n.emit("error",new Error("timeout"))},a);this.opts.autoUnref&&s.unref(),this.subs.push(function(){clearTimeout(s)})}return this.subs.push(i),this.subs.push(o),this}connect(t){return this.open(t)}onopen(){this.cleanup(),this._readyState="open",this.emitReserved("open");const t=this.engine;this.subs.push(gs(t,"ping",this.onping.bind(this)),gs(t,"data",this.ondata.bind(this)),gs(t,"error",this.onerror.bind(this)),gs(t,"close",this.onclose.bind(this)),gs(this.decoder,"decoded",this.ondecoded.bind(this)))}onping(){this.emitReserved("ping")}ondata(t){try{this.decoder.add(t)}catch(n){this.onclose("parse error",n)}}ondecoded(t){AH(()=>{this.emitReserved("packet",t)},this.setTimeoutFn)}onerror(t){this.emitReserved("error",t)}socket(t,n){let r=this.nsps[t];return r||(r=new MH(this,t,n),this.nsps[t]=r),r}_destroy(t){const n=Object.keys(this.nsps);for(const r of n)if(this.nsps[r].active)return;this._close()}_packet(t){const n=this.encoder.encode(t);for(let r=0;r<n.length;r++)this.engine.write(n[r],t.options)}cleanup(){this.subs.forEach(t=>t()),this.subs.length=0,this.decoder.destroy()}_close(){this.skipReconnect=!0,this._reconnecting=!1,this.onclose("forced close"),this.engine&&this.engine.close()}disconnect(){return this._close()}onclose(t,n){this.cleanup(),this.backoff.reset(),this._readyState="closed",this.emitReserved("close",t,n),this._reconnection&&!this.skipReconnect&&this.reconnect()}reconnect(){if(this._reconnecting||this.skipReconnect)return this;const t=this;if(this.backoff.attempts>=this._reconnectionAttempts)this.backoff.reset(),this.emitReserved("reconnect_failed"),this._reconnecting=!1;else{const n=this.backoff.duration();this._reconnecting=!0;const r=this.setTimeoutFn(()=>{t.skipReconnect||(this.emitReserved("reconnect_attempt",t.backoff.attempts),!t.skipReconnect&&t.open(i=>{i?(t._reconnecting=!1,t.reconnect(),this.emitReserved("reconnect_error",i)):t.onreconnect()}))},n);this.opts.autoUnref&&r.unref(),this.subs.push(function(){clearTimeout(r)})}}onreconnect(){const t=this.backoff.attempts;this._reconnecting=!1,this.backoff.reset(),this.emitReserved("reconnect",t)}}const gg={};function v3(e,t){typeof e=="object"&&(t=e,e=void 0),t=t||{};const n=R6e(e,t.path||"/socket.io"),r=n.source,i=n.id,o=n.path,a=gg[i]&&o in gg[i].nsps,s=t.forceNew||t["force new connection"]||t.multiplex===!1||a;let l;return s?l=new zC(r,t):(gg[i]||(gg[i]=new zC(r,t)),l=gg[i]),n.query&&!t.query&&(t.query=n.queryKey),l.socket(n.path,t)}Object.assign(v3,{Manager:zC,Socket:MH,io:v3,connect:v3});let Ty;const G6e=new Uint8Array(16);function j6e(){if(!Ty&&(Ty=typeof crypto<"u"&&crypto.getRandomValues&&crypto.getRandomValues.bind(crypto),!Ty))throw new 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gCe(t,[{key:"componentDidMount",value:function(){this._unsubscribe=this.props.persistor.subscribe(this.handlePersistorState),this.handlePersistorState()}},{key:"componentWillUnmount",value:function(){this._unsubscribe&&this._unsubscribe()}},{key:"render",value:function(){return typeof this.props.children=="function"?this.props.children(this.state.bootstrapped):this.state.bootstrapped?this.props.children:this.props.loading}}]),t}(C.exports.PureComponent);b3(OH,"defaultProps",{children:null,loading:null});const iI=Xre({config:{initialColorMode:"dark",useSystemColorMode:!1},components:{Tooltip:{baseStyle:e=>({textColor:e.colorMode==="dark"?"gray.800":"gray.100"})},Accordion:{baseStyle:e=>({button:{fontWeight:"bold",_hover:{bgColor:e.colorMode==="dark"?"rgba(255,255,255,0.05)":"rgba(0,0,0,0.05)"}},panel:{paddingBottom:2}})},FormLabel:{baseStyle:{fontWeight:"light"}},Button:{variants:{imageHoverIconButton:e=>({bg:e.colorMode==="dark"?"blackAlpha.700":"whiteAlpha.800",color:e.colorMode==="dark"?"whiteAlpha.700":"blackAlpha.700",_hover:{bg:e.colorMode==="dark"?"blackAlpha.800":"whiteAlpha.800",color:e.colorMode==="dark"?"whiteAlpha.900":"blackAlpha.900"}})}}}}),NH=()=>w(Dn,{width:"100vw",height:"100vh",alignItems:"center",justifyContent:"center",children:w(A0,{thickness:"2px",speed:"1s",emptyColor:"gray.200",color:"gray.400",size:"xl"})}),yCe=jt(e=>e.system,e=>({isProcessing:e.isProcessing,currentStep:e.currentStep,totalSteps:e.totalSteps,currentStatusHasSteps:e.currentStatusHasSteps}),{memoizeOptions:{resultEqualityCheck:la.exports.isEqual}}),xCe=()=>{const{isProcessing:e,currentStep:t,totalSteps:n,currentStatusHasSteps:r}=$e(yCe),i=t?Math.round(t*100/n):0;return 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sr(sr({},e),{},{isDragActive:t.isDragActive,isDragAccept:t.isDragAccept,isDragReject:t.isDragReject});case"setFiles":return sr(sr({},e),{},{acceptedFiles:t.acceptedFiles,fileRejections:t.fileRejections});case"reset":return sr({},UC);default:return e}}function gI(){}const T9e=e=>{const{children:t}=e,n=ct(),r=$e(Ga),i=od({}),[o,a]=C.exports.useState(!1),s=C.exports.useCallback(E=>{a(!0);const P=E.errors.reduce((k,L)=>k+`
+`+L.message,"");i({title:"Upload failed",description:P,status:"error",isClosable:!0})},[i]),l=C.exports.useCallback(E=>{a(!0);const P={file:E};["img2img","inpainting"].includes(r)&&(P.destination=r),n(NL(P))},[n,r]),d=C.exports.useCallback((E,P)=>{P.forEach(k=>{s(k)}),E.forEach(k=>{l(k)})},[l,s]),{getRootProps:p,getInputProps:g,isDragAccept:m,isDragReject:y,isDragActive:b,open:S}=jH({accept:{"image/png":[".png"],"image/jpeg":[".jpg",".jpeg",".png"]},noClick:!0,onDrop:d,maxFiles:1});return C.exports.useEffect(()=>{const E=P=>{const k=P.clipboardData?.items;if(!k)return;const L=[];for(const N of k)N.kind==="file"&&["image/png","image/jpg"].includes(N.type)&&L.push(N);if(!L.length)return;if(P.stopImmediatePropagation(),L.length>1){i({description:"Multiple images pasted, may only upload one image at a time",status:"error",isClosable:!0});return}const I=L[0].getAsFile();if(!I){i({description:"Unable to load file",status:"error",isClosable:!0});return}const O={file:I};["img2img","inpainting"].includes(r)&&(O.destination=r),n(NL(O))};return document.addEventListener("paste",E),()=>{document.removeEventListener("paste",E)}},[n,i,r]),w(AB.Provider,{value:S,children:te("div",{...p({style:{}}),children:[w("input",{...g()}),t,b&&te("div",{className:"dropzone-container",children:[m&&w("div",{className:"dropzone-overlay is-drag-accept",children:w(Af,{size:"lg",children:"Drop Images"})}),y&&te("div",{className:"dropzone-overlay is-drag-reject",children:[w(Af,{size:"lg",children:"Invalid Upload"}),w(Af,{size:"md",children:"Must be single JPEG or PNG image"})]}),o&&w("div",{className:"dropzone-overlay is-handling-upload",children:w(A0,{})})]})]})})},L9e=()=>{const e=ct();return w(sn,{tooltip:"Show Gallery (G)",tooltipPlacement:"top","aria-label":"Show Gallery",styleClass:"floating-show-hide-button right",onMouseOver:()=>{e(nC(!0))},children:w(pB,{})})};function A9e(e){return mt({tag:"svg",attr:{viewBox:"0 0 512 512"},child:[{tag:"path",attr:{d:"M32 384h272v32H32zM400 384h80v32h-80zM384 447.5c0 17.949-14.327 32.5-32 32.5-17.673 0-32-14.551-32-32.5v-95c0-17.949 14.327-32.5 32-32.5 17.673 0 32 14.551 32 32.5v95z"}},{tag:"g",attr:{},child:[{tag:"path",attr:{d:"M32 240h80v32H32zM208 240h272v32H208zM192 303.5c0 17.949-14.327 32.5-32 32.5-17.673 0-32-14.551-32-32.5v-95c0-17.949 14.327-32.5 32-32.5 17.673 0 32 14.551 32 32.5v95z"}}]},{tag:"g",attr:{},child:[{tag:"path",attr:{d:"M32 96h272v32H32zM400 96h80v32h-80zM384 159.5c0 17.949-14.327 32.5-32 32.5-17.673 0-32-14.551-32-32.5v-95c0-17.949 14.327-32.5 32-32.5 17.673 0 32 14.551 32 32.5v95z"}}]}]})(e)}const I9e=jt(e=>e.options,e=>{const{shouldPinOptionsPanel:t,shouldShowOptionsPanel:n}=e;return{shouldShowProcessButtons:!t||!n}},{memoizeOptions:{resultEqualityCheck:Wt.isEqual}}),M9e=()=>{const e=ct(),{shouldShowProcessButtons:t}=$e(I9e);return te("div",{className:"show-hide-button-options",children:[w(sn,{tooltip:"Show Options Panel (O)",tooltipPlacement:"top","aria-label":"Show Options Panel",onClick:()=>{e(MC(!0))},children:w(A9e,{})}),t&&te(Fn,{children:[w(yB,{iconButton:!0}),w(bB,{}),w(xB,{})]})]})};OCe();const R9e=jt([e=>e.gallery,e=>e.options,e=>e.system,Ga],(e,t,n,r)=>{const{shouldShowGallery:i,shouldHoldGalleryOpen:o,shouldPinGallery:a}=e,{shouldShowOptionsPanel:s,shouldHoldOptionsPanelOpen:l,shouldPinOptionsPanel:d}=t,p=Wt.reduce(n.model_list,(y,b,S)=>(b.status==="active"&&(y=S),y),""),g=!(i||o&&!a),m=!(s||l&&!d)&&["txt2img","img2img","inpainting"].includes(r);return{modelStatusText:p,shouldShowGalleryButton:g,shouldShowOptionsPanelButton:m}},{memoizeOptions:{resultEqualityCheck:Wt.isEqual}}),O9e=()=>{const e=ct(),[t,n]=C.exports.useState(!1),{shouldShowGalleryButton:r,shouldShowOptionsPanelButton:i}=$e(R9e);return C.exports.useEffect(()=>{e(Q2e()),n(!0)},[e]),t?w("div",{className:"App",children:te(T9e,{children:[w(xCe,{}),te("div",{className:"app-content",children:[w(ACe,{}),w(zwe,{})]}),w("div",{className:"app-console",children:w(RCe,{})}),r&&w(L9e,{}),i&&w(M9e,{})]})}):w(NH,{})};const qH=Wme(RH);GS.createRoot(document.getElementById("root")).render(w(ne.StrictMode,{children:w(xme,{store:RH,children:w(OH,{loading:w(NH,{}),persistor:qH,children:te(oge,{theme:iI,children:[w(QK,{initialColorMode:iI.config.initialColorMode}),w(O9e,{})]})})})}));
diff --git a/frontend/src/features/system/SettingsModal/SettingsModalItem.tsx b/frontend/src/features/system/SettingsModal/SettingsModalItem.tsx
deleted file mode 100644
index 0f69c87395..0000000000
--- a/frontend/src/features/system/SettingsModal/SettingsModalItem.tsx
+++ /dev/null
@@ -1,50 +0,0 @@
-import { useAppDispatch } from '../../../app/store';
-import IAISelect from '../../../common/components/IAISelect';
-import IAISwitch from '../../../common/components/IAISwitch';
-
-export function SettingsModalItem({
-  settingTitle,
-  isChecked,
-  dispatcher,
-}: {
-  settingTitle: string;
-  isChecked: boolean;
-  dispatcher: any;
-}) {
-  const dispatch = useAppDispatch();
-  return (
-    <IAISwitch
-      styleClass="settings-modal-item"
-      label={settingTitle}
-      isChecked={isChecked}
-      onChange={(e) => dispatch(dispatcher(e.target.checked))}
-    />
-  );
-}
-
-
-export function SettingsModalSelectItem({
-  settingTitle,
-  validValues,
-  defaultValue,
-  dispatcher,
-}: {
-  settingTitle: string;
-  validValues: 
-      Array<number | string>
-    | Array<{ key: string; value: string | number }>;
-  defaultValue: string;
-  dispatcher: any;
-}) {
-  const dispatch = useAppDispatch();
-  return (
-    <IAISelect
-      styleClass="settings-modal-item"
-      label={settingTitle}
-      validValues={validValues}
-      defaultValue={defaultValue}
-      onChange={(e) => dispatch(dispatcher(e.target.value))}
-    />
-  );
-}
-
diff --git a/installer/WinLongPathsEnabled.reg b/installer/WinLongPathsEnabled.reg
new file mode 100644
index 0000000000000000000000000000000000000000..778782b2724d59ad0caa2bd5ce8c0d38dc74f39b
GIT binary patch
literal 284
zcmXw!%WA?v6o$XG(033YK<c6!!9@v*(niG;OQ{kRgAK$4q9T3yw%?4D!*J%moZIiu
zJ0&#-8g88FvZCN9cjiLNx6D|z6gA>5YxP#O6xZS>`aBvBbmIGD%8NB0<h-&V;n|#%
zniI7dbJY!*j*K_|sVL!U*Vf*(lakmw%x|c46>`B7CjPS<ChkzFWo{X^EjY1_JM^3w
iyhoPoRKC>5;vM@~aiGV@t8Thl^DtfzuDHh_cKHL!axnJ*

literal 0
HcmV?d00001

diff --git a/installer/create_installers.sh b/installer/create_installers.sh
index 1b1d634ca6..833ae82239 100755
--- a/installer/create_installers.sh
+++ b/installer/create_installers.sh
@@ -1,22 +1,29 @@
-#!/bin/bash
+#!/usr/bin/env bash
 
-cd "$(dirname "${BASH_SOURCE[0]}")"
+set -euo pipefail
+IFS=$'\n\t'
+
+echo "Be certain that you're in the 'installer' directory before continuing."
+read -p "Press any key to continue, or CTRL-C to exit..."
 
 # make the installer zip for linux and mac
-rm -rf invokeAI
-mkdir -p invokeAI
-cp install.sh invokeAI
-cp readme.txt invokeAI
+rm -rf InvokeAI
+mkdir -p InvokeAI
+cp install.sh InvokeAI
+cp readme.txt InvokeAI
 
-zip -r invokeAI-linux.zip invokeAI
-zip -r invokeAI-mac.zip invokeAI
+zip -r InvokeAI-linux.zip InvokeAI
+zip -r InvokeAI-mac.zip InvokeAI
 
 # make the installer zip for windows
-rm -rf invokeAI
-mkdir -p invokeAI
-cp install.bat invokeAI
-cp readme.txt invokeAI
+rm -rf InvokeAI
+mkdir -p InvokeAI
+cp install.bat InvokeAI
+cp readme.txt InvokeAI
+cp WinLongPathsEnabled.reg InvokeAI
 
-zip -r invokeAI-windows.zip invokeAI
+zip -r InvokeAI-windows.zip InvokeAI
 
-echo "The installer zips are ready to be distributed.."
+rm -rf InvokeAI
+
+echo "The installer zips are ready for distribution."
diff --git a/installer/install.bat b/installer/install.bat
index c0ebc061bc..7fd6e0459d 100644
--- a/installer/install.bat
+++ b/installer/install.bat
@@ -1,115 +1,164 @@
 @echo off
 
-@rem This script will install git and conda (if not found on the PATH variable)
+@rem This script will install git (if not found on the PATH variable)
 @rem  using micromamba (an 8mb static-linked single-file binary, conda replacement).
-@rem For users who already have git and conda, this step will be skipped.
+@rem For users who already have git, this step will be skipped.
 
-@rem Next, it'll checkout the project's git repo, if necessary.
-@rem Finally, it'll create the conda environment and preload the models.
+@rem Next, it'll download the project's source code.
+@rem Then it will download a self-contained, standalone Python and unpack it.
+@rem Finally, it'll create the Python virtual environment and preload the models.
 
-@rem This enables a user to install this project without manually installing conda and git.
+@rem This enables a user to install this project without manually installing git or Python
 
-echo "Installing InvokeAI.."
-echo.
+echo ***** Installing InvokeAI.. *****
 
-@rem config
-set MAMBA_ROOT_PREFIX=%cd%\installer_files\mamba
+set PATH=c:\windows\system32
+
+@rem Config
 set INSTALL_ENV_DIR=%cd%\installer_files\env
+@rem https://mamba.readthedocs.io/en/latest/installation.html
 set MICROMAMBA_DOWNLOAD_URL=https://github.com/cmdr2/stable-diffusion-ui/releases/download/v1.1/micromamba.exe
-set REPO_URL=https://github.com/invoke-ai/InvokeAI.git
-set umamba_exists=F
-@rem Change the download URL to an InvokeAI repo's release URL
-
-@rem figure out whether git and conda needs to be installed
-if exist "%INSTALL_ENV_DIR%" set PATH=%INSTALL_ENV_DIR%;%INSTALL_ENV_DIR%\Library\bin;%INSTALL_ENV_DIR%\Scripts;%INSTALL_ENV_DIR%\Library\usr\bin;%PATH%
+set RELEASE_URL=https://github.com/invoke-ai/InvokeAI
+set RELEASE_SOURCEBALL=/archive/refs/heads/v2.1.3.tar.gz
+set PYTHON_BUILD_STANDALONE_URL=https://github.com/indygreg/python-build-standalone/releases/download
+set PYTHON_BUILD_STANDALONE=20221002/cpython-3.10.7+20221002-x86_64-pc-windows-msvc-shared-install_only.tar.gz
 
 set PACKAGES_TO_INSTALL=
 
-call conda --version >.tmp1 2>.tmp2
-if "%ERRORLEVEL%" NEQ "0" set PACKAGES_TO_INSTALL=%PACKAGES_TO_INSTALL% conda
-
 call git --version >.tmp1 2>.tmp2
 if "%ERRORLEVEL%" NEQ "0" set PACKAGES_TO_INSTALL=%PACKAGES_TO_INSTALL% git
 
-call "%MAMBA_ROOT_PREFIX%\micromamba.exe" --version >.tmp1 2>.tmp2
-if "%ERRORLEVEL%" EQU "0" set umamba_exists=T
+@rem Cleanup
+del /q .tmp1 .tmp2
 
-@rem (if necessary) install git and conda into a contained environment
+@rem (if necessary) install git into a contained environment
 if "%PACKAGES_TO_INSTALL%" NEQ "" (
     @rem download micromamba
-    if "%umamba_exists%" == "F" (
-        echo "Downloading micromamba from %MICROMAMBA_DOWNLOAD_URL% to %MAMBA_ROOT_PREFIX%\micromamba.exe"
+    echo ***** Downloading micromamba from %MICROMAMBA_DOWNLOAD_URL% to micromamba.exe *****
 
-        mkdir "%MAMBA_ROOT_PREFIX%"
-        call curl -L "%MICROMAMBA_DOWNLOAD_URL%" > "%MAMBA_ROOT_PREFIX%\micromamba.exe"
+    call curl -L "%MICROMAMBA_DOWNLOAD_URL%" > micromamba.exe
 
-        @rem test the mamba binary
-        echo Micromamba version:
-        call "%MAMBA_ROOT_PREFIX%\micromamba.exe" --version
-    )
+    @rem test the mamba binary
+    echo ***** Micromamba version:  *****
+    call micromamba.exe --version
 
     @rem create the installer env
     if not exist "%INSTALL_ENV_DIR%" (
-        call "%MAMBA_ROOT_PREFIX%\micromamba.exe" create -y --prefix "%INSTALL_ENV_DIR%"
+        call micromamba.exe create -y --prefix "%INSTALL_ENV_DIR%"
     )
 
-    echo "Packages to install:%PACKAGES_TO_INSTALL%"
+    echo ***** Packages to install:%PACKAGES_TO_INSTALL% *****
 
-    call "%MAMBA_ROOT_PREFIX%\micromamba.exe" install -y --prefix "%INSTALL_ENV_DIR%" -c conda-forge %PACKAGES_TO_INSTALL%
+    call micromamba.exe install -y --prefix "%INSTALL_ENV_DIR%" -c conda-forge %PACKAGES_TO_INSTALL%
 
     if not exist "%INSTALL_ENV_DIR%" (
-        echo "There was a problem while installing%PACKAGES_TO_INSTALL% using micromamba. Cannot continue."
+        echo ----- There was a problem while installing "%PACKAGES_TO_INSTALL%" using micromamba. Cannot continue. -----
         pause
         exit /b
     )
 )
 
-set PATH=%INSTALL_ENV_DIR%;%INSTALL_ENV_DIR%\Library\bin;%INSTALL_ENV_DIR%\Scripts;%INSTALL_ENV_DIR%\Library\usr\bin;%PATH%
+del /q micromamba.exe
 
-@rem get the repo (and load into the current directory)
-if not exist ".git" (
-    call git config --global init.defaultBranch main
-    call git init
-    call git remote add origin %REPO_URL%
-    call git fetch
-#    call git checkout origin/main -ft
-    call git checkout origin/release-candidate-2-1 -ft
-)
+@rem For 'git' only
+set PATH=%INSTALL_ENV_DIR%\Library\bin;%PATH%
 
-@rem activate the base env
-call conda activate
+@rem Download/unpack/clean up InvokeAI release sourceball
+set err_msg=----- InvokeAI source download failed -----
+curl -L %RELEASE_URL%/%RELEASE_SOURCEBALL% --output InvokeAI.tgz
+if %errorlevel% neq 0 goto err_exit
 
-@rem create the environment
-call conda env remove -n invokeai
-call conda env create
-if "%ERRORLEVEL%" NEQ "0" (
-   echo ""
-   echo "Something went wrong while installing Python libraries and cannot continue.
-   echo "Please visit https://invoke-ai.github.io/InvokeAI/#installation for alternative"
-   echo "installation methods."
-   echo "Press any key to continue"
-   pause
-   exit /b
-)
+set err_msg=----- InvokeAI source unpack failed -----
+tar -zxf InvokeAI.tgz
+if %errorlevel% neq 0 goto err_exit
+
+del /q InvokeAI.tgz
+
+set err_msg=----- InvokeAI source copy failed -----
+cd InvokeAI-*
+xcopy . .. /e /h
+if %errorlevel% neq 0 goto err_exit
+cd ..
+
+@rem cleanup
+for /f %%i in ('dir /b InvokeAI-*') do rd /s /q %%i
+rd /s /q .dev_scripts .github docker-build tests
+del /q requirements.in requirements-mkdocs.txt shell.nix
+
+echo ***** Unpacked InvokeAI source *****
+
+@rem Download/unpack/clean up python-build-standalone
+set err_msg=----- Python download failed -----
+curl -L %PYTHON_BUILD_STANDALONE_URL%/%PYTHON_BUILD_STANDALONE% --output python.tgz
+if %errorlevel% neq 0 goto err_exit
+
+set err_msg=----- Python unpack failed -----
+tar -zxf python.tgz
+if %errorlevel% neq 0 goto err_exit
+
+del /q python.tgz
+
+echo ***** Unpacked python-build-standalone *****
+
+@rem create venv
+set err_msg=----- problem creating venv -----
+.\python\python -E -s -m venv .venv
+@rem In reality, the following is ALL that 'activate.bat' does,
+@rem aside from setting the prompt, which we don't care about
+set PYTHONPATH=
+set PATH=.venv\Scripts;%PATH%
+if %errorlevel% neq 0 goto err_exit
+
+echo ***** Created Python virtual environment *****
+
+@rem Print venv's Python version
+set err_msg=----- problem calling venv's python -----
+echo We're running under
+.venv\Scripts\python --version
+if %errorlevel% neq 0 goto err_exit
+
+set err_msg=----- pip update failed -----
+.venv\Scripts\python -m pip install --no-cache-dir --no-warn-script-location --upgrade pip
+if %errorlevel% neq 0 goto err_exit
+
+echo ***** Updated pip *****
+
+set err_msg=----- requirements file copy failed -----
+copy installer\py3.10-windows-x86_64-cuda-reqs.txt requirements.txt
+if %errorlevel% neq 0 goto err_exit
+
+set err_msg=----- main pip install failed -----
+.venv\Scripts\python -m pip install --no-cache-dir --no-warn-script-location -r requirements.txt
+if %errorlevel% neq 0 goto err_exit
+
+set err_msg=----- clipseg install failed -----
+.venv\Scripts\python -m pip install --no-cache-dir --no-warn-script-location git+https://github.com/invoke-ai/clipseg.git@relaxed-python-requirement#egg=clipseg
+if %errorlevel% neq 0 goto err_exit
+
+set err_msg=----- InvokeAI setup failed -----
+.venv\Scripts\python -m pip install --no-cache-dir --no-warn-script-location -e .
+if %errorlevel% neq 0 goto err_exit
+
+echo ***** Installed Python dependencies *****
 
-call conda activate invokeai
 @rem preload the models
-call python scripts\preload_models.py
-if "%ERRORLEVEL%" NEQ "0" (
-   echo ""
-   echo "The preload_models.py script crashed or was cancelled."
-   echo "InvokeAI is not ready to run. To run preload_models.py again,"
-   echo "run the command 'update.bat' in this directory."
-   echo "Press any key to continue"
-   pause
-   exit /b
-)
+call .venv\Scripts\python scripts\preload_models.py
+set err_msg=----- model download clone failed -----
+if %errorlevel% neq 0 goto err_exit
+
+echo ***** Finished downloading models *****
+
+echo ***** Installing invoke.bat ******
+copy installer\invoke.bat .\invoke.bat
+echo All done! Execute the file invoke.bat in this directory to start InvokeAI
+
+@rem more cleanup
+rd /s /q installer installer_files
 
-@rem tell the user their next steps
-echo ""
-echo "* InvokeAI installed successfully *"
-echo "You can now start generating images by double-clicking the 'invoke.bat' file (inside this folder)
-echo "Press any key to continue"
 pause
-exit 0
+exit
 
+:err_exit
+    echo %err_msg%
+    pause
+    exit
diff --git a/installer/install.sh b/installer/install.sh
index 96ee9c9c25..c104b2e80d 100755
--- a/installer/install.sh
+++ b/installer/install.sh
@@ -1,130 +1,211 @@
-#!/bin/bash
+#!/usr/bin/env bash
 
-# This script will install git and conda (if not found on the PATH variable)
+set -euo pipefail
+IFS=$'\n\t'
+
+function _err_exit {
+    if test "$1" -ne 0
+    then
+        echo -e "Error code $1; Error caught was '$2'"
+        read -p "Press any key to exit..."
+        exit
+    fi
+}
+
+# This script will install git (if not found on the PATH variable)
 #  using micromamba (an 8mb static-linked single-file binary, conda replacement).
-# For users who already have git and conda, this step will be skipped.
+# For users who already have git, this step will be skipped.
 
-# Next, it'll checkout the project's git repo, if necessary.
-# Finally, it'll create the conda environment and preload the models.
+# Next, it'll download the project's source code.
+# Then it will download a self-contained, standalone Python and unpack it.
+# Finally, it'll create the Python virtual environment and preload the models.
 
-# This enables a user to install this project without manually installing conda and git.
+# This enables a user to install this project without manually installing git or Python
 
-cd "$(dirname "${BASH_SOURCE[0]}")"
+echo -e "\n***** Installing InvokeAI... *****\n"
 
-echo "Installing InvokeAI.."
-echo ""
 
 OS_NAME=$(uname -s)
 case "${OS_NAME}" in
     Linux*)     OS_NAME="linux";;
-    Darwin*)    OS_NAME="mac";;
-    *)          echo "Unknown OS: $OS_NAME! This script runs only on Linux or Mac" && exit
+    Darwin*)    OS_NAME="darwin";;
+    *)          echo -e "\n----- Unknown OS: $OS_NAME! This script runs only on Linux or MacOS -----\n" && exit
 esac
 
 OS_ARCH=$(uname -m)
 case "${OS_ARCH}" in
-    x86_64*)    OS_ARCH="64";;
-    arm64*)     OS_ARCH="arm64";;
-    *)          echo "Unknown system architecture: $OS_ARCH! This script runs only on x86_64 or arm64" && exit
+    x86_64*)    ;;
+    arm64*)     ;;
+    *)          echo -e "\n----- Unknown system architecture: $OS_ARCH! This script runs only on x86_64 or arm64 -----\n" && exit
 esac
 
 # https://mamba.readthedocs.io/en/latest/installation.html
-if [ "$OS_NAME" == "linux" ] && [ "$OS_ARCH" == "arm64" ]; then OS_ARCH="aarch64"; fi
+MAMBA_OS_NAME=$OS_NAME
+MAMBA_ARCH=$OS_ARCH
+if [ "$OS_NAME" == "darwin" ]; then
+    MAMBA_OS_NAME="osx"
+fi
+
+if [ "$OS_ARCH" == "linux" ]; then
+    MAMBA_ARCH="aarch64"
+fi
+
+if [ "$OS_ARCH" == "x86_64" ]; then
+    MAMBA_ARCH="64"
+fi
+
+PY_ARCH=$OS_ARCH
+if [ "$OS_ARCH" == "arm64" ]; then
+    PY_ARCH="aarch64"
+fi
+
+# Compute device ('cd' segment of reqs files) detect goes here
+# This needs a ton of work
+# Suggestions:
+#   - lspci
+#   - check $PATH for nvidia-smi, gtt CUDA/GPU version from output
+#   - Surely there's a similar utility for AMD?
+CD="cuda"
+if [ "$OS_NAME" == "darwin" ] && [ "$OS_ARCH" == "arm64" ]; then
+    CD="mps"
+fi
 
 # config
-export MAMBA_ROOT_PREFIX="$(pwd)/installer_files/mamba"
 INSTALL_ENV_DIR="$(pwd)/installer_files/env"
-MICROMAMBA_DOWNLOAD_URL="https://micro.mamba.pm/api/micromamba/${OS_NAME}-${OS_ARCH}/latest"
-REPO_URL="https://github.com/invoke-ai/InvokeAI.git"
-umamba_exists="F"
-
-# figure out whether git and conda needs to be installed
-if [ -e "$INSTALL_ENV_DIR" ]; then export PATH="$INSTALL_ENV_DIR/bin:$PATH"; fi
+MICROMAMBA_DOWNLOAD_URL="https://micro.mamba.pm/api/micromamba/${MAMBA_OS_NAME}-${MAMBA_ARCH}/latest"
+RELEASE_URL=https://github.com/invoke-ai/InvokeAI
+RELEASE_SOURCEBALL=/archive/refs/heads/v2.1.3.tar.gz
+PYTHON_BUILD_STANDALONE_URL=https://github.com/indygreg/python-build-standalone/releases/download
+if [ "$OS_NAME" == "darwin" ]; then
+    PYTHON_BUILD_STANDALONE=20221002/cpython-3.10.7+20221002-${PY_ARCH}-apple-darwin-install_only.tar.gz
+elif [ "$OS_NAME" == "linux" ]; then
+    PYTHON_BUILD_STANDALONE=20221002/cpython-3.10.7+20221002-${PY_ARCH}-unknown-linux-gnu-install_only.tar.gz
+fi
 
 PACKAGES_TO_INSTALL=""
 
-if ! hash "conda" &>/dev/null; then PACKAGES_TO_INSTALL="$PACKAGES_TO_INSTALL conda"; fi
 if ! hash "git" &>/dev/null; then PACKAGES_TO_INSTALL="$PACKAGES_TO_INSTALL git"; fi
 
-if "$MAMBA_ROOT_PREFIX/micromamba" --version &>/dev/null; then umamba_exists="T"; fi
-
 # (if necessary) install git and conda into a contained environment
 if [ "$PACKAGES_TO_INSTALL" != "" ]; then
     # download micromamba
-    if [ "$umamba_exists" == "F" ]; then
-        echo "Downloading micromamba from $MICROMAMBA_DOWNLOAD_URL to $MAMBA_ROOT_PREFIX/micromamba"
+    echo -e "\n***** Downloading micromamba from $MICROMAMBA_DOWNLOAD_URL to micromamba *****\n"
 
-        mkdir -p "$MAMBA_ROOT_PREFIX"
-        curl -L "$MICROMAMBA_DOWNLOAD_URL" | tar -xvj bin/micromamba -O > "$MAMBA_ROOT_PREFIX/micromamba"
+    curl -L "$MICROMAMBA_DOWNLOAD_URL" | tar -xvj bin/micromamba -O > micromamba
 
-        chmod u+x "$MAMBA_ROOT_PREFIX/micromamba"
+    chmod u+x "micromamba"
 
-        # test the mamba binary
-        echo "Micromamba version:"
-        "$MAMBA_ROOT_PREFIX/micromamba" --version
-    fi
+    # test the mamba binary
+    echo -e "\n***** Micromamba version: *****\n"
+    "micromamba" --version
 
     # create the installer env
     if [ ! -e "$INSTALL_ENV_DIR" ]; then
-        "$MAMBA_ROOT_PREFIX/micromamba" create -y --prefix "$INSTALL_ENV_DIR"
+        "micromamba" create -y --prefix "$INSTALL_ENV_DIR"
     fi
 
-    echo "Packages to install:$PACKAGES_TO_INSTALL"
+    echo -e "\n***** Packages to install:$PACKAGES_TO_INSTALL *****\n"
 
-    "$MAMBA_ROOT_PREFIX/micromamba" install -y --prefix "$INSTALL_ENV_DIR" -c conda-forge $PACKAGES_TO_INSTALL
+    "micromamba" install -y --prefix "$INSTALL_ENV_DIR" -c conda-forge $PACKAGES_TO_INSTALL
 
     if [ ! -e "$INSTALL_ENV_DIR" ]; then
-        echo "There was a problem while initializing micromamba. Cannot continue."
+        echo -e "\n----- There was a problem while initializing micromamba. Cannot continue. -----\n"
         exit
     fi
 fi
 
-if [ -e "$INSTALL_ENV_DIR" ]; then export PATH="$INSTALL_ENV_DIR/bin:$PATH"; fi
+rm -f micromamba.exe
 
-# get the repo (and load into the current directory)
-if [ ! -e ".git" ]; then
-    git config --global init.defaultBranch main
-    git init
-    git remote add origin "$REPO_URL"
-    git fetch
-    git checkout origin/release-candidate-2-1 -ft
-fi
+export PATH="$INSTALL_ENV_DIR/bin:$PATH"
 
-# create the environment
-CONDA_BASEPATH=$(conda info --base)
-source "$CONDA_BASEPATH/etc/profile.d/conda.sh" # otherwise conda complains about 'shell not initialized' (needed when running in a script)
+# Download/unpack/clean up InvokeAI release sourceball
+_err_msg="\n----- InvokeAI source download failed -----\n"
+curl -L $RELEASE_URL/$RELEASE_SOURCEBALL --output InvokeAI.tgz
+_err_exit $? _err_msg
+_err_msg="\n----- InvokeAI source unpack failed -----\n"
+tar -zxf InvokeAI.tgz
+_err_exit $? _err_msg
 
-conda activate
+rm -f InvokeAI.tgz
 
-if [ "$OS_NAME" == "mac" ]; then
-    PIP_EXISTS_ACTION=w CONDA_SUBDIR=osx-${OS_ARCH} conda env create -f environment-mac.yml
-else
-    conda env remove -n invokeai
-    conda env create -f environment.yml
-fi
+_err_msg="\n----- InvokeAI source copy failed -----\n"
+cd InvokeAI-*
+cp -r . ..
+_err_exit $? _err_msg
+cd ..
 
-status=$?
+# cleanup
+rm -rf InvokeAI-*/
+rm -rf .dev_scripts/ .github/ docker-build/ tests/ requirements.in requirements-mkdocs.txt shell.nix
 
-if test $status -ne 0
-then
-   echo "Something went wrong while installing Python libraries and cannot continue."
-   echo "Please visit https://invoke-ai.github.io/InvokeAI/#installation for alternative"
-   echo "installation methods"
-else
-    conda activate invokeai
-    # preload the models
-    echo "Calling the preload_models.py script"
-    python scripts/preload_models.py
-    status=$?
-    if test $status -ne 0
-       then
-	   echo "The preload_models.py script crashed or was cancelled."
-           echo "InvokeAI is not ready to run. Try again by running"
-           echo "update.sh in this directory."
-       else
-           # tell the user their next steps
-	   echo "You can now start generating images by running invoke.sh (inside this folder), using ./invoke.sh"
-       fi
-fi   
+echo -e "\n***** Unpacked InvokeAI source *****\n"
 
-conda activate invokeai
+# Download/unpack/clean up python-build-standalone
+_err_msg="\n----- Python download failed -----\n"
+curl -L $PYTHON_BUILD_STANDALONE_URL/$PYTHON_BUILD_STANDALONE --output python.tgz
+_err_exit $? _err_msg
+_err_msg="\n----- Python unpack failed -----\n"
+tar -zxf python.tgz
+_err_exit $? _err_msg
+
+rm -f python.tgz
+
+echo -e "\n***** Unpacked python-build-standalone *****\n"
+
+# create venv
+_err_msg="\n----- problem creating venv -----\n"
+./python/bin/python3 -E -s -m venv .venv
+_err_exit $? _err_msg
+# In reality, the following is ALL that 'activate.bat' does,
+# aside from setting the prompt, which we don't care about
+export PYTHONPATH=
+export PATH=.venv/bin:$PATH
+
+echo -e "\n***** Created Python virtual environment *****\n"
+
+# Print venv's Python version
+_err_msg="\n----- problem calling venv's python -----\n"
+echo -e "We're running under"
+.venv/bin/python3 --version
+_err_exit $? _err_msg
+
+_err_msg="\n----- pip update failed -----\n"
+.venv/bin/python3 -m pip install --no-cache-dir --no-warn-script-location --upgrade pip
+_err_exit $? _err_msg
+
+echo -e "\n***** Updated pip *****\n"
+
+_err_msg="\n----- requirements file copy failed -----\n"
+cp installer/py3.10-${OS_NAME}-"${OS_ARCH}"-${CD}-reqs.txt requirements.txt
+_err_exit $? _err_msg
+
+_err_msg="\n----- main pip install failed -----\n"
+.venv/bin/python3 -m pip install --no-cache-dir --no-warn-script-location -r requirements.txt
+_err_exit $? _err_msg
+
+_err_msg="\n----- clipseg install failed -----\n"
+.venv/bin/python3 -m pip install --no-cache-dir --no-warn-script-location git+https://github.com/invoke-ai/clipseg.git@relaxed-python-requirement#egg=clipseg
+_err_exit $? _err_msg
+
+_err_msg="\n----- InvokeAI setup failed -----\n"
+.venv/bin/python3 -m pip install --no-cache-dir --no-warn-script-location -e .
+_err_exit $? _err_msg
+
+echo -e "\n***** Installed Python dependencies *****\n"
+
+# preload the models
+.venv/bin/python3 scripts/preload_models.py
+_err_msg="\n----- model download clone failed -----\n"
+_err_exit $? _err_msg
+
+echo -e "\n***** Finished downloading models *****\n"
+
+echo -e "\n***** Installing invoke.sh ******\n"
+cp installer/invoke.sh .
+
+# more cleanup
+rm -rf installer/ installer_files/
+
+echo "All done! Run the command './invoke.sh' to start InvokeAI."
+read -p "Press any key to exit..."
+exit
diff --git a/installer/invoke.bat b/installer/invoke.bat
new file mode 100644
index 0000000000..c00975fd3b
--- /dev/null
+++ b/installer/invoke.bat
@@ -0,0 +1,27 @@
+@echo off
+
+set PATH=c:\windows\system32
+set PATH=.venv\Scripts;%PATH%
+
+echo Do you want to generate images using the
+echo 1. command-line
+echo 2. browser-based UI
+echo 3. open the developer console
+set /P restore="Please enter 1, 2 or 3: "
+IF /I "%restore%" == "1" (
+    echo Starting the InvokeAI command-line..
+    .venv\Scripts\python scripts\invoke.py
+) ELSE IF /I "%restore%" == "2" (
+    echo Starting the InvokeAI browser-based UI..
+    .venv\Scripts\python scripts\invoke.py --web
+) ELSE IF /I "%restore%" == "3" (
+    echo Developer Console
+    call where python
+    call python --version
+
+    cmd /k
+) ELSE (
+    echo Invalid selection
+    pause
+    exit /b
+)
diff --git a/installer/invoke.sh b/installer/invoke.sh
new file mode 100755
index 0000000000..58f342d471
--- /dev/null
+++ b/installer/invoke.sh
@@ -0,0 +1,24 @@
+#!/usr/bin/env bash
+
+set -euo pipefail
+IFS=$'\n\t'
+
+PATH=.venv/scripts:$PATH
+
+if [ "$0" != "bash" ]; then
+    echo "Do you want to generate images using the"
+    echo "1. command-line"
+    echo "2. browser-based UI"
+    echo "3. open the developer console"
+    read -p "Please enter 1, 2, or 3: " yn
+    case $yn in
+        1 ) printf "\nStarting the InvokeAI command-line..\n"; .venv/bin/python scripts/invoke.py;;
+        2 ) printf "\nStarting the InvokeAI browser-based UI..\n"; .venv/bin/python scripts/invoke.py --web;;
+        3 ) printf "\nDeveloper Console:\n"; file_name=$(basename "${BASH_SOURCE[0]}"); bash --init-file "$file_name";;
+        * ) echo "Invalid selection"; exit;;
+    esac
+else # in developer console
+    python --version
+    echo "Press ^D to exit"
+    export PS1="(InvokeAI) \u@\h \w> "
+fi
diff --git a/installer/py3.10-darwin-arm64-mps-reqs.txt b/installer/py3.10-darwin-arm64-mps-reqs.txt
new file mode 100644
index 0000000000..116f67ded1
--- /dev/null
+++ b/installer/py3.10-darwin-arm64-mps-reqs.txt
@@ -0,0 +1,2069 @@
+#
+# This file is autogenerated by pip-compile with python 3.10
+# To update, run:
+#
+#    pip-compile --allow-unsafe --generate-hashes --output-file=installer/py3.10-darwin-arm64-mps-reqs.txt requirements.in
+#
+--extra-index-url https://download.pytorch.org/whl/cu116
+--trusted-host https
+
+absl-py==1.3.0 \
+    --hash=sha256:34995df9bd7a09b3b8749e230408f5a2a2dd7a68a0d33c12a3d0cb15a041a507 \
+    --hash=sha256:463c38a08d2e4cef6c498b76ba5bd4858e4c6ef51da1a5a1f27139a022e20248
+    # via
+    #   tb-nightly
+    #   tensorboard
+accelerate==0.13.2 \
+    --hash=sha256:dd6f08b010077f252dda5a7699d87b02885335c456770939c536e65ff07ed760 \
+    --hash=sha256:e22180d7094e4c1bfb05a2b078297c222f6b4fa595fde8916946c3f377cdf019
+    # via k-diffusion
+addict==2.4.0 \
+    --hash=sha256:249bb56bbfd3cdc2a004ea0ff4c2b6ddc84d53bc2194761636eb314d5cfa5dfc \
+    --hash=sha256:b3b2210e0e067a281f5646c8c5db92e99b7231ea8b0eb5f74dbdf9e259d4e494
+    # via basicsr
+aiohttp==3.8.3 \
+    --hash=sha256:02f9a2c72fc95d59b881cf38a4b2be9381b9527f9d328771e90f72ac76f31ad8 \
+    --hash=sha256:059a91e88f2c00fe40aed9031b3606c3f311414f86a90d696dd982e7aec48142 \
+    --hash=sha256:05a3c31c6d7cd08c149e50dc7aa2568317f5844acd745621983380597f027a18 \
+    --hash=sha256:08c78317e950e0762c2983f4dd58dc5e6c9ff75c8a0efeae299d363d439c8e34 \
+    --hash=sha256:09e28f572b21642128ef31f4e8372adb6888846f32fecb288c8b0457597ba61a \
+    --hash=sha256:0d2c6d8c6872df4a6ec37d2ede71eff62395b9e337b4e18efd2177de883a5033 \
+    --hash=sha256:16c121ba0b1ec2b44b73e3a8a171c4f999b33929cd2397124a8c7fcfc8cd9e06 \
+    --hash=sha256:1d90043c1882067f1bd26196d5d2db9aa6d268def3293ed5fb317e13c9413ea4 \
+    --hash=sha256:1e56b9cafcd6531bab5d9b2e890bb4937f4165109fe98e2b98ef0dcfcb06ee9d \
+    --hash=sha256:20acae4f268317bb975671e375493dbdbc67cddb5f6c71eebdb85b34444ac46b \
+    --hash=sha256:21b30885a63c3f4ff5b77a5d6caf008b037cb521a5f33eab445dc566f6d092cc \
+    --hash=sha256:21d69797eb951f155026651f7e9362877334508d39c2fc37bd04ff55b2007091 \
+    --hash=sha256:256deb4b29fe5e47893fa32e1de2d73c3afe7407738bd3c63829874661d4822d \
+    --hash=sha256:25892c92bee6d9449ffac82c2fe257f3a6f297792cdb18ad784737d61e7a9a85 \
+    --hash=sha256:2ca9af5f8f5812d475c5259393f52d712f6d5f0d7fdad9acdb1107dd9e3cb7eb \
+    --hash=sha256:2d252771fc85e0cf8da0b823157962d70639e63cb9b578b1dec9868dd1f4f937 \
+    --hash=sha256:2dea10edfa1a54098703cb7acaa665c07b4e7568472a47f4e64e6319d3821ccf \
+    --hash=sha256:2df5f139233060578d8c2c975128fb231a89ca0a462b35d4b5fcf7c501ebdbe1 \
+    --hash=sha256:2feebbb6074cdbd1ac276dbd737b40e890a1361b3cc30b74ac2f5e24aab41f7b \
+    --hash=sha256:309aa21c1d54b8ef0723181d430347d7452daaff93e8e2363db8e75c72c2fb2d \
+    --hash=sha256:3828fb41b7203176b82fe5d699e0d845435f2374750a44b480ea6b930f6be269 \
+    --hash=sha256:398701865e7a9565d49189f6c90868efaca21be65c725fc87fc305906be915da \
+    --hash=sha256:43046a319664a04b146f81b40e1545d4c8ac7b7dd04c47e40bf09f65f2437346 \
+    --hash=sha256:437399385f2abcd634865705bdc180c8314124b98299d54fe1d4c8990f2f9494 \
+    --hash=sha256:45d88b016c849d74ebc6f2b6e8bc17cabf26e7e40c0661ddd8fae4c00f015697 \
+    --hash=sha256:47841407cc89a4b80b0c52276f3cc8138bbbfba4b179ee3acbd7d77ae33f7ac4 \
+    --hash=sha256:4a4fbc769ea9b6bd97f4ad0b430a6807f92f0e5eb020f1e42ece59f3ecfc4585 \
+    --hash=sha256:4ab94426ddb1ecc6a0b601d832d5d9d421820989b8caa929114811369673235c \
+    --hash=sha256:4b0f30372cef3fdc262f33d06e7b411cd59058ce9174ef159ad938c4a34a89da \
+    --hash=sha256:4e3a23ec214e95c9fe85a58470b660efe6534b83e6cbe38b3ed52b053d7cb6ad \
+    --hash=sha256:512bd5ab136b8dc0ffe3fdf2dfb0c4b4f49c8577f6cae55dca862cd37a4564e2 \
+    --hash=sha256:527b3b87b24844ea7865284aabfab08eb0faf599b385b03c2aa91fc6edd6e4b6 \
+    --hash=sha256:54d107c89a3ebcd13228278d68f1436d3f33f2dd2af5415e3feaeb1156e1a62c \
+    --hash=sha256:5835f258ca9f7c455493a57ee707b76d2d9634d84d5d7f62e77be984ea80b849 \
+    --hash=sha256:598adde339d2cf7d67beaccda3f2ce7c57b3b412702f29c946708f69cf8222aa \
+    --hash=sha256:599418aaaf88a6d02a8c515e656f6faf3d10618d3dd95866eb4436520096c84b \
+    --hash=sha256:5bf651afd22d5f0c4be16cf39d0482ea494f5c88f03e75e5fef3a85177fecdeb \
+    --hash=sha256:5c59fcd80b9049b49acd29bd3598cada4afc8d8d69bd4160cd613246912535d7 \
+    --hash=sha256:653acc3880459f82a65e27bd6526e47ddf19e643457d36a2250b85b41a564715 \
+    --hash=sha256:66bd5f950344fb2b3dbdd421aaa4e84f4411a1a13fca3aeb2bcbe667f80c9f76 \
+    --hash=sha256:6f3553510abdbec67c043ca85727396ceed1272eef029b050677046d3387be8d \
+    --hash=sha256:7018ecc5fe97027214556afbc7c502fbd718d0740e87eb1217b17efd05b3d276 \
+    --hash=sha256:713d22cd9643ba9025d33c4af43943c7a1eb8547729228de18d3e02e278472b6 \
+    --hash=sha256:73a4131962e6d91109bca6536416aa067cf6c4efb871975df734f8d2fd821b37 \
+    --hash=sha256:75880ed07be39beff1881d81e4a907cafb802f306efd6d2d15f2b3c69935f6fb \
+    --hash=sha256:75e14eac916f024305db517e00a9252714fce0abcb10ad327fb6dcdc0d060f1d \
+    --hash=sha256:8135fa153a20d82ffb64f70a1b5c2738684afa197839b34cc3e3c72fa88d302c \
+    --hash=sha256:84b14f36e85295fe69c6b9789b51a0903b774046d5f7df538176516c3e422446 \
+    --hash=sha256:86fc24e58ecb32aee09f864cb11bb91bc4c1086615001647dbfc4dc8c32f4008 \
+    --hash=sha256:87f44875f2804bc0511a69ce44a9595d5944837a62caecc8490bbdb0e18b1342 \
+    --hash=sha256:88c70ed9da9963d5496d38320160e8eb7e5f1886f9290475a881db12f351ab5d \
+    --hash=sha256:88e5be56c231981428f4f506c68b6a46fa25c4123a2e86d156c58a8369d31ab7 \
+    --hash=sha256:89d2e02167fa95172c017732ed7725bc8523c598757f08d13c5acca308e1a061 \
+    --hash=sha256:8d6aaa4e7155afaf994d7924eb290abbe81a6905b303d8cb61310a2aba1c68ba \
+    --hash=sha256:92a2964319d359f494f16011e23434f6f8ef0434acd3cf154a6b7bec511e2fb7 \
+    --hash=sha256:96372fc29471646b9b106ee918c8eeb4cca423fcbf9a34daa1b93767a88a2290 \
+    --hash=sha256:978b046ca728073070e9abc074b6299ebf3501e8dee5e26efacb13cec2b2dea0 \
+    --hash=sha256:9c7149272fb5834fc186328e2c1fa01dda3e1fa940ce18fded6d412e8f2cf76d \
+    --hash=sha256:a0239da9fbafd9ff82fd67c16704a7d1bccf0d107a300e790587ad05547681c8 \
+    --hash=sha256:ad5383a67514e8e76906a06741febd9126fc7c7ff0f599d6fcce3e82b80d026f \
+    --hash=sha256:ad61a9639792fd790523ba072c0555cd6be5a0baf03a49a5dd8cfcf20d56df48 \
+    --hash=sha256:b29bfd650ed8e148f9c515474a6ef0ba1090b7a8faeee26b74a8ff3b33617502 \
+    --hash=sha256:b97decbb3372d4b69e4d4c8117f44632551c692bb1361b356a02b97b69e18a62 \
+    --hash=sha256:ba71c9b4dcbb16212f334126cc3d8beb6af377f6703d9dc2d9fb3874fd667ee9 \
+    --hash=sha256:c37c5cce780349d4d51739ae682dec63573847a2a8dcb44381b174c3d9c8d403 \
+    --hash=sha256:c971bf3786b5fad82ce5ad570dc6ee420f5b12527157929e830f51c55dc8af77 \
+    --hash=sha256:d1fde0f44029e02d02d3993ad55ce93ead9bb9b15c6b7ccd580f90bd7e3de476 \
+    --hash=sha256:d24b8bb40d5c61ef2d9b6a8f4528c2f17f1c5d2d31fed62ec860f6006142e83e \
+    --hash=sha256:d5ba88df9aa5e2f806650fcbeedbe4f6e8736e92fc0e73b0400538fd25a4dd96 \
+    --hash=sha256:d6f76310355e9fae637c3162936e9504b4767d5c52ca268331e2756e54fd4ca5 \
+    --hash=sha256:d737fc67b9a970f3234754974531dc9afeea11c70791dcb7db53b0cf81b79784 \
+    --hash=sha256:da22885266bbfb3f78218dc40205fed2671909fbd0720aedba39b4515c038091 \
+    --hash=sha256:da37dcfbf4b7f45d80ee386a5f81122501ec75672f475da34784196690762f4b \
+    --hash=sha256:db19d60d846283ee275d0416e2a23493f4e6b6028825b51290ac05afc87a6f97 \
+    --hash=sha256:db4c979b0b3e0fa7e9e69ecd11b2b3174c6963cebadeecfb7ad24532ffcdd11a \
+    --hash=sha256:e164e0a98e92d06da343d17d4e9c4da4654f4a4588a20d6c73548a29f176abe2 \
+    --hash=sha256:e168a7560b7c61342ae0412997b069753f27ac4862ec7867eff74f0fe4ea2ad9 \
+    --hash=sha256:e381581b37db1db7597b62a2e6b8b57c3deec95d93b6d6407c5b61ddc98aca6d \
+    --hash=sha256:e65bc19919c910127c06759a63747ebe14f386cda573d95bcc62b427ca1afc73 \
+    --hash=sha256:e7b8813be97cab8cb52b1375f41f8e6804f6507fe4660152e8ca5c48f0436017 \
+    --hash=sha256:e8a78079d9a39ca9ca99a8b0ac2fdc0c4d25fc80c8a8a82e5c8211509c523363 \
+    --hash=sha256:ebf909ea0a3fc9596e40d55d8000702a85e27fd578ff41a5500f68f20fd32e6c \
+    --hash=sha256:ec40170327d4a404b0d91855d41bfe1fe4b699222b2b93e3d833a27330a87a6d \
+    --hash=sha256:f178d2aadf0166be4df834c4953da2d7eef24719e8aec9a65289483eeea9d618 \
+    --hash=sha256:f88df3a83cf9df566f171adba39d5bd52814ac0b94778d2448652fc77f9eb491 \
+    --hash=sha256:f973157ffeab5459eefe7b97a804987876dd0a55570b8fa56b4e1954bf11329b \
+    --hash=sha256:ff25f48fc8e623d95eca0670b8cc1469a83783c924a602e0fbd47363bb54aaca
+    # via fsspec
+aiosignal==1.2.0 \
+    --hash=sha256:26e62109036cd181df6e6ad646f91f0dcfd05fe16d0cb924138ff2ab75d64e3a \
+    --hash=sha256:78ed67db6c7b7ced4f98e495e572106d5c432a93e1ddd1bf475e1dc05f5b7df2
+    # via aiohttp
+albumentations==1.3.0 \
+    --hash=sha256:294165d87d03bc8323e484927f0a5c1a3c64b0e7b9c32a979582a6c93c363bdf \
+    --hash=sha256:be1af36832c8893314f2a5550e8ac19801e04770734c1b70fa3c996b41f37bed
+    # via -r requirements.in
+altair==4.2.0 \
+    --hash=sha256:0c724848ae53410c13fa28be2b3b9a9dcb7b5caa1a70f7f217bd663bb419935a \
+    --hash=sha256:d87d9372e63b48cd96b2a6415f0cf9457f50162ab79dc7a31cd7e024dd840026
+    # via streamlit
+antlr4-python3-runtime==4.9.3 \
+    --hash=sha256:f224469b4168294902bb1efa80a8bf7855f24c99aef99cbefc1bcd3cce77881b
+    # via omegaconf
+async-timeout==4.0.2 \
+    --hash=sha256:2163e1640ddb52b7a8c80d0a67a08587e5d245cc9c553a74a847056bc2976b15 \
+    --hash=sha256:8ca1e4fcf50d07413d66d1a5e416e42cfdf5851c981d679a09851a6853383b3c
+    # via aiohttp
+attrs==22.1.0 \
+    --hash=sha256:29adc2665447e5191d0e7c568fde78b21f9672d344281d0c6e1ab085429b22b6 \
+    --hash=sha256:86efa402f67bf2df34f51a335487cf46b1ec130d02b8d39fd248abfd30da551c
+    # via
+    #   aiohttp
+    #   jsonschema
+basicsr==1.4.2 \
+    --hash=sha256:b89b595a87ef964cda9913b4d99380ddb6554c965577c0c10cb7b78e31301e87
+    # via
+    #   gfpgan
+    #   realesrgan
+bidict==0.22.0 \
+    --hash=sha256:415126d23a0c81e1a8c584a8fb1f6905ea090c772571803aeee0a2242e8e7ba0 \
+    --hash=sha256:5c826b3e15e97cc6e615de295756847c282a79b79c5430d3bfc909b1ac9f5bd8
+    # via python-socketio
+blinker==1.5 \
+    --hash=sha256:1eb563df6fdbc39eeddc177d953203f99f097e9bf0e2b8f9f3cf18b6ca425e36 \
+    --hash=sha256:923e5e2f69c155f2cc42dafbbd70e16e3fde24d2d4aa2ab72fbe386238892462
+    # via streamlit
+cachetools==5.2.0 \
+    --hash=sha256:6a94c6402995a99c3970cc7e4884bb60b4a8639938157eeed436098bf9831757 \
+    --hash=sha256:f9f17d2aec496a9aa6b76f53e3b614c965223c061982d434d160f930c698a9db
+    # via
+    #   google-auth
+    #   streamlit
+certifi==2022.9.24 \
+    --hash=sha256:0d9c601124e5a6ba9712dbc60d9c53c21e34f5f641fe83002317394311bdce14 \
+    --hash=sha256:90c1a32f1d68f940488354e36370f6cca89f0f106db09518524c88d6ed83f382
+    # via
+    #   requests
+    #   sentry-sdk
+chardet==4.0.0 \
+    --hash=sha256:0d6f53a15db4120f2b08c94f11e7d93d2c911ee118b6b30a04ec3ee8310179fa \
+    --hash=sha256:f864054d66fd9118f2e67044ac8981a54775ec5b67aed0441892edb553d21da5
+    # via requests
+charset-normalizer==2.1.1 \
+    --hash=sha256:5a3d016c7c547f69d6f81fb0db9449ce888b418b5b9952cc5e6e66843e9dd845 \
+    --hash=sha256:83e9a75d1911279afd89352c68b45348559d1fc0506b054b346651b5e7fee29f
+    # via aiohttp
+clean-fid==0.1.34 \
+    --hash=sha256:2997f85a67a28c95adaae7899a33fc10537164fef4cdd424e3257bffad79a901
+    # via k-diffusion
+click==8.1.3 \
+    --hash=sha256:7682dc8afb30297001674575ea00d1814d808d6a36af415a82bd481d37ba7b8e \
+    --hash=sha256:bb4d8133cb15a609f44e8213d9b391b0809795062913b383c62be0ee95b1db48
+    # via
+    #   flask
+    #   streamlit
+    #   wandb
+clip @ https://github.com/openai/CLIP/archive/d50d76daa670286dd6cacf3bcd80b5e4823fc8e1.zip \
+    --hash=sha256:b5842c25da441d6c581b53a5c60e0c2127ebafe0f746f8e15561a006c6c3be6a
+    # via -r requirements.in
+colorama==0.4.6 \
+    --hash=sha256:08695f5cb7ed6e0531a20572697297273c47b8cae5a63ffc6d6ed5c201be6e44 \
+    --hash=sha256:4f1d9991f5acc0ca119f9d443620b77f9d6b33703e51011c16baf57afb285fc6
+    # via
+    #   click
+    #   tqdm
+commonmark==0.9.1 \
+    --hash=sha256:452f9dc859be7f06631ddcb328b6919c67984aca654e5fefb3914d54691aed60 \
+    --hash=sha256:da2f38c92590f83de410ba1a3cbceafbc74fee9def35f9251ba9a971d6d66fd9
+    # via rich
+contourpy==1.0.6 \
+    --hash=sha256:0236875c5a0784215b49d00ebbe80c5b6b5d5244b3655a36dda88105334dea17 \
+    --hash=sha256:03d1b9c6b44a9e30d554654c72be89af94fab7510b4b9f62356c64c81cec8b7d \
+    --hash=sha256:0537cc1195245bbe24f2913d1f9211b8f04eb203de9044630abd3664c6cc339c \
+    --hash=sha256:06ca79e1efbbe2df795822df2fa173d1a2b38b6e0f047a0ec7903fbca1d1847e \
+    --hash=sha256:08e8d09d96219ace6cb596506fb9b64ea5f270b2fb9121158b976d88871fcfd1 \
+    --hash=sha256:0b1e66346acfb17694d46175a0cea7d9036f12ed0c31dfe86f0f405eedde2bdd \
+    --hash=sha256:0b97454ed5b1368b66ed414c754cba15b9750ce69938fc6153679787402e4cdf \
+    --hash=sha256:0e4854cc02006ad6684ce092bdadab6f0912d131f91c2450ce6dbdea78ee3c0b \
+    --hash=sha256:12a7dc8439544ed05c6553bf026d5e8fa7fad48d63958a95d61698df0e00092b \
+    --hash=sha256:1b1ee48a130da4dd0eb8055bbab34abf3f6262957832fd575e0cab4979a15a41 \
+    --hash=sha256:1c0e1308307a75e07d1f1b5f0f56b5af84538a5e9027109a7bcf6cb47c434e72 \
+    --hash=sha256:1dedf4c64185a216c35eb488e6f433297c660321275734401760dafaeb0ad5c2 \
+    --hash=sha256:208bc904889c910d95aafcf7be9e677726df9ef71e216780170dbb7e37d118fa \
+    --hash=sha256:211dfe2bd43bf5791d23afbe23a7952e8ac8b67591d24be3638cabb648b3a6eb \
+    --hash=sha256:341330ed19074f956cb20877ad8d2ae50e458884bfa6a6df3ae28487cc76c768 \
+    --hash=sha256:344cb3badf6fc7316ad51835f56ac387bdf86c8e1b670904f18f437d70da4183 \
+    --hash=sha256:358f6364e4873f4d73360b35da30066f40387dd3c427a3e5432c6b28dd24a8fa \
+    --hash=sha256:371f6570a81dfdddbb837ba432293a63b4babb942a9eb7aaa699997adfb53278 \
+    --hash=sha256:375d81366afd547b8558c4720337218345148bc2fcffa3a9870cab82b29667f2 \
+    --hash=sha256:3a1917d3941dd58732c449c810fa7ce46cc305ce9325a11261d740118b85e6f3 \
+    --hash=sha256:4081918147fc4c29fad328d5066cfc751da100a1098398742f9f364be63803fc \
+    --hash=sha256:444fb776f58f4906d8d354eb6f6ce59d0a60f7b6a720da6c1ccb839db7c80eb9 \
+    --hash=sha256:46deb310a276cc5c1fd27958e358cce68b1e8a515fa5a574c670a504c3a3fe30 \
+    --hash=sha256:494efed2c761f0f37262815f9e3c4bb9917c5c69806abdee1d1cb6611a7174a0 \
+    --hash=sha256:50627bf76abb6ba291ad08db583161939c2c5fab38c38181b7833423ab9c7de3 \
+    --hash=sha256:5641927cc5ae66155d0c80195dc35726eae060e7defc18b7ab27600f39dd1fe7 \
+    --hash=sha256:5b117d29433fc8393b18a696d794961464e37afb34a6eeb8b2c37b5f4128a83e \
+    --hash=sha256:613c665529899b5d9fade7e5d1760111a0b011231277a0d36c49f0d3d6914bd6 \
+    --hash=sha256:6e459ebb8bb5ee4c22c19cc000174f8059981971a33ce11e17dddf6aca97a142 \
+    --hash=sha256:6f56515e7c6fae4529b731f6c117752247bef9cdad2b12fc5ddf8ca6a50965a5 \
+    --hash=sha256:730c27978a0003b47b359935478b7d63fd8386dbb2dcd36c1e8de88cbfc1e9de \
+    --hash=sha256:75a2e638042118118ab39d337da4c7908c1af74a8464cad59f19fbc5bbafec9b \
+    --hash=sha256:78ced51807ccb2f45d4ea73aca339756d75d021069604c2fccd05390dc3c28eb \
+    --hash=sha256:7ee394502026d68652c2824348a40bf50f31351a668977b51437131a90d777ea \
+    --hash=sha256:8468b40528fa1e15181cccec4198623b55dcd58306f8815a793803f51f6c474a \
+    --hash=sha256:84c593aeff7a0171f639da92cb86d24954bbb61f8a1b530f74eb750a14685832 \
+    --hash=sha256:913bac9d064cff033cf3719e855d4f1db9f1c179e0ecf3ba9fdef21c21c6a16a \
+    --hash=sha256:9447c45df407d3ecb717d837af3b70cfef432138530712263730783b3d016512 \
+    --hash=sha256:9b0e7fe7f949fb719b206548e5cde2518ffb29936afa4303d8a1c4db43dcb675 \
+    --hash=sha256:9bc407a6af672da20da74823443707e38ece8b93a04009dca25856c2d9adadb1 \
+    --hash=sha256:9e8e686a6db92a46111a1ee0ee6f7fbfae4048f0019de207149f43ac1812cf95 \
+    --hash=sha256:9fc4e7973ed0e1fe689435842a6e6b330eb7ccc696080dda9a97b1a1b78e41db \
+    --hash=sha256:a457ee72d9032e86730f62c5eeddf402e732fdf5ca8b13b41772aa8ae13a4563 \
+    --hash=sha256:a628bba09ba72e472bf7b31018b6281fd4cc903f0888049a3724afba13b6e0b8 \
+    --hash=sha256:a79d239fc22c3b8d9d3de492aa0c245533f4f4c7608e5749af866949c0f1b1b9 \
+    --hash=sha256:aa4674cf3fa2bd9c322982644967f01eed0c91bb890f624e0e0daf7a5c3383e9 \
+    --hash=sha256:acd2bd02f1a7adff3a1f33e431eb96ab6d7987b039d2946a9b39fe6fb16a1036 \
+    --hash=sha256:b3b1bd7577c530eaf9d2bc52d1a93fef50ac516a8b1062c3d1b9bcec9ebe329b \
+    --hash=sha256:b48d94386f1994db7c70c76b5808c12e23ed7a4ee13693c2fc5ab109d60243c0 \
+    --hash=sha256:b64f747e92af7da3b85631a55d68c45a2d728b4036b03cdaba4bd94bcc85bd6f \
+    --hash=sha256:b98c820608e2dca6442e786817f646d11057c09a23b68d2b3737e6dcb6e4a49b \
+    --hash=sha256:c1baa49ab9fedbf19d40d93163b7d3e735d9cd8d5efe4cce9907902a6dad391f \
+    --hash=sha256:c38c6536c2d71ca2f7e418acaf5bca30a3af7f2a2fa106083c7d738337848dbe \
+    --hash=sha256:c78bfbc1a7bff053baf7e508449d2765964d67735c909b583204e3240a2aca45 \
+    --hash=sha256:cd2bc0c8f2e8de7dd89a7f1c10b8844e291bca17d359373203ef2e6100819edd \
+    --hash=sha256:d2eff2af97ea0b61381828b1ad6cd249bbd41d280e53aea5cccd7b2b31b8225c \
+    --hash=sha256:d8834c14b8c3dd849005e06703469db9bf96ba2d66a3f88ecc539c9a8982e0ee \
+    --hash=sha256:d912f0154a20a80ea449daada904a7eb6941c83281a9fab95de50529bfc3a1da \
+    --hash=sha256:da1ef35fd79be2926ba80fbb36327463e3656c02526e9b5b4c2b366588b74d9a \
+    --hash=sha256:dbe6fe7a1166b1ddd7b6d887ea6fa8389d3f28b5ed3f73a8f40ece1fc5a3d340 \
+    --hash=sha256:dcd556c8fc37a342dd636d7eef150b1399f823a4462f8c968e11e1ebeabee769 \
+    --hash=sha256:e13b31d1b4b68db60b3b29f8e337908f328c7f05b9add4b1b5c74e0691180109 \
+    --hash=sha256:e1739496c2f0108013629aa095cc32a8c6363444361960c07493818d0dea2da4 \
+    --hash=sha256:e43255a83835a129ef98f75d13d643844d8c646b258bebd11e4a0975203e018f \
+    --hash=sha256:e626cefff8491bce356221c22af5a3ea528b0b41fbabc719c00ae233819ea0bf \
+    --hash=sha256:eadad75bf91897f922e0fb3dca1b322a58b1726a953f98c2e5f0606bd8408621 \
+    --hash=sha256:f33da6b5d19ad1bb5e7ad38bb8ba5c426d2178928bc2b2c44e8823ea0ecb6ff3 \
+    --hash=sha256:f4052a8a4926d4468416fc7d4b2a7b2a3e35f25b39f4061a7e2a3a2748c4fc48 \
+    --hash=sha256:f6ca38dd8d988eca8f07305125dec6f54ac1c518f1aaddcc14d08c01aebb6efc
+    # via matplotlib
+cycler==0.11.0 \
+    --hash=sha256:3a27e95f763a428a739d2add979fa7494c912a32c17c4c38c4d5f082cad165a3 \
+    --hash=sha256:9c87405839a19696e837b3b818fed3f5f69f16f1eec1a1ad77e043dcea9c772f
+    # via matplotlib
+decorator==5.1.1 \
+    --hash=sha256:637996211036b6385ef91435e4fae22989472f9d571faba8927ba8253acbc330 \
+    --hash=sha256:b8c3f85900b9dc423225913c5aace94729fe1fa9763b38939a95226f02d37186
+    # via validators
+diffusers==0.7.2 \
+    --hash=sha256:4a5f8b3a5fbd936bba7d459611cb35ec62875030367be32b232f9e19543e25a9 \
+    --hash=sha256:fb814ffd150cc6f470380b8c6a521181a77beb2f44134d2aad2e4cd8aa2ced0e
+    # via -r requirements.in
+dnspython==2.2.1 \
+    --hash=sha256:0f7569a4a6ff151958b64304071d370daa3243d15941a7beedf0c9fe5105603e \
+    --hash=sha256:a851e51367fb93e9e1361732c1d60dab63eff98712e503ea7d92e6eccb109b4f
+    # via eventlet
+docker-pycreds==0.4.0 \
+    --hash=sha256:6ce3270bcaf404cc4c3e27e4b6c70d3521deae82fb508767870fdbf772d584d4 \
+    --hash=sha256:7266112468627868005106ec19cd0d722702d2b7d5912a28e19b826c3d37af49
+    # via wandb
+einops==0.5.0 \
+    --hash=sha256:055de7eeb3cb9e9710ef3085a811090c6b52e809b7044e8785824ed185f486d1 \
+    --hash=sha256:8b7a83cffc1ea88e306df099b7cbb9c3ba5003bd84d05ae44be5655864abb8d3
+    # via k-diffusion
+entrypoints==0.4 \
+    --hash=sha256:b706eddaa9218a19ebcd67b56818f05bb27589b1ca9e8d797b74affad4ccacd4 \
+    --hash=sha256:f174b5ff827504fd3cd97cc3f8649f3693f51538c7e4bdf3ef002c8429d42f9f
+    # via altair
+eventlet==0.33.1 \
+    --hash=sha256:a085922698e5029f820cf311a648ac324d73cec0e4792877609d978a4b5bbf31 \
+    --hash=sha256:afbe17f06a58491e9aebd7a4a03e70b0b63fd4cf76d8307bae07f280479b1515
+    # via -r requirements.in
+facexlib==0.2.5 \
+    --hash=sha256:31e20cc4ed5d63562d380e4564bae14ac0d5d1899a079bad87621e13564567e4 \
+    --hash=sha256:cc7ceb56c5424319c47223cf75eef6828c34c66082707c6eb35b95d39779f02d
+    # via
+    #   gfpgan
+    #   realesrgan
+filelock==3.8.0 \
+    --hash=sha256:55447caa666f2198c5b6b13a26d2084d26fa5b115c00d065664b2124680c4edc \
+    --hash=sha256:617eb4e5eedc82fc5f47b6d61e4d11cb837c56cb4544e39081099fa17ad109d4
+    # via
+    #   diffusers
+    #   huggingface-hub
+    #   transformers
+filterpy==1.4.5 \
+    --hash=sha256:4f2a4d39e4ea601b9ab42b2db08b5918a9538c168cff1c6895ae26646f3d73b1
+    # via facexlib
+flask==2.2.2 \
+    --hash=sha256:642c450d19c4ad482f96729bd2a8f6d32554aa1e231f4f6b4e7e5264b16cca2b \
+    --hash=sha256:b9c46cc36662a7949f34b52d8ec7bb59c0d74ba08ba6cb9ce9adc1d8676d9526
+    # via
+    #   flask-cors
+    #   flask-socketio
+flask-cors==3.0.10 \
+    --hash=sha256:74efc975af1194fc7891ff5cd85b0f7478be4f7f59fe158102e91abb72bb4438 \
+    --hash=sha256:b60839393f3b84a0f3746f6cdca56c1ad7426aa738b70d6c61375857823181de
+    # via -r requirements.in
+flask-socketio==5.3.1 \
+    --hash=sha256:fd0ed0fc1341671d92d5f5b2f5503916deb7aa7e2940e6636cfa2c087c828bf9 \
+    --hash=sha256:ff0c721f20bff1e2cfba77948727a8db48f187e89a72fe50c34478ce6efb3353
+    # via -r requirements.in
+flaskwebgui==0.3.7 \
+    --hash=sha256:4a69955308eaa8bb256ba04a994dc8f58a48dcd6f9599694ab1bcd9f43d88a5d \
+    --hash=sha256:535974ce2672dcc74787c254de24cceed4101be75d96952dae82014dd57f061e
+    # via -r requirements.in
+fonttools==4.38.0 \
+    --hash=sha256:2bb244009f9bf3fa100fc3ead6aeb99febe5985fa20afbfbaa2f8946c2fbdaf1 \
+    --hash=sha256:820466f43c8be8c3009aef8b87e785014133508f0de64ec469e4efb643ae54fb
+    # via matplotlib
+frozenlist==1.3.1 \
+    --hash=sha256:022178b277cb9277d7d3b3f2762d294f15e85cd2534047e68a118c2bb0058f3e \
+    --hash=sha256:086ca1ac0a40e722d6833d4ce74f5bf1aba2c77cbfdc0cd83722ffea6da52a04 \
+    --hash=sha256:0bc75692fb3770cf2b5856a6c2c9de967ca744863c5e89595df64e252e4b3944 \
+    --hash=sha256:0dde791b9b97f189874d654c55c24bf7b6782343e14909c84beebd28b7217845 \
+    --hash=sha256:12607804084d2244a7bd4685c9d0dca5df17a6a926d4f1967aa7978b1028f89f \
+    --hash=sha256:19127f8dcbc157ccb14c30e6f00392f372ddb64a6ffa7106b26ff2196477ee9f \
+    --hash=sha256:1b51eb355e7f813bcda00276b0114c4172872dc5fb30e3fea059b9367c18fbcb \
+    --hash=sha256:1e1cf7bc8cbbe6ce3881863671bac258b7d6bfc3706c600008925fb799a256e2 \
+    --hash=sha256:219a9676e2eae91cb5cc695a78b4cb43d8123e4160441d2b6ce8d2c70c60e2f3 \
+    --hash=sha256:2743bb63095ef306041c8f8ea22bd6e4d91adabf41887b1ad7886c4c1eb43d5f \
+    --hash=sha256:2af6f7a4e93f5d08ee3f9152bce41a6015b5cf87546cb63872cc19b45476e98a \
+    --hash=sha256:31b44f1feb3630146cffe56344704b730c33e042ffc78d21f2125a6a91168131 \
+    --hash=sha256:31bf9539284f39ff9398deabf5561c2b0da5bb475590b4e13dd8b268d7a3c5c1 \
+    --hash=sha256:35c3d79b81908579beb1fb4e7fcd802b7b4921f1b66055af2578ff7734711cfa \
+    --hash=sha256:3a735e4211a04ccfa3f4833547acdf5d2f863bfeb01cfd3edaffbc251f15cec8 \
+    --hash=sha256:42719a8bd3792744c9b523674b752091a7962d0d2d117f0b417a3eba97d1164b \
+    --hash=sha256:49459f193324fbd6413e8e03bd65789e5198a9fa3095e03f3620dee2f2dabff2 \
+    --hash=sha256:4c0c99e31491a1d92cde8648f2e7ccad0e9abb181f6ac3ddb9fc48b63301808e \
+    --hash=sha256:52137f0aea43e1993264a5180c467a08a3e372ca9d378244c2d86133f948b26b \
+    --hash=sha256:526d5f20e954d103b1d47232e3839f3453c02077b74203e43407b962ab131e7b \
+    --hash=sha256:53b2b45052e7149ee8b96067793db8ecc1ae1111f2f96fe1f88ea5ad5fd92d10 \
+    --hash=sha256:572ce381e9fe027ad5e055f143763637dcbac2542cfe27f1d688846baeef5170 \
+    --hash=sha256:58fb94a01414cddcdc6839807db77ae8057d02ddafc94a42faee6004e46c9ba8 \
+    --hash=sha256:5e77a8bd41e54b05e4fb2708dc6ce28ee70325f8c6f50f3df86a44ecb1d7a19b \
+    --hash=sha256:5f271c93f001748fc26ddea409241312a75e13466b06c94798d1a341cf0e6989 \
+    --hash=sha256:5f63c308f82a7954bf8263a6e6de0adc67c48a8b484fab18ff87f349af356efd \
+    --hash=sha256:61d7857950a3139bce035ad0b0945f839532987dfb4c06cfe160254f4d19df03 \
+    --hash=sha256:61e8cb51fba9f1f33887e22488bad1e28dd8325b72425f04517a4d285a04c519 \
+    --hash=sha256:625d8472c67f2d96f9a4302a947f92a7adbc1e20bedb6aff8dbc8ff039ca6189 \
+    --hash=sha256:6e19add867cebfb249b4e7beac382d33215d6d54476bb6be46b01f8cafb4878b \
+    --hash=sha256:717470bfafbb9d9be624da7780c4296aa7935294bd43a075139c3d55659038ca \
+    --hash=sha256:74140933d45271c1a1283f708c35187f94e1256079b3c43f0c2267f9db5845ff \
+    --hash=sha256:74e6b2b456f21fc93ce1aff2b9728049f1464428ee2c9752a4b4f61e98c4db96 \
+    --hash=sha256:9494122bf39da6422b0972c4579e248867b6b1b50c9b05df7e04a3f30b9a413d \
+    --hash=sha256:94e680aeedc7fd3b892b6fa8395b7b7cc4b344046c065ed4e7a1e390084e8cb5 \
+    --hash=sha256:97d9e00f3ac7c18e685320601f91468ec06c58acc185d18bb8e511f196c8d4b2 \
+    --hash=sha256:9c6ef8014b842f01f5d2b55315f1af5cbfde284eb184075c189fd657c2fd8204 \
+    --hash=sha256:a027f8f723d07c3f21963caa7d585dcc9b089335565dabe9c814b5f70c52705a \
+    --hash=sha256:a718b427ff781c4f4e975525edb092ee2cdef6a9e7bc49e15063b088961806f8 \
+    --hash=sha256:ab386503f53bbbc64d1ad4b6865bf001414930841a870fc97f1546d4d133f141 \
+    --hash=sha256:ab6fa8c7871877810e1b4e9392c187a60611fbf0226a9e0b11b7b92f5ac72792 \
+    --hash=sha256:b47d64cdd973aede3dd71a9364742c542587db214e63b7529fbb487ed67cddd9 \
+    --hash=sha256:b499c6abe62a7a8d023e2c4b2834fce78a6115856ae95522f2f974139814538c \
+    --hash=sha256:bbb1a71b1784e68870800b1bc9f3313918edc63dbb8f29fbd2e767ce5821696c \
+    --hash=sha256:c3b31180b82c519b8926e629bf9f19952c743e089c41380ddca5db556817b221 \
+    --hash=sha256:c56c299602c70bc1bb5d1e75f7d8c007ca40c9d7aebaf6e4ba52925d88ef826d \
+    --hash=sha256:c92deb5d9acce226a501b77307b3b60b264ca21862bd7d3e0c1f3594022f01bc \
+    --hash=sha256:cc2f3e368ee5242a2cbe28323a866656006382872c40869b49b265add546703f \
+    --hash=sha256:d82bed73544e91fb081ab93e3725e45dd8515c675c0e9926b4e1f420a93a6ab9 \
+    --hash=sha256:da1cdfa96425cbe51f8afa43e392366ed0b36ce398f08b60de6b97e3ed4affef \
+    --hash=sha256:da5ba7b59d954f1f214d352308d1d86994d713b13edd4b24a556bcc43d2ddbc3 \
+    --hash=sha256:e0c8c803f2f8db7217898d11657cb6042b9b0553a997c4a0601f48a691480fab \
+    --hash=sha256:ee4c5120ddf7d4dd1eaf079af3af7102b56d919fa13ad55600a4e0ebe532779b \
+    --hash=sha256:eee0c5ecb58296580fc495ac99b003f64f82a74f9576a244d04978a7e97166db \
+    --hash=sha256:f5abc8b4d0c5b556ed8cd41490b606fe99293175a82b98e652c3f2711b452988 \
+    --hash=sha256:f810e764617b0748b49a731ffaa525d9bb36ff38332411704c2400125af859a6 \
+    --hash=sha256:f89139662cc4e65a4813f4babb9ca9544e42bddb823d2ec434e18dad582543bc \
+    --hash=sha256:fa47319a10e0a076709644a0efbcaab9e91902c8bd8ef74c6adb19d320f69b83 \
+    --hash=sha256:fabb953ab913dadc1ff9dcc3a7a7d3dc6a92efab3a0373989b8063347f8705be
+    # via
+    #   aiohttp
+    #   aiosignal
+fsspec[http]==2022.10.0 \
+    --hash=sha256:6b7c6ab3b476cdf17efcfeccde7fca28ef5a48f73a71010aaceec5fc15bf9ebf \
+    --hash=sha256:cb6092474e90487a51de768170f3afa50ca8982c26150a59072b16433879ff1d
+    # via pytorch-lightning
+ftfy==6.1.1 \
+    --hash=sha256:0ffd33fce16b54cccaec78d6ec73d95ad370e5df5a25255c8966a6147bd667ca \
+    --hash=sha256:bfc2019f84fcd851419152320a6375604a0f1459c281b5b199b2cd0d2e727f8f
+    # via clip
+future==0.18.2 \
+    --hash=sha256:b1bead90b70cf6ec3f0710ae53a525360fa360d306a86583adc6bf83a4db537d
+    # via
+    #   basicsr
+    #   test-tube
+getpass-asterisk==1.0.1 \
+    --hash=sha256:20d45cafda0066d761961e0919728526baf7bb5151fbf48a7d5ea4034127d857 \
+    --hash=sha256:7cc357a924cf62fa4e15b73cb4e5e30685c9084e464ffdc3fd9000a2b54ea9e9
+    # via -r requirements.in
+gfpgan @ https://github.com/TencentARC/GFPGAN/archive/2eac2033893ca7f427f4035d80fe95b92649ac56.zip \
+    --hash=sha256:79e6d71c8f1df7c7ccb0ac6b9a2ccb615ad5cde818c8b6f285a8711c05aebf85
+    # via
+    #   -r requirements.in
+    #   realesrgan
+gitdb==4.0.9 \
+    --hash=sha256:8033ad4e853066ba6ca92050b9df2f89301b8fc8bf7e9324d412a63f8bf1a8fd \
+    --hash=sha256:bac2fd45c0a1c9cf619e63a90d62bdc63892ef92387424b855792a6cabe789aa
+    # via gitpython
+gitpython==3.1.29 \
+    --hash=sha256:41eea0deec2deea139b459ac03656f0dd28fc4a3387240ec1d3c259a2c47850f \
+    --hash=sha256:cc36bfc4a3f913e66805a28e84703e419d9c264c1077e537b54f0e1af85dbefd
+    # via
+    #   streamlit
+    #   wandb
+google-auth==2.14.0 \
+    --hash=sha256:1ad5b0e6eba5f69645971abb3d2c197537d5914070a8c6d30299dfdb07c5c700 \
+    --hash=sha256:cf24817855d874ede2efd071aa22125445f555de1685b739a9782fcf408c2a3d
+    # via
+    #   google-auth-oauthlib
+    #   tb-nightly
+    #   tensorboard
+google-auth-oauthlib==0.4.6 \
+    --hash=sha256:3f2a6e802eebbb6fb736a370fbf3b055edcb6b52878bf2f26330b5e041316c73 \
+    --hash=sha256:a90a072f6993f2c327067bf65270046384cda5a8ecb20b94ea9a687f1f233a7a
+    # via
+    #   tb-nightly
+    #   tensorboard
+greenlet==2.0.0.post0 \
+    --hash=sha256:00ebdaf0fa51c284fd2172837d751731a15971e0c20d1a9163cfbdf620ce8b49 \
+    --hash=sha256:029ca674b3a7e8427db8f5c65d5ed4e24a7417af2a415a5958598aefd71980c4 \
+    --hash=sha256:02bdb1e373b275bd705c43b249426e776c4f8a8ff2afaf8ec5ea0dde487d8a14 \
+    --hash=sha256:08dc04f49ed1ea5e6772bb5e8cf2a77d1b1744566f4eca471a55b35af1278b31 \
+    --hash=sha256:08f44e938d142271b954405afb6570e0be48a9f556b6bf4d42d2e3ae6a251fad \
+    --hash=sha256:0a5c03e2a68ec2ff1cba74ceaed899ec8cd353285f4f985c30c8cfbef9d3a3be \
+    --hash=sha256:0fee3240093b745efc857392f09379514ad84db4ca324514594bbdf6380016c8 \
+    --hash=sha256:118e708dd7bc88beaeeaa5a8601a7743b8835b7bbaf7c8f23ffa78f8bc8faf28 \
+    --hash=sha256:13d492a807a5c7334b5931e9b6d9b181991ccc6a40555a7b177f189feff59b4b \
+    --hash=sha256:1cac9e9895aeff26434325404558783ee54f4ff3aec8daa56b8706796f7b01a0 \
+    --hash=sha256:2146d15429b4eeb412428737594acb5660a5bc0fdd1488d8a2a74a5ee32391fa \
+    --hash=sha256:21ee1ae26d072b195edea764218623f6c15eba4ae06816908f33c82e0af018d3 \
+    --hash=sha256:22eca421e3f2f3c18f4f54c0ff525aa9d397c6f116fce9ebd37b420174dbc027 \
+    --hash=sha256:2bab49783858cf724fff6868395cbeb81d1188cba23616b53e79de0beda29f42 \
+    --hash=sha256:2fbdec204ca40b3d0c0992a19c1ba627441c17983ac4ffc45baec7f5f53e20ca \
+    --hash=sha256:30ce47525f9a1515566429ac7de6b1ae76d32c3ccede256e3517a1a6419cf659 \
+    --hash=sha256:335dcf676d5e4122e4006c16ae11eda2467af5461b949c265ce120b6b959ffe2 \
+    --hash=sha256:3407b843b05da71fef0f1dd666059c08ad0e0f4afc3b9c93c998a2e53fac95e5 \
+    --hash=sha256:35827f98fd0d768862b8f15777e6dbb03fe6ac6e7bd1bee3f3ded4536f350347 \
+    --hash=sha256:3a22e5988f9d66b3e9ae9583bf9d8ef792b09f23afeb78707e6a4f47ab57cc5e \
+    --hash=sha256:3c3327da2bdab61078e42e695307465c425671a5a9251e6c29ee130d51943f28 \
+    --hash=sha256:3ca723dfc2789c1fb991809822811896b198ecf0909dbccea4a07170d18c3e1b \
+    --hash=sha256:46156ae88ee71c37b6c4f7af63fff5d3ab8f45ef72e1a660bcf6386c1647f106 \
+    --hash=sha256:4bbe2d074292e3646704371eb640ee52c386d633ed72ff223dadcd3fe8ecd8f9 \
+    --hash=sha256:4c4310f0e42154995d92810f27b44ab7116a4a696feb0ff141ae2de59196efd7 \
+    --hash=sha256:4cfa629de5b2dea27c81b334c4536463e9a49ac0877e2008a276d58d4c72868a \
+    --hash=sha256:4e144ab0de56b4d2a2cf0d2fb9d568b59fce49aab3e129badf17c12b0252047d \
+    --hash=sha256:4ea67f303cec384b148774667c7e3cf02311e7026fc02bdcdcd206dfe4ea4fc9 \
+    --hash=sha256:538c9e8f65a32413ace426f8117ef019021adf8175f7c491fed65f5fe2083e0c \
+    --hash=sha256:56565ac9ab4ff3dd473bfe959e0bf2a5062aabb89b7c94cabb417beb162c9fff \
+    --hash=sha256:5e22485256bb1c60bbcc6f8509b1a11042358a2462d5ecdb9a82dc472d2fdd60 \
+    --hash=sha256:602a69c24f1a9755dd1760b3b31bdfc495c4613260c876a01b7e6d5eb9bcae1b \
+    --hash=sha256:6393ec3cecda53b20241e88bc33d87cbd8126cc10870fc69fa16ca2e20a5ac1b \
+    --hash=sha256:6442bbfb047dc1e47658954b72e1589f2bc4e12e67d51bbad0059a626180daa1 \
+    --hash=sha256:666d2a0b269a68cd4fe0976544ab97970c5334d35d0e47ae9be1723f734d8204 \
+    --hash=sha256:697cfbfc19815c40213badcfe5f076418e0f9100cd25a66f513f32c1026b8bf4 \
+    --hash=sha256:6a1a6745c5dce202aa3f29a1736c53cf2179e9c3b280dc62cea9cb8c69977c83 \
+    --hash=sha256:6fc73fc8dd81d9efa842a55033b6b4cb233b134a0270e127c6874d053ef2049b \
+    --hash=sha256:7e9e0d4c5c618b0442396715ffe6c2f84a60d593dad7e0184388aed36d568a65 \
+    --hash=sha256:81fdcf7c0c2df46a99ca421a552c4370117851c5e4dbd6cb53d569b896d62322 \
+    --hash=sha256:8b26932be686f3582df039d79fe96f7ca13d63b39468162f816f9ff29584b9a4 \
+    --hash=sha256:8b7e5191b974fb66fcbac1818ba494d3512da9cf6eaef7acd952f9862eaaa20c \
+    --hash=sha256:8c80e9c41a83d8c90399af8c7dcdeae0c03c48b40b9d0ab84457533d5d7882bf \
+    --hash=sha256:9f2f110b9cc325f6543e0e3f4ab8008c272a59052f9464047c29d4be4511ce05 \
+    --hash=sha256:a339e510a079dc8372e39ce1c7629414db51966235c9670c58d529def79243a2 \
+    --hash=sha256:ad9abc3e4d2370cecb524421cc5c8a664006aa11d5c1cb3c9250e3bf65ab546e \
+    --hash=sha256:b043782c8f6cccc8fae3a16db397eca1d36a41b0706cbf6f514aea1e1a260bab \
+    --hash=sha256:b31de27313abbb567c528ed123380fcf18a5dfd03134570dfd12227e21ac1184 \
+    --hash=sha256:b75e5644cc353328cd57ec8dafaaf5f81b2c3ecf7c4b278b907e99ad53ba7839 \
+    --hash=sha256:b8cfc8fc944bd7b704691bc28225a2635e377e92dc413459845868d3f7724982 \
+    --hash=sha256:c2055c52260808d87622293b57df1c68aeb12ddd8a0cfc0223fb57a5f629e202 \
+    --hash=sha256:c416106b3b8e905b6ab0e84ec90047a6401021aa023f9aa93978e57cd8f8189f \
+    --hash=sha256:d0e210e17a6181a3fd3f8dce957043a4e74177ffa9f295514984b2b633940dce \
+    --hash=sha256:d9453135e48cd631e3e9f06d9da9100d17c9f662e4a6d8b552c29be6c834a6b9 \
+    --hash=sha256:dd0198006278291d9469309d655093df1f5e5107c0261e242b5f390baee32199 \
+    --hash=sha256:e1781bda1e787d3ad33788cc3be47f6e47a9581676d02670c15ee36c9460adfe \
+    --hash=sha256:e56a5a9f303e3ac011ba445a6d84f05d08666bf8db094afafcec5228622c30f5 \
+    --hash=sha256:e93ae35f0fd3caf75e58c76a1cab71e6ece169aaa1b281782ef9efde0a6b83f2 \
+    --hash=sha256:eb36b6570646227a63eda03916f1cc6f3744ee96d28f7a0a5629c59267a8055f \
+    --hash=sha256:f8c425a130e04d5404edaf6f5906e5ab12f3aa1168a1828aba6dfadac5910469
+    # via eventlet
+grpcio==1.50.0 \
+    --hash=sha256:05f7c248e440f538aaad13eee78ef35f0541e73498dd6f832fe284542ac4b298 \
+    --hash=sha256:080b66253f29e1646ac53ef288c12944b131a2829488ac3bac8f52abb4413c0d \
+    --hash=sha256:12b479839a5e753580b5e6053571de14006157f2ef9b71f38c56dc9b23b95ad6 \
+    --hash=sha256:156f8009e36780fab48c979c5605eda646065d4695deea4cfcbcfdd06627ddb6 \
+    --hash=sha256:15f9e6d7f564e8f0776770e6ef32dac172c6f9960c478616c366862933fa08b4 \
+    --hash=sha256:177afaa7dba3ab5bfc211a71b90da1b887d441df33732e94e26860b3321434d9 \
+    --hash=sha256:1a4cd8cb09d1bc70b3ea37802be484c5ae5a576108bad14728f2516279165dd7 \
+    --hash=sha256:1d8d02dbb616c0a9260ce587eb751c9c7dc689bc39efa6a88cc4fa3e9c138a7b \
+    --hash=sha256:2b71916fa8f9eb2abd93151fafe12e18cebb302686b924bd4ec39266211da525 \
+    --hash=sha256:2d9fd6e38b16c4d286a01e1776fdf6c7a4123d99ae8d6b3f0b4a03a34bf6ce45 \
+    --hash=sha256:3b611b3de3dfd2c47549ca01abfa9bbb95937eb0ea546ea1d762a335739887be \
+    --hash=sha256:3e4244c09cc1b65c286d709658c061f12c61c814be0b7030a2d9966ff02611e0 \
+    --hash=sha256:40838061e24f960b853d7bce85086c8e1b81c6342b1f4c47ff0edd44bbae2722 \
+    --hash=sha256:4b123fbb7a777a2fedec684ca0b723d85e1d2379b6032a9a9b7851829ed3ca9a \
+    --hash=sha256:531f8b46f3d3db91d9ef285191825d108090856b3bc86a75b7c3930f16ce432f \
+    --hash=sha256:67dd41a31f6fc5c7db097a5c14a3fa588af54736ffc174af4411d34c4f306f68 \
+    --hash=sha256:7489dbb901f4fdf7aec8d3753eadd40839c9085967737606d2c35b43074eea24 \
+    --hash=sha256:8d4c8e73bf20fb53fe5a7318e768b9734cf122fe671fcce75654b98ba12dfb75 \
+    --hash=sha256:8e69aa4e9b7f065f01d3fdcecbe0397895a772d99954bb82eefbb1682d274518 \
+    --hash=sha256:8e8999a097ad89b30d584c034929f7c0be280cd7851ac23e9067111167dcbf55 \
+    --hash=sha256:906f4d1beb83b3496be91684c47a5d870ee628715227d5d7c54b04a8de802974 \
+    --hash=sha256:92d7635d1059d40d2ec29c8bf5ec58900120b3ce5150ef7414119430a4b2dd5c \
+    --hash=sha256:931e746d0f75b2a5cff0a1197d21827a3a2f400c06bace036762110f19d3d507 \
+    --hash=sha256:95ce51f7a09491fb3da8cf3935005bff19983b77c4e9437ef77235d787b06842 \
+    --hash=sha256:9eea18a878cffc804506d39c6682d71f6b42ec1c151d21865a95fae743fda500 \
+    --hash=sha256:a23d47f2fc7111869f0ff547f771733661ff2818562b04b9ed674fa208e261f4 \
+    --hash=sha256:a4c23e54f58e016761b576976da6a34d876420b993f45f66a2bfb00363ecc1f9 \
+    --hash=sha256:a50a1be449b9e238b9bd43d3857d40edf65df9416dea988929891d92a9f8a778 \
+    --hash=sha256:ab5d0e3590f0a16cb88de4a3fa78d10eb66a84ca80901eb2c17c1d2c308c230f \
+    --hash=sha256:ae23daa7eda93c1c49a9ecc316e027ceb99adbad750fbd3a56fa9e4a2ffd5ae0 \
+    --hash=sha256:af98d49e56605a2912cf330b4627e5286243242706c3a9fa0bcec6e6f68646fc \
+    --hash=sha256:b2f77a90ba7b85bfb31329f8eab9d9540da2cf8a302128fb1241d7ea239a5469 \
+    --hash=sha256:baab51dcc4f2aecabf4ed1e2f57bceab240987c8b03533f1cef90890e6502067 \
+    --hash=sha256:ca8a2254ab88482936ce941485c1c20cdeaef0efa71a61dbad171ab6758ec998 \
+    --hash=sha256:cb11464f480e6103c59d558a3875bd84eed6723f0921290325ebe97262ae1347 \
+    --hash=sha256:ce8513aee0af9c159319692bfbf488b718d1793d764798c3d5cff827a09e25ef \
+    --hash=sha256:cf151f97f5f381163912e8952eb5b3afe89dec9ed723d1561d59cabf1e219a35 \
+    --hash=sha256:d144ad10eeca4c1d1ce930faa105899f86f5d99cecfe0d7224f3c4c76265c15e \
+    --hash=sha256:d534d169673dd5e6e12fb57cc67664c2641361e1a0885545495e65a7b761b0f4 \
+    --hash=sha256:d75061367a69808ab2e84c960e9dce54749bcc1e44ad3f85deee3a6c75b4ede9 \
+    --hash=sha256:d84d04dec64cc4ed726d07c5d17b73c343c8ddcd6b59c7199c801d6bbb9d9ed1 \
+    --hash=sha256:de411d2b030134b642c092e986d21aefb9d26a28bf5a18c47dd08ded411a3bc5 \
+    --hash=sha256:e07fe0d7ae395897981d16be61f0db9791f482f03fee7d1851fe20ddb4f69c03 \
+    --hash=sha256:ea8ccf95e4c7e20419b7827aa5b6da6f02720270686ac63bd3493a651830235c \
+    --hash=sha256:f7025930039a011ed7d7e7ef95a1cb5f516e23c5a6ecc7947259b67bea8e06ca
+    # via
+    #   tb-nightly
+    #   tensorboard
+huggingface-hub==0.10.1 \
+    --hash=sha256:5c188d5b16bec4b78449f8681f9975ff9d321c16046cc29bcf0d7e464ff29276 \
+    --hash=sha256:dc3b0e9a663fe6cad6a8522055c02a9d8673dbd527223288e2442bc028c253db
+    # via
+    #   diffusers
+    #   transformers
+idna==2.10 \
+    --hash=sha256:b307872f855b18632ce0c21c5e45be78c0ea7ae4c15c828c20788b26921eb3f6 \
+    --hash=sha256:b97d804b1e9b523befed77c48dacec60e6dcb0b5391d57af6a65a312a90648c0
+    # via
+    #   requests
+    #   yarl
+imageio==2.22.3 \
+    --hash=sha256:63f007b7f2a082306e36922b3fd529a7aa305d2b78f46195bab8e22bbfe866e9 \
+    --hash=sha256:a4b88f9f3d428b8c0ceeb7e297df8c346a642bb7e3111743eb85717d60b26f6f
+    # via
+    #   scikit-image
+    #   test-tube
+imageio-ffmpeg==0.4.7 \
+    --hash=sha256:27b48c32becae1658aa81c3a6b922538e4099edf5fbcbdb4ff5dbc84b8ffd3d3 \
+    --hash=sha256:6514f1380daf42815bc8c83aad63f33e0b8b47133421ddafe7b410cd8dfbbea5 \
+    --hash=sha256:6aba52ddf0a64442ffcb8d30ac6afb668186acec99ecbc7ae5bd171c4f500bbc \
+    --hash=sha256:7a08838f97f363e37ca41821b864fd3fdc99ab1fe2421040c78eb5f56a9e723e \
+    --hash=sha256:8e724d12dfe83e2a6eb39619e820243ca96c81c47c2648e66e05f7ee24e14312 \
+    --hash=sha256:fc60686ef03c2d0f842901b206223c30051a6a120384458761390104470846fd
+    # via -r requirements.in
+importlib-metadata==5.0.0 \
+    --hash=sha256:da31db32b304314d044d3c12c79bd59e307889b287ad12ff387b3500835fc2ab \
+    --hash=sha256:ddb0e35065e8938f867ed4928d0ae5bf2a53b7773871bfe6bcc7e4fcdc7dea43
+    # via
+    #   diffusers
+    #   streamlit
+itsdangerous==2.1.2 \
+    --hash=sha256:2c2349112351b88699d8d4b6b075022c0808887cb7ad10069318a8b0bc88db44 \
+    --hash=sha256:5dbbc68b317e5e42f327f9021763545dc3fc3bfe22e6deb96aaf1fc38874156a
+    # via flask
+jinja2==3.1.2 \
+    --hash=sha256:31351a702a408a9e7595a8fc6150fc3f43bb6bf7e319770cbc0db9df9437e852 \
+    --hash=sha256:6088930bfe239f0e6710546ab9c19c9ef35e29792895fed6e6e31a023a182a61
+    # via
+    #   altair
+    #   flask
+    #   pydeck
+joblib==1.2.0 \
+    --hash=sha256:091138ed78f800342968c523bdde947e7a305b8594b910a0fea2ab83c3c6d385 \
+    --hash=sha256:e1cee4a79e4af22881164f218d4311f60074197fb707e082e803b61f6d137018
+    # via scikit-learn
+jsonmerge==1.9.0 \
+    --hash=sha256:a2d1f80021c5c1d70a49e31f862b5f068f9db066080d8561e80654de74a3584d
+    # via k-diffusion
+jsonschema==4.17.0 \
+    --hash=sha256:5bfcf2bca16a087ade17e02b282d34af7ccd749ef76241e7f9bd7c0cb8a9424d \
+    --hash=sha256:f660066c3966db7d6daeaea8a75e0b68237a48e51cf49882087757bb59916248
+    # via
+    #   altair
+    #   jsonmerge
+k-diffusion @ https://github.com/invoke-ai/k-diffusion/archive/7f16b2c33411f26b3eae78d10648d625cb0c1095.zip \
+    --hash=sha256:c3f2c84036aa98c3abf4552fafab04df5ca472aa639982795e05bb1db43ce5e4
+    # via -r requirements.in
+kiwisolver==1.4.4 \
+    --hash=sha256:02f79693ec433cb4b5f51694e8477ae83b3205768a6fb48ffba60549080e295b \
+    --hash=sha256:03baab2d6b4a54ddbb43bba1a3a2d1627e82d205c5cf8f4c924dc49284b87166 \
+    --hash=sha256:1041feb4cda8708ce73bb4dcb9ce1ccf49d553bf87c3954bdfa46f0c3f77252c \
+    --hash=sha256:10ee06759482c78bdb864f4109886dff7b8a56529bc1609d4f1112b93fe6423c \
+    --hash=sha256:1d1573129aa0fd901076e2bfb4275a35f5b7aa60fbfb984499d661ec950320b0 \
+    --hash=sha256:283dffbf061a4ec60391d51e6155e372a1f7a4f5b15d59c8505339454f8989e4 \
+    --hash=sha256:28bc5b299f48150b5f822ce68624e445040595a4ac3d59251703779836eceff9 \
+    --hash=sha256:2a66fdfb34e05b705620dd567f5a03f239a088d5a3f321e7b6ac3239d22aa286 \
+    --hash=sha256:2e307eb9bd99801f82789b44bb45e9f541961831c7311521b13a6c85afc09767 \
+    --hash=sha256:2e407cb4bd5a13984a6c2c0fe1845e4e41e96f183e5e5cd4d77a857d9693494c \
+    --hash=sha256:2f5e60fabb7343a836360c4f0919b8cd0d6dbf08ad2ca6b9cf90bf0c76a3c4f6 \
+    --hash=sha256:36dafec3d6d6088d34e2de6b85f9d8e2324eb734162fba59d2ba9ed7a2043d5b \
+    --hash=sha256:3fe20f63c9ecee44560d0e7f116b3a747a5d7203376abeea292ab3152334d004 \
+    --hash=sha256:41dae968a94b1ef1897cb322b39360a0812661dba7c682aa45098eb8e193dbdf \
+    --hash=sha256:4bd472dbe5e136f96a4b18f295d159d7f26fd399136f5b17b08c4e5f498cd494 \
+    --hash=sha256:4ea39b0ccc4f5d803e3337dd46bcce60b702be4d86fd0b3d7531ef10fd99a1ac \
+    --hash=sha256:5853eb494c71e267912275e5586fe281444eb5e722de4e131cddf9d442615626 \
+    --hash=sha256:5bce61af018b0cb2055e0e72e7d65290d822d3feee430b7b8203d8a855e78766 \
+    --hash=sha256:6295ecd49304dcf3bfbfa45d9a081c96509e95f4b9d0eb7ee4ec0530c4a96514 \
+    --hash=sha256:62ac9cc684da4cf1778d07a89bf5f81b35834cb96ca523d3a7fb32509380cbf6 \
+    --hash=sha256:70e7c2e7b750585569564e2e5ca9845acfaa5da56ac46df68414f29fea97be9f \
+    --hash=sha256:7577c1987baa3adc4b3c62c33bd1118c3ef5c8ddef36f0f2c950ae0b199e100d \
+    --hash=sha256:75facbe9606748f43428fc91a43edb46c7ff68889b91fa31f53b58894503a191 \
+    --hash=sha256:787518a6789009c159453da4d6b683f468ef7a65bbde796bcea803ccf191058d \
+    --hash=sha256:78d6601aed50c74e0ef02f4204da1816147a6d3fbdc8b3872d263338a9052c51 \
+    --hash=sha256:7c43e1e1206cd421cd92e6b3280d4385d41d7166b3ed577ac20444b6995a445f \
+    --hash=sha256:81e38381b782cc7e1e46c4e14cd997ee6040768101aefc8fa3c24a4cc58e98f8 \
+    --hash=sha256:841293b17ad704d70c578f1f0013c890e219952169ce8a24ebc063eecf775454 \
+    --hash=sha256:872b8ca05c40d309ed13eb2e582cab0c5a05e81e987ab9c521bf05ad1d5cf5cb \
+    --hash=sha256:877272cf6b4b7e94c9614f9b10140e198d2186363728ed0f701c6eee1baec1da \
+    --hash=sha256:8c808594c88a025d4e322d5bb549282c93c8e1ba71b790f539567932722d7bd8 \
+    --hash=sha256:8ed58b8acf29798b036d347791141767ccf65eee7f26bde03a71c944449e53de \
+    --hash=sha256:91672bacaa030f92fc2f43b620d7b337fd9a5af28b0d6ed3f77afc43c4a64b5a \
+    --hash=sha256:968f44fdbf6dd757d12920d63b566eeb4d5b395fd2d00d29d7ef00a00582aac9 \
+    --hash=sha256:9f85003f5dfa867e86d53fac6f7e6f30c045673fa27b603c397753bebadc3008 \
+    --hash=sha256:a553dadda40fef6bfa1456dc4be49b113aa92c2a9a9e8711e955618cd69622e3 \
+    --hash=sha256:a68b62a02953b9841730db7797422f983935aeefceb1679f0fc85cbfbd311c32 \
+    --hash=sha256:abbe9fa13da955feb8202e215c4018f4bb57469b1b78c7a4c5c7b93001699938 \
+    --hash=sha256:ad881edc7ccb9d65b0224f4e4d05a1e85cf62d73aab798943df6d48ab0cd79a1 \
+    --hash=sha256:b1792d939ec70abe76f5054d3f36ed5656021dcad1322d1cc996d4e54165cef9 \
+    --hash=sha256:b428ef021242344340460fa4c9185d0b1f66fbdbfecc6c63eff4b7c29fad429d \
+    --hash=sha256:b533558eae785e33e8c148a8d9921692a9fe5aa516efbdff8606e7d87b9d5824 \
+    --hash=sha256:ba59c92039ec0a66103b1d5fe588fa546373587a7d68f5c96f743c3396afc04b \
+    --hash=sha256:bc8d3bd6c72b2dd9decf16ce70e20abcb3274ba01b4e1c96031e0c4067d1e7cd \
+    --hash=sha256:bc9db8a3efb3e403e4ecc6cd9489ea2bac94244f80c78e27c31dcc00d2790ac2 \
+    --hash=sha256:bf7d9fce9bcc4752ca4a1b80aabd38f6d19009ea5cbda0e0856983cf6d0023f5 \
+    --hash=sha256:c2dbb44c3f7e6c4d3487b31037b1bdbf424d97687c1747ce4ff2895795c9bf69 \
+    --hash=sha256:c79ebe8f3676a4c6630fd3f777f3cfecf9289666c84e775a67d1d358578dc2e3 \
+    --hash=sha256:c97528e64cb9ebeff9701e7938653a9951922f2a38bd847787d4a8e498cc83ae \
+    --hash=sha256:d0611a0a2a518464c05ddd5a3a1a0e856ccc10e67079bb17f265ad19ab3c7597 \
+    --hash=sha256:d06adcfa62a4431d404c31216f0f8ac97397d799cd53800e9d3efc2fbb3cf14e \
+    --hash=sha256:d41997519fcba4a1e46eb4a2fe31bc12f0ff957b2b81bac28db24744f333e955 \
+    --hash=sha256:d5b61785a9ce44e5a4b880272baa7cf6c8f48a5180c3e81c59553ba0cb0821ca \
+    --hash=sha256:da152d8cdcab0e56e4f45eb08b9aea6455845ec83172092f09b0e077ece2cf7a \
+    --hash=sha256:da7e547706e69e45d95e116e6939488d62174e033b763ab1496b4c29b76fabea \
+    --hash=sha256:db5283d90da4174865d520e7366801a93777201e91e79bacbac6e6927cbceede \
+    --hash=sha256:db608a6757adabb32f1cfe6066e39b3706d8c3aa69bbc353a5b61edad36a5cb4 \
+    --hash=sha256:e0ea21f66820452a3f5d1655f8704a60d66ba1191359b96541eaf457710a5fc6 \
+    --hash=sha256:e7da3fec7408813a7cebc9e4ec55afed2d0fd65c4754bc376bf03498d4e92686 \
+    --hash=sha256:e92a513161077b53447160b9bd8f522edfbed4bd9759e4c18ab05d7ef7e49408 \
+    --hash=sha256:ecb1fa0db7bf4cff9dac752abb19505a233c7f16684c5826d1f11ebd9472b871 \
+    --hash=sha256:efda5fc8cc1c61e4f639b8067d118e742b812c930f708e6667a5ce0d13499e29 \
+    --hash=sha256:f0a1dbdb5ecbef0d34eb77e56fcb3e95bbd7e50835d9782a45df81cc46949750 \
+    --hash=sha256:f0a71d85ecdd570ded8ac3d1c0f480842f49a40beb423bb8014539a9f32a5897 \
+    --hash=sha256:f4f270de01dd3e129a72efad823da90cc4d6aafb64c410c9033aba70db9f1ff0 \
+    --hash=sha256:f6cb459eea32a4e2cf18ba5fcece2dbdf496384413bc1bae15583f19e567f3b2 \
+    --hash=sha256:f8ad8285b01b0d4695102546b342b493b3ccc6781fc28c8c6a1bb63e95d22f09 \
+    --hash=sha256:f9f39e2f049db33a908319cf46624a569b36983c7c78318e9726a4cb8923b26c
+    # via matplotlib
+kornia==0.6.8 \
+    --hash=sha256:0985e02453c0ab4f030e8d22a3a7554dab312ffa8f8a54ec872190e6f0b58c56 \
+    --hash=sha256:0d6d69330b4fd24da742337b8134da0ce01b4d7da66770db5498d58e8b4a0832
+    # via k-diffusion
+llvmlite==0.39.1 \
+    --hash=sha256:03aee0ccd81735696474dc4f8b6be60774892a2929d6c05d093d17392c237f32 \
+    --hash=sha256:1578f5000fdce513712e99543c50e93758a954297575610f48cb1fd71b27c08a \
+    --hash=sha256:16f56eb1eec3cda3a5c526bc3f63594fc24e0c8d219375afeb336f289764c6c7 \
+    --hash=sha256:1ec3d70b3e507515936e475d9811305f52d049281eaa6c8273448a61c9b5b7e2 \
+    --hash=sha256:22d36591cd5d02038912321d9ab8e4668e53ae2211da5523f454e992b5e13c36 \
+    --hash=sha256:3803f11ad5f6f6c3d2b545a303d68d9fabb1d50e06a8d6418e6fcd2d0df00959 \
+    --hash=sha256:39dc2160aed36e989610fc403487f11b8764b6650017ff367e45384dff88ffbf \
+    --hash=sha256:3fc14e757bc07a919221f0cbaacb512704ce5774d7fcada793f1996d6bc75f2a \
+    --hash=sha256:4c6ebace910410daf0bebda09c1859504fc2f33d122e9a971c4c349c89cca630 \
+    --hash=sha256:50aea09a2b933dab7c9df92361b1844ad3145bfb8dd2deb9cd8b8917d59306fb \
+    --hash=sha256:60f8dd1e76f47b3dbdee4b38d9189f3e020d22a173c00f930b52131001d801f9 \
+    --hash=sha256:62c0ea22e0b9dffb020601bb65cb11dd967a095a488be73f07d8867f4e327ca5 \
+    --hash=sha256:6546bed4e02a1c3d53a22a0bced254b3b6894693318b16c16c8e43e29d6befb6 \
+    --hash=sha256:6717c7a6e93c9d2c3d07c07113ec80ae24af45cde536b34363d4bcd9188091d9 \
+    --hash=sha256:7ebf1eb9badc2a397d4f6a6c8717447c81ac011db00064a00408bc83c923c0e4 \
+    --hash=sha256:9ffc84ade195abd4abcf0bd3b827b9140ae9ef90999429b9ea84d5df69c9058c \
+    --hash=sha256:a3f331a323d0f0ada6b10d60182ef06c20a2f01be21699999d204c5750ffd0b4 \
+    --hash=sha256:b1a0bbdb274fb683f993198775b957d29a6f07b45d184c571ef2a721ce4388cf \
+    --hash=sha256:b43abd7c82e805261c425d50335be9a6c4f84264e34d6d6e475207300005d572 \
+    --hash=sha256:c0f158e4708dda6367d21cf15afc58de4ebce979c7a1aa2f6b977aae737e2a54 \
+    --hash=sha256:d0bfd18c324549c0fec2c5dc610fd024689de6f27c6cc67e4e24a07541d6e49b \
+    --hash=sha256:ddab526c5a2c4ccb8c9ec4821fcea7606933dc53f510e2a6eebb45a418d3488a \
+    --hash=sha256:e172c73fccf7d6db4bd6f7de963dedded900d1a5c6778733241d878ba613980e \
+    --hash=sha256:e2c00ff204afa721b0bb9835b5bf1ba7fba210eefcec5552a9e05a63219ba0dc \
+    --hash=sha256:e31f4b799d530255aaf0566e3da2df5bfc35d3cd9d6d5a3dcc251663656c27b1 \
+    --hash=sha256:e4f212c018db951da3e1dc25c2651abc688221934739721f2dad5ff1dd5f90e7 \
+    --hash=sha256:fa9b26939ae553bf30a9f5c4c754db0fb2d2677327f2511e674aa2f5df941789 \
+    --hash=sha256:fb62fc7016b592435d3e3a8f680e3ea8897c3c9e62e6e6cc58011e7a4801439e
+    # via numba
+lmdb==1.3.0 \
+    --hash=sha256:008243762decf8f6c90430a9bced56290ebbcdb5e877d90e42343bb97033e494 \
+    --hash=sha256:08f4b5129f4683802569b02581142e415c8dcc0ff07605983ec1b07804cecbad \
+    --hash=sha256:17215a42a4b9814c383deabecb160581e4fb75d00198eef0e3cea54f230ffbea \
+    --hash=sha256:18c69fabdaf04efaf246587739cc1062b3e57c6ef0743f5c418df89e5e7e7b9b \
+    --hash=sha256:2cfa4aa9c67f8aee89b23005e98d1f3f32490b6b905fd1cb604b207cbd5755ab \
+    --hash=sha256:2df38115dd9428a54d59ae7c712a4c7cce0d6b1d66056de4b1a8c38718066106 \
+    --hash=sha256:394df860c3f93cfd92b6f4caba785f38208cc9614c18b3803f83a2cc1695042f \
+    --hash=sha256:41318717ab5d15ad2d6d263d34fbf614a045210f64b25e59ce734bb2105e421f \
+    --hash=sha256:4172fba19417d7b29409beca7d73c067b54e5d8ab1fb9b51d7b4c1445d20a167 \
+    --hash=sha256:5a14aca2651c3af6f0d0a6b9168200eea0c8f2d27c40b01a442f33329a6e8dff \
+    --hash=sha256:5ddd590e1c7fcb395931aa3782fb89b9db4550ab2d81d006ecd239e0d462bc41 \
+    --hash=sha256:60a11efc21aaf009d06518996360eed346f6000bfc9de05114374230879f992e \
+    --hash=sha256:6260a526e4ad85b1f374a5ba9475bf369fb07e7728ea6ec57226b02c40d1976b \
+    --hash=sha256:62ab28e3593bdc318ea2f2fa1574e5fca3b6d1f264686d773ba54a637d4f563b \
+    --hash=sha256:63cb73fe7ce9eb93d992d632c85a0476b4332670d9e6a2802b5062f603b7809f \
+    --hash=sha256:65334eafa5d430b18d81ebd5362559a41483c362e1931f6e1b15bab2ecb7d75d \
+    --hash=sha256:7da05d70fcc6561ac6b09e9fb1bf64b7ca294652c64c8a2889273970cee796b9 \
+    --hash=sha256:abbc439cd9fe60ffd6197009087ea885ac150017dc85384093b1d376f83f0ec4 \
+    --hash=sha256:c6adbd6f7f9048e97f31a069e652eb51020a81e80a0ce92dbb9810d21da2409a \
+    --hash=sha256:d6a816954d212f40fd15007cd81ab7a6bebb77436d949a6a9ae04af57fc127f3 \
+    --hash=sha256:d9103aa4908f0bca43c5911ca067d4e3d01f682dff0c0381a1239bd2bd757984 \
+    --hash=sha256:df2724bad7820114a205472994091097d0fa65a3e5fff5a8e688d123fb8c6326 \
+    --hash=sha256:e568ae0887ae196340947d9800136e90feaed6b86a261ef01f01b2ba65fc8106 \
+    --hash=sha256:e6a704b3baced9182836c7f77b769f23856f3a8f62d0282b1bc1feaf81a86712 \
+    --hash=sha256:eefb392f6b5cd43aada49258c5a79be11cb2c8cd3fc3e2d9319a1e0b9f906458 \
+    --hash=sha256:f291e3f561f58dddf63a92a5a6a4b8af3a0920b6705d35e2f80e52e86ee238a2 \
+    --hash=sha256:fa6439356e591d3249ab0e1778a6f8d8408e993f66dc911914c78208f5310309
+    # via
+    #   basicsr
+    #   gfpgan
+markdown==3.4.1 \
+    --hash=sha256:08fb8465cffd03d10b9dd34a5c3fea908e20391a2a90b88d66362cb05beed186 \
+    --hash=sha256:3b809086bb6efad416156e00a0da66fe47618a5d6918dd688f53f40c8e4cfeff
+    # via
+    #   tb-nightly
+    #   tensorboard
+markupsafe==2.1.1 \
+    --hash=sha256:0212a68688482dc52b2d45013df70d169f542b7394fc744c02a57374a4207003 \
+    --hash=sha256:089cf3dbf0cd6c100f02945abeb18484bd1ee57a079aefd52cffd17fba910b88 \
+    --hash=sha256:10c1bfff05d95783da83491be968e8fe789263689c02724e0c691933c52994f5 \
+    --hash=sha256:33b74d289bd2f5e527beadcaa3f401e0df0a89927c1559c8566c066fa4248ab7 \
+    --hash=sha256:3799351e2336dc91ea70b034983ee71cf2f9533cdff7c14c90ea126bfd95d65a \
+    --hash=sha256:3ce11ee3f23f79dbd06fb3d63e2f6af7b12db1d46932fe7bd8afa259a5996603 \
+    --hash=sha256:421be9fbf0ffe9ffd7a378aafebbf6f4602d564d34be190fc19a193232fd12b1 \
+    --hash=sha256:43093fb83d8343aac0b1baa75516da6092f58f41200907ef92448ecab8825135 \
+    --hash=sha256:46d00d6cfecdde84d40e572d63735ef81423ad31184100411e6e3388d405e247 \
+    --hash=sha256:4a33dea2b688b3190ee12bd7cfa29d39c9ed176bda40bfa11099a3ce5d3a7ac6 \
+    --hash=sha256:4b9fe39a2ccc108a4accc2676e77da025ce383c108593d65cc909add5c3bd601 \
+    --hash=sha256:56442863ed2b06d19c37f94d999035e15ee982988920e12a5b4ba29b62ad1f77 \
+    --hash=sha256:671cd1187ed5e62818414afe79ed29da836dde67166a9fac6d435873c44fdd02 \
+    --hash=sha256:694deca8d702d5db21ec83983ce0bb4b26a578e71fbdbd4fdcd387daa90e4d5e \
+    --hash=sha256:6a074d34ee7a5ce3effbc526b7083ec9731bb3cbf921bbe1d3005d4d2bdb3a63 \
+    --hash=sha256:6d0072fea50feec76a4c418096652f2c3238eaa014b2f94aeb1d56a66b41403f \
+    --hash=sha256:6fbf47b5d3728c6aea2abb0589b5d30459e369baa772e0f37a0320185e87c980 \
+    --hash=sha256:7f91197cc9e48f989d12e4e6fbc46495c446636dfc81b9ccf50bb0ec74b91d4b \
+    --hash=sha256:86b1f75c4e7c2ac2ccdaec2b9022845dbb81880ca318bb7a0a01fbf7813e3812 \
+    --hash=sha256:8dc1c72a69aa7e082593c4a203dcf94ddb74bb5c8a731e4e1eb68d031e8498ff \
+    --hash=sha256:8e3dcf21f367459434c18e71b2a9532d96547aef8a871872a5bd69a715c15f96 \
+    --hash=sha256:8e576a51ad59e4bfaac456023a78f6b5e6e7651dcd383bcc3e18d06f9b55d6d1 \
+    --hash=sha256:96e37a3dc86e80bf81758c152fe66dbf60ed5eca3d26305edf01892257049925 \
+    --hash=sha256:97a68e6ada378df82bc9f16b800ab77cbf4b2fada0081794318520138c088e4a \
+    --hash=sha256:99a2a507ed3ac881b975a2976d59f38c19386d128e7a9a18b7df6fff1fd4c1d6 \
+    --hash=sha256:a49907dd8420c5685cfa064a1335b6754b74541bbb3706c259c02ed65b644b3e \
+    --hash=sha256:b09bf97215625a311f669476f44b8b318b075847b49316d3e28c08e41a7a573f \
+    --hash=sha256:b7bd98b796e2b6553da7225aeb61f447f80a1ca64f41d83612e6139ca5213aa4 \
+    --hash=sha256:b87db4360013327109564f0e591bd2a3b318547bcef31b468a92ee504d07ae4f \
+    --hash=sha256:bcb3ed405ed3222f9904899563d6fc492ff75cce56cba05e32eff40e6acbeaa3 \
+    --hash=sha256:d4306c36ca495956b6d568d276ac11fdd9c30a36f1b6eb928070dc5360b22e1c \
+    --hash=sha256:d5ee4f386140395a2c818d149221149c54849dfcfcb9f1debfe07a8b8bd63f9a \
+    --hash=sha256:dda30ba7e87fbbb7eab1ec9f58678558fd9a6b8b853530e176eabd064da81417 \
+    --hash=sha256:e04e26803c9c3851c931eac40c695602c6295b8d432cbe78609649ad9bd2da8a \
+    --hash=sha256:e1c0b87e09fa55a220f058d1d49d3fb8df88fbfab58558f1198e08c1e1de842a \
+    --hash=sha256:e72591e9ecd94d7feb70c1cbd7be7b3ebea3f548870aa91e2732960fa4d57a37 \
+    --hash=sha256:e8c843bbcda3a2f1e3c2ab25913c80a3c5376cd00c6e8c4a86a89a28c8dc5452 \
+    --hash=sha256:efc1913fd2ca4f334418481c7e595c00aad186563bbc1ec76067848c7ca0a933 \
+    --hash=sha256:f121a1420d4e173a5d96e47e9a0c0dcff965afdf1626d28de1460815f7c4ee7a \
+    --hash=sha256:fc7b548b17d238737688817ab67deebb30e8073c95749d55538ed473130ec0c7
+    # via
+    #   jinja2
+    #   werkzeug
+matplotlib==3.6.2 \
+    --hash=sha256:0844523dfaaff566e39dbfa74e6f6dc42e92f7a365ce80929c5030b84caa563a \
+    --hash=sha256:0eda9d1b43f265da91fb9ae10d6922b5a986e2234470a524e6b18f14095b20d2 \
+    --hash=sha256:168093410b99f647ba61361b208f7b0d64dde1172b5b1796d765cd243cadb501 \
+    --hash=sha256:1836f366272b1557a613f8265db220eb8dd883202bbbabe01bad5a4eadfd0c95 \
+    --hash=sha256:19d61ee6414c44a04addbe33005ab1f87539d9f395e25afcbe9a3c50ce77c65c \
+    --hash=sha256:252957e208c23db72ca9918cb33e160c7833faebf295aaedb43f5b083832a267 \
+    --hash=sha256:32d29c8c26362169c80c5718ce367e8c64f4dd068a424e7110df1dd2ed7bd428 \
+    --hash=sha256:380d48c15ec41102a2b70858ab1dedfa33eb77b2c0982cb65a200ae67a48e9cb \
+    --hash=sha256:3964934731fd7a289a91d315919cf757f293969a4244941ab10513d2351b4e83 \
+    --hash=sha256:3cef89888a466228fc4e4b2954e740ce8e9afde7c4315fdd18caa1b8de58ca17 \
+    --hash=sha256:4426c74761790bff46e3d906c14c7aab727543293eed5a924300a952e1a3a3c1 \
+    --hash=sha256:5024b8ed83d7f8809982d095d8ab0b179bebc07616a9713f86d30cf4944acb73 \
+    --hash=sha256:52c2bdd7cd0bf9d5ccdf9c1816568fd4ccd51a4d82419cc5480f548981b47dd0 \
+    --hash=sha256:54fa9fe27f5466b86126ff38123261188bed568c1019e4716af01f97a12fe812 \
+    --hash=sha256:5ba73aa3aca35d2981e0b31230d58abb7b5d7ca104e543ae49709208d8ce706a \
+    --hash=sha256:5e16dcaecffd55b955aa5e2b8a804379789c15987e8ebd2f32f01398a81e975b \
+    --hash=sha256:5ecfc6559132116dedfc482d0ad9df8a89dc5909eebffd22f3deb684132d002f \
+    --hash=sha256:74153008bd24366cf099d1f1e83808d179d618c4e32edb0d489d526523a94d9f \
+    --hash=sha256:78ec3c3412cf277e6252764ee4acbdbec6920cc87ad65862272aaa0e24381eee \
+    --hash=sha256:795ad83940732b45d39b82571f87af0081c120feff2b12e748d96bb191169e33 \
+    --hash=sha256:7f716b6af94dc1b6b97c46401774472f0867e44595990fe80a8ba390f7a0a028 \
+    --hash=sha256:83dc89c5fd728fdb03b76f122f43b4dcee8c61f1489e232d9ad0f58020523e1c \
+    --hash=sha256:8a0ae37576ed444fe853709bdceb2be4c7df6f7acae17b8378765bd28e61b3ae \
+    --hash=sha256:8a8dbe2cb7f33ff54b16bb5c500673502a35f18ac1ed48625e997d40c922f9cc \
+    --hash=sha256:8a9d899953c722b9afd7e88dbefd8fb276c686c3116a43c577cfabf636180558 \
+    --hash=sha256:8d0068e40837c1d0df6e3abf1cdc9a34a6d2611d90e29610fa1d2455aeb4e2e5 \
+    --hash=sha256:9347cc6822f38db2b1d1ce992f375289670e595a2d1c15961aacbe0977407dfc \
+    --hash=sha256:9f335e5625feb90e323d7e3868ec337f7b9ad88b5d633f876e3b778813021dab \
+    --hash=sha256:b03fd10a1709d0101c054883b550f7c4c5e974f751e2680318759af005964990 \
+    --hash=sha256:b0ca2c60d3966dfd6608f5f8c49b8a0fcf76de6654f2eda55fc6ef038d5a6f27 \
+    --hash=sha256:b2604c6450f9dd2c42e223b1f5dca9643a23cfecc9fde4a94bb38e0d2693b136 \
+    --hash=sha256:ca0e7a658fbafcddcaefaa07ba8dae9384be2343468a8e011061791588d839fa \
+    --hash=sha256:d0e9ac04065a814d4cf2c6791a2ad563f739ae3ae830d716d54245c2b96fead6 \
+    --hash=sha256:d50e8c1e571ee39b5dfbc295c11ad65988879f68009dd281a6e1edbc2ff6c18c \
+    --hash=sha256:d840adcad7354be6f2ec28d0706528b0026e4c3934cc6566b84eac18633eab1b \
+    --hash=sha256:e0bbee6c2a5bf2a0017a9b5e397babb88f230e6f07c3cdff4a4c4bc75ed7c617 \
+    --hash=sha256:e5afe0a7ea0e3a7a257907060bee6724a6002b7eec55d0db16fd32409795f3e1 \
+    --hash=sha256:e68be81cd8c22b029924b6d0ee814c337c0e706b8d88495a617319e5dd5441c3 \
+    --hash=sha256:ec9be0f4826cdb3a3a517509dcc5f87f370251b76362051ab59e42b6b765f8c4 \
+    --hash=sha256:f04f97797df35e442ed09f529ad1235d1f1c0f30878e2fe09a2676b71a8801e0 \
+    --hash=sha256:f41e57ad63d336fe50d3a67bb8eaa26c09f6dda6a59f76777a99b8ccd8e26aec
+    # via filterpy
+multidict==6.0.2 \
+    --hash=sha256:0327292e745a880459ef71be14e709aaea2f783f3537588fb4ed09b6c01bca60 \
+    --hash=sha256:041b81a5f6b38244b34dc18c7b6aba91f9cdaf854d9a39e5ff0b58e2b5773b9c \
+    --hash=sha256:0556a1d4ea2d949efe5fd76a09b4a82e3a4a30700553a6725535098d8d9fb672 \
+    --hash=sha256:05f6949d6169878a03e607a21e3b862eaf8e356590e8bdae4227eedadacf6e51 \
+    --hash=sha256:07a017cfa00c9890011628eab2503bee5872f27144936a52eaab449be5eaf032 \
+    --hash=sha256:0b9e95a740109c6047602f4db4da9949e6c5945cefbad34a1299775ddc9a62e2 \
+    --hash=sha256:19adcfc2a7197cdc3987044e3f415168fc5dc1f720c932eb1ef4f71a2067e08b \
+    --hash=sha256:19d9bad105dfb34eb539c97b132057a4e709919ec4dd883ece5838bcbf262b80 \
+    --hash=sha256:225383a6603c086e6cef0f2f05564acb4f4d5f019a4e3e983f572b8530f70c88 \
+    --hash=sha256:23b616fdc3c74c9fe01d76ce0d1ce872d2d396d8fa8e4899398ad64fb5aa214a \
+    --hash=sha256:2957489cba47c2539a8eb7ab32ff49101439ccf78eab724c828c1a54ff3ff98d \
+    --hash=sha256:2d36e929d7f6a16d4eb11b250719c39560dd70545356365b494249e2186bc389 \
+    --hash=sha256:2e4a0785b84fb59e43c18a015ffc575ba93f7d1dbd272b4cdad9f5134b8a006c \
+    --hash=sha256:3368bf2398b0e0fcbf46d85795adc4c259299fec50c1416d0f77c0a843a3eed9 \
+    --hash=sha256:373ba9d1d061c76462d74e7de1c0c8e267e9791ee8cfefcf6b0b2495762c370c \
+    --hash=sha256:4070613ea2227da2bfb2c35a6041e4371b0af6b0be57f424fe2318b42a748516 \
+    --hash=sha256:45183c96ddf61bf96d2684d9fbaf6f3564d86b34cb125761f9a0ef9e36c1d55b \
+    --hash=sha256:4571f1beddff25f3e925eea34268422622963cd8dc395bb8778eb28418248e43 \
+    --hash=sha256:47e6a7e923e9cada7c139531feac59448f1f47727a79076c0b1ee80274cd8eee \
+    --hash=sha256:47fbeedbf94bed6547d3aa632075d804867a352d86688c04e606971595460227 \
+    --hash=sha256:497988d6b6ec6ed6f87030ec03280b696ca47dbf0648045e4e1d28b80346560d \
+    --hash=sha256:4bae31803d708f6f15fd98be6a6ac0b6958fcf68fda3c77a048a4f9073704aae \
+    --hash=sha256:50bd442726e288e884f7be9071016c15a8742eb689a593a0cac49ea093eef0a7 \
+    --hash=sha256:514fe2b8d750d6cdb4712346a2c5084a80220821a3e91f3f71eec11cf8d28fd4 \
+    --hash=sha256:5774d9218d77befa7b70d836004a768fb9aa4fdb53c97498f4d8d3f67bb9cfa9 \
+    --hash=sha256:5fdda29a3c7e76a064f2477c9aab1ba96fd94e02e386f1e665bca1807fc5386f \
+    --hash=sha256:5ff3bd75f38e4c43f1f470f2df7a4d430b821c4ce22be384e1459cb57d6bb013 \
+    --hash=sha256:626fe10ac87851f4cffecee161fc6f8f9853f0f6f1035b59337a51d29ff3b4f9 \
+    --hash=sha256:6701bf8a5d03a43375909ac91b6980aea74b0f5402fbe9428fc3f6edf5d9677e \
+    --hash=sha256:684133b1e1fe91eda8fa7447f137c9490a064c6b7f392aa857bba83a28cfb693 \
+    --hash=sha256:6f3cdef8a247d1eafa649085812f8a310e728bdf3900ff6c434eafb2d443b23a \
+    --hash=sha256:75bdf08716edde767b09e76829db8c1e5ca9d8bb0a8d4bd94ae1eafe3dac5e15 \
+    --hash=sha256:7c40b7bbece294ae3a87c1bc2abff0ff9beef41d14188cda94ada7bcea99b0fb \
+    --hash=sha256:8004dca28e15b86d1b1372515f32eb6f814bdf6f00952699bdeb541691091f96 \
+    --hash=sha256:8064b7c6f0af936a741ea1efd18690bacfbae4078c0c385d7c3f611d11f0cf87 \
+    --hash=sha256:89171b2c769e03a953d5969b2f272efa931426355b6c0cb508022976a17fd376 \
+    --hash=sha256:8cbf0132f3de7cc6c6ce00147cc78e6439ea736cee6bca4f068bcf892b0fd658 \
+    --hash=sha256:9cc57c68cb9139c7cd6fc39f211b02198e69fb90ce4bc4a094cf5fe0d20fd8b0 \
+    --hash=sha256:a007b1638e148c3cfb6bf0bdc4f82776cef0ac487191d093cdc316905e504071 \
+    --hash=sha256:a2c34a93e1d2aa35fbf1485e5010337c72c6791407d03aa5f4eed920343dd360 \
+    --hash=sha256:a45e1135cb07086833ce969555df39149680e5471c04dfd6a915abd2fc3f6dbc \
+    --hash=sha256:ac0e27844758d7177989ce406acc6a83c16ed4524ebc363c1f748cba184d89d3 \
+    --hash=sha256:aef9cc3d9c7d63d924adac329c33835e0243b5052a6dfcbf7732a921c6e918ba \
+    --hash=sha256:b9d153e7f1f9ba0b23ad1568b3b9e17301e23b042c23870f9ee0522dc5cc79e8 \
+    --hash=sha256:bfba7c6d5d7c9099ba21f84662b037a0ffd4a5e6b26ac07d19e423e6fdf965a9 \
+    --hash=sha256:c207fff63adcdf5a485969131dc70e4b194327666b7e8a87a97fbc4fd80a53b2 \
+    --hash=sha256:d0509e469d48940147e1235d994cd849a8f8195e0bca65f8f5439c56e17872a3 \
+    --hash=sha256:d16cce709ebfadc91278a1c005e3c17dd5f71f5098bfae1035149785ea6e9c68 \
+    --hash=sha256:d48b8ee1d4068561ce8033d2c344cf5232cb29ee1a0206a7b828c79cbc5982b8 \
+    --hash=sha256:de989b195c3d636ba000ee4281cd03bb1234635b124bf4cd89eeee9ca8fcb09d \
+    --hash=sha256:e07c8e79d6e6fd37b42f3250dba122053fddb319e84b55dd3a8d6446e1a7ee49 \
+    --hash=sha256:e2c2e459f7050aeb7c1b1276763364884595d47000c1cddb51764c0d8976e608 \
+    --hash=sha256:e5b20e9599ba74391ca0cfbd7b328fcc20976823ba19bc573983a25b32e92b57 \
+    --hash=sha256:e875b6086e325bab7e680e4316d667fc0e5e174bb5611eb16b3ea121c8951b86 \
+    --hash=sha256:f4f052ee022928d34fe1f4d2bc743f32609fb79ed9c49a1710a5ad6b2198db20 \
+    --hash=sha256:fcb91630817aa8b9bc4a74023e4198480587269c272c58b3279875ed7235c293 \
+    --hash=sha256:fd9fc9c4849a07f3635ccffa895d57abce554b467d611a5009ba4f39b78a8849 \
+    --hash=sha256:feba80698173761cddd814fa22e88b0661e98cb810f9f986c54aa34d281e4937 \
+    --hash=sha256:feea820722e69451743a3d56ad74948b68bf456984d63c1a92e8347b7b88452d
+    # via
+    #   aiohttp
+    #   yarl
+networkx==2.8.8 \
+    --hash=sha256:230d388117af870fce5647a3c52401fcf753e94720e6ea6b4197a5355648885e \
+    --hash=sha256:e435dfa75b1d7195c7b8378c3859f0445cd88c6b0375c181ed66823a9ceb7524
+    # via scikit-image
+numba==0.56.4 \
+    --hash=sha256:0240f9026b015e336069329839208ebd70ec34ae5bfbf402e4fcc8e06197528e \
+    --hash=sha256:03634579d10a6129181129de293dd6b5eaabee86881369d24d63f8fe352dd6cb \
+    --hash=sha256:03fe94cd31e96185cce2fae005334a8cc712fc2ba7756e52dff8c9400718173f \
+    --hash=sha256:0611e6d3eebe4cb903f1a836ffdb2bda8d18482bcd0a0dcc56e79e2aa3fefef5 \
+    --hash=sha256:0da583c532cd72feefd8e551435747e0e0fbb3c0530357e6845fcc11e38d6aea \
+    --hash=sha256:14dbbabf6ffcd96ee2ac827389afa59a70ffa9f089576500434c34abf9b054a4 \
+    --hash=sha256:32d9fef412c81483d7efe0ceb6cf4d3310fde8b624a9cecca00f790573ac96ee \
+    --hash=sha256:3a993349b90569518739009d8f4b523dfedd7e0049e6838c0e17435c3e70dcc4 \
+    --hash=sha256:3cb1a07a082a61df80a468f232e452d818f5ae254b40c26390054e4e868556e0 \
+    --hash=sha256:42f9e1be942b215df7e6cc9948cf9c15bb8170acc8286c063a9e57994ef82fd1 \
+    --hash=sha256:4373da9757049db7c90591e9ec55a2e97b2b36ba7ae3bf9c956a513374077470 \
+    --hash=sha256:4e08e203b163ace08bad500b0c16f6092b1eb34fd1fce4feaf31a67a3a5ecf3b \
+    --hash=sha256:553da2ce74e8862e18a72a209ed3b6d2924403bdd0fb341fa891c6455545ba7c \
+    --hash=sha256:720886b852a2d62619ae3900fe71f1852c62db4f287d0c275a60219e1643fc04 \
+    --hash=sha256:85dbaed7a05ff96492b69a8900c5ba605551afb9b27774f7f10511095451137c \
+    --hash=sha256:8a95ca9cc77ea4571081f6594e08bd272b66060634b8324e99cd1843020364f9 \
+    --hash=sha256:91f021145a8081f881996818474ef737800bcc613ffb1e618a655725a0f9e246 \
+    --hash=sha256:9f62672145f8669ec08762895fe85f4cf0ead08ce3164667f2b94b2f62ab23c3 \
+    --hash=sha256:a12ef323c0f2101529d455cfde7f4135eaa147bad17afe10b48634f796d96abd \
+    --hash=sha256:c602d015478b7958408d788ba00a50272649c5186ea8baa6cf71d4a1c761bba1 \
+    --hash=sha256:c75e8a5f810ce80a0cfad6e74ee94f9fde9b40c81312949bf356b7304ef20740 \
+    --hash=sha256:d0ae9270a7a5cc0ede63cd234b4ff1ce166c7a749b91dbbf45e0000c56d3eade \
+    --hash=sha256:d69ad934e13c15684e7887100a8f5f0f61d7a8e57e0fd29d9993210089a5b531 \
+    --hash=sha256:dbcc847bac2d225265d054993a7f910fda66e73d6662fe7156452cac0325b073 \
+    --hash=sha256:e64d338b504c9394a4a34942df4627e1e6cb07396ee3b49fe7b8d6420aa5104f \
+    --hash=sha256:f4cfc3a19d1e26448032049c79fc60331b104f694cf570a9e94f4e2c9d0932bb \
+    --hash=sha256:fbfb45e7b297749029cb28694abf437a78695a100e7c2033983d69f0ba2698d4 \
+    --hash=sha256:fcdf84ba3ed8124eb7234adfbb8792f311991cbf8aed1cad4b1b1a7ee08380c1
+    # via facexlib
+numpy==1.23.4 \
+    --hash=sha256:0fe563fc8ed9dc4474cbf70742673fc4391d70f4363f917599a7fa99f042d5a8 \
+    --hash=sha256:12ac457b63ec8ded85d85c1e17d85efd3c2b0967ca39560b307a35a6703a4735 \
+    --hash=sha256:2341f4ab6dba0834b685cce16dad5f9b6606ea8a00e6da154f5dbded70fdc4dd \
+    --hash=sha256:296d17aed51161dbad3c67ed6d164e51fcd18dbcd5dd4f9d0a9c6055dce30810 \
+    --hash=sha256:488a66cb667359534bc70028d653ba1cf307bae88eab5929cd707c761ff037db \
+    --hash=sha256:4d52914c88b4930dafb6c48ba5115a96cbab40f45740239d9f4159c4ba779962 \
+    --hash=sha256:5e13030f8793e9ee42f9c7d5777465a560eb78fa7e11b1c053427f2ccab90c79 \
+    --hash=sha256:61be02e3bf810b60ab74e81d6d0d36246dbfb644a462458bb53b595791251911 \
+    --hash=sha256:7607b598217745cc40f751da38ffd03512d33ec06f3523fb0b5f82e09f6f676d \
+    --hash=sha256:7a70a7d3ce4c0e9284e92285cba91a4a3f5214d87ee0e95928f3614a256a1488 \
+    --hash=sha256:7ab46e4e7ec63c8a5e6dbf5c1b9e1c92ba23a7ebecc86c336cb7bf3bd2fb10e5 \
+    --hash=sha256:8981d9b5619569899666170c7c9748920f4a5005bf79c72c07d08c8a035757b0 \
+    --hash=sha256:8c053d7557a8f022ec823196d242464b6955a7e7e5015b719e76003f63f82d0f \
+    --hash=sha256:926db372bc4ac1edf81cfb6c59e2a881606b409ddc0d0920b988174b2e2a767f \
+    --hash=sha256:95d79ada05005f6f4f337d3bb9de8a7774f259341c70bc88047a1f7b96a4bcb2 \
+    --hash=sha256:95de7dc7dc47a312f6feddd3da2500826defdccbc41608d0031276a24181a2c0 \
+    --hash=sha256:a0882323e0ca4245eb0a3d0a74f88ce581cc33aedcfa396e415e5bba7bf05f68 \
+    --hash=sha256:a8365b942f9c1a7d0f0dc974747d99dd0a0cdfc5949a33119caf05cb314682d3 \
+    --hash=sha256:a8aae2fb3180940011b4862b2dd3756616841c53db9734b27bb93813cd79fce6 \
+    --hash=sha256:c237129f0e732885c9a6076a537e974160482eab8f10db6292e92154d4c67d71 \
+    --hash=sha256:c67b833dbccefe97cdd3f52798d430b9d3430396af7cdb2a0c32954c3ef73894 \
+    --hash=sha256:ce03305dd694c4873b9429274fd41fc7eb4e0e4dea07e0af97a933b079a5814f \
+    --hash=sha256:d331afac87c92373826af83d2b2b435f57b17a5c74e6268b79355b970626e329 \
+    --hash=sha256:dada341ebb79619fe00a291185bba370c9803b1e1d7051610e01ed809ef3a4ba \
+    --hash=sha256:ed2cc92af0efad20198638c69bb0fc2870a58dabfba6eb722c933b48556c686c \
+    --hash=sha256:f260da502d7441a45695199b4e7fd8ca87db659ba1c78f2bbf31f934fe76ae0e \
+    --hash=sha256:f2f390aa4da44454db40a1f0201401f9036e8d578a25f01a6e237cea238337ef \
+    --hash=sha256:f76025acc8e2114bb664294a07ede0727aa75d63a06d2fae96bf29a81747e4a7
+    # via
+    #   accelerate
+    #   albumentations
+    #   altair
+    #   basicsr
+    #   clean-fid
+    #   contourpy
+    #   diffusers
+    #   facexlib
+    #   filterpy
+    #   gfpgan
+    #   imageio
+    #   matplotlib
+    #   numba
+    #   opencv-python
+    #   opencv-python-headless
+    #   pandas
+    #   pyarrow
+    #   pydeck
+    #   pytorch-lightning
+    #   pywavelets
+    #   qudida
+    #   realesrgan
+    #   scikit-image
+    #   scikit-learn
+    #   scipy
+    #   streamlit
+    #   taming-transformers-rom1504
+    #   tb-nightly
+    #   tensorboard
+    #   test-tube
+    #   tifffile
+    #   torch-fidelity
+    #   torchmetrics
+    #   torchvision
+    #   transformers
+oauthlib==3.2.2 \
+    --hash=sha256:8139f29aac13e25d502680e9e19963e83f16838d48a0d71c287fe40e7067fbca \
+    --hash=sha256:9859c40929662bec5d64f34d01c99e093149682a3f38915dc0655d5a633dd918
+    # via requests-oauthlib
+omegaconf==2.2.3 \
+    --hash=sha256:59ff9fba864ffbb5fb710b64e8a9ba37c68fa339a2e2bb4f1b648d6901552523 \
+    --hash=sha256:d6f2cbf79a992899eb76c6cb1aedfcf0fe7456a8654382edd5ee0c1b199c0657
+    # via taming-transformers-rom1504
+opencv-python==4.6.0.66 \
+    --hash=sha256:0dc82a3d8630c099d2f3ac1b1aabee164e8188db54a786abb7a4e27eba309440 \
+    --hash=sha256:5af8ba35a4fcb8913ffb86e92403e9a656a4bff4a645d196987468f0f8947875 \
+    --hash=sha256:6e32af22e3202748bd233ed8f538741876191863882eba44e332d1a34993165b \
+    --hash=sha256:c5bfae41ad4031e66bb10ec4a0a2ffd3e514d092652781e8b1ac98d1b59f1158 \
+    --hash=sha256:dbdc84a9b4ea2cbae33861652d25093944b9959279200b7ae0badd32439f74de \
+    --hash=sha256:e6e448b62afc95c5b58f97e87ef84699e6607fe5c58730a03301c52496005cae \
+    --hash=sha256:f482e78de6e7b0b060ff994ffd859bddc3f7f382bb2019ef157b0ea8ca8712f5
+    # via
+    #   basicsr
+    #   facexlib
+    #   gfpgan
+    #   realesrgan
+opencv-python-headless==4.6.0.66 \
+    --hash=sha256:21e70f8b0c04098cdf466d27184fe6c3820aaef944a22548db95099959c95889 \
+    --hash=sha256:2c032c373e447c3fc2a670bca20e2918a1205a6e72854df60425fd3f82c78c32 \
+    --hash=sha256:3bacd806cce1f1988e58f3d6f761538e0215d6621d316de94c009dc0acbd6ad3 \
+    --hash=sha256:d5291d7e10aa2c19cab6fd86f0d61af8617290ecd2d7ffcb051e446868d04cc5 \
+    --hash=sha256:e6c069bc963d7e8fcec21b3e33e594d35948badd63eccb2e80f88b0a12102c03 \
+    --hash=sha256:eec6281054346103d6af93f173b7c6a206beb2663d3adc04aa3ddc66e85093df \
+    --hash=sha256:ffbf26fcd697af996408440a93bc69c49c05a36845771f984156dfbeaa95d497
+    # via
+    #   albumentations
+    #   qudida
+packaging==21.3 \
+    --hash=sha256:dd47c42927d89ab911e606518907cc2d3a1f38bbd026385970643f9c5b8ecfeb \
+    --hash=sha256:ef103e05f519cdc783ae24ea4e2e0f508a9c99b2d4969652eed6a2e1ea5bd522
+    # via
+    #   accelerate
+    #   huggingface-hub
+    #   kornia
+    #   matplotlib
+    #   pytorch-lightning
+    #   scikit-image
+    #   streamlit
+    #   torchmetrics
+    #   transformers
+pandas==1.5.1 \
+    --hash=sha256:04e51b01d5192499390c0015630975f57836cc95c7411415b499b599b05c0c96 \
+    --hash=sha256:05c527c64ee02a47a24031c880ee0ded05af0623163494173204c5b72ddce658 \
+    --hash=sha256:0a78e05ec09731c5b3bd7a9805927ea631fe6f6cb06f0e7c63191a9a778d52b4 \
+    --hash=sha256:17da7035d9e6f9ea9cdc3a513161f8739b8f8489d31dc932bc5a29a27243f93d \
+    --hash=sha256:249cec5f2a5b22096440bd85c33106b6102e0672204abd2d5c014106459804ee \
+    --hash=sha256:2c25e5c16ee5c0feb6cf9d982b869eec94a22ddfda9aa2fbed00842cbb697624 \
+    --hash=sha256:32e3d9f65606b3f6e76555bfd1d0b68d94aff0929d82010b791b6254bf5a4b96 \
+    --hash=sha256:36aa1f8f680d7584e9b572c3203b20d22d697c31b71189322f16811d4ecfecd3 \
+    --hash=sha256:5b0c970e2215572197b42f1cff58a908d734503ea54b326412c70d4692256391 \
+    --hash=sha256:5cee0c74e93ed4f9d39007e439debcaadc519d7ea5c0afc3d590a3a7b2edf060 \
+    --hash=sha256:669c8605dba6c798c1863157aefde959c1796671ffb342b80fcb80a4c0bc4c26 \
+    --hash=sha256:66a1ad667b56e679e06ba73bb88c7309b3f48a4c279bd3afea29f65a766e9036 \
+    --hash=sha256:683779e5728ac9138406c59a11e09cd98c7d2c12f0a5fc2b9c5eecdbb4a00075 \
+    --hash=sha256:6bb391659a747cf4f181a227c3e64b6d197100d53da98dcd766cc158bdd9ec68 \
+    --hash=sha256:81f0674fa50b38b6793cd84fae5d67f58f74c2d974d2cb4e476d26eee33343d0 \
+    --hash=sha256:927e59c694e039c75d7023465d311277a1fc29ed7236b5746e9dddf180393113 \
+    --hash=sha256:932d2d7d3cab44cfa275601c982f30c2d874722ef6396bb539e41e4dc4618ed4 \
+    --hash=sha256:a52419d9ba5906db516109660b114faf791136c94c1a636ed6b29cbfff9187ee \
+    --hash=sha256:b156a971bc451c68c9e1f97567c94fd44155f073e3bceb1b0d195fd98ed12048 \
+    --hash=sha256:bcf1a82b770b8f8c1e495b19a20d8296f875a796c4fe6e91da5ef107f18c5ecb \
+    --hash=sha256:cb2a9cf1150302d69bb99861c5cddc9c25aceacb0a4ef5299785d0f5389a3209 \
+    --hash=sha256:d8c709f4700573deb2036d240d140934df7e852520f4a584b2a8d5443b71f54d \
+    --hash=sha256:db45b94885000981522fb92349e6b76f5aee0924cc5315881239c7859883117d \
+    --hash=sha256:ddf46b940ef815af4e542697eaf071f0531449407a7607dd731bf23d156e20a7 \
+    --hash=sha256:e675f8fe9aa6c418dc8d3aac0087b5294c1a4527f1eacf9fe5ea671685285454 \
+    --hash=sha256:eb7e8cf2cf11a2580088009b43de84cabbf6f5dae94ceb489f28dba01a17cb77 \
+    --hash=sha256:f340331a3f411910adfb4bbe46c2ed5872d9e473a783d7f14ecf49bc0869c594
+    # via
+    #   altair
+    #   streamlit
+    #   test-tube
+pathtools==0.1.2 \
+    --hash=sha256:7c35c5421a39bb82e58018febd90e3b6e5db34c5443aaaf742b3f33d4655f1c0
+    # via wandb
+pillow==9.3.0 \
+    --hash=sha256:03150abd92771742d4a8cd6f2fa6246d847dcd2e332a18d0c15cc75bf6703040 \
+    --hash=sha256:073adb2ae23431d3b9bcbcff3fe698b62ed47211d0716b067385538a1b0f28b8 \
+    --hash=sha256:0b07fffc13f474264c336298d1b4ce01d9c5a011415b79d4ee5527bb69ae6f65 \
+    --hash=sha256:0b7257127d646ff8676ec8a15520013a698d1fdc48bc2a79ba4e53df792526f2 \
+    --hash=sha256:12ce4932caf2ddf3e41d17fc9c02d67126935a44b86df6a206cf0d7161548627 \
+    --hash=sha256:15c42fb9dea42465dfd902fb0ecf584b8848ceb28b41ee2b58f866411be33f07 \
+    --hash=sha256:18498994b29e1cf86d505edcb7edbe814d133d2232d256db8c7a8ceb34d18cef \
+    --hash=sha256:1c7c8ae3864846fc95f4611c78129301e203aaa2af813b703c55d10cc1628535 \
+    --hash=sha256:22b012ea2d065fd163ca096f4e37e47cd8b59cf4b0fd47bfca6abb93df70b34c \
+    --hash=sha256:276a5ca930c913f714e372b2591a22c4bd3b81a418c0f6635ba832daec1cbcfc \
+    --hash=sha256:2e0918e03aa0c72ea56edbb00d4d664294815aa11291a11504a377ea018330d3 \
+    --hash=sha256:3033fbe1feb1b59394615a1cafaee85e49d01b51d54de0cbf6aa8e64182518a1 \
+    --hash=sha256:3168434d303babf495d4ba58fc22d6604f6e2afb97adc6a423e917dab828939c \
+    --hash=sha256:32a44128c4bdca7f31de5be641187367fe2a450ad83b833ef78910397db491aa \
+    --hash=sha256:3dd6caf940756101205dffc5367babf288a30043d35f80936f9bfb37f8355b32 \
+    --hash=sha256:40e1ce476a7804b0fb74bcfa80b0a2206ea6a882938eaba917f7a0f004b42502 \
+    --hash=sha256:41e0051336807468be450d52b8edd12ac60bebaa97fe10c8b660f116e50b30e4 \
+    --hash=sha256:4390e9ce199fc1951fcfa65795f239a8a4944117b5935a9317fb320e7767b40f \
+    --hash=sha256:502526a2cbfa431d9fc2a079bdd9061a2397b842bb6bc4239bb176da00993812 \
+    --hash=sha256:51e0e543a33ed92db9f5ef69a0356e0b1a7a6b6a71b80df99f1d181ae5875636 \
+    --hash=sha256:57751894f6618fd4308ed8e0c36c333e2f5469744c34729a27532b3db106ee20 \
+    --hash=sha256:5d77adcd56a42d00cc1be30843d3426aa4e660cab4a61021dc84467123f7a00c \
+    --hash=sha256:655a83b0058ba47c7c52e4e2df5ecf484c1b0b0349805896dd350cbc416bdd91 \
+    --hash=sha256:68943d632f1f9e3dce98908e873b3a090f6cba1cbb1b892a9e8d97c938871fbe \
+    --hash=sha256:6c738585d7a9961d8c2821a1eb3dcb978d14e238be3d70f0a706f7fa9316946b \
+    --hash=sha256:73bd195e43f3fadecfc50c682f5055ec32ee2c933243cafbfdec69ab1aa87cad \
+    --hash=sha256:772a91fc0e03eaf922c63badeca75e91baa80fe2f5f87bdaed4280662aad25c9 \
+    --hash=sha256:77ec3e7be99629898c9a6d24a09de089fa5356ee408cdffffe62d67bb75fdd72 \
+    --hash=sha256:7db8b751ad307d7cf238f02101e8e36a128a6cb199326e867d1398067381bff4 \
+    --hash=sha256:801ec82e4188e935c7f5e22e006d01611d6b41661bba9fe45b60e7ac1a8f84de \
+    --hash=sha256:82409ffe29d70fd733ff3c1025a602abb3e67405d41b9403b00b01debc4c9a29 \
+    --hash=sha256:828989c45c245518065a110434246c44a56a8b2b2f6347d1409c787e6e4651ee \
+    --hash=sha256:829f97c8e258593b9daa80638aee3789b7df9da5cf1336035016d76f03b8860c \
+    --hash=sha256:871b72c3643e516db4ecf20efe735deb27fe30ca17800e661d769faab45a18d7 \
+    --hash=sha256:89dca0ce00a2b49024df6325925555d406b14aa3efc2f752dbb5940c52c56b11 \
+    --hash=sha256:90fb88843d3902fe7c9586d439d1e8c05258f41da473952aa8b328d8b907498c \
+    --hash=sha256:97aabc5c50312afa5e0a2b07c17d4ac5e865b250986f8afe2b02d772567a380c \
+    --hash=sha256:9aaa107275d8527e9d6e7670b64aabaaa36e5b6bd71a1015ddd21da0d4e06448 \
+    --hash=sha256:9f47eabcd2ded7698106b05c2c338672d16a6f2a485e74481f524e2a23c2794b \
+    --hash=sha256:a0a06a052c5f37b4ed81c613a455a81f9a3a69429b4fd7bb913c3fa98abefc20 \
+    --hash=sha256:ab388aaa3f6ce52ac1cb8e122c4bd46657c15905904b3120a6248b5b8b0bc228 \
+    --hash=sha256:ad58d27a5b0262c0c19b47d54c5802db9b34d38bbf886665b626aff83c74bacd \
+    --hash=sha256:ae5331c23ce118c53b172fa64a4c037eb83c9165aba3a7ba9ddd3ec9fa64a699 \
+    --hash=sha256:af0372acb5d3598f36ec0914deed2a63f6bcdb7b606da04dc19a88d31bf0c05b \
+    --hash=sha256:afa4107d1b306cdf8953edde0534562607fe8811b6c4d9a486298ad31de733b2 \
+    --hash=sha256:b03ae6f1a1878233ac620c98f3459f79fd77c7e3c2b20d460284e1fb370557d4 \
+    --hash=sha256:b0915e734b33a474d76c28e07292f196cdf2a590a0d25bcc06e64e545f2d146c \
+    --hash=sha256:b4012d06c846dc2b80651b120e2cdd787b013deb39c09f407727ba90015c684f \
+    --hash=sha256:b472b5ea442148d1c3e2209f20f1e0bb0eb556538690fa70b5e1f79fa0ba8dc2 \
+    --hash=sha256:b59430236b8e58840a0dfb4099a0e8717ffb779c952426a69ae435ca1f57210c \
+    --hash=sha256:b90f7616ea170e92820775ed47e136208e04c967271c9ef615b6fbd08d9af0e3 \
+    --hash=sha256:b9a65733d103311331875c1dca05cb4606997fd33d6acfed695b1232ba1df193 \
+    --hash=sha256:bac18ab8d2d1e6b4ce25e3424f709aceef668347db8637c2296bcf41acb7cf48 \
+    --hash=sha256:bca31dd6014cb8b0b2db1e46081b0ca7d936f856da3b39744aef499db5d84d02 \
+    --hash=sha256:be55f8457cd1eac957af0c3f5ece7bc3f033f89b114ef30f710882717670b2a8 \
+    --hash=sha256:c7025dce65566eb6e89f56c9509d4f628fddcedb131d9465cacd3d8bac337e7e \
+    --hash=sha256:c935a22a557a560108d780f9a0fc426dd7459940dc54faa49d83249c8d3e760f \
+    --hash=sha256:dbb8e7f2abee51cef77673be97760abff1674ed32847ce04b4af90f610144c7b \
+    --hash=sha256:e6ea6b856a74d560d9326c0f5895ef8050126acfdc7ca08ad703eb0081e82b74 \
+    --hash=sha256:ebf2029c1f464c59b8bdbe5143c79fa2045a581ac53679733d3a91d400ff9efb \
+    --hash=sha256:f1ff2ee69f10f13a9596480335f406dd1f70c3650349e2be67ca3139280cade0
+    # via
+    #   basicsr
+    #   clean-fid
+    #   diffusers
+    #   facexlib
+    #   imageio
+    #   k-diffusion
+    #   matplotlib
+    #   realesrgan
+    #   scikit-image
+    #   streamlit
+    #   torch-fidelity
+    #   torchvision
+promise==2.3 \
+    --hash=sha256:dfd18337c523ba4b6a58801c164c1904a9d4d1b1747c7d5dbf45b693a49d93d0
+    # via wandb
+protobuf==3.19.6 \
+    --hash=sha256:010be24d5a44be7b0613750ab40bc8b8cedc796db468eae6c779b395f50d1fa1 \
+    --hash=sha256:0469bc66160180165e4e29de7f445e57a34ab68f49357392c5b2f54c656ab25e \
+    --hash=sha256:0c0714b025ec057b5a7600cb66ce7c693815f897cfda6d6efb58201c472e3437 \
+    --hash=sha256:11478547958c2dfea921920617eb457bc26867b0d1aa065ab05f35080c5d9eb6 \
+    --hash=sha256:14082457dc02be946f60b15aad35e9f5c69e738f80ebbc0900a19bc83734a5a4 \
+    --hash=sha256:2b2d2913bcda0e0ec9a784d194bc490f5dc3d9d71d322d070b11a0ade32ff6ba \
+    --hash=sha256:30a15015d86b9c3b8d6bf78d5b8c7749f2512c29f168ca259c9d7727604d0e39 \
+    --hash=sha256:30f5370d50295b246eaa0296533403961f7e64b03ea12265d6dfce3a391d8992 \
+    --hash=sha256:347b393d4dd06fb93a77620781e11c058b3b0a5289262f094379ada2920a3730 \
+    --hash=sha256:4bc98de3cdccfb5cd769620d5785b92c662b6bfad03a202b83799b6ed3fa1fa7 \
+    --hash=sha256:5057c64052a1f1dd7d4450e9aac25af6bf36cfbfb3a1cd89d16393a036c49157 \
+    --hash=sha256:559670e006e3173308c9254d63facb2c03865818f22204037ab76f7a0ff70b5f \
+    --hash=sha256:5a0d7539a1b1fb7e76bf5faa0b44b30f812758e989e59c40f77a7dab320e79b9 \
+    --hash=sha256:5f5540d57a43042389e87661c6eaa50f47c19c6176e8cf1c4f287aeefeccb5c4 \
+    --hash=sha256:7a552af4dc34793803f4e735aabe97ffc45962dfd3a237bdde242bff5a3de684 \
+    --hash=sha256:84a04134866861b11556a82dd91ea6daf1f4925746b992f277b84013a7cc1229 \
+    --hash=sha256:878b4cd080a21ddda6ac6d1e163403ec6eea2e206cf225982ae04567d39be7b0 \
+    --hash=sha256:90b0d02163c4e67279ddb6dc25e063db0130fc299aefabb5d481053509fae5c8 \
+    --hash=sha256:91d5f1e139ff92c37e0ff07f391101df77e55ebb97f46bbc1535298d72019462 \
+    --hash=sha256:a8ce5ae0de28b51dff886fb922012dad885e66176663950cb2344c0439ecb473 \
+    --hash=sha256:aa3b82ca1f24ab5326dcf4ea00fcbda703e986b22f3d27541654f749564d778b \
+    --hash=sha256:bb6776bd18f01ffe9920e78e03a8676530a5d6c5911934c6a1ac6eb78973ecb6 \
+    --hash=sha256:bbf5cea5048272e1c60d235c7bd12ce1b14b8a16e76917f371c718bd3005f045 \
+    --hash=sha256:c0ccd3f940fe7f3b35a261b1dd1b4fc850c8fde9f74207015431f174be5976b3 \
+    --hash=sha256:d0b635cefebd7a8a0f92020562dead912f81f401af7e71f16bf9506ff3bdbb38
+    # via
+    #   streamlit
+    #   tb-nightly
+    #   tensorboard
+    #   wandb
+psutil==5.9.3 \
+    --hash=sha256:07d880053c6461c9b89cd5d4808f3b8336665fa3acdefd6777662c5ed73a851a \
+    --hash=sha256:12500d761ac091f2426567f19f95fd3f15a197d96befb44a5c1e3cbe6db5752c \
+    --hash=sha256:1b540599481c73408f6b392cdffef5b01e8ff7a2ac8caae0a91b8222e88e8f1e \
+    --hash=sha256:35feafe232d1aaf35d51bd42790cbccb882456f9f18cdc411532902370d660df \
+    --hash=sha256:3a7826e68b0cf4ce2c1ee385d64eab7d70e3133171376cac53d7c1790357ec8f \
+    --hash=sha256:46907fa62acaac364fff0b8a9da7b360265d217e4fdeaca0a2397a6883dffba2 \
+    --hash=sha256:4bd4854f0c83aa84a5a40d3b5d0eb1f3c128f4146371e03baed4589fe4f3c931 \
+    --hash=sha256:538fcf6ae856b5e12d13d7da25ad67f02113c96f5989e6ad44422cb5994ca7fc \
+    --hash=sha256:547ebb02031fdada635452250ff39942db8310b5c4a8102dfe9384ee5791e650 \
+    --hash=sha256:5e8b50241dd3c2ed498507f87a6602825073c07f3b7e9560c58411c14fe1e1c9 \
+    --hash=sha256:5fa88e3d5d0b480602553d362c4b33a63e0c40bfea7312a7bf78799e01e0810b \
+    --hash=sha256:68fa227c32240c52982cb931801c5707a7f96dd8927f9102d6c7771ea1ff5698 \
+    --hash=sha256:6ced1ad823ecfa7d3ce26fe8aa4996e2e53fb49b7fed8ad81c80958501ec0619 \
+    --hash=sha256:71b1206e7909792d16933a0d2c1c7f04ae196186c51ba8567abae1d041f06dcb \
+    --hash=sha256:767ef4fa33acda16703725c0473a91e1832d296c37c63896c7153ba81698f1ab \
+    --hash=sha256:7ccfcdfea4fc4b0a02ca2c31de7fcd186beb9cff8207800e14ab66f79c773af6 \
+    --hash=sha256:7e4939ff75149b67aef77980409f156f0082fa36accc475d45c705bb00c6c16a \
+    --hash=sha256:828c9dc9478b34ab96be75c81942d8df0c2bb49edbb481f597314d92b6441d89 \
+    --hash=sha256:8a4e07611997acf178ad13b842377e3d8e9d0a5bac43ece9bfc22a96735d9a4f \
+    --hash=sha256:941a6c2c591da455d760121b44097781bc970be40e0e43081b9139da485ad5b7 \
+    --hash=sha256:9a4af6ed1094f867834f5f07acd1250605a0874169a5fcadbcec864aec2496a6 \
+    --hash=sha256:9ec296f565191f89c48f33d9544d8d82b0d2af7dd7d2d4e6319f27a818f8d1cc \
+    --hash=sha256:9ec95df684583b5596c82bb380c53a603bb051cf019d5c849c47e117c5064395 \
+    --hash=sha256:a04a1836894c8279e5e0a0127c0db8e198ca133d28be8a2a72b4db16f6cf99c1 \
+    --hash=sha256:a3d81165b8474087bb90ec4f333a638ccfd1d69d34a9b4a1a7eaac06648f9fbe \
+    --hash=sha256:b4a247cd3feaae39bb6085fcebf35b3b8ecd9b022db796d89c8f05067ca28e71 \
+    --hash=sha256:ba38cf9984d5462b506e239cf4bc24e84ead4b1d71a3be35e66dad0d13ded7c1 \
+    --hash=sha256:beb57d8a1ca0ae0eb3d08ccaceb77e1a6d93606f0e1754f0d60a6ebd5c288837 \
+    --hash=sha256:d266cd05bd4a95ca1c2b9b5aac50d249cf7c94a542f47e0b22928ddf8b80d1ef \
+    --hash=sha256:d8c3cc6bb76492133474e130a12351a325336c01c96a24aae731abf5a47fe088 \
+    --hash=sha256:db8e62016add2235cc87fb7ea000ede9e4ca0aa1f221b40cef049d02d5d2593d \
+    --hash=sha256:e7507f6c7b0262d3e7b0eeda15045bf5881f4ada70473b87bc7b7c93b992a7d7 \
+    --hash=sha256:ed15edb14f52925869250b1375f0ff58ca5c4fa8adefe4883cfb0737d32f5c02 \
+    --hash=sha256:f57d63a2b5beaf797b87024d018772439f9d3103a395627b77d17a8d72009543 \
+    --hash=sha256:fa5e32c7d9b60b2528108ade2929b115167fe98d59f89555574715054f50fa31 \
+    --hash=sha256:fe79b4ad4836e3da6c4650cb85a663b3a51aef22e1a829c384e18fae87e5e727
+    # via
+    #   accelerate
+    #   wandb
+pyarrow==10.0.0 \
+    --hash=sha256:10e031794d019425d34406edffe7e32157359e9455f9edb97a1732f8dabf802f \
+    --hash=sha256:25f51dca780fc22cfd7ac30f6bdfe70eb99145aee9acfda987f2c49955d66ed9 \
+    --hash=sha256:2d326a9d47ac237d81b8c4337e9d30a0b361835b536fc7ea53991455ce761fbd \
+    --hash=sha256:3d2694f08c8d4482d14e3798ff036dbd81ae6b1c47948f52515e1aa90fbec3f0 \
+    --hash=sha256:4051664d354b14939b5da35cfa77821ade594bc1cf56dd2032b3068c96697d74 \
+    --hash=sha256:511735040b83f2993f78d7fb615e7b88253d75f41500e87e587c40156ff88120 \
+    --hash=sha256:65d4a312f3ced318423704355acaccc7f7bdfe242472e59bdd54aa0f8837adf8 \
+    --hash=sha256:68ccb82c04c0f7abf7a95541d5e9d9d94290fc66a2d36d3f6ea0777f40c15654 \
+    --hash=sha256:69b8a1fd99201178799b02f18498633847109b701856ec762f314352a431b7d0 \
+    --hash=sha256:758284e1ebd3f2a9abb30544bfec28d151a398bb7c0f2578cbca5ee5b000364a \
+    --hash=sha256:7be7f42f713068293308c989a4a3a2de03b70199bdbe753901c6595ff8640c64 \
+    --hash=sha256:7ce026274cd5d9934cd3694e89edecde4e036018bbc6cb735fd33b9e967e7d47 \
+    --hash=sha256:7e6b837cc44cd62a0e280c8fc4de94ebce503d6d1190e6e94157ab49a8bea67b \
+    --hash=sha256:b153b05765393557716e3729cf988442b3ae4f5567364ded40d58c07feed27c2 \
+    --hash=sha256:b3e3148468d3eed3779d68241f1d13ed4ee7cca4c6dbc7c07e5062b93ad4da33 \
+    --hash=sha256:b45f969ed924282e9d4ede38f3430630d809c36dbff65452cabce03141943d28 \
+    --hash=sha256:b9f63ceb8346aac0bcb487fafe9faca642ad448ca649fcf66a027c6e120cbc12 \
+    --hash=sha256:c79300e1a3e23f2bf4defcf0d70ff5ea25ef6ebf6f121d8670ee14bb662bb7ca \
+    --hash=sha256:d45a59e2f47826544c0ca70bc0f7ed8ffa5ad23f93b0458230c7e983bcad1acf \
+    --hash=sha256:e4c6da9f9e1ff96781ee1478f7cc0860e66c23584887b8e297c4b9905c3c9066 \
+    --hash=sha256:f329951d56b3b943c353f7b27c894e02367a7efbb9fef7979c6b24e02dbfcf55 \
+    --hash=sha256:f76157d9579571c865860e5fd004537c03e21139db76692d96fd8a186adab1f2
+    # via streamlit
+pyasn1==0.4.8 \
+    --hash=sha256:39c7e2ec30515947ff4e87fb6f456dfc6e84857d34be479c9d4a4ba4bf46aa5d \
+    --hash=sha256:aef77c9fb94a3ac588e87841208bdec464471d9871bd5050a287cc9a475cd0ba
+    # via
+    #   pyasn1-modules
+    #   rsa
+pyasn1-modules==0.2.8 \
+    --hash=sha256:905f84c712230b2c592c19470d3ca8d552de726050d1d1716282a1f6146be65e \
+    --hash=sha256:a50b808ffeb97cb3601dd25981f6b016cbb3d31fbf57a8b8a87428e6158d0c74
+    # via google-auth
+pydeck==0.8.0 \
+    --hash=sha256:07edde833f7cfcef6749124351195aa7dcd24663d4909fd7898dbd0b6fbc01ec \
+    --hash=sha256:a8fa7757c6f24bba033af39db3147cb020eef44012ba7e60d954de187f9ed4d5
+    # via streamlit
+pydeprecate==0.3.2 \
+    --hash=sha256:d481116cc5d7f6c473e7c4be820efdd9b90a16b594b350276e9e66a6cb5bdd29 \
+    --hash=sha256:ed86b68ed837e6465245904a3de2f59bf9eef78ac7a2502ee280533d04802457
+    # via pytorch-lightning
+pygments==2.13.0 \
+    --hash=sha256:56a8508ae95f98e2b9bdf93a6be5ae3f7d8af858b43e02c5a2ff083726be40c1 \
+    --hash=sha256:f643f331ab57ba3c9d89212ee4a2dabc6e94f117cf4eefde99a0574720d14c42
+    # via rich
+pympler==1.0.1 \
+    --hash=sha256:993f1a3599ca3f4fcd7160c7545ad06310c9e12f70174ae7ae8d4e25f6c5d3fa \
+    --hash=sha256:d260dda9ae781e1eab6ea15bacb84015849833ba5555f141d2d9b7b7473b307d
+    # via streamlit
+pyparsing==3.0.9 \
+    --hash=sha256:2b020ecf7d21b687f219b71ecad3631f644a47f01403fa1d1036b0c6416d70fb \
+    --hash=sha256:5026bae9a10eeaefb61dab2f09052b9f4307d44aee4eda64b309723d8d206bbc
+    # via
+    #   matplotlib
+    #   packaging
+pyreadline3==3.4.1 \
+    --hash=sha256:6f3d1f7b8a31ba32b73917cefc1f28cc660562f39aea8646d30bd6eff21f7bae \
+    --hash=sha256:b0efb6516fd4fb07b45949053826a62fa4cb353db5be2bbb4a7aa1fdd1e345fb
+    # via -r requirements.in
+pyrsistent==0.19.2 \
+    --hash=sha256:055ab45d5911d7cae397dc418808d8802fb95262751872c841c170b0dbf51eed \
+    --hash=sha256:111156137b2e71f3a9936baf27cb322e8024dac3dc54ec7fb9f0bcf3249e68bb \
+    --hash=sha256:187d5730b0507d9285a96fca9716310d572e5464cadd19f22b63a6976254d77a \
+    --hash=sha256:21455e2b16000440e896ab99e8304617151981ed40c29e9507ef1c2e4314ee95 \
+    --hash=sha256:2aede922a488861de0ad00c7630a6e2d57e8023e4be72d9d7147a9fcd2d30712 \
+    --hash=sha256:3ba4134a3ff0fc7ad225b6b457d1309f4698108fb6b35532d015dca8f5abed73 \
+    --hash=sha256:456cb30ca8bff00596519f2c53e42c245c09e1a4543945703acd4312949bfd41 \
+    --hash=sha256:71d332b0320642b3261e9fee47ab9e65872c2bd90260e5d225dabeed93cbd42b \
+    --hash=sha256:879b4c2f4d41585c42df4d7654ddffff1239dc4065bc88b745f0341828b83e78 \
+    --hash=sha256:9cd3e9978d12b5d99cbdc727a3022da0430ad007dacf33d0bf554b96427f33ab \
+    --hash=sha256:a178209e2df710e3f142cbd05313ba0c5ebed0a55d78d9945ac7a4e09d923308 \
+    --hash=sha256:b39725209e06759217d1ac5fcdb510e98670af9e37223985f330b611f62e7425 \
+    --hash=sha256:bfa0351be89c9fcbcb8c9879b826f4353be10f58f8a677efab0c017bf7137ec2 \
+    --hash=sha256:bfd880614c6237243ff53a0539f1cb26987a6dc8ac6e66e0c5a40617296a045e \
+    --hash=sha256:c43bec251bbd10e3cb58ced80609c5c1eb238da9ca78b964aea410fb820d00d6 \
+    --hash=sha256:d690b18ac4b3e3cab73b0b7aa7dbe65978a172ff94970ff98d82f2031f8971c2 \
+    --hash=sha256:d6982b5a0237e1b7d876b60265564648a69b14017f3b5f908c5be2de3f9abb7a \
+    --hash=sha256:dec3eac7549869365fe263831f576c8457f6c833937c68542d08fde73457d291 \
+    --hash=sha256:e371b844cec09d8dc424d940e54bba8f67a03ebea20ff7b7b0d56f526c71d584 \
+    --hash=sha256:e5d8f84d81e3729c3b506657dddfe46e8ba9c330bf1858ee33108f8bb2adb38a \
+    --hash=sha256:ea6b79a02a28550c98b6ca9c35b9f492beaa54d7c5c9e9949555893c8a9234d0 \
+    --hash=sha256:f1258f4e6c42ad0b20f9cfcc3ada5bd6b83374516cd01c0960e3cb75fdca6770
+    # via jsonschema
+python-dateutil==2.8.2 \
+    --hash=sha256:0123cacc1627ae19ddf3c27a5de5bd67ee4586fbdd6440d9748f8abb483d3e86 \
+    --hash=sha256:961d03dc3453ebbc59dbdea9e4e11c5651520a876d0f4db161e8674aae935da9
+    # via
+    #   matplotlib
+    #   pandas
+    #   streamlit
+python-engineio==4.3.4 \
+    --hash=sha256:7454314a529bba20e745928601ffeaf101c1b5aca9a6c4e48ad397803d10ea0c \
+    --hash=sha256:d8d8b072799c36cadcdcc2b40d2a560ce09797ab3d2d596b2ad519a5e4df19ae
+    # via python-socketio
+python-socketio==5.7.2 \
+    --hash=sha256:92395062d9db3c13d30e7cdedaa0e1330bba78505645db695415f9a3c628d097 \
+    --hash=sha256:d9a9f047e6fdd306c852fbac36516f4b495c2096f8ad9ceb8803b8e5ff5622e3
+    # via flask-socketio
+pytorch-lightning==1.7.7 \
+    --hash=sha256:27c2dd01a18db2415168e3fa3775ccb5a1fa1e2961a50439ad9365507fe9d4ae \
+    --hash=sha256:4438b8284d7f7fdb06cf3566a7b5b6f102ac8971cf7bb6d3f1b1de64628241f3
+    # via taming-transformers-rom1504
+pytz==2022.6 \
+    --hash=sha256:222439474e9c98fced559f1709d89e6c9cbf8d79c794ff3eb9f8800064291427 \
+    --hash=sha256:e89512406b793ca39f5971bc999cc538ce125c0e51c27941bef4568b460095e2
+    # via pandas
+pytz-deprecation-shim==0.1.0.post0 \
+    --hash=sha256:8314c9692a636c8eb3bda879b9f119e350e93223ae83e70e80c31675a0fdc1a6 \
+    --hash=sha256:af097bae1b616dde5c5744441e2ddc69e74dfdcb0c263129610d85b87445a59d
+    # via tzlocal
+pywavelets==1.4.1 \
+    --hash=sha256:030670a213ee8fefa56f6387b0c8e7d970c7f7ad6850dc048bd7c89364771b9b \
+    --hash=sha256:058b46434eac4c04dd89aeef6fa39e4b6496a951d78c500b6641fd5b2cc2f9f4 \
+    --hash=sha256:231b0e0b1cdc1112f4af3c24eea7bf181c418d37922a67670e9bf6cfa2d544d4 \
+    --hash=sha256:23bafd60350b2b868076d976bdd92f950b3944f119b4754b1d7ff22b7acbf6c6 \
+    --hash=sha256:3f19327f2129fb7977bc59b966b4974dfd72879c093e44a7287500a7032695de \
+    --hash=sha256:47cac4fa25bed76a45bc781a293c26ac63e8eaae9eb8f9be961758d22b58649c \
+    --hash=sha256:578af438a02a86b70f1975b546f68aaaf38f28fb082a61ceb799816049ed18aa \
+    --hash=sha256:6437af3ddf083118c26d8f97ab43b0724b956c9f958e9ea788659f6a2834ba93 \
+    --hash=sha256:64c6bac6204327321db30b775060fbe8e8642316e6bff17f06b9f34936f88875 \
+    --hash=sha256:67a0d28a08909f21400cb09ff62ba94c064882ffd9e3a6b27880a111211d59bd \
+    --hash=sha256:71ab30f51ee4470741bb55fc6b197b4a2b612232e30f6ac069106f0156342356 \
+    --hash=sha256:7231461d7a8eb3bdc7aa2d97d9f67ea5a9f8902522818e7e2ead9c2b3408eeb1 \
+    --hash=sha256:754fa5085768227c4f4a26c1e0c78bc509a266d9ebd0eb69a278be7e3ece943c \
+    --hash=sha256:7ab8d9db0fe549ab2ee0bea61f614e658dd2df419d5b75fba47baa761e95f8f2 \
+    --hash=sha256:875d4d620eee655346e3589a16a73790cf9f8917abba062234439b594e706784 \
+    --hash=sha256:88aa5449e109d8f5e7f0adef85f7f73b1ab086102865be64421a3a3d02d277f4 \
+    --hash=sha256:91d3d393cffa634f0e550d88c0e3f217c96cfb9e32781f2960876f1808d9b45b \
+    --hash=sha256:9cb5ca8d11d3f98e89e65796a2125be98424d22e5ada360a0dbabff659fca0fc \
+    --hash=sha256:ab7da0a17822cd2f6545626946d3b82d1a8e106afc4b50e3387719ba01c7b966 \
+    --hash=sha256:ad987748f60418d5f4138db89d82ba0cb49b086e0cbb8fd5c3ed4a814cfb705e \
+    --hash=sha256:d0e56cd7a53aed3cceca91a04d62feb3a0aca6725b1912d29546c26f6ea90426 \
+    --hash=sha256:d854411eb5ee9cb4bc5d0e66e3634aeb8f594210f6a1bed96dbed57ec70f181c \
+    --hash=sha256:da7b9c006171be1f9ddb12cc6e0d3d703b95f7f43cb5e2c6f5f15d3233fcf202 \
+    --hash=sha256:daf0aa79842b571308d7c31a9c43bc99a30b6328e6aea3f50388cd8f69ba7dbc \
+    --hash=sha256:de7cd61a88a982edfec01ea755b0740e94766e00a1ceceeafef3ed4c85c605cd
+    # via scikit-image
+pyyaml==6.0 \
+    --hash=sha256:01b45c0191e6d66c470b6cf1b9531a771a83c1c4208272ead47a3ae4f2f603bf \
+    --hash=sha256:0283c35a6a9fbf047493e3a0ce8d79ef5030852c51e9d911a27badfde0605293 \
+    --hash=sha256:055d937d65826939cb044fc8c9b08889e8c743fdc6a32b33e2390f66013e449b \
+    --hash=sha256:07751360502caac1c067a8132d150cf3d61339af5691fe9e87803040dbc5db57 \
+    --hash=sha256:0b4624f379dab24d3725ffde76559cff63d9ec94e1736b556dacdfebe5ab6d4b \
+    --hash=sha256:0ce82d761c532fe4ec3f87fc45688bdd3a4c1dc5e0b4a19814b9009a29baefd4 \
+    --hash=sha256:1e4747bc279b4f613a09eb64bba2ba602d8a6664c6ce6396a4d0cd413a50ce07 \
+    --hash=sha256:213c60cd50106436cc818accf5baa1aba61c0189ff610f64f4a3e8c6726218ba \
+    --hash=sha256:231710d57adfd809ef5d34183b8ed1eeae3f76459c18fb4a0b373ad56bedcdd9 \
+    --hash=sha256:277a0ef2981ca40581a47093e9e2d13b3f1fbbeffae064c1d21bfceba2030287 \
+    --hash=sha256:2cd5df3de48857ed0544b34e2d40e9fac445930039f3cfe4bcc592a1f836d513 \
+    --hash=sha256:40527857252b61eacd1d9af500c3337ba8deb8fc298940291486c465c8b46ec0 \
+    --hash=sha256:432557aa2c09802be39460360ddffd48156e30721f5e8d917f01d31694216782 \
+    --hash=sha256:473f9edb243cb1935ab5a084eb238d842fb8f404ed2193a915d1784b5a6b5fc0 \
+    --hash=sha256:48c346915c114f5fdb3ead70312bd042a953a8ce5c7106d5bfb1a5254e47da92 \
+    --hash=sha256:50602afada6d6cbfad699b0c7bb50d5ccffa7e46a3d738092afddc1f9758427f \
+    --hash=sha256:68fb519c14306fec9720a2a5b45bc9f0c8d1b9c72adf45c37baedfcd949c35a2 \
+    --hash=sha256:77f396e6ef4c73fdc33a9157446466f1cff553d979bd00ecb64385760c6babdc \
+    --hash=sha256:81957921f441d50af23654aa6c5e5eaf9b06aba7f0a19c18a538dc7ef291c5a1 \
+    --hash=sha256:819b3830a1543db06c4d4b865e70ded25be52a2e0631ccd2f6a47a2822f2fd7c \
+    --hash=sha256:897b80890765f037df3403d22bab41627ca8811ae55e9a722fd0392850ec4d86 \
+    --hash=sha256:98c4d36e99714e55cfbaaee6dd5badbc9a1ec339ebfc3b1f52e293aee6bb71a4 \
+    --hash=sha256:9df7ed3b3d2e0ecfe09e14741b857df43adb5a3ddadc919a2d94fbdf78fea53c \
+    --hash=sha256:9fa600030013c4de8165339db93d182b9431076eb98eb40ee068700c9c813e34 \
+    --hash=sha256:a80a78046a72361de73f8f395f1f1e49f956c6be882eed58505a15f3e430962b \
+    --hash=sha256:afa17f5bc4d1b10afd4466fd3a44dc0e245382deca5b3c353d8b757f9e3ecb8d \
+    --hash=sha256:b3d267842bf12586ba6c734f89d1f5b871df0273157918b0ccefa29deb05c21c \
+    --hash=sha256:b5b9eccad747aabaaffbc6064800670f0c297e52c12754eb1d976c57e4f74dcb \
+    --hash=sha256:bfaef573a63ba8923503d27530362590ff4f576c626d86a9fed95822a8255fd7 \
+    --hash=sha256:c5687b8d43cf58545ade1fe3e055f70eac7a5a1a0bf42824308d868289a95737 \
+    --hash=sha256:cba8c411ef271aa037d7357a2bc8f9ee8b58b9965831d9e51baf703280dc73d3 \
+    --hash=sha256:d15a181d1ecd0d4270dc32edb46f7cb7733c7c508857278d3d378d14d606db2d \
+    --hash=sha256:d4b0ba9512519522b118090257be113b9468d804b19d63c71dbcf4a48fa32358 \
+    --hash=sha256:d4db7c7aef085872ef65a8fd7d6d09a14ae91f691dec3e87ee5ee0539d516f53 \
+    --hash=sha256:d4eccecf9adf6fbcc6861a38015c2a64f38b9d94838ac1810a9023a0609e1b78 \
+    --hash=sha256:d67d839ede4ed1b28a4e8909735fc992a923cdb84e618544973d7dfc71540803 \
+    --hash=sha256:daf496c58a8c52083df09b80c860005194014c3698698d1a57cbcfa182142a3a \
+    --hash=sha256:dbad0e9d368bb989f4515da330b88a057617d16b6a8245084f1b05400f24609f \
+    --hash=sha256:e61ceaab6f49fb8bdfaa0f92c4b57bcfbea54c09277b1b4f7ac376bfb7a7c174 \
+    --hash=sha256:f84fbc98b019fef2ee9a1cb3ce93e3187a6df0b2538a651bfb890254ba9f90b5
+    # via
+    #   accelerate
+    #   albumentations
+    #   basicsr
+    #   gfpgan
+    #   huggingface-hub
+    #   omegaconf
+    #   pytorch-lightning
+    #   transformers
+    #   wandb
+qudida==0.0.4 \
+    --hash=sha256:4519714c40cd0f2e6c51e1735edae8f8b19f4efe1f33be13e9d644ca5f736dd6 \
+    --hash=sha256:db198e2887ab0c9aa0023e565afbff41dfb76b361f85fd5e13f780d75ba18cc8
+    # via albumentations
+realesrgan==0.3.0 \
+    --hash=sha256:0d36da96ab9f447071606e91f502ccdfb08f80cc82ee4f8caf720c7745ccec7e \
+    --hash=sha256:59336c16c30dd5130eff350dd27424acb9b7281d18a6810130e265606c9a6088
+    # via -r requirements.in
+regex==2022.10.31 \
+    --hash=sha256:052b670fafbe30966bbe5d025e90b2a491f85dfe5b2583a163b5e60a85a321ad \
+    --hash=sha256:0653d012b3bf45f194e5e6a41df9258811ac8fc395579fa82958a8b76286bea4 \
+    --hash=sha256:0a069c8483466806ab94ea9068c34b200b8bfc66b6762f45a831c4baaa9e8cdd \
+    --hash=sha256:0cf0da36a212978be2c2e2e2d04bdff46f850108fccc1851332bcae51c8907cc \
+    --hash=sha256:131d4be09bea7ce2577f9623e415cab287a3c8e0624f778c1d955ec7c281bd4d \
+    --hash=sha256:144486e029793a733e43b2e37df16a16df4ceb62102636ff3db6033994711066 \
+    --hash=sha256:1ddf14031a3882f684b8642cb74eea3af93a2be68893901b2b387c5fd92a03ec \
+    --hash=sha256:1eba476b1b242620c266edf6325b443a2e22b633217a9835a52d8da2b5c051f9 \
+    --hash=sha256:20f61c9944f0be2dc2b75689ba409938c14876c19d02f7585af4460b6a21403e \
+    --hash=sha256:22960019a842777a9fa5134c2364efaed5fbf9610ddc5c904bd3a400973b0eb8 \
+    --hash=sha256:22e7ebc231d28393dfdc19b185d97e14a0f178bedd78e85aad660e93b646604e \
+    --hash=sha256:23cbb932cc53a86ebde0fb72e7e645f9a5eec1a5af7aa9ce333e46286caef783 \
+    --hash=sha256:29c04741b9ae13d1e94cf93fca257730b97ce6ea64cfe1eba11cf9ac4e85afb6 \
+    --hash=sha256:2bde29cc44fa81c0a0c8686992c3080b37c488df167a371500b2a43ce9f026d1 \
+    --hash=sha256:2cdc55ca07b4e70dda898d2ab7150ecf17c990076d3acd7a5f3b25cb23a69f1c \
+    --hash=sha256:370f6e97d02bf2dd20d7468ce4f38e173a124e769762d00beadec3bc2f4b3bc4 \
+    --hash=sha256:395161bbdbd04a8333b9ff9763a05e9ceb4fe210e3c7690f5e68cedd3d65d8e1 \
+    --hash=sha256:44136355e2f5e06bf6b23d337a75386371ba742ffa771440b85bed367c1318d1 \
+    --hash=sha256:44a6c2f6374e0033873e9ed577a54a3602b4f609867794c1a3ebba65e4c93ee7 \
+    --hash=sha256:4919899577ba37f505aaebdf6e7dc812d55e8f097331312db7f1aab18767cce8 \
+    --hash=sha256:4b4b1fe58cd102d75ef0552cf17242705ce0759f9695334a56644ad2d83903fe \
+    --hash=sha256:4bdd56ee719a8f751cf5a593476a441c4e56c9b64dc1f0f30902858c4ef8771d \
+    --hash=sha256:4bf41b8b0a80708f7e0384519795e80dcb44d7199a35d52c15cc674d10b3081b \
+    --hash=sha256:4cac3405d8dda8bc6ed499557625585544dd5cbf32072dcc72b5a176cb1271c8 \
+    --hash=sha256:4fe7fda2fe7c8890d454f2cbc91d6c01baf206fbc96d89a80241a02985118c0c \
+    --hash=sha256:50921c140561d3db2ab9f5b11c5184846cde686bb5a9dc64cae442926e86f3af \
+    --hash=sha256:5217c25229b6a85049416a5c1e6451e9060a1edcf988641e309dbe3ab26d3e49 \
+    --hash=sha256:5352bea8a8f84b89d45ccc503f390a6be77917932b1c98c4cdc3565137acc714 \
+    --hash=sha256:542e3e306d1669b25936b64917285cdffcd4f5c6f0247636fec037187bd93542 \
+    --hash=sha256:543883e3496c8b6d58bd036c99486c3c8387c2fc01f7a342b760c1ea3158a318 \
+    --hash=sha256:586b36ebda81e6c1a9c5a5d0bfdc236399ba6595e1397842fd4a45648c30f35e \
+    --hash=sha256:597f899f4ed42a38df7b0e46714880fb4e19a25c2f66e5c908805466721760f5 \
+    --hash=sha256:5a260758454580f11dd8743fa98319bb046037dfab4f7828008909d0aa5292bc \
+    --hash=sha256:5aefb84a301327ad115e9d346c8e2760009131d9d4b4c6b213648d02e2abe144 \
+    --hash=sha256:5e6a5567078b3eaed93558842346c9d678e116ab0135e22eb72db8325e90b453 \
+    --hash=sha256:5ff525698de226c0ca743bfa71fc6b378cda2ddcf0d22d7c37b1cc925c9650a5 \
+    --hash=sha256:61edbca89aa3f5ef7ecac8c23d975fe7261c12665f1d90a6b1af527bba86ce61 \
+    --hash=sha256:659175b2144d199560d99a8d13b2228b85e6019b6e09e556209dfb8c37b78a11 \
+    --hash=sha256:6a9a19bea8495bb419dc5d38c4519567781cd8d571c72efc6aa959473d10221a \
+    --hash=sha256:6b30bddd61d2a3261f025ad0f9ee2586988c6a00c780a2fb0a92cea2aa702c54 \
+    --hash=sha256:6ffd55b5aedc6f25fd8d9f905c9376ca44fcf768673ffb9d160dd6f409bfda73 \
+    --hash=sha256:702d8fc6f25bbf412ee706bd73019da5e44a8400861dfff7ff31eb5b4a1276dc \
+    --hash=sha256:74bcab50a13960f2a610cdcd066e25f1fd59e23b69637c92ad470784a51b1347 \
+    --hash=sha256:75f591b2055523fc02a4bbe598aa867df9e953255f0b7f7715d2a36a9c30065c \
+    --hash=sha256:763b64853b0a8f4f9cfb41a76a4a85a9bcda7fdda5cb057016e7706fde928e66 \
+    --hash=sha256:76c598ca73ec73a2f568e2a72ba46c3b6c8690ad9a07092b18e48ceb936e9f0c \
+    --hash=sha256:78d680ef3e4d405f36f0d6d1ea54e740366f061645930072d39bca16a10d8c93 \
+    --hash=sha256:7b280948d00bd3973c1998f92e22aa3ecb76682e3a4255f33e1020bd32adf443 \
+    --hash=sha256:7db345956ecce0c99b97b042b4ca7326feeec6b75facd8390af73b18e2650ffc \
+    --hash=sha256:7dbdce0c534bbf52274b94768b3498abdf675a691fec5f751b6057b3030f34c1 \
+    --hash=sha256:7ef6b5942e6bfc5706301a18a62300c60db9af7f6368042227ccb7eeb22d0892 \
+    --hash=sha256:7f5a3ffc731494f1a57bd91c47dc483a1e10048131ffb52d901bfe2beb6102e8 \
+    --hash=sha256:8a45b6514861916c429e6059a55cf7db74670eaed2052a648e3e4d04f070e001 \
+    --hash=sha256:8ad241da7fac963d7573cc67a064c57c58766b62a9a20c452ca1f21050868dfa \
+    --hash=sha256:8b0886885f7323beea6f552c28bff62cbe0983b9fbb94126531693ea6c5ebb90 \
+    --hash=sha256:8ca88da1bd78990b536c4a7765f719803eb4f8f9971cc22d6ca965c10a7f2c4c \
+    --hash=sha256:8e0caeff18b96ea90fc0eb6e3bdb2b10ab5b01a95128dfeccb64a7238decf5f0 \
+    --hash=sha256:957403a978e10fb3ca42572a23e6f7badff39aa1ce2f4ade68ee452dc6807692 \
+    --hash=sha256:9af69f6746120998cd9c355e9c3c6aec7dff70d47247188feb4f829502be8ab4 \
+    --hash=sha256:9c94f7cc91ab16b36ba5ce476f1904c91d6c92441f01cd61a8e2729442d6fcf5 \
+    --hash=sha256:a37d51fa9a00d265cf73f3de3930fa9c41548177ba4f0faf76e61d512c774690 \
+    --hash=sha256:a3a98921da9a1bf8457aeee6a551948a83601689e5ecdd736894ea9bbec77e83 \
+    --hash=sha256:a3c1ebd4ed8e76e886507c9eddb1a891673686c813adf889b864a17fafcf6d66 \
+    --hash=sha256:a5f9505efd574d1e5b4a76ac9dd92a12acb2b309551e9aa874c13c11caefbe4f \
+    --hash=sha256:a8ff454ef0bb061e37df03557afda9d785c905dab15584860f982e88be73015f \
+    --hash=sha256:a9d0b68ac1743964755ae2d89772c7e6fb0118acd4d0b7464eaf3921c6b49dd4 \
+    --hash=sha256:aa62a07ac93b7cb6b7d0389d8ef57ffc321d78f60c037b19dfa78d6b17c928ee \
+    --hash=sha256:ac741bf78b9bb432e2d314439275235f41656e189856b11fb4e774d9f7246d81 \
+    --hash=sha256:ae1e96785696b543394a4e3f15f3f225d44f3c55dafe3f206493031419fedf95 \
+    --hash=sha256:b683e5fd7f74fb66e89a1ed16076dbab3f8e9f34c18b1979ded614fe10cdc4d9 \
+    --hash=sha256:b7a8b43ee64ca8f4befa2bea4083f7c52c92864d8518244bfa6e88c751fa8fff \
+    --hash=sha256:b8e38472739028e5f2c3a4aded0ab7eadc447f0d84f310c7a8bb697ec417229e \
+    --hash=sha256:bfff48c7bd23c6e2aec6454aaf6edc44444b229e94743b34bdcdda2e35126cf5 \
+    --hash=sha256:c14b63c9d7bab795d17392c7c1f9aaabbffd4cf4387725a0ac69109fb3b550c6 \
+    --hash=sha256:c27cc1e4b197092e50ddbf0118c788d9977f3f8f35bfbbd3e76c1846a3443df7 \
+    --hash=sha256:c28d3309ebd6d6b2cf82969b5179bed5fefe6142c70f354ece94324fa11bf6a1 \
+    --hash=sha256:c670f4773f2f6f1957ff8a3962c7dd12e4be54d05839b216cb7fd70b5a1df394 \
+    --hash=sha256:ce6910b56b700bea7be82c54ddf2e0ed792a577dfaa4a76b9af07d550af435c6 \
+    --hash=sha256:d0213671691e341f6849bf33cd9fad21f7b1cb88b89e024f33370733fec58742 \
+    --hash=sha256:d03fe67b2325cb3f09be029fd5da8df9e6974f0cde2c2ac6a79d2634e791dd57 \
+    --hash=sha256:d0e5af9a9effb88535a472e19169e09ce750c3d442fb222254a276d77808620b \
+    --hash=sha256:d243b36fbf3d73c25e48014961e83c19c9cc92530516ce3c43050ea6276a2ab7 \
+    --hash=sha256:d26166acf62f731f50bdd885b04b38828436d74e8e362bfcb8df221d868b5d9b \
+    --hash=sha256:d403d781b0e06d2922435ce3b8d2376579f0c217ae491e273bab8d092727d244 \
+    --hash=sha256:d8716f82502997b3d0895d1c64c3b834181b1eaca28f3f6336a71777e437c2af \
+    --hash=sha256:e4f781ffedd17b0b834c8731b75cce2639d5a8afe961c1e58ee7f1f20b3af185 \
+    --hash=sha256:e613a98ead2005c4ce037c7b061f2409a1a4e45099edb0ef3200ee26ed2a69a8 \
+    --hash=sha256:ef4163770525257876f10e8ece1cf25b71468316f61451ded1a6f44273eedeb5
+    # via
+    #   clip
+    #   diffusers
+    #   transformers
+requests==2.25.1 \
+    --hash=sha256:27973dd4a904a4f13b263a19c866c13b92a39ed1c964655f025f3f8d3d75b804 \
+    --hash=sha256:c210084e36a42ae6b9219e00e48287def368a26d03a048ddad7bfee44f75871e
+    # via
+    #   basicsr
+    #   clean-fid
+    #   diffusers
+    #   fsspec
+    #   huggingface-hub
+    #   requests-oauthlib
+    #   streamlit
+    #   tb-nightly
+    #   tensorboard
+    #   torchvision
+    #   transformers
+    #   wandb
+requests-oauthlib==1.3.1 \
+    --hash=sha256:2577c501a2fb8d05a304c09d090d6e47c306fef15809d102b327cf8364bddab5 \
+    --hash=sha256:75beac4a47881eeb94d5ea5d6ad31ef88856affe2332b9aafb52c6452ccf0d7a
+    # via google-auth-oauthlib
+resize-right==0.0.2 \
+    --hash=sha256:78351ca3eda0872208fcbc90861b45de559f90fb4027ce41825fdeb9b995005c \
+    --hash=sha256:7dc35b72ce4012b77f7cc9049835163793ab98a58ab8893610fb119fe59af520
+    # via k-diffusion
+rich==12.6.0 \
+    --hash=sha256:a4eb26484f2c82589bd9a17c73d32a010b1e29d89f1604cd9bf3a2097b81bb5e \
+    --hash=sha256:ba3a3775974105c221d31141f2c116f4fd65c5ceb0698657a11e9f295ec93fd0
+    # via streamlit
+rsa==4.9 \
+    --hash=sha256:90260d9058e514786967344d0ef75fa8727eed8a7d2e43ce9f4bcf1b536174f7 \
+    --hash=sha256:e38464a49c6c85d7f1351b0126661487a7e0a14a50f1675ec50eb34d4f20ef21
+    # via google-auth
+scikit-image==0.19.3 \
+    --hash=sha256:03779a7e1736fdf89d83c0ba67d44110496edd736a3bfce61a2b5177a1c8a099 \
+    --hash=sha256:0b0a199157ce8487c77de4fde0edc0b42d6d42818881c11f459262351d678b2d \
+    --hash=sha256:19a21a101a20c587a3b611a2cf6f86c35aae9f8d9563279b987e83ee1c9a9790 \
+    --hash=sha256:24b5367de1762da6ee126dd8f30cc4e7efda474e0d7d70685433f0e3aa2ec450 \
+    --hash=sha256:2a02d1bd0e2b53e36b952bd5fd6118d9ccc3ee51de35705d63d8eb1f2e86adef \
+    --hash=sha256:2f50b923f8099c1045fcde7418d86b206c87e333e43da980f41d8577b9605245 \
+    --hash=sha256:32fb88cc36203b99c9672fb972c9ef98635deaa5fc889fe969f3e11c44f22919 \
+    --hash=sha256:33dfd463ee6cc509defa279b963829f2230c9e0639ccd3931045be055878eea6 \
+    --hash=sha256:3a01372ae4bca223873304b0bff79b9d92446ac6d6177f73d89b45561e2d09d8 \
+    --hash=sha256:651de1c2ce1fbee834753b46b8e7d81cb12a5594898babba63ac82b30ddad49d \
+    --hash=sha256:6b6a8f98f2ac9bb73706461fd1dec875f6a5141759ed526850a5a49e90003d19 \
+    --hash=sha256:7f9f8a1387afc6c70f2bed007c3854a2d7489f9f7713c242f16f32ee05934bc2 \
+    --hash=sha256:84baa3179f3ae983c3a5d81c1e404bc92dcf7daeb41bfe9369badcda3fb22b92 \
+    --hash=sha256:8d8917fcf85b987b1f287f823f3a1a7dac38b70aaca759bc0200f3bc292d5ced \
+    --hash=sha256:9439e5294de3f18d6e82ec8eee2c46590231cf9c690da80545e83a0733b7a69e \
+    --hash=sha256:9fb0923a3bfa99457c5e17888f27b3b8a83a3600b4fef317992e7b7234764732 \
+    --hash=sha256:a7c3985c68bfe05f7571167ee021d14f5b8d1a4a250c91f0b13be7fb07e6af34 \
+    --hash=sha256:a8714348ddd671f819457a797c97d4c672166f093def66d66c3254cbd1d43f83 \
+    --hash=sha256:ad5d8000207a264d1a55681a9276e6a739d3f05cf4429004ad00d61d1892235f \
+    --hash=sha256:cc24177de3fdceca5d04807ad9c87d665f0bf01032ed94a9055cd1ed2b3f33e9 \
+    --hash=sha256:ce3d2207f253b8eb2c824e30d145a9f07a34a14212d57f3beca9f7e03c383cbe \
+    --hash=sha256:cfbb073f23deb48e0e60c47f8741d8089121d89cc78629ea8c5b51096efc5be7 \
+    --hash=sha256:e207c6ce5ce121d7d9b9d2b61b9adca57d1abed112c902d8ffbfdc20fb42c12b \
+    --hash=sha256:fd9dd3994bb6f9f7a35f228323f3c4dc44b3cf2ff15fd72d895216e9333550c6 \
+    --hash=sha256:fdf48d9b1f13af69e4e2c78e05067e322e9c8c97463c315cd0ecb47a94e259fc \
+    --hash=sha256:ff3b1025356508d41f4fe48528e509d95f9e4015e90cf158cd58c56dc63e0ac5
+    # via
+    #   albumentations
+    #   basicsr
+    #   k-diffusion
+scikit-learn==1.1.3 \
+    --hash=sha256:23fb9e74b813cc2528b5167d82ed08950b11106ccf50297161875e45152fb311 \
+    --hash=sha256:250da993701da88bf475e7c5746abf1285ea0ae47e4d0917cd13afd6600bb162 \
+    --hash=sha256:28b2bd6a1419acd522ff45d282c8ba23dbccb5338802ab0ee12baa4ade0aba4c \
+    --hash=sha256:2ee2c649f2231b68511aabb0dc827edd8936aad682acc6263c34aed11bc95dac \
+    --hash=sha256:30e27721adc308e8fd9f419f43068e43490005f911edf4476a9e585059fa8a83 \
+    --hash=sha256:38814f66285318f2e241305cca545eaa9b4126c65aa5dd78c69371f235f78e2b \
+    --hash=sha256:4d3a19166d4e1cdfcab975c68f471e046ce01e74c42a9a33fa89a14c2fcedf60 \
+    --hash=sha256:5699cded6c0685426433c7e5afe0fecad80ec831ec7fa264940e50c796775cc5 \
+    --hash=sha256:6785b8a3093329bf90ac01801be5525551728ae73edb11baa175df660820add4 \
+    --hash=sha256:6d1c1394e38a3319ace620381f6f23cc807d8780e9915c152449a86fc8f1db21 \
+    --hash=sha256:701181792a28c82fecae12adb5d15d0ecf57bffab7cf4bdbb52c7b3fd428d540 \
+    --hash=sha256:748f2bd632d6993e8918d43f1a26c380aeda4e122a88840d4c3a9af99d4239fe \
+    --hash=sha256:8e9dd76c7274055d1acf4526b8efb16a3531c26dcda714a0c16da99bf9d41900 \
+    --hash=sha256:bef51978a51ec19977700fe7b86aecea49c825884f3811756b74a3b152bb4e35 \
+    --hash=sha256:cd55c6fbef7608dbce1f22baf289dfcc6eb323247daa3c3542f73d389c724786 \
+    --hash=sha256:da5a2e95fef9805b1750e4abda4e834bf8835d26fc709a391543b53feee7bd0e \
+    --hash=sha256:ee47f68d973cee7009f06edb956f2f5588a0f230f24a2a70175fd0ecf36e2653 \
+    --hash=sha256:f4931f2a6c06e02c6c17a05f8ae397e2545965bc7a0a6cb38c8cd7d4fba8624d \
+    --hash=sha256:f5644663987ee221f5d1f47a593271b966c271c236fe05634e6bdc06041b5a2b \
+    --hash=sha256:f5d4231af7199531e77da1b78a4cc6b3d960a00b1ec672578ac818aae2b9c35d \
+    --hash=sha256:fd3ee69d36d42a7dcbb17e355a5653af5fd241a7dfd9133080b3dde8d9e2aafb
+    # via qudida
+scipy==1.9.3 \
+    --hash=sha256:06d2e1b4c491dc7d8eacea139a1b0b295f74e1a1a0f704c375028f8320d16e31 \
+    --hash=sha256:0d54222d7a3ba6022fdf5773931b5d7c56efe41ede7f7128c7b1637700409108 \
+    --hash=sha256:1884b66a54887e21addf9c16fb588720a8309a57b2e258ae1c7986d4444d3bc0 \
+    --hash=sha256:1a72d885fa44247f92743fc20732ae55564ff2a519e8302fb7e18717c5355a8b \
+    --hash=sha256:2318bef588acc7a574f5bfdff9c172d0b1bf2c8143d9582e05f878e580a3781e \
+    --hash=sha256:4db5b30849606a95dcf519763dd3ab6fe9bd91df49eba517359e450a7d80ce2e \
+    --hash=sha256:545c83ffb518094d8c9d83cce216c0c32f8c04aaf28b92cc8283eda0685162d5 \
+    --hash=sha256:5a04cd7d0d3eff6ea4719371cbc44df31411862b9646db617c99718ff68d4840 \
+    --hash=sha256:5b88e6d91ad9d59478fafe92a7c757d00c59e3bdc3331be8ada76a4f8d683f58 \
+    --hash=sha256:68239b6aa6f9c593da8be1509a05cb7f9efe98b80f43a5861cd24c7557e98523 \
+    --hash=sha256:83b89e9586c62e787f5012e8475fbb12185bafb996a03257e9675cd73d3736dd \
+    --hash=sha256:83c06e62a390a9167da60bedd4575a14c1f58ca9dfde59830fc42e5197283dab \
+    --hash=sha256:90453d2b93ea82a9f434e4e1cba043e779ff67b92f7a0e85d05d286a3625df3c \
+    --hash=sha256:abaf921531b5aeaafced90157db505e10345e45038c39e5d9b6c7922d68085cb \
+    --hash=sha256:b41bc822679ad1c9a5f023bc93f6d0543129ca0f37c1ce294dd9d386f0a21096 \
+    --hash=sha256:c68db6b290cbd4049012990d7fe71a2abd9ffbe82c0056ebe0f01df8be5436b0 \
+    --hash=sha256:cff3a5295234037e39500d35316a4c5794739433528310e117b8a9a0c76d20fc \
+    --hash=sha256:d01e1dd7b15bd2449c8bfc6b7cc67d630700ed655654f0dfcf121600bad205c9 \
+    --hash=sha256:d644a64e174c16cb4b2e41dfea6af722053e83d066da7343f333a54dae9bc31c \
+    --hash=sha256:da8245491d73ed0a994ed9c2e380fd058ce2fa8a18da204681f2fe1f57f98f95 \
+    --hash=sha256:fbc5c05c85c1a02be77b1ff591087c83bc44579c6d2bd9fb798bb64ea5e1a027
+    # via
+    #   albumentations
+    #   basicsr
+    #   clean-fid
+    #   facexlib
+    #   filterpy
+    #   gfpgan
+    #   k-diffusion
+    #   scikit-image
+    #   scikit-learn
+    #   torch-fidelity
+    #   torchdiffeq
+semver==2.13.0 \
+    --hash=sha256:ced8b23dceb22134307c1b8abfa523da14198793d9787ac838e70e29e77458d4 \
+    --hash=sha256:fa0fe2722ee1c3f57eac478820c3a5ae2f624af8264cbdf9000c980ff7f75e3f
+    # via streamlit
+send2trash==1.8.0 \
+    --hash=sha256:d2c24762fd3759860a0aff155e45871447ea58d2be6bdd39b5c8f966a0c99c2d \
+    --hash=sha256:f20eaadfdb517eaca5ce077640cb261c7d2698385a6a0f072a4a5447fd49fa08
+    # via -r requirements.in
+sentry-sdk==1.10.1 \
+    --hash=sha256:06c0fa9ccfdc80d7e3b5d2021978d6eb9351fa49db9b5847cf4d1f2a473414ad \
+    --hash=sha256:105faf7bd7b7fa25653404619ee261527266b14103fe1389e0ce077bd23a9691
+    # via wandb
+setproctitle==1.3.2 \
+    --hash=sha256:1c5d5dad7c28bdd1ec4187d818e43796f58a845aa892bb4481587010dc4d362b \
+    --hash=sha256:1c8d9650154afaa86a44ff195b7b10d683c73509d085339d174e394a22cccbb9 \
+    --hash=sha256:1f0cde41857a644b7353a0060b5f94f7ba7cf593ebde5a1094da1be581ac9a31 \
+    --hash=sha256:1f29b75e86260b0ab59adb12661ef9f113d2f93a59951373eb6d68a852b13e83 \
+    --hash=sha256:1fa1a0fbee72b47dc339c87c890d3c03a72ea65c061ade3204f285582f2da30f \
+    --hash=sha256:1ff863a20d1ff6ba2c24e22436a3daa3cd80be1dfb26891aae73f61b54b04aca \
+    --hash=sha256:265ecbe2c6eafe82e104f994ddd7c811520acdd0647b73f65c24f51374cf9494 \
+    --hash=sha256:288943dec88e178bb2fd868adf491197cc0fc8b6810416b1c6775e686bab87fe \
+    --hash=sha256:2e3ac25bfc4a0f29d2409650c7532d5ddfdbf29f16f8a256fc31c47d0dc05172 \
+    --hash=sha256:2fbd8187948284293f43533c150cd69a0e4192c83c377da837dbcd29f6b83084 \
+    --hash=sha256:4058564195b975ddc3f0462375c533cce310ccdd41b80ac9aed641c296c3eff4 \
+    --hash=sha256:4749a2b0c9ac52f864d13cee94546606f92b981b50e46226f7f830a56a9dc8e1 \
+    --hash=sha256:4d8938249a7cea45ab7e1e48b77685d0f2bab1ebfa9dde23e94ab97968996a7c \
+    --hash=sha256:5194b4969f82ea842a4f6af2f82cd16ebdc3f1771fb2771796e6add9835c1973 \
+    --hash=sha256:55ce1e9925ce1765865442ede9dca0ba9bde10593fcd570b1f0fa25d3ec6b31c \
+    --hash=sha256:589be87172b238f839e19f146b9ea47c71e413e951ef0dc6db4218ddacf3c202 \
+    --hash=sha256:5b932c3041aa924163f4aab970c2f0e6b4d9d773f4d50326e0ea1cd69240e5c5 \
+    --hash=sha256:5fb4f769c02f63fac90989711a3fee83919f47ae9afd4758ced5d86596318c65 \
+    --hash=sha256:630f6fe5e24a619ccf970c78e084319ee8be5be253ecc9b5b216b0f474f5ef18 \
+    --hash=sha256:65d884e22037b23fa25b2baf1a3316602ed5c5971eb3e9d771a38c3a69ce6e13 \
+    --hash=sha256:6c877691b90026670e5a70adfbcc735460a9f4c274d35ec5e8a43ce3f8443005 \
+    --hash=sha256:710e16fa3bade3b026907e4a5e841124983620046166f355bbb84be364bf2a02 \
+    --hash=sha256:7a55fe05f15c10e8c705038777656fe45e3bd676d49ad9ac8370b75c66dd7cd7 \
+    --hash=sha256:7aa0aac1711fadffc1d51e9d00a3bea61f68443d6ac0241a224e4d622489d665 \
+    --hash=sha256:7f0bed90a216ef28b9d227d8d73e28a8c9b88c0f48a082d13ab3fa83c581488f \
+    --hash=sha256:7f2719a398e1a2c01c2a63bf30377a34d0b6ef61946ab9cf4d550733af8f1ef1 \
+    --hash=sha256:7fe9df7aeb8c64db6c34fc3b13271a363475d77bc157d3f00275a53910cb1989 \
+    --hash=sha256:8ff3c8cb26afaed25e8bca7b9dd0c1e36de71f35a3a0706b5c0d5172587a3827 \
+    --hash=sha256:9124bedd8006b0e04d4e8a71a0945da9b67e7a4ab88fdad7b1440dc5b6122c42 \
+    --hash=sha256:92c626edc66169a1b09e9541b9c0c9f10488447d8a2b1d87c8f0672e771bc927 \
+    --hash=sha256:a149a5f7f2c5a065d4e63cb0d7a4b6d3b66e6e80f12e3f8827c4f63974cbf122 \
+    --hash=sha256:a47d97a75fd2d10c37410b180f67a5835cb1d8fdea2648fd7f359d4277f180b9 \
+    --hash=sha256:a499fff50387c1520c085a07578a000123f519e5f3eee61dd68e1d301659651f \
+    --hash=sha256:ab45146c71ca6592c9cc8b354a2cc9cc4843c33efcbe1d245d7d37ce9696552d \
+    --hash=sha256:b2c9cb2705fc84cb8798f1ba74194f4c080aaef19d9dae843591c09b97678e98 \
+    --hash=sha256:b34baef93bfb20a8ecb930e395ccd2ae3268050d8cf4fe187de5e2bd806fd796 \
+    --hash=sha256:b617f12c9be61e8f4b2857be4a4319754756845dbbbd9c3718f468bbb1e17bcb \
+    --hash=sha256:b9fb97907c830d260fa0658ed58afd48a86b2b88aac521135c352ff7fd3477fd \
+    --hash=sha256:bae283e85fc084b18ffeb92e061ff7ac5af9e183c9d1345c93e178c3e5069cbe \
+    --hash=sha256:c2c46200656280a064073447ebd363937562debef329482fd7e570c8d498f806 \
+    --hash=sha256:c8a09d570b39517de10ee5b718730e171251ce63bbb890c430c725c8c53d4484 \
+    --hash=sha256:c91b9bc8985d00239f7dc08a49927a7ca1ca8a6af2c3890feec3ed9665b6f91e \
+    --hash=sha256:dad42e676c5261eb50fdb16bdf3e2771cf8f99a79ef69ba88729aeb3472d8575 \
+    --hash=sha256:de3a540cd1817ede31f530d20e6a4935bbc1b145fd8f8cf393903b1e02f1ae76 \
+    --hash=sha256:e00c9d5c541a2713ba0e657e0303bf96ddddc412ef4761676adc35df35d7c246 \
+    --hash=sha256:e1aafc91cbdacc9e5fe712c52077369168e6b6c346f3a9d51bf600b53eae56bb \
+    --hash=sha256:e425be62524dc0c593985da794ee73eb8a17abb10fe692ee43bb39e201d7a099 \
+    --hash=sha256:e43f315c68aa61cbdef522a2272c5a5b9b8fd03c301d3167b5e1343ef50c676c \
+    --hash=sha256:e49ae693306d7624015f31cb3e82708916759d592c2e5f72a35c8f4cc8aef258 \
+    --hash=sha256:e5c50e164cd2459bc5137c15288a9ef57160fd5cbf293265ea3c45efe7870865 \
+    --hash=sha256:e8579a43eafd246e285eb3a5b939e7158073d5087aacdd2308f23200eac2458b \
+    --hash=sha256:e85e50b9c67854f89635a86247412f3ad66b132a4d8534ac017547197c88f27d \
+    --hash=sha256:f0452282258dfcc01697026a8841258dd2057c4438b43914b611bccbcd048f10 \
+    --hash=sha256:f4bfc89bd33ebb8e4c0e9846a09b1f5a4a86f5cb7a317e75cc42fee1131b4f4f \
+    --hash=sha256:fa2f50678f04fda7a75d0fe5dd02bbdd3b13cbe6ed4cf626e4472a7ccf47ae94 \
+    --hash=sha256:faec934cfe5fd6ac1151c02e67156c3f526e82f96b24d550b5d51efa4a5527c6 \
+    --hash=sha256:fcd3cf4286a60fdc95451d8d14e0389a6b4f5cebe02c7f2609325eb016535963 \
+    --hash=sha256:fe8a988c7220c002c45347430993830666e55bc350179d91fcee0feafe64e1d4 \
+    --hash=sha256:fed18e44711c5af4b681c2b3b18f85e6f0f1b2370a28854c645d636d5305ccd8 \
+    --hash=sha256:ffc61a388a5834a97953d6444a2888c24a05f2e333f9ed49f977a87bb1ad4761
+    # via wandb
+shortuuid==1.0.9 \
+    --hash=sha256:459f12fa1acc34ff213b1371467c0325169645a31ed989e268872339af7563d5 \
+    --hash=sha256:b2bb9eb7773170e253bb7ba25971023acb473517a8b76803d9618668cb1dd46f
+    # via wandb
+six==1.16.0 \
+    --hash=sha256:1e61c37477a1626458e36f7b1d82aa5c9b094fa4802892072e49de9c60c4c926 \
+    --hash=sha256:8abb2f1d86890a2dfb989f9a77cfcfd3e47c2a354b01111771326f8aa26e0254
+    # via
+    #   docker-pycreds
+    #   eventlet
+    #   flask-cors
+    #   google-auth
+    #   grpcio
+    #   promise
+    #   python-dateutil
+    #   validators
+    #   wandb
+smmap==5.0.0 \
+    --hash=sha256:2aba19d6a040e78d8b09de5c57e96207b09ed71d8e55ce0959eeee6c8e190d94 \
+    --hash=sha256:c840e62059cd3be204b0c9c9f74be2c09d5648eddd4580d9314c3ecde0b30936
+    # via gitdb
+streamlit==1.14.0 \
+    --hash=sha256:62556d873567e1b3427bcd118a57ee6946619f363bd6bba38df2d1f8225ecba0 \
+    --hash=sha256:e078b8143d150ba721bdb9194218e311c5fe1d6d4156473a2dea6cc848a6c9fc
+    # via -r requirements.in
+taming-transformers-rom1504==0.0.6 \
+    --hash=sha256:051b5804c58caa247bcd51d17ddb525b4d5f892a29d42dc460f40e3e9e34e5d8 \
+    --hash=sha256:73fe5fc1108accee4236ee6976e0987ab236afad0af06cb9f037641a908d2c32
+    # via -r requirements.in
+tb-nightly==2.11.0a20221106 \
+    --hash=sha256:8940457ee42db92f01da8bcdbbea1a476735eda559dde5976f5728919960af4a
+    # via
+    #   basicsr
+    #   gfpgan
+tensorboard==2.10.1 \
+    --hash=sha256:fb9222c1750e2fa35ef170d998a1e229f626eeced3004494a8849c88c15d8c1c
+    # via
+    #   pytorch-lightning
+    #   test-tube
+tensorboard-data-server==0.6.1 \
+    --hash=sha256:809fe9887682d35c1f7d1f54f0f40f98bb1f771b14265b453ca051e2ce58fca7 \
+    --hash=sha256:d8237580755e58eff68d1f3abefb5b1e39ae5c8b127cc40920f9c4fb33f4b98a \
+    --hash=sha256:fa8cef9be4fcae2f2363c88176638baf2da19c5ec90addb49b1cde05c95c88ee
+    # via
+    #   tb-nightly
+    #   tensorboard
+tensorboard-plugin-wit==1.8.1 \
+    --hash=sha256:ff26bdd583d155aa951ee3b152b3d0cffae8005dc697f72b44a8e8c2a77a8cbe
+    # via
+    #   tb-nightly
+    #   tensorboard
+test-tube==0.7.5 \
+    --hash=sha256:1379c33eb8cde3e9b36610f87da0f16c2e06496b1cfebac473df4e7be2faa124
+    # via -r requirements.in
+threadpoolctl==3.1.0 \
+    --hash=sha256:8b99adda265feb6773280df41eece7b2e6561b772d21ffd52e372f999024907b \
+    --hash=sha256:a335baacfaa4400ae1f0d8e3a58d6674d2f8828e3716bb2802c44955ad391380
+    # via scikit-learn
+tifffile==2022.10.10 \
+    --hash=sha256:50b61ba943b866d191295bc38a00191c9fdab23ece063544c7f1a264e3f6aa8e \
+    --hash=sha256:87f3aee8a0d06b74655269a105de75c1958a24653e1930d523eb516100043503
+    # via scikit-image
+tokenizers==0.13.1 \
+    --hash=sha256:0a3412830ad66a643723d6b0fc3202e64e9e299bd9c9eda04b2914b5b1e0ddb0 \
+    --hash=sha256:126bcb18a77cf65961ece04f54bd10ef3713412195e543d9d3eda2f0e4fd677c \
+    --hash=sha256:16434c61c5eb72f6692b9bc52052b07ca92d3eba9dd72a1bc371890e1bdc3f07 \
+    --hash=sha256:1d4acfdb6e7ef974677bb8445462db7fed240185fdc0f5b061db357d4ef8d85d \
+    --hash=sha256:3333d1cee5c8f47c96362ea0abc1f81c77c9b92c6c3d11cbf1d01985f0d5cf1d \
+    --hash=sha256:3acf3cae4c4739fc9ec49fa0e6cce224c1aa0fabc8f8d1358fd7de5c7d49cdca \
+    --hash=sha256:3ba43b3f6f6b41c97c1d041785342cd72ba142999f6c4605d628e8e937398f20 \
+    --hash=sha256:3c69a8389fd88bc32115e99db70f63bef577ba5c54f40a632580038a49612856 \
+    --hash=sha256:3de653a551cc616a442a123da21706cb3a3163cf6919973f978f0921eee1bdf0 \
+    --hash=sha256:4b3be8af87b357340b9b049d28067287b5e5e296e3120b6e4875d3b8469b67e6 \
+    --hash=sha256:680bc0e357b7da6d0d01634bffbd002e866fdaccde303e1d1af58f32464cf308 \
+    --hash=sha256:70de69681a264a5808d39f4bb6331be9a4dec51fd48cd1b959a94da76c4939cc \
+    --hash=sha256:73198cda6e1d991c583ed798514863e16763aa600eb7aa6df7722373290575b2 \
+    --hash=sha256:80864f456f715829f901ad5bb85af97e9ae52fc902270944804f6476ab8c6699 \
+    --hash=sha256:80b9552295fdce0a2513dcb795a3f8591eca1a8dcf8afe0de3214209e6924ad1 \
+    --hash=sha256:84fa41b58a8d3b7363ecdf3397d4b38f345fcf7d4dd14565b4360e7bffc9cae0 \
+    --hash=sha256:890d2139100b5c8ac6d585438d5e145ede1d7b32b4090a6c078db6db0ef1daea \
+    --hash=sha256:8b3f97041f7716998e474d3c7ffd19ac6941f117616696aef2b5ba927bf091e3 \
+    --hash=sha256:910479e92d5fbdf91e8106b4c658fd43d418893d7cfd5fb11983c54a1ff53869 \
+    --hash=sha256:96a1beef1e64d44597627f4e29d794047a66ad4d7474d93daf5a0ee27928e012 \
+    --hash=sha256:98bef54cf51ac335fda1408112df7ff3e584107633bd9066616033e12b0bd519 \
+    --hash=sha256:afcb1bd6d9ed59d5c8e633765589cab12f98aae09804f156b5965b4463b8b8e3 \
+    --hash=sha256:b72dec85488af3e1e8d58fb4b86b5dbe5171c176002b5e098ea6d52a70004bb5 \
+    --hash=sha256:c3109ba62bea56c68c7c2a976250b040afee61b5f86fc791f17afaa2a09fce94 \
+    --hash=sha256:c73b9e6c107e980e65077b89c54311d8d645f6a9efdde82990777fa43c0a8cae \
+    --hash=sha256:d8fca8b492a4697b0182e0c40b164cb0c44a9669d9c98033fec2f88174605eb0 \
+    --hash=sha256:db6872294339bf35c158219fc65bad939ba87d14c936ae7a33a3ca2d1532c5b1 \
+    --hash=sha256:e1a90bc97f53600f52e902f3ae097424de712d8ae0e42d957efc7ed908573a20 \
+    --hash=sha256:f75f476fe183c03c515a0f0f5d195cb05d93fcdc76e31fe3c9753d01f3ee990b \
+    --hash=sha256:fd17b14f84bec0b171869abd17ca0d9bfc564aa1e7f3451f44da526949a911c1 \
+    --hash=sha256:fea71780b66f8c278ebae7221c8959404cf7343b8d2f4b7308aa668cf6f02364
+    # via transformers
+toml==0.10.2 \
+    --hash=sha256:806143ae5bfb6a3c6e736a764057db0e6a0e05e338b5630894a5f779cabb4f9b \
+    --hash=sha256:b3bda1d108d5dd99f4a20d24d9c348e91c4db7ab1b749200bded2f839ccbe68f
+    # via streamlit
+toolz==0.12.0 \
+    --hash=sha256:2059bd4148deb1884bb0eb770a3cde70e7f954cfbbdc2285f1f2de01fd21eb6f \
+    --hash=sha256:88c570861c440ee3f2f6037c4654613228ff40c93a6c25e0eba70d17282c6194
+    # via altair
+torch==1.13.0 \
+    --hash=sha256:0fdd38c96230947b1ed870fed4a560252f8d23c3a2bf4dab9d2d42b18f2e67c8 \
+    --hash=sha256:220325d0f4e69ee9edf00c04208244ef7cf22ebce083815ce272c7491f0603f5 \
+    --hash=sha256:43db0723fc66ad6486f86dc4890c497937f7cd27429f28f73fb7e4d74b7482e2 \
+    --hash=sha256:47fe6228386bff6d74319a2ffe9d4ed943e6e85473d78e80502518c607d644d2 \
+    --hash=sha256:49a949b8136b32b2ec0724cbf4c6678b54e974b7d68f19f1231eea21cde5c23b \
+    --hash=sha256:4a378f5091307381abfb30eb821174e12986f39b1cf7c4522bf99155256819eb \
+    --hash=sha256:635dbb99d981a6483ca533b3dc7be18ef08dd9e1e96fb0bb0e6a99d79e85a130 \
+    --hash=sha256:6c227c16626e4ce766cca5351cc62a2358a11e8e466410a298487b9dff159eb1 \
+    --hash=sha256:857c7d5b1624c5fd979f66d2b074765733dba3f5e1cc97b7d6909155a2aae3ce \
+    --hash=sha256:9197ec216833b836b67e4d68e513d31fb38d9789d7cd998a08fba5b499c38454 \
+    --hash=sha256:922a4910613b310fbeb87707f00cb76fec328eb60cc1349ed2173e7c9b6edcd8 \
+    --hash=sha256:9ac382cedaf2f70afea41380ad8e7c06acef6b5b7e2aef3971cdad666ca6e185 \
+    --hash=sha256:bb33a911460475d1594a8c8cb73f58c08293211760796d99cae8c2509b86d7f1 \
+    --hash=sha256:cd1e67db6575e1b173a626077a54e4911133178557aac50683db03a34e2b636a \
+    --hash=sha256:d2d2753519415d154de4d3e64d2eaaeefdba6b6fd7d69d5ffaef595988117700 \
+    --hash=sha256:e20df14d874b024851c58e8bb3846249cb120e677f7463f60c986e3661f88680 \
+    --hash=sha256:e643ac8d086706e82f77b5d4dfcf145a9dd37b69e03e64177fc23821754d2ed7 \
+    --hash=sha256:ef934a21da6f6a516d0a9c712a80d09c56128abdc6af8dc151bee5199b4c3b4e \
+    --hash=sha256:f01a9ae0d4b69d2fc4145e8beab45b7877342dddbd4838a7d3c11ca7f6680745 \
+    --hash=sha256:f68edfea71ade3862039ba66bcedf954190a2db03b0c41a9b79afd72210abd97 \
+    --hash=sha256:fa768432ce4b8ffa29184c79a3376ab3de4a57b302cdf3c026a6be4c5a8ab75b
+    # via
+    #   accelerate
+    #   basicsr
+    #   clean-fid
+    #   clip
+    #   facexlib
+    #   gfpgan
+    #   k-diffusion
+    #   kornia
+    #   pytorch-lightning
+    #   realesrgan
+    #   taming-transformers-rom1504
+    #   test-tube
+    #   torch-fidelity
+    #   torchdiffeq
+    #   torchmetrics
+    #   torchvision
+torch-fidelity==0.3.0 \
+    --hash=sha256:3d3e33db98919759cc4f3f24cb27e1e74bdc7c905d90a780630e4e1c18492b66 \
+    --hash=sha256:d01284825595feb7dc3eae3dc9a0d8ced02be764813a3483f109bc142b52a1d3
+    # via -r requirements.in
+torchdiffeq==0.2.3 \
+    --hash=sha256:b5b01ec1294a2d8d5f77e567bf17c5de1237c0573cb94deefa88326f0e18c338 \
+    --hash=sha256:fe75f434b9090ac0c27702e02bed21472b0f87035be6581f51edc5d4013ea31a
+    # via k-diffusion
+torchmetrics==0.10.2 \
+    --hash=sha256:43757d82266969906fc74b6e80766fcb2a0d52d6c3d09e3b7c98cf3b733fd20c \
+    --hash=sha256:daa29d96bff5cff04d80eec5b9f5076993d6ac9c2d2163e88b6b31f8d38f7c25
+    # via pytorch-lightning
+torchvision==0.13.1 ; platform_system == "Darwin" \
+    --hash=sha256:0298bae3b09ac361866088434008d82b99d6458fe8888c8df90720ef4b347d44 \
+    --hash=sha256:08f592ea61836ebeceb5c97f4d7a813b9d7dc651bbf7ce4401563ccfae6a21fc \
+    --hash=sha256:099874088df104d54d8008f2a28539ca0117b512daed8bf3c2bbfa2b7ccb187a \
+    --hash=sha256:0e77706cc90462653620e336bb90daf03d7bf1b88c3a9a3037df8d111823a56e \
+    --hash=sha256:19286a733c69dcbd417b86793df807bd227db5786ed787c17297741a9b0d0fc7 \
+    --hash=sha256:3567fb3def829229ec217c1e38f08c5128ff7fb65854cac17ebac358ff7aa309 \
+    --hash=sha256:4d8bf321c4380854ef04613935fdd415dce29d1088a7ff99e06e113f0efe9203 \
+    --hash=sha256:5e631241bee3661de64f83616656224af2e3512eb2580da7c08e08b8c965a8ac \
+    --hash=sha256:7552e80fa222252b8b217a951c85e172a710ea4cad0ae0c06fbb67addece7871 \
+    --hash=sha256:7cb789ceefe6dcd0dc8eeda37bfc45efb7cf34770eac9533861d51ca508eb5b3 \
+    --hash=sha256:83e9e2457f23110fd53b0177e1bc621518d6ea2108f570e853b768ce36b7c679 \
+    --hash=sha256:87c137f343197769a51333076e66bfcd576301d2cd8614b06657187c71b06c4f \
+    --hash=sha256:899eec0b9f3b99b96d6f85b9aa58c002db41c672437677b553015b9135b3be7e \
+    --hash=sha256:8e4d02e4d8a203e0c09c10dfb478214c224d080d31efc0dbf36d9c4051f7f3c6 \
+    --hash=sha256:b167934a5943242da7b1e59318f911d2d253feeca0d13ad5d832b58eed943401 \
+    --hash=sha256:c5ed609c8bc88c575226400b2232e0309094477c82af38952e0373edef0003fd \
+    --hash=sha256:e9a563894f9fa40692e24d1aa58c3ef040450017cfed3598ff9637f404f3fe3b \
+    --hash=sha256:ef5fe3ec1848123cd0ec74c07658192b3147dcd38e507308c790d5943e87b88c \
+    --hash=sha256:f230a1a40ed70d51e463ce43df243ec520902f8725de2502e485efc5eea9d864
+    # via
+    #   -r requirements.in
+    #   basicsr
+    #   clean-fid
+    #   clip
+    #   facexlib
+    #   gfpgan
+    #   k-diffusion
+    #   realesrgan
+    #   taming-transformers-rom1504
+    #   torch-fidelity
+tornado==6.2 \
+    --hash=sha256:1d54d13ab8414ed44de07efecb97d4ef7c39f7438cf5e976ccd356bebb1b5fca \
+    --hash=sha256:20f638fd8cc85f3cbae3c732326e96addff0a15e22d80f049e00121651e82e72 \
+    --hash=sha256:5c87076709343557ef8032934ce5f637dbb552efa7b21d08e89ae7619ed0eb23 \
+    --hash=sha256:5f8c52d219d4995388119af7ccaa0bcec289535747620116a58d830e7c25d8a8 \
+    --hash=sha256:6fdfabffd8dfcb6cf887428849d30cf19a3ea34c2c248461e1f7d718ad30b66b \
+    --hash=sha256:87dcafae3e884462f90c90ecc200defe5e580a7fbbb4365eda7c7c1eb809ebc9 \
+    --hash=sha256:9b630419bde84ec666bfd7ea0a4cb2a8a651c2d5cccdbdd1972a0c859dfc3c13 \
+    --hash=sha256:b8150f721c101abdef99073bf66d3903e292d851bee51910839831caba341a75 \
+    --hash=sha256:ba09ef14ca9893954244fd872798b4ccb2367c165946ce2dd7376aebdde8e3ac \
+    --hash=sha256:d3a2f5999215a3a06a4fc218026cd84c61b8b2b40ac5296a6db1f1451ef04c1e \
+    --hash=sha256:e5f923aa6a47e133d1cf87d60700889d7eae68988704e20c75fb2d65677a8e4b
+    # via streamlit
+tqdm==4.64.1 \
+    --hash=sha256:5f4f682a004951c1b450bc753c710e9280c5746ce6ffedee253ddbcbf54cf1e4 \
+    --hash=sha256:6fee160d6ffcd1b1c68c65f14c829c22832bc401726335ce92c52d395944a6a1
+    # via
+    #   basicsr
+    #   clean-fid
+    #   clip
+    #   facexlib
+    #   gfpgan
+    #   huggingface-hub
+    #   k-diffusion
+    #   pytorch-lightning
+    #   realesrgan
+    #   taming-transformers-rom1504
+    #   torch-fidelity
+    #   transformers
+transformers==4.24.0 \
+    --hash=sha256:486f353a8e594002e48be0e2aba723d96eda839e63bfe274702a4b5eda85559b \
+    --hash=sha256:b7ab50039ef9bf817eff14ab974f306fd20a72350bdc9df3a858fd009419322e
+    # via -r requirements.in
+typing-extensions==4.4.0 \
+    --hash=sha256:1511434bb92bf8dd198c12b1cc812e800d4181cfcb867674e0f8279cc93087aa \
+    --hash=sha256:16fa4864408f655d35ec496218b85f79b3437c829e93320c7c9215ccfd92489e
+    # via
+    #   huggingface-hub
+    #   pytorch-lightning
+    #   qudida
+    #   streamlit
+    #   torch
+    #   torchvision
+tzdata==2022.6 \
+    --hash=sha256:04a680bdc5b15750c39c12a448885a51134a27ec9af83667663f0b3a1bf3f342 \
+    --hash=sha256:91f11db4503385928c15598c98573e3af07e7229181bee5375bd30f1695ddcae
+    # via
+    #   pytz-deprecation-shim
+    #   tzlocal
+tzlocal==4.2 \
+    --hash=sha256:89885494684c929d9191c57aa27502afc87a579be5cdd3225c77c463ea043745 \
+    --hash=sha256:ee5842fa3a795f023514ac2d801c4a81d1743bbe642e3940143326b3a00addd7
+    # via streamlit
+urllib3==1.26.12 \
+    --hash=sha256:3fa96cf423e6987997fc326ae8df396db2a8b7c667747d47ddd8ecba91f4a74e \
+    --hash=sha256:b930dd878d5a8afb066a637fbb35144fe7901e3b209d1cd4f524bd0e9deee997
+    # via
+    #   requests
+    #   sentry-sdk
+validators==0.18.2 \
+    --hash=sha256:0143dcca8a386498edaf5780cbd5960da1a4c85e0719f3ee5c9b41249c4fefbd \
+    --hash=sha256:37cd9a9213278538ad09b5b9f9134266e7c226ab1fede1d500e29e0a8fbb9ea6
+    # via streamlit
+wandb==0.13.5 \
+    --hash=sha256:11f30a22e30abaa9c187e8b6aa4c12d76160b40bbe98a6f14b0dde9297bbfbe2 \
+    --hash=sha256:60d5bcc524b8a314c8e072c03f7702dbd5406261b00a4ce75e7556b805fdc765
+    # via k-diffusion
+wcwidth==0.2.5 \
+    --hash=sha256:beb4802a9cebb9144e99086eff703a642a13d6a0052920003a230f3294bbe784 \
+    --hash=sha256:c4d647b99872929fdb7bdcaa4fbe7f01413ed3d98077df798530e5b04f116c83
+    # via ftfy
+werkzeug==2.2.2 \
+    --hash=sha256:7ea2d48322cc7c0f8b3a215ed73eabd7b5d75d0b50e31ab006286ccff9e00b8f \
+    --hash=sha256:f979ab81f58d7318e064e99c4506445d60135ac5cd2e177a2de0089bfd4c9bd5
+    # via
+    #   flask
+    #   tb-nightly
+    #   tensorboard
+wheel==0.38.2 \
+    --hash=sha256:3d492ef22379a156ec923d2a77051cedfd4df4b667864e9e41ba83f0b70b7149 \
+    --hash=sha256:7a5a3095dceca97a3cac869b8fef4e89b83fafde21b6688f47b6fda7600eb441
+    # via
+    #   tb-nightly
+    #   tensorboard
+whichcraft==0.6.1 \
+    --hash=sha256:acdbb91b63d6a15efbd6430d1d7b2d36e44a71697e93e19b7ded477afd9fce87 \
+    --hash=sha256:deda9266fbb22b8c64fd3ee45c050d61139cd87419765f588e37c8d23e236dd9
+    # via flaskwebgui
+yapf==0.32.0 \
+    --hash=sha256:8fea849025584e486fd06d6ba2bed717f396080fd3cc236ba10cb97c4c51cf32 \
+    --hash=sha256:a3f5085d37ef7e3e004c4ba9f9b3e40c54ff1901cd111f05145ae313a7c67d1b
+    # via
+    #   basicsr
+    #   gfpgan
+yarl==1.8.1 \
+    --hash=sha256:076eede537ab978b605f41db79a56cad2e7efeea2aa6e0fa8f05a26c24a034fb \
+    --hash=sha256:07b21e274de4c637f3e3b7104694e53260b5fc10d51fb3ec5fed1da8e0f754e3 \
+    --hash=sha256:0ab5a138211c1c366404d912824bdcf5545ccba5b3ff52c42c4af4cbdc2c5035 \
+    --hash=sha256:0c03f456522d1ec815893d85fccb5def01ffaa74c1b16ff30f8aaa03eb21e453 \
+    --hash=sha256:12768232751689c1a89b0376a96a32bc7633c08da45ad985d0c49ede691f5c0d \
+    --hash=sha256:19cd801d6f983918a3f3a39f3a45b553c015c5aac92ccd1fac619bd74beece4a \
+    --hash=sha256:1ca7e596c55bd675432b11320b4eacc62310c2145d6801a1f8e9ad160685a231 \
+    --hash=sha256:1e4808f996ca39a6463f45182e2af2fae55e2560be586d447ce8016f389f626f \
+    --hash=sha256:205904cffd69ae972a1707a1bd3ea7cded594b1d773a0ce66714edf17833cdae \
+    --hash=sha256:20df6ff4089bc86e4a66e3b1380460f864df3dd9dccaf88d6b3385d24405893b \
+    --hash=sha256:21ac44b763e0eec15746a3d440f5e09ad2ecc8b5f6dcd3ea8cb4773d6d4703e3 \
+    --hash=sha256:29e256649f42771829974e742061c3501cc50cf16e63f91ed8d1bf98242e5507 \
+    --hash=sha256:2d800b9c2eaf0684c08be5f50e52bfa2aa920e7163c2ea43f4f431e829b4f0fd \
+    --hash=sha256:2d93a049d29df172f48bcb09acf9226318e712ce67374f893b460b42cc1380ae \
+    --hash=sha256:31a9a04ecccd6b03e2b0e12e82131f1488dea5555a13a4d32f064e22a6003cfe \
+    --hash=sha256:3d1a50e461615747dd93c099f297c1994d472b0f4d2db8a64e55b1edf704ec1c \
+    --hash=sha256:449c957ffc6bc2309e1fbe67ab7d2c1efca89d3f4912baeb8ead207bb3cc1cd4 \
+    --hash=sha256:4a88510731cd8d4befaba5fbd734a7dd914de5ab8132a5b3dde0bbd6c9476c64 \
+    --hash=sha256:4c322cbaa4ed78a8aac89b2174a6df398faf50e5fc12c4c191c40c59d5e28357 \
+    --hash=sha256:5395da939ffa959974577eff2cbfc24b004a2fb6c346918f39966a5786874e54 \
+    --hash=sha256:5587bba41399854703212b87071c6d8638fa6e61656385875f8c6dff92b2e461 \
+    --hash=sha256:56c11efb0a89700987d05597b08a1efcd78d74c52febe530126785e1b1a285f4 \
+    --hash=sha256:5999c4662631cb798496535afbd837a102859568adc67d75d2045e31ec3ac497 \
+    --hash=sha256:59ddd85a1214862ce7c7c66457f05543b6a275b70a65de366030d56159a979f0 \
+    --hash=sha256:6347f1a58e658b97b0a0d1ff7658a03cb79bdbda0331603bed24dd7054a6dea1 \
+    --hash=sha256:6628d750041550c5d9da50bb40b5cf28a2e63b9388bac10fedd4f19236ef4957 \
+    --hash=sha256:6afb336e23a793cd3b6476c30f030a0d4c7539cd81649683b5e0c1b0ab0bf350 \
+    --hash=sha256:6c8148e0b52bf9535c40c48faebb00cb294ee577ca069d21bd5c48d302a83780 \
+    --hash=sha256:76577f13333b4fe345c3704811ac7509b31499132ff0181f25ee26619de2c843 \
+    --hash=sha256:7c0da7e44d0c9108d8b98469338705e07f4bb7dab96dbd8fa4e91b337db42548 \
+    --hash=sha256:7de89c8456525650ffa2bb56a3eee6af891e98f498babd43ae307bd42dca98f6 \
+    --hash=sha256:7ec362167e2c9fd178f82f252b6d97669d7245695dc057ee182118042026da40 \
+    --hash=sha256:7fce6cbc6c170ede0221cc8c91b285f7f3c8b9fe28283b51885ff621bbe0f8ee \
+    --hash=sha256:85cba594433915d5c9a0d14b24cfba0339f57a2fff203a5d4fd070e593307d0b \
+    --hash=sha256:8b0af1cf36b93cee99a31a545fe91d08223e64390c5ecc5e94c39511832a4bb6 \
+    --hash=sha256:9130ddf1ae9978abe63808b6b60a897e41fccb834408cde79522feb37fb72fb0 \
+    --hash=sha256:99449cd5366fe4608e7226c6cae80873296dfa0cde45d9b498fefa1de315a09e \
+    --hash=sha256:9de955d98e02fab288c7718662afb33aab64212ecb368c5dc866d9a57bf48880 \
+    --hash=sha256:a0fb2cb4204ddb456a8e32381f9a90000429489a25f64e817e6ff94879d432fc \
+    --hash=sha256:a165442348c211b5dea67c0206fc61366212d7082ba8118c8c5c1c853ea4d82e \
+    --hash=sha256:ab2a60d57ca88e1d4ca34a10e9fb4ab2ac5ad315543351de3a612bbb0560bead \
+    --hash=sha256:abc06b97407868ef38f3d172762f4069323de52f2b70d133d096a48d72215d28 \
+    --hash=sha256:af887845b8c2e060eb5605ff72b6f2dd2aab7a761379373fd89d314f4752abbf \
+    --hash=sha256:b19255dde4b4f4c32e012038f2c169bb72e7f081552bea4641cab4d88bc409dd \
+    --hash=sha256:b3ded839a5c5608eec8b6f9ae9a62cb22cd037ea97c627f38ae0841a48f09eae \
+    --hash=sha256:c1445a0c562ed561d06d8cbc5c8916c6008a31c60bc3655cdd2de1d3bf5174a0 \
+    --hash=sha256:d0272228fabe78ce00a3365ffffd6f643f57a91043e119c289aaba202f4095b0 \
+    --hash=sha256:d0b51530877d3ad7a8d47b2fff0c8df3b8f3b8deddf057379ba50b13df2a5eae \
+    --hash=sha256:d0f77539733e0ec2475ddcd4e26777d08996f8cd55d2aef82ec4d3896687abda \
+    --hash=sha256:d2b8f245dad9e331540c350285910b20dd913dc86d4ee410c11d48523c4fd546 \
+    --hash=sha256:dd032e8422a52e5a4860e062eb84ac94ea08861d334a4bcaf142a63ce8ad4802 \
+    --hash=sha256:de49d77e968de6626ba7ef4472323f9d2e5a56c1d85b7c0e2a190b2173d3b9be \
+    --hash=sha256:de839c3a1826a909fdbfe05f6fe2167c4ab033f1133757b5936efe2f84904c07 \
+    --hash=sha256:e80ed5a9939ceb6fda42811542f31c8602be336b1fb977bccb012e83da7e4936 \
+    --hash=sha256:ea30a42dc94d42f2ba4d0f7c0ffb4f4f9baa1b23045910c0c32df9c9902cb272 \
+    --hash=sha256:ea513a25976d21733bff523e0ca836ef1679630ef4ad22d46987d04b372d57fc \
+    --hash=sha256:ed19b74e81b10b592084a5ad1e70f845f0aacb57577018d31de064e71ffa267a \
+    --hash=sha256:f5af52738e225fcc526ae64071b7e5342abe03f42e0e8918227b38c9aa711e28 \
+    --hash=sha256:fae37373155f5ef9b403ab48af5136ae9851151f7aacd9926251ab26b953118b
+    # via aiohttp
+zipp==3.10.0 \
+    --hash=sha256:4fcb6f278987a6605757302a6e40e896257570d11c51628968ccb2a47e80c6c1 \
+    --hash=sha256:7a7262fd930bd3e36c50b9a64897aec3fafff3dfdeec9623ae22b40e93f99bb8
+    # via importlib-metadata
+
+# The following packages are considered to be unsafe in a requirements file:
+setuptools==65.5.1 \
+    --hash=sha256:d0b9a8433464d5800cbe05094acf5c6d52a91bfac9b52bcfc4d41382be5d5d31 \
+    --hash=sha256:e197a19aa8ec9722928f2206f8de752def0e4c9fc6953527360d1c36d94ddb2f
+    # via
+    #   numba
+    #   tb-nightly
+    #   tensorboard
+    #   wandb
diff --git a/installer/py3.10-darwin-x86_64-cpu-reqs.txt b/installer/py3.10-darwin-x86_64-cpu-reqs.txt
new file mode 100644
index 0000000000..2b3d061213
--- /dev/null
+++ b/installer/py3.10-darwin-x86_64-cpu-reqs.txt
@@ -0,0 +1,2069 @@
+#
+# This file is autogenerated by pip-compile with python 3.10
+# To update, run:
+#
+#    pip-compile --allow-unsafe --generate-hashes --output-file=installer/py3.10-darwin-x86_64-cpu-reqs.txt requirements.in
+#
+--extra-index-url https://download.pytorch.org/whl/cu116
+--trusted-host https
+
+absl-py==1.3.0 \
+    --hash=sha256:34995df9bd7a09b3b8749e230408f5a2a2dd7a68a0d33c12a3d0cb15a041a507 \
+    --hash=sha256:463c38a08d2e4cef6c498b76ba5bd4858e4c6ef51da1a5a1f27139a022e20248
+    # via
+    #   tb-nightly
+    #   tensorboard
+accelerate==0.13.2 \
+    --hash=sha256:dd6f08b010077f252dda5a7699d87b02885335c456770939c536e65ff07ed760 \
+    --hash=sha256:e22180d7094e4c1bfb05a2b078297c222f6b4fa595fde8916946c3f377cdf019
+    # via k-diffusion
+addict==2.4.0 \
+    --hash=sha256:249bb56bbfd3cdc2a004ea0ff4c2b6ddc84d53bc2194761636eb314d5cfa5dfc \
+    --hash=sha256:b3b2210e0e067a281f5646c8c5db92e99b7231ea8b0eb5f74dbdf9e259d4e494
+    # via basicsr
+aiohttp==3.8.3 \
+    --hash=sha256:02f9a2c72fc95d59b881cf38a4b2be9381b9527f9d328771e90f72ac76f31ad8 \
+    --hash=sha256:059a91e88f2c00fe40aed9031b3606c3f311414f86a90d696dd982e7aec48142 \
+    --hash=sha256:05a3c31c6d7cd08c149e50dc7aa2568317f5844acd745621983380597f027a18 \
+    --hash=sha256:08c78317e950e0762c2983f4dd58dc5e6c9ff75c8a0efeae299d363d439c8e34 \
+    --hash=sha256:09e28f572b21642128ef31f4e8372adb6888846f32fecb288c8b0457597ba61a \
+    --hash=sha256:0d2c6d8c6872df4a6ec37d2ede71eff62395b9e337b4e18efd2177de883a5033 \
+    --hash=sha256:16c121ba0b1ec2b44b73e3a8a171c4f999b33929cd2397124a8c7fcfc8cd9e06 \
+    --hash=sha256:1d90043c1882067f1bd26196d5d2db9aa6d268def3293ed5fb317e13c9413ea4 \
+    --hash=sha256:1e56b9cafcd6531bab5d9b2e890bb4937f4165109fe98e2b98ef0dcfcb06ee9d \
+    --hash=sha256:20acae4f268317bb975671e375493dbdbc67cddb5f6c71eebdb85b34444ac46b \
+    --hash=sha256:21b30885a63c3f4ff5b77a5d6caf008b037cb521a5f33eab445dc566f6d092cc \
+    --hash=sha256:21d69797eb951f155026651f7e9362877334508d39c2fc37bd04ff55b2007091 \
+    --hash=sha256:256deb4b29fe5e47893fa32e1de2d73c3afe7407738bd3c63829874661d4822d \
+    --hash=sha256:25892c92bee6d9449ffac82c2fe257f3a6f297792cdb18ad784737d61e7a9a85 \
+    --hash=sha256:2ca9af5f8f5812d475c5259393f52d712f6d5f0d7fdad9acdb1107dd9e3cb7eb \
+    --hash=sha256:2d252771fc85e0cf8da0b823157962d70639e63cb9b578b1dec9868dd1f4f937 \
+    --hash=sha256:2dea10edfa1a54098703cb7acaa665c07b4e7568472a47f4e64e6319d3821ccf \
+    --hash=sha256:2df5f139233060578d8c2c975128fb231a89ca0a462b35d4b5fcf7c501ebdbe1 \
+    --hash=sha256:2feebbb6074cdbd1ac276dbd737b40e890a1361b3cc30b74ac2f5e24aab41f7b \
+    --hash=sha256:309aa21c1d54b8ef0723181d430347d7452daaff93e8e2363db8e75c72c2fb2d \
+    --hash=sha256:3828fb41b7203176b82fe5d699e0d845435f2374750a44b480ea6b930f6be269 \
+    --hash=sha256:398701865e7a9565d49189f6c90868efaca21be65c725fc87fc305906be915da \
+    --hash=sha256:43046a319664a04b146f81b40e1545d4c8ac7b7dd04c47e40bf09f65f2437346 \
+    --hash=sha256:437399385f2abcd634865705bdc180c8314124b98299d54fe1d4c8990f2f9494 \
+    --hash=sha256:45d88b016c849d74ebc6f2b6e8bc17cabf26e7e40c0661ddd8fae4c00f015697 \
+    --hash=sha256:47841407cc89a4b80b0c52276f3cc8138bbbfba4b179ee3acbd7d77ae33f7ac4 \
+    --hash=sha256:4a4fbc769ea9b6bd97f4ad0b430a6807f92f0e5eb020f1e42ece59f3ecfc4585 \
+    --hash=sha256:4ab94426ddb1ecc6a0b601d832d5d9d421820989b8caa929114811369673235c \
+    --hash=sha256:4b0f30372cef3fdc262f33d06e7b411cd59058ce9174ef159ad938c4a34a89da \
+    --hash=sha256:4e3a23ec214e95c9fe85a58470b660efe6534b83e6cbe38b3ed52b053d7cb6ad \
+    --hash=sha256:512bd5ab136b8dc0ffe3fdf2dfb0c4b4f49c8577f6cae55dca862cd37a4564e2 \
+    --hash=sha256:527b3b87b24844ea7865284aabfab08eb0faf599b385b03c2aa91fc6edd6e4b6 \
+    --hash=sha256:54d107c89a3ebcd13228278d68f1436d3f33f2dd2af5415e3feaeb1156e1a62c \
+    --hash=sha256:5835f258ca9f7c455493a57ee707b76d2d9634d84d5d7f62e77be984ea80b849 \
+    --hash=sha256:598adde339d2cf7d67beaccda3f2ce7c57b3b412702f29c946708f69cf8222aa \
+    --hash=sha256:599418aaaf88a6d02a8c515e656f6faf3d10618d3dd95866eb4436520096c84b \
+    --hash=sha256:5bf651afd22d5f0c4be16cf39d0482ea494f5c88f03e75e5fef3a85177fecdeb \
+    --hash=sha256:5c59fcd80b9049b49acd29bd3598cada4afc8d8d69bd4160cd613246912535d7 \
+    --hash=sha256:653acc3880459f82a65e27bd6526e47ddf19e643457d36a2250b85b41a564715 \
+    --hash=sha256:66bd5f950344fb2b3dbdd421aaa4e84f4411a1a13fca3aeb2bcbe667f80c9f76 \
+    --hash=sha256:6f3553510abdbec67c043ca85727396ceed1272eef029b050677046d3387be8d \
+    --hash=sha256:7018ecc5fe97027214556afbc7c502fbd718d0740e87eb1217b17efd05b3d276 \
+    --hash=sha256:713d22cd9643ba9025d33c4af43943c7a1eb8547729228de18d3e02e278472b6 \
+    --hash=sha256:73a4131962e6d91109bca6536416aa067cf6c4efb871975df734f8d2fd821b37 \
+    --hash=sha256:75880ed07be39beff1881d81e4a907cafb802f306efd6d2d15f2b3c69935f6fb \
+    --hash=sha256:75e14eac916f024305db517e00a9252714fce0abcb10ad327fb6dcdc0d060f1d \
+    --hash=sha256:8135fa153a20d82ffb64f70a1b5c2738684afa197839b34cc3e3c72fa88d302c \
+    --hash=sha256:84b14f36e85295fe69c6b9789b51a0903b774046d5f7df538176516c3e422446 \
+    --hash=sha256:86fc24e58ecb32aee09f864cb11bb91bc4c1086615001647dbfc4dc8c32f4008 \
+    --hash=sha256:87f44875f2804bc0511a69ce44a9595d5944837a62caecc8490bbdb0e18b1342 \
+    --hash=sha256:88c70ed9da9963d5496d38320160e8eb7e5f1886f9290475a881db12f351ab5d \
+    --hash=sha256:88e5be56c231981428f4f506c68b6a46fa25c4123a2e86d156c58a8369d31ab7 \
+    --hash=sha256:89d2e02167fa95172c017732ed7725bc8523c598757f08d13c5acca308e1a061 \
+    --hash=sha256:8d6aaa4e7155afaf994d7924eb290abbe81a6905b303d8cb61310a2aba1c68ba \
+    --hash=sha256:92a2964319d359f494f16011e23434f6f8ef0434acd3cf154a6b7bec511e2fb7 \
+    --hash=sha256:96372fc29471646b9b106ee918c8eeb4cca423fcbf9a34daa1b93767a88a2290 \
+    --hash=sha256:978b046ca728073070e9abc074b6299ebf3501e8dee5e26efacb13cec2b2dea0 \
+    --hash=sha256:9c7149272fb5834fc186328e2c1fa01dda3e1fa940ce18fded6d412e8f2cf76d \
+    --hash=sha256:a0239da9fbafd9ff82fd67c16704a7d1bccf0d107a300e790587ad05547681c8 \
+    --hash=sha256:ad5383a67514e8e76906a06741febd9126fc7c7ff0f599d6fcce3e82b80d026f \
+    --hash=sha256:ad61a9639792fd790523ba072c0555cd6be5a0baf03a49a5dd8cfcf20d56df48 \
+    --hash=sha256:b29bfd650ed8e148f9c515474a6ef0ba1090b7a8faeee26b74a8ff3b33617502 \
+    --hash=sha256:b97decbb3372d4b69e4d4c8117f44632551c692bb1361b356a02b97b69e18a62 \
+    --hash=sha256:ba71c9b4dcbb16212f334126cc3d8beb6af377f6703d9dc2d9fb3874fd667ee9 \
+    --hash=sha256:c37c5cce780349d4d51739ae682dec63573847a2a8dcb44381b174c3d9c8d403 \
+    --hash=sha256:c971bf3786b5fad82ce5ad570dc6ee420f5b12527157929e830f51c55dc8af77 \
+    --hash=sha256:d1fde0f44029e02d02d3993ad55ce93ead9bb9b15c6b7ccd580f90bd7e3de476 \
+    --hash=sha256:d24b8bb40d5c61ef2d9b6a8f4528c2f17f1c5d2d31fed62ec860f6006142e83e \
+    --hash=sha256:d5ba88df9aa5e2f806650fcbeedbe4f6e8736e92fc0e73b0400538fd25a4dd96 \
+    --hash=sha256:d6f76310355e9fae637c3162936e9504b4767d5c52ca268331e2756e54fd4ca5 \
+    --hash=sha256:d737fc67b9a970f3234754974531dc9afeea11c70791dcb7db53b0cf81b79784 \
+    --hash=sha256:da22885266bbfb3f78218dc40205fed2671909fbd0720aedba39b4515c038091 \
+    --hash=sha256:da37dcfbf4b7f45d80ee386a5f81122501ec75672f475da34784196690762f4b \
+    --hash=sha256:db19d60d846283ee275d0416e2a23493f4e6b6028825b51290ac05afc87a6f97 \
+    --hash=sha256:db4c979b0b3e0fa7e9e69ecd11b2b3174c6963cebadeecfb7ad24532ffcdd11a \
+    --hash=sha256:e164e0a98e92d06da343d17d4e9c4da4654f4a4588a20d6c73548a29f176abe2 \
+    --hash=sha256:e168a7560b7c61342ae0412997b069753f27ac4862ec7867eff74f0fe4ea2ad9 \
+    --hash=sha256:e381581b37db1db7597b62a2e6b8b57c3deec95d93b6d6407c5b61ddc98aca6d \
+    --hash=sha256:e65bc19919c910127c06759a63747ebe14f386cda573d95bcc62b427ca1afc73 \
+    --hash=sha256:e7b8813be97cab8cb52b1375f41f8e6804f6507fe4660152e8ca5c48f0436017 \
+    --hash=sha256:e8a78079d9a39ca9ca99a8b0ac2fdc0c4d25fc80c8a8a82e5c8211509c523363 \
+    --hash=sha256:ebf909ea0a3fc9596e40d55d8000702a85e27fd578ff41a5500f68f20fd32e6c \
+    --hash=sha256:ec40170327d4a404b0d91855d41bfe1fe4b699222b2b93e3d833a27330a87a6d \
+    --hash=sha256:f178d2aadf0166be4df834c4953da2d7eef24719e8aec9a65289483eeea9d618 \
+    --hash=sha256:f88df3a83cf9df566f171adba39d5bd52814ac0b94778d2448652fc77f9eb491 \
+    --hash=sha256:f973157ffeab5459eefe7b97a804987876dd0a55570b8fa56b4e1954bf11329b \
+    --hash=sha256:ff25f48fc8e623d95eca0670b8cc1469a83783c924a602e0fbd47363bb54aaca
+    # via fsspec
+aiosignal==1.2.0 \
+    --hash=sha256:26e62109036cd181df6e6ad646f91f0dcfd05fe16d0cb924138ff2ab75d64e3a \
+    --hash=sha256:78ed67db6c7b7ced4f98e495e572106d5c432a93e1ddd1bf475e1dc05f5b7df2
+    # via aiohttp
+albumentations==1.3.0 \
+    --hash=sha256:294165d87d03bc8323e484927f0a5c1a3c64b0e7b9c32a979582a6c93c363bdf \
+    --hash=sha256:be1af36832c8893314f2a5550e8ac19801e04770734c1b70fa3c996b41f37bed
+    # via -r requirements.in
+altair==4.2.0 \
+    --hash=sha256:0c724848ae53410c13fa28be2b3b9a9dcb7b5caa1a70f7f217bd663bb419935a \
+    --hash=sha256:d87d9372e63b48cd96b2a6415f0cf9457f50162ab79dc7a31cd7e024dd840026
+    # via streamlit
+antlr4-python3-runtime==4.9.3 \
+    --hash=sha256:f224469b4168294902bb1efa80a8bf7855f24c99aef99cbefc1bcd3cce77881b
+    # via omegaconf
+async-timeout==4.0.2 \
+    --hash=sha256:2163e1640ddb52b7a8c80d0a67a08587e5d245cc9c553a74a847056bc2976b15 \
+    --hash=sha256:8ca1e4fcf50d07413d66d1a5e416e42cfdf5851c981d679a09851a6853383b3c
+    # via aiohttp
+attrs==22.1.0 \
+    --hash=sha256:29adc2665447e5191d0e7c568fde78b21f9672d344281d0c6e1ab085429b22b6 \
+    --hash=sha256:86efa402f67bf2df34f51a335487cf46b1ec130d02b8d39fd248abfd30da551c
+    # via
+    #   aiohttp
+    #   jsonschema
+basicsr==1.4.2 \
+    --hash=sha256:b89b595a87ef964cda9913b4d99380ddb6554c965577c0c10cb7b78e31301e87
+    # via
+    #   gfpgan
+    #   realesrgan
+bidict==0.22.0 \
+    --hash=sha256:415126d23a0c81e1a8c584a8fb1f6905ea090c772571803aeee0a2242e8e7ba0 \
+    --hash=sha256:5c826b3e15e97cc6e615de295756847c282a79b79c5430d3bfc909b1ac9f5bd8
+    # via python-socketio
+blinker==1.5 \
+    --hash=sha256:1eb563df6fdbc39eeddc177d953203f99f097e9bf0e2b8f9f3cf18b6ca425e36 \
+    --hash=sha256:923e5e2f69c155f2cc42dafbbd70e16e3fde24d2d4aa2ab72fbe386238892462
+    # via streamlit
+cachetools==5.2.0 \
+    --hash=sha256:6a94c6402995a99c3970cc7e4884bb60b4a8639938157eeed436098bf9831757 \
+    --hash=sha256:f9f17d2aec496a9aa6b76f53e3b614c965223c061982d434d160f930c698a9db
+    # via
+    #   google-auth
+    #   streamlit
+certifi==2022.9.24 \
+    --hash=sha256:0d9c601124e5a6ba9712dbc60d9c53c21e34f5f641fe83002317394311bdce14 \
+    --hash=sha256:90c1a32f1d68f940488354e36370f6cca89f0f106db09518524c88d6ed83f382
+    # via
+    #   requests
+    #   sentry-sdk
+chardet==4.0.0 \
+    --hash=sha256:0d6f53a15db4120f2b08c94f11e7d93d2c911ee118b6b30a04ec3ee8310179fa \
+    --hash=sha256:f864054d66fd9118f2e67044ac8981a54775ec5b67aed0441892edb553d21da5
+    # via requests
+charset-normalizer==2.1.1 \
+    --hash=sha256:5a3d016c7c547f69d6f81fb0db9449ce888b418b5b9952cc5e6e66843e9dd845 \
+    --hash=sha256:83e9a75d1911279afd89352c68b45348559d1fc0506b054b346651b5e7fee29f
+    # via aiohttp
+clean-fid==0.1.34 \
+    --hash=sha256:2997f85a67a28c95adaae7899a33fc10537164fef4cdd424e3257bffad79a901
+    # via k-diffusion
+click==8.1.3 \
+    --hash=sha256:7682dc8afb30297001674575ea00d1814d808d6a36af415a82bd481d37ba7b8e \
+    --hash=sha256:bb4d8133cb15a609f44e8213d9b391b0809795062913b383c62be0ee95b1db48
+    # via
+    #   flask
+    #   streamlit
+    #   wandb
+clip @ https://github.com/openai/CLIP/archive/d50d76daa670286dd6cacf3bcd80b5e4823fc8e1.zip \
+    --hash=sha256:b5842c25da441d6c581b53a5c60e0c2127ebafe0f746f8e15561a006c6c3be6a
+    # via -r requirements.in
+colorama==0.4.6 \
+    --hash=sha256:08695f5cb7ed6e0531a20572697297273c47b8cae5a63ffc6d6ed5c201be6e44 \
+    --hash=sha256:4f1d9991f5acc0ca119f9d443620b77f9d6b33703e51011c16baf57afb285fc6
+    # via
+    #   click
+    #   tqdm
+commonmark==0.9.1 \
+    --hash=sha256:452f9dc859be7f06631ddcb328b6919c67984aca654e5fefb3914d54691aed60 \
+    --hash=sha256:da2f38c92590f83de410ba1a3cbceafbc74fee9def35f9251ba9a971d6d66fd9
+    # via rich
+contourpy==1.0.6 \
+    --hash=sha256:0236875c5a0784215b49d00ebbe80c5b6b5d5244b3655a36dda88105334dea17 \
+    --hash=sha256:03d1b9c6b44a9e30d554654c72be89af94fab7510b4b9f62356c64c81cec8b7d \
+    --hash=sha256:0537cc1195245bbe24f2913d1f9211b8f04eb203de9044630abd3664c6cc339c \
+    --hash=sha256:06ca79e1efbbe2df795822df2fa173d1a2b38b6e0f047a0ec7903fbca1d1847e \
+    --hash=sha256:08e8d09d96219ace6cb596506fb9b64ea5f270b2fb9121158b976d88871fcfd1 \
+    --hash=sha256:0b1e66346acfb17694d46175a0cea7d9036f12ed0c31dfe86f0f405eedde2bdd \
+    --hash=sha256:0b97454ed5b1368b66ed414c754cba15b9750ce69938fc6153679787402e4cdf \
+    --hash=sha256:0e4854cc02006ad6684ce092bdadab6f0912d131f91c2450ce6dbdea78ee3c0b \
+    --hash=sha256:12a7dc8439544ed05c6553bf026d5e8fa7fad48d63958a95d61698df0e00092b \
+    --hash=sha256:1b1ee48a130da4dd0eb8055bbab34abf3f6262957832fd575e0cab4979a15a41 \
+    --hash=sha256:1c0e1308307a75e07d1f1b5f0f56b5af84538a5e9027109a7bcf6cb47c434e72 \
+    --hash=sha256:1dedf4c64185a216c35eb488e6f433297c660321275734401760dafaeb0ad5c2 \
+    --hash=sha256:208bc904889c910d95aafcf7be9e677726df9ef71e216780170dbb7e37d118fa \
+    --hash=sha256:211dfe2bd43bf5791d23afbe23a7952e8ac8b67591d24be3638cabb648b3a6eb \
+    --hash=sha256:341330ed19074f956cb20877ad8d2ae50e458884bfa6a6df3ae28487cc76c768 \
+    --hash=sha256:344cb3badf6fc7316ad51835f56ac387bdf86c8e1b670904f18f437d70da4183 \
+    --hash=sha256:358f6364e4873f4d73360b35da30066f40387dd3c427a3e5432c6b28dd24a8fa \
+    --hash=sha256:371f6570a81dfdddbb837ba432293a63b4babb942a9eb7aaa699997adfb53278 \
+    --hash=sha256:375d81366afd547b8558c4720337218345148bc2fcffa3a9870cab82b29667f2 \
+    --hash=sha256:3a1917d3941dd58732c449c810fa7ce46cc305ce9325a11261d740118b85e6f3 \
+    --hash=sha256:4081918147fc4c29fad328d5066cfc751da100a1098398742f9f364be63803fc \
+    --hash=sha256:444fb776f58f4906d8d354eb6f6ce59d0a60f7b6a720da6c1ccb839db7c80eb9 \
+    --hash=sha256:46deb310a276cc5c1fd27958e358cce68b1e8a515fa5a574c670a504c3a3fe30 \
+    --hash=sha256:494efed2c761f0f37262815f9e3c4bb9917c5c69806abdee1d1cb6611a7174a0 \
+    --hash=sha256:50627bf76abb6ba291ad08db583161939c2c5fab38c38181b7833423ab9c7de3 \
+    --hash=sha256:5641927cc5ae66155d0c80195dc35726eae060e7defc18b7ab27600f39dd1fe7 \
+    --hash=sha256:5b117d29433fc8393b18a696d794961464e37afb34a6eeb8b2c37b5f4128a83e \
+    --hash=sha256:613c665529899b5d9fade7e5d1760111a0b011231277a0d36c49f0d3d6914bd6 \
+    --hash=sha256:6e459ebb8bb5ee4c22c19cc000174f8059981971a33ce11e17dddf6aca97a142 \
+    --hash=sha256:6f56515e7c6fae4529b731f6c117752247bef9cdad2b12fc5ddf8ca6a50965a5 \
+    --hash=sha256:730c27978a0003b47b359935478b7d63fd8386dbb2dcd36c1e8de88cbfc1e9de \
+    --hash=sha256:75a2e638042118118ab39d337da4c7908c1af74a8464cad59f19fbc5bbafec9b \
+    --hash=sha256:78ced51807ccb2f45d4ea73aca339756d75d021069604c2fccd05390dc3c28eb \
+    --hash=sha256:7ee394502026d68652c2824348a40bf50f31351a668977b51437131a90d777ea \
+    --hash=sha256:8468b40528fa1e15181cccec4198623b55dcd58306f8815a793803f51f6c474a \
+    --hash=sha256:84c593aeff7a0171f639da92cb86d24954bbb61f8a1b530f74eb750a14685832 \
+    --hash=sha256:913bac9d064cff033cf3719e855d4f1db9f1c179e0ecf3ba9fdef21c21c6a16a \
+    --hash=sha256:9447c45df407d3ecb717d837af3b70cfef432138530712263730783b3d016512 \
+    --hash=sha256:9b0e7fe7f949fb719b206548e5cde2518ffb29936afa4303d8a1c4db43dcb675 \
+    --hash=sha256:9bc407a6af672da20da74823443707e38ece8b93a04009dca25856c2d9adadb1 \
+    --hash=sha256:9e8e686a6db92a46111a1ee0ee6f7fbfae4048f0019de207149f43ac1812cf95 \
+    --hash=sha256:9fc4e7973ed0e1fe689435842a6e6b330eb7ccc696080dda9a97b1a1b78e41db \
+    --hash=sha256:a457ee72d9032e86730f62c5eeddf402e732fdf5ca8b13b41772aa8ae13a4563 \
+    --hash=sha256:a628bba09ba72e472bf7b31018b6281fd4cc903f0888049a3724afba13b6e0b8 \
+    --hash=sha256:a79d239fc22c3b8d9d3de492aa0c245533f4f4c7608e5749af866949c0f1b1b9 \
+    --hash=sha256:aa4674cf3fa2bd9c322982644967f01eed0c91bb890f624e0e0daf7a5c3383e9 \
+    --hash=sha256:acd2bd02f1a7adff3a1f33e431eb96ab6d7987b039d2946a9b39fe6fb16a1036 \
+    --hash=sha256:b3b1bd7577c530eaf9d2bc52d1a93fef50ac516a8b1062c3d1b9bcec9ebe329b \
+    --hash=sha256:b48d94386f1994db7c70c76b5808c12e23ed7a4ee13693c2fc5ab109d60243c0 \
+    --hash=sha256:b64f747e92af7da3b85631a55d68c45a2d728b4036b03cdaba4bd94bcc85bd6f \
+    --hash=sha256:b98c820608e2dca6442e786817f646d11057c09a23b68d2b3737e6dcb6e4a49b \
+    --hash=sha256:c1baa49ab9fedbf19d40d93163b7d3e735d9cd8d5efe4cce9907902a6dad391f \
+    --hash=sha256:c38c6536c2d71ca2f7e418acaf5bca30a3af7f2a2fa106083c7d738337848dbe \
+    --hash=sha256:c78bfbc1a7bff053baf7e508449d2765964d67735c909b583204e3240a2aca45 \
+    --hash=sha256:cd2bc0c8f2e8de7dd89a7f1c10b8844e291bca17d359373203ef2e6100819edd \
+    --hash=sha256:d2eff2af97ea0b61381828b1ad6cd249bbd41d280e53aea5cccd7b2b31b8225c \
+    --hash=sha256:d8834c14b8c3dd849005e06703469db9bf96ba2d66a3f88ecc539c9a8982e0ee \
+    --hash=sha256:d912f0154a20a80ea449daada904a7eb6941c83281a9fab95de50529bfc3a1da \
+    --hash=sha256:da1ef35fd79be2926ba80fbb36327463e3656c02526e9b5b4c2b366588b74d9a \
+    --hash=sha256:dbe6fe7a1166b1ddd7b6d887ea6fa8389d3f28b5ed3f73a8f40ece1fc5a3d340 \
+    --hash=sha256:dcd556c8fc37a342dd636d7eef150b1399f823a4462f8c968e11e1ebeabee769 \
+    --hash=sha256:e13b31d1b4b68db60b3b29f8e337908f328c7f05b9add4b1b5c74e0691180109 \
+    --hash=sha256:e1739496c2f0108013629aa095cc32a8c6363444361960c07493818d0dea2da4 \
+    --hash=sha256:e43255a83835a129ef98f75d13d643844d8c646b258bebd11e4a0975203e018f \
+    --hash=sha256:e626cefff8491bce356221c22af5a3ea528b0b41fbabc719c00ae233819ea0bf \
+    --hash=sha256:eadad75bf91897f922e0fb3dca1b322a58b1726a953f98c2e5f0606bd8408621 \
+    --hash=sha256:f33da6b5d19ad1bb5e7ad38bb8ba5c426d2178928bc2b2c44e8823ea0ecb6ff3 \
+    --hash=sha256:f4052a8a4926d4468416fc7d4b2a7b2a3e35f25b39f4061a7e2a3a2748c4fc48 \
+    --hash=sha256:f6ca38dd8d988eca8f07305125dec6f54ac1c518f1aaddcc14d08c01aebb6efc
+    # via matplotlib
+cycler==0.11.0 \
+    --hash=sha256:3a27e95f763a428a739d2add979fa7494c912a32c17c4c38c4d5f082cad165a3 \
+    --hash=sha256:9c87405839a19696e837b3b818fed3f5f69f16f1eec1a1ad77e043dcea9c772f
+    # via matplotlib
+decorator==5.1.1 \
+    --hash=sha256:637996211036b6385ef91435e4fae22989472f9d571faba8927ba8253acbc330 \
+    --hash=sha256:b8c3f85900b9dc423225913c5aace94729fe1fa9763b38939a95226f02d37186
+    # via validators
+diffusers==0.7.2 \
+    --hash=sha256:4a5f8b3a5fbd936bba7d459611cb35ec62875030367be32b232f9e19543e25a9 \
+    --hash=sha256:fb814ffd150cc6f470380b8c6a521181a77beb2f44134d2aad2e4cd8aa2ced0e
+    # via -r requirements.in
+dnspython==2.2.1 \
+    --hash=sha256:0f7569a4a6ff151958b64304071d370daa3243d15941a7beedf0c9fe5105603e \
+    --hash=sha256:a851e51367fb93e9e1361732c1d60dab63eff98712e503ea7d92e6eccb109b4f
+    # via eventlet
+docker-pycreds==0.4.0 \
+    --hash=sha256:6ce3270bcaf404cc4c3e27e4b6c70d3521deae82fb508767870fdbf772d584d4 \
+    --hash=sha256:7266112468627868005106ec19cd0d722702d2b7d5912a28e19b826c3d37af49
+    # via wandb
+einops==0.5.0 \
+    --hash=sha256:055de7eeb3cb9e9710ef3085a811090c6b52e809b7044e8785824ed185f486d1 \
+    --hash=sha256:8b7a83cffc1ea88e306df099b7cbb9c3ba5003bd84d05ae44be5655864abb8d3
+    # via k-diffusion
+entrypoints==0.4 \
+    --hash=sha256:b706eddaa9218a19ebcd67b56818f05bb27589b1ca9e8d797b74affad4ccacd4 \
+    --hash=sha256:f174b5ff827504fd3cd97cc3f8649f3693f51538c7e4bdf3ef002c8429d42f9f
+    # via altair
+eventlet==0.33.1 \
+    --hash=sha256:a085922698e5029f820cf311a648ac324d73cec0e4792877609d978a4b5bbf31 \
+    --hash=sha256:afbe17f06a58491e9aebd7a4a03e70b0b63fd4cf76d8307bae07f280479b1515
+    # via -r requirements.in
+facexlib==0.2.5 \
+    --hash=sha256:31e20cc4ed5d63562d380e4564bae14ac0d5d1899a079bad87621e13564567e4 \
+    --hash=sha256:cc7ceb56c5424319c47223cf75eef6828c34c66082707c6eb35b95d39779f02d
+    # via
+    #   gfpgan
+    #   realesrgan
+filelock==3.8.0 \
+    --hash=sha256:55447caa666f2198c5b6b13a26d2084d26fa5b115c00d065664b2124680c4edc \
+    --hash=sha256:617eb4e5eedc82fc5f47b6d61e4d11cb837c56cb4544e39081099fa17ad109d4
+    # via
+    #   diffusers
+    #   huggingface-hub
+    #   transformers
+filterpy==1.4.5 \
+    --hash=sha256:4f2a4d39e4ea601b9ab42b2db08b5918a9538c168cff1c6895ae26646f3d73b1
+    # via facexlib
+flask==2.2.2 \
+    --hash=sha256:642c450d19c4ad482f96729bd2a8f6d32554aa1e231f4f6b4e7e5264b16cca2b \
+    --hash=sha256:b9c46cc36662a7949f34b52d8ec7bb59c0d74ba08ba6cb9ce9adc1d8676d9526
+    # via
+    #   flask-cors
+    #   flask-socketio
+flask-cors==3.0.10 \
+    --hash=sha256:74efc975af1194fc7891ff5cd85b0f7478be4f7f59fe158102e91abb72bb4438 \
+    --hash=sha256:b60839393f3b84a0f3746f6cdca56c1ad7426aa738b70d6c61375857823181de
+    # via -r requirements.in
+flask-socketio==5.3.1 \
+    --hash=sha256:fd0ed0fc1341671d92d5f5b2f5503916deb7aa7e2940e6636cfa2c087c828bf9 \
+    --hash=sha256:ff0c721f20bff1e2cfba77948727a8db48f187e89a72fe50c34478ce6efb3353
+    # via -r requirements.in
+flaskwebgui==0.3.7 \
+    --hash=sha256:4a69955308eaa8bb256ba04a994dc8f58a48dcd6f9599694ab1bcd9f43d88a5d \
+    --hash=sha256:535974ce2672dcc74787c254de24cceed4101be75d96952dae82014dd57f061e
+    # via -r requirements.in
+fonttools==4.38.0 \
+    --hash=sha256:2bb244009f9bf3fa100fc3ead6aeb99febe5985fa20afbfbaa2f8946c2fbdaf1 \
+    --hash=sha256:820466f43c8be8c3009aef8b87e785014133508f0de64ec469e4efb643ae54fb
+    # via matplotlib
+frozenlist==1.3.1 \
+    --hash=sha256:022178b277cb9277d7d3b3f2762d294f15e85cd2534047e68a118c2bb0058f3e \
+    --hash=sha256:086ca1ac0a40e722d6833d4ce74f5bf1aba2c77cbfdc0cd83722ffea6da52a04 \
+    --hash=sha256:0bc75692fb3770cf2b5856a6c2c9de967ca744863c5e89595df64e252e4b3944 \
+    --hash=sha256:0dde791b9b97f189874d654c55c24bf7b6782343e14909c84beebd28b7217845 \
+    --hash=sha256:12607804084d2244a7bd4685c9d0dca5df17a6a926d4f1967aa7978b1028f89f \
+    --hash=sha256:19127f8dcbc157ccb14c30e6f00392f372ddb64a6ffa7106b26ff2196477ee9f \
+    --hash=sha256:1b51eb355e7f813bcda00276b0114c4172872dc5fb30e3fea059b9367c18fbcb \
+    --hash=sha256:1e1cf7bc8cbbe6ce3881863671bac258b7d6bfc3706c600008925fb799a256e2 \
+    --hash=sha256:219a9676e2eae91cb5cc695a78b4cb43d8123e4160441d2b6ce8d2c70c60e2f3 \
+    --hash=sha256:2743bb63095ef306041c8f8ea22bd6e4d91adabf41887b1ad7886c4c1eb43d5f \
+    --hash=sha256:2af6f7a4e93f5d08ee3f9152bce41a6015b5cf87546cb63872cc19b45476e98a \
+    --hash=sha256:31b44f1feb3630146cffe56344704b730c33e042ffc78d21f2125a6a91168131 \
+    --hash=sha256:31bf9539284f39ff9398deabf5561c2b0da5bb475590b4e13dd8b268d7a3c5c1 \
+    --hash=sha256:35c3d79b81908579beb1fb4e7fcd802b7b4921f1b66055af2578ff7734711cfa \
+    --hash=sha256:3a735e4211a04ccfa3f4833547acdf5d2f863bfeb01cfd3edaffbc251f15cec8 \
+    --hash=sha256:42719a8bd3792744c9b523674b752091a7962d0d2d117f0b417a3eba97d1164b \
+    --hash=sha256:49459f193324fbd6413e8e03bd65789e5198a9fa3095e03f3620dee2f2dabff2 \
+    --hash=sha256:4c0c99e31491a1d92cde8648f2e7ccad0e9abb181f6ac3ddb9fc48b63301808e \
+    --hash=sha256:52137f0aea43e1993264a5180c467a08a3e372ca9d378244c2d86133f948b26b \
+    --hash=sha256:526d5f20e954d103b1d47232e3839f3453c02077b74203e43407b962ab131e7b \
+    --hash=sha256:53b2b45052e7149ee8b96067793db8ecc1ae1111f2f96fe1f88ea5ad5fd92d10 \
+    --hash=sha256:572ce381e9fe027ad5e055f143763637dcbac2542cfe27f1d688846baeef5170 \
+    --hash=sha256:58fb94a01414cddcdc6839807db77ae8057d02ddafc94a42faee6004e46c9ba8 \
+    --hash=sha256:5e77a8bd41e54b05e4fb2708dc6ce28ee70325f8c6f50f3df86a44ecb1d7a19b \
+    --hash=sha256:5f271c93f001748fc26ddea409241312a75e13466b06c94798d1a341cf0e6989 \
+    --hash=sha256:5f63c308f82a7954bf8263a6e6de0adc67c48a8b484fab18ff87f349af356efd \
+    --hash=sha256:61d7857950a3139bce035ad0b0945f839532987dfb4c06cfe160254f4d19df03 \
+    --hash=sha256:61e8cb51fba9f1f33887e22488bad1e28dd8325b72425f04517a4d285a04c519 \
+    --hash=sha256:625d8472c67f2d96f9a4302a947f92a7adbc1e20bedb6aff8dbc8ff039ca6189 \
+    --hash=sha256:6e19add867cebfb249b4e7beac382d33215d6d54476bb6be46b01f8cafb4878b \
+    --hash=sha256:717470bfafbb9d9be624da7780c4296aa7935294bd43a075139c3d55659038ca \
+    --hash=sha256:74140933d45271c1a1283f708c35187f94e1256079b3c43f0c2267f9db5845ff \
+    --hash=sha256:74e6b2b456f21fc93ce1aff2b9728049f1464428ee2c9752a4b4f61e98c4db96 \
+    --hash=sha256:9494122bf39da6422b0972c4579e248867b6b1b50c9b05df7e04a3f30b9a413d \
+    --hash=sha256:94e680aeedc7fd3b892b6fa8395b7b7cc4b344046c065ed4e7a1e390084e8cb5 \
+    --hash=sha256:97d9e00f3ac7c18e685320601f91468ec06c58acc185d18bb8e511f196c8d4b2 \
+    --hash=sha256:9c6ef8014b842f01f5d2b55315f1af5cbfde284eb184075c189fd657c2fd8204 \
+    --hash=sha256:a027f8f723d07c3f21963caa7d585dcc9b089335565dabe9c814b5f70c52705a \
+    --hash=sha256:a718b427ff781c4f4e975525edb092ee2cdef6a9e7bc49e15063b088961806f8 \
+    --hash=sha256:ab386503f53bbbc64d1ad4b6865bf001414930841a870fc97f1546d4d133f141 \
+    --hash=sha256:ab6fa8c7871877810e1b4e9392c187a60611fbf0226a9e0b11b7b92f5ac72792 \
+    --hash=sha256:b47d64cdd973aede3dd71a9364742c542587db214e63b7529fbb487ed67cddd9 \
+    --hash=sha256:b499c6abe62a7a8d023e2c4b2834fce78a6115856ae95522f2f974139814538c \
+    --hash=sha256:bbb1a71b1784e68870800b1bc9f3313918edc63dbb8f29fbd2e767ce5821696c \
+    --hash=sha256:c3b31180b82c519b8926e629bf9f19952c743e089c41380ddca5db556817b221 \
+    --hash=sha256:c56c299602c70bc1bb5d1e75f7d8c007ca40c9d7aebaf6e4ba52925d88ef826d \
+    --hash=sha256:c92deb5d9acce226a501b77307b3b60b264ca21862bd7d3e0c1f3594022f01bc \
+    --hash=sha256:cc2f3e368ee5242a2cbe28323a866656006382872c40869b49b265add546703f \
+    --hash=sha256:d82bed73544e91fb081ab93e3725e45dd8515c675c0e9926b4e1f420a93a6ab9 \
+    --hash=sha256:da1cdfa96425cbe51f8afa43e392366ed0b36ce398f08b60de6b97e3ed4affef \
+    --hash=sha256:da5ba7b59d954f1f214d352308d1d86994d713b13edd4b24a556bcc43d2ddbc3 \
+    --hash=sha256:e0c8c803f2f8db7217898d11657cb6042b9b0553a997c4a0601f48a691480fab \
+    --hash=sha256:ee4c5120ddf7d4dd1eaf079af3af7102b56d919fa13ad55600a4e0ebe532779b \
+    --hash=sha256:eee0c5ecb58296580fc495ac99b003f64f82a74f9576a244d04978a7e97166db \
+    --hash=sha256:f5abc8b4d0c5b556ed8cd41490b606fe99293175a82b98e652c3f2711b452988 \
+    --hash=sha256:f810e764617b0748b49a731ffaa525d9bb36ff38332411704c2400125af859a6 \
+    --hash=sha256:f89139662cc4e65a4813f4babb9ca9544e42bddb823d2ec434e18dad582543bc \
+    --hash=sha256:fa47319a10e0a076709644a0efbcaab9e91902c8bd8ef74c6adb19d320f69b83 \
+    --hash=sha256:fabb953ab913dadc1ff9dcc3a7a7d3dc6a92efab3a0373989b8063347f8705be
+    # via
+    #   aiohttp
+    #   aiosignal
+fsspec[http]==2022.10.0 \
+    --hash=sha256:6b7c6ab3b476cdf17efcfeccde7fca28ef5a48f73a71010aaceec5fc15bf9ebf \
+    --hash=sha256:cb6092474e90487a51de768170f3afa50ca8982c26150a59072b16433879ff1d
+    # via pytorch-lightning
+ftfy==6.1.1 \
+    --hash=sha256:0ffd33fce16b54cccaec78d6ec73d95ad370e5df5a25255c8966a6147bd667ca \
+    --hash=sha256:bfc2019f84fcd851419152320a6375604a0f1459c281b5b199b2cd0d2e727f8f
+    # via clip
+future==0.18.2 \
+    --hash=sha256:b1bead90b70cf6ec3f0710ae53a525360fa360d306a86583adc6bf83a4db537d
+    # via
+    #   basicsr
+    #   test-tube
+getpass-asterisk==1.0.1 \
+    --hash=sha256:20d45cafda0066d761961e0919728526baf7bb5151fbf48a7d5ea4034127d857 \
+    --hash=sha256:7cc357a924cf62fa4e15b73cb4e5e30685c9084e464ffdc3fd9000a2b54ea9e9
+    # via -r requirements.in
+gfpgan @ https://github.com/TencentARC/GFPGAN/archive/2eac2033893ca7f427f4035d80fe95b92649ac56.zip \
+    --hash=sha256:79e6d71c8f1df7c7ccb0ac6b9a2ccb615ad5cde818c8b6f285a8711c05aebf85
+    # via
+    #   -r requirements.in
+    #   realesrgan
+gitdb==4.0.9 \
+    --hash=sha256:8033ad4e853066ba6ca92050b9df2f89301b8fc8bf7e9324d412a63f8bf1a8fd \
+    --hash=sha256:bac2fd45c0a1c9cf619e63a90d62bdc63892ef92387424b855792a6cabe789aa
+    # via gitpython
+gitpython==3.1.29 \
+    --hash=sha256:41eea0deec2deea139b459ac03656f0dd28fc4a3387240ec1d3c259a2c47850f \
+    --hash=sha256:cc36bfc4a3f913e66805a28e84703e419d9c264c1077e537b54f0e1af85dbefd
+    # via
+    #   streamlit
+    #   wandb
+google-auth==2.14.0 \
+    --hash=sha256:1ad5b0e6eba5f69645971abb3d2c197537d5914070a8c6d30299dfdb07c5c700 \
+    --hash=sha256:cf24817855d874ede2efd071aa22125445f555de1685b739a9782fcf408c2a3d
+    # via
+    #   google-auth-oauthlib
+    #   tb-nightly
+    #   tensorboard
+google-auth-oauthlib==0.4.6 \
+    --hash=sha256:3f2a6e802eebbb6fb736a370fbf3b055edcb6b52878bf2f26330b5e041316c73 \
+    --hash=sha256:a90a072f6993f2c327067bf65270046384cda5a8ecb20b94ea9a687f1f233a7a
+    # via
+    #   tb-nightly
+    #   tensorboard
+greenlet==2.0.0.post0 \
+    --hash=sha256:00ebdaf0fa51c284fd2172837d751731a15971e0c20d1a9163cfbdf620ce8b49 \
+    --hash=sha256:029ca674b3a7e8427db8f5c65d5ed4e24a7417af2a415a5958598aefd71980c4 \
+    --hash=sha256:02bdb1e373b275bd705c43b249426e776c4f8a8ff2afaf8ec5ea0dde487d8a14 \
+    --hash=sha256:08dc04f49ed1ea5e6772bb5e8cf2a77d1b1744566f4eca471a55b35af1278b31 \
+    --hash=sha256:08f44e938d142271b954405afb6570e0be48a9f556b6bf4d42d2e3ae6a251fad \
+    --hash=sha256:0a5c03e2a68ec2ff1cba74ceaed899ec8cd353285f4f985c30c8cfbef9d3a3be \
+    --hash=sha256:0fee3240093b745efc857392f09379514ad84db4ca324514594bbdf6380016c8 \
+    --hash=sha256:118e708dd7bc88beaeeaa5a8601a7743b8835b7bbaf7c8f23ffa78f8bc8faf28 \
+    --hash=sha256:13d492a807a5c7334b5931e9b6d9b181991ccc6a40555a7b177f189feff59b4b \
+    --hash=sha256:1cac9e9895aeff26434325404558783ee54f4ff3aec8daa56b8706796f7b01a0 \
+    --hash=sha256:2146d15429b4eeb412428737594acb5660a5bc0fdd1488d8a2a74a5ee32391fa \
+    --hash=sha256:21ee1ae26d072b195edea764218623f6c15eba4ae06816908f33c82e0af018d3 \
+    --hash=sha256:22eca421e3f2f3c18f4f54c0ff525aa9d397c6f116fce9ebd37b420174dbc027 \
+    --hash=sha256:2bab49783858cf724fff6868395cbeb81d1188cba23616b53e79de0beda29f42 \
+    --hash=sha256:2fbdec204ca40b3d0c0992a19c1ba627441c17983ac4ffc45baec7f5f53e20ca \
+    --hash=sha256:30ce47525f9a1515566429ac7de6b1ae76d32c3ccede256e3517a1a6419cf659 \
+    --hash=sha256:335dcf676d5e4122e4006c16ae11eda2467af5461b949c265ce120b6b959ffe2 \
+    --hash=sha256:3407b843b05da71fef0f1dd666059c08ad0e0f4afc3b9c93c998a2e53fac95e5 \
+    --hash=sha256:35827f98fd0d768862b8f15777e6dbb03fe6ac6e7bd1bee3f3ded4536f350347 \
+    --hash=sha256:3a22e5988f9d66b3e9ae9583bf9d8ef792b09f23afeb78707e6a4f47ab57cc5e \
+    --hash=sha256:3c3327da2bdab61078e42e695307465c425671a5a9251e6c29ee130d51943f28 \
+    --hash=sha256:3ca723dfc2789c1fb991809822811896b198ecf0909dbccea4a07170d18c3e1b \
+    --hash=sha256:46156ae88ee71c37b6c4f7af63fff5d3ab8f45ef72e1a660bcf6386c1647f106 \
+    --hash=sha256:4bbe2d074292e3646704371eb640ee52c386d633ed72ff223dadcd3fe8ecd8f9 \
+    --hash=sha256:4c4310f0e42154995d92810f27b44ab7116a4a696feb0ff141ae2de59196efd7 \
+    --hash=sha256:4cfa629de5b2dea27c81b334c4536463e9a49ac0877e2008a276d58d4c72868a \
+    --hash=sha256:4e144ab0de56b4d2a2cf0d2fb9d568b59fce49aab3e129badf17c12b0252047d \
+    --hash=sha256:4ea67f303cec384b148774667c7e3cf02311e7026fc02bdcdcd206dfe4ea4fc9 \
+    --hash=sha256:538c9e8f65a32413ace426f8117ef019021adf8175f7c491fed65f5fe2083e0c \
+    --hash=sha256:56565ac9ab4ff3dd473bfe959e0bf2a5062aabb89b7c94cabb417beb162c9fff \
+    --hash=sha256:5e22485256bb1c60bbcc6f8509b1a11042358a2462d5ecdb9a82dc472d2fdd60 \
+    --hash=sha256:602a69c24f1a9755dd1760b3b31bdfc495c4613260c876a01b7e6d5eb9bcae1b \
+    --hash=sha256:6393ec3cecda53b20241e88bc33d87cbd8126cc10870fc69fa16ca2e20a5ac1b \
+    --hash=sha256:6442bbfb047dc1e47658954b72e1589f2bc4e12e67d51bbad0059a626180daa1 \
+    --hash=sha256:666d2a0b269a68cd4fe0976544ab97970c5334d35d0e47ae9be1723f734d8204 \
+    --hash=sha256:697cfbfc19815c40213badcfe5f076418e0f9100cd25a66f513f32c1026b8bf4 \
+    --hash=sha256:6a1a6745c5dce202aa3f29a1736c53cf2179e9c3b280dc62cea9cb8c69977c83 \
+    --hash=sha256:6fc73fc8dd81d9efa842a55033b6b4cb233b134a0270e127c6874d053ef2049b \
+    --hash=sha256:7e9e0d4c5c618b0442396715ffe6c2f84a60d593dad7e0184388aed36d568a65 \
+    --hash=sha256:81fdcf7c0c2df46a99ca421a552c4370117851c5e4dbd6cb53d569b896d62322 \
+    --hash=sha256:8b26932be686f3582df039d79fe96f7ca13d63b39468162f816f9ff29584b9a4 \
+    --hash=sha256:8b7e5191b974fb66fcbac1818ba494d3512da9cf6eaef7acd952f9862eaaa20c \
+    --hash=sha256:8c80e9c41a83d8c90399af8c7dcdeae0c03c48b40b9d0ab84457533d5d7882bf \
+    --hash=sha256:9f2f110b9cc325f6543e0e3f4ab8008c272a59052f9464047c29d4be4511ce05 \
+    --hash=sha256:a339e510a079dc8372e39ce1c7629414db51966235c9670c58d529def79243a2 \
+    --hash=sha256:ad9abc3e4d2370cecb524421cc5c8a664006aa11d5c1cb3c9250e3bf65ab546e \
+    --hash=sha256:b043782c8f6cccc8fae3a16db397eca1d36a41b0706cbf6f514aea1e1a260bab \
+    --hash=sha256:b31de27313abbb567c528ed123380fcf18a5dfd03134570dfd12227e21ac1184 \
+    --hash=sha256:b75e5644cc353328cd57ec8dafaaf5f81b2c3ecf7c4b278b907e99ad53ba7839 \
+    --hash=sha256:b8cfc8fc944bd7b704691bc28225a2635e377e92dc413459845868d3f7724982 \
+    --hash=sha256:c2055c52260808d87622293b57df1c68aeb12ddd8a0cfc0223fb57a5f629e202 \
+    --hash=sha256:c416106b3b8e905b6ab0e84ec90047a6401021aa023f9aa93978e57cd8f8189f \
+    --hash=sha256:d0e210e17a6181a3fd3f8dce957043a4e74177ffa9f295514984b2b633940dce \
+    --hash=sha256:d9453135e48cd631e3e9f06d9da9100d17c9f662e4a6d8b552c29be6c834a6b9 \
+    --hash=sha256:dd0198006278291d9469309d655093df1f5e5107c0261e242b5f390baee32199 \
+    --hash=sha256:e1781bda1e787d3ad33788cc3be47f6e47a9581676d02670c15ee36c9460adfe \
+    --hash=sha256:e56a5a9f303e3ac011ba445a6d84f05d08666bf8db094afafcec5228622c30f5 \
+    --hash=sha256:e93ae35f0fd3caf75e58c76a1cab71e6ece169aaa1b281782ef9efde0a6b83f2 \
+    --hash=sha256:eb36b6570646227a63eda03916f1cc6f3744ee96d28f7a0a5629c59267a8055f \
+    --hash=sha256:f8c425a130e04d5404edaf6f5906e5ab12f3aa1168a1828aba6dfadac5910469
+    # via eventlet
+grpcio==1.50.0 \
+    --hash=sha256:05f7c248e440f538aaad13eee78ef35f0541e73498dd6f832fe284542ac4b298 \
+    --hash=sha256:080b66253f29e1646ac53ef288c12944b131a2829488ac3bac8f52abb4413c0d \
+    --hash=sha256:12b479839a5e753580b5e6053571de14006157f2ef9b71f38c56dc9b23b95ad6 \
+    --hash=sha256:156f8009e36780fab48c979c5605eda646065d4695deea4cfcbcfdd06627ddb6 \
+    --hash=sha256:15f9e6d7f564e8f0776770e6ef32dac172c6f9960c478616c366862933fa08b4 \
+    --hash=sha256:177afaa7dba3ab5bfc211a71b90da1b887d441df33732e94e26860b3321434d9 \
+    --hash=sha256:1a4cd8cb09d1bc70b3ea37802be484c5ae5a576108bad14728f2516279165dd7 \
+    --hash=sha256:1d8d02dbb616c0a9260ce587eb751c9c7dc689bc39efa6a88cc4fa3e9c138a7b \
+    --hash=sha256:2b71916fa8f9eb2abd93151fafe12e18cebb302686b924bd4ec39266211da525 \
+    --hash=sha256:2d9fd6e38b16c4d286a01e1776fdf6c7a4123d99ae8d6b3f0b4a03a34bf6ce45 \
+    --hash=sha256:3b611b3de3dfd2c47549ca01abfa9bbb95937eb0ea546ea1d762a335739887be \
+    --hash=sha256:3e4244c09cc1b65c286d709658c061f12c61c814be0b7030a2d9966ff02611e0 \
+    --hash=sha256:40838061e24f960b853d7bce85086c8e1b81c6342b1f4c47ff0edd44bbae2722 \
+    --hash=sha256:4b123fbb7a777a2fedec684ca0b723d85e1d2379b6032a9a9b7851829ed3ca9a \
+    --hash=sha256:531f8b46f3d3db91d9ef285191825d108090856b3bc86a75b7c3930f16ce432f \
+    --hash=sha256:67dd41a31f6fc5c7db097a5c14a3fa588af54736ffc174af4411d34c4f306f68 \
+    --hash=sha256:7489dbb901f4fdf7aec8d3753eadd40839c9085967737606d2c35b43074eea24 \
+    --hash=sha256:8d4c8e73bf20fb53fe5a7318e768b9734cf122fe671fcce75654b98ba12dfb75 \
+    --hash=sha256:8e69aa4e9b7f065f01d3fdcecbe0397895a772d99954bb82eefbb1682d274518 \
+    --hash=sha256:8e8999a097ad89b30d584c034929f7c0be280cd7851ac23e9067111167dcbf55 \
+    --hash=sha256:906f4d1beb83b3496be91684c47a5d870ee628715227d5d7c54b04a8de802974 \
+    --hash=sha256:92d7635d1059d40d2ec29c8bf5ec58900120b3ce5150ef7414119430a4b2dd5c \
+    --hash=sha256:931e746d0f75b2a5cff0a1197d21827a3a2f400c06bace036762110f19d3d507 \
+    --hash=sha256:95ce51f7a09491fb3da8cf3935005bff19983b77c4e9437ef77235d787b06842 \
+    --hash=sha256:9eea18a878cffc804506d39c6682d71f6b42ec1c151d21865a95fae743fda500 \
+    --hash=sha256:a23d47f2fc7111869f0ff547f771733661ff2818562b04b9ed674fa208e261f4 \
+    --hash=sha256:a4c23e54f58e016761b576976da6a34d876420b993f45f66a2bfb00363ecc1f9 \
+    --hash=sha256:a50a1be449b9e238b9bd43d3857d40edf65df9416dea988929891d92a9f8a778 \
+    --hash=sha256:ab5d0e3590f0a16cb88de4a3fa78d10eb66a84ca80901eb2c17c1d2c308c230f \
+    --hash=sha256:ae23daa7eda93c1c49a9ecc316e027ceb99adbad750fbd3a56fa9e4a2ffd5ae0 \
+    --hash=sha256:af98d49e56605a2912cf330b4627e5286243242706c3a9fa0bcec6e6f68646fc \
+    --hash=sha256:b2f77a90ba7b85bfb31329f8eab9d9540da2cf8a302128fb1241d7ea239a5469 \
+    --hash=sha256:baab51dcc4f2aecabf4ed1e2f57bceab240987c8b03533f1cef90890e6502067 \
+    --hash=sha256:ca8a2254ab88482936ce941485c1c20cdeaef0efa71a61dbad171ab6758ec998 \
+    --hash=sha256:cb11464f480e6103c59d558a3875bd84eed6723f0921290325ebe97262ae1347 \
+    --hash=sha256:ce8513aee0af9c159319692bfbf488b718d1793d764798c3d5cff827a09e25ef \
+    --hash=sha256:cf151f97f5f381163912e8952eb5b3afe89dec9ed723d1561d59cabf1e219a35 \
+    --hash=sha256:d144ad10eeca4c1d1ce930faa105899f86f5d99cecfe0d7224f3c4c76265c15e \
+    --hash=sha256:d534d169673dd5e6e12fb57cc67664c2641361e1a0885545495e65a7b761b0f4 \
+    --hash=sha256:d75061367a69808ab2e84c960e9dce54749bcc1e44ad3f85deee3a6c75b4ede9 \
+    --hash=sha256:d84d04dec64cc4ed726d07c5d17b73c343c8ddcd6b59c7199c801d6bbb9d9ed1 \
+    --hash=sha256:de411d2b030134b642c092e986d21aefb9d26a28bf5a18c47dd08ded411a3bc5 \
+    --hash=sha256:e07fe0d7ae395897981d16be61f0db9791f482f03fee7d1851fe20ddb4f69c03 \
+    --hash=sha256:ea8ccf95e4c7e20419b7827aa5b6da6f02720270686ac63bd3493a651830235c \
+    --hash=sha256:f7025930039a011ed7d7e7ef95a1cb5f516e23c5a6ecc7947259b67bea8e06ca
+    # via
+    #   tb-nightly
+    #   tensorboard
+huggingface-hub==0.10.1 \
+    --hash=sha256:5c188d5b16bec4b78449f8681f9975ff9d321c16046cc29bcf0d7e464ff29276 \
+    --hash=sha256:dc3b0e9a663fe6cad6a8522055c02a9d8673dbd527223288e2442bc028c253db
+    # via
+    #   diffusers
+    #   transformers
+idna==2.10 \
+    --hash=sha256:b307872f855b18632ce0c21c5e45be78c0ea7ae4c15c828c20788b26921eb3f6 \
+    --hash=sha256:b97d804b1e9b523befed77c48dacec60e6dcb0b5391d57af6a65a312a90648c0
+    # via
+    #   requests
+    #   yarl
+imageio==2.22.3 \
+    --hash=sha256:63f007b7f2a082306e36922b3fd529a7aa305d2b78f46195bab8e22bbfe866e9 \
+    --hash=sha256:a4b88f9f3d428b8c0ceeb7e297df8c346a642bb7e3111743eb85717d60b26f6f
+    # via
+    #   scikit-image
+    #   test-tube
+imageio-ffmpeg==0.4.7 \
+    --hash=sha256:27b48c32becae1658aa81c3a6b922538e4099edf5fbcbdb4ff5dbc84b8ffd3d3 \
+    --hash=sha256:6514f1380daf42815bc8c83aad63f33e0b8b47133421ddafe7b410cd8dfbbea5 \
+    --hash=sha256:6aba52ddf0a64442ffcb8d30ac6afb668186acec99ecbc7ae5bd171c4f500bbc \
+    --hash=sha256:7a08838f97f363e37ca41821b864fd3fdc99ab1fe2421040c78eb5f56a9e723e \
+    --hash=sha256:8e724d12dfe83e2a6eb39619e820243ca96c81c47c2648e66e05f7ee24e14312 \
+    --hash=sha256:fc60686ef03c2d0f842901b206223c30051a6a120384458761390104470846fd
+    # via -r requirements.in
+importlib-metadata==5.0.0 \
+    --hash=sha256:da31db32b304314d044d3c12c79bd59e307889b287ad12ff387b3500835fc2ab \
+    --hash=sha256:ddb0e35065e8938f867ed4928d0ae5bf2a53b7773871bfe6bcc7e4fcdc7dea43
+    # via
+    #   diffusers
+    #   streamlit
+itsdangerous==2.1.2 \
+    --hash=sha256:2c2349112351b88699d8d4b6b075022c0808887cb7ad10069318a8b0bc88db44 \
+    --hash=sha256:5dbbc68b317e5e42f327f9021763545dc3fc3bfe22e6deb96aaf1fc38874156a
+    # via flask
+jinja2==3.1.2 \
+    --hash=sha256:31351a702a408a9e7595a8fc6150fc3f43bb6bf7e319770cbc0db9df9437e852 \
+    --hash=sha256:6088930bfe239f0e6710546ab9c19c9ef35e29792895fed6e6e31a023a182a61
+    # via
+    #   altair
+    #   flask
+    #   pydeck
+joblib==1.2.0 \
+    --hash=sha256:091138ed78f800342968c523bdde947e7a305b8594b910a0fea2ab83c3c6d385 \
+    --hash=sha256:e1cee4a79e4af22881164f218d4311f60074197fb707e082e803b61f6d137018
+    # via scikit-learn
+jsonmerge==1.9.0 \
+    --hash=sha256:a2d1f80021c5c1d70a49e31f862b5f068f9db066080d8561e80654de74a3584d
+    # via k-diffusion
+jsonschema==4.17.0 \
+    --hash=sha256:5bfcf2bca16a087ade17e02b282d34af7ccd749ef76241e7f9bd7c0cb8a9424d \
+    --hash=sha256:f660066c3966db7d6daeaea8a75e0b68237a48e51cf49882087757bb59916248
+    # via
+    #   altair
+    #   jsonmerge
+k-diffusion @ https://github.com/invoke-ai/k-diffusion/archive/7f16b2c33411f26b3eae78d10648d625cb0c1095.zip \
+    --hash=sha256:c3f2c84036aa98c3abf4552fafab04df5ca472aa639982795e05bb1db43ce5e4
+    # via -r requirements.in
+kiwisolver==1.4.4 \
+    --hash=sha256:02f79693ec433cb4b5f51694e8477ae83b3205768a6fb48ffba60549080e295b \
+    --hash=sha256:03baab2d6b4a54ddbb43bba1a3a2d1627e82d205c5cf8f4c924dc49284b87166 \
+    --hash=sha256:1041feb4cda8708ce73bb4dcb9ce1ccf49d553bf87c3954bdfa46f0c3f77252c \
+    --hash=sha256:10ee06759482c78bdb864f4109886dff7b8a56529bc1609d4f1112b93fe6423c \
+    --hash=sha256:1d1573129aa0fd901076e2bfb4275a35f5b7aa60fbfb984499d661ec950320b0 \
+    --hash=sha256:283dffbf061a4ec60391d51e6155e372a1f7a4f5b15d59c8505339454f8989e4 \
+    --hash=sha256:28bc5b299f48150b5f822ce68624e445040595a4ac3d59251703779836eceff9 \
+    --hash=sha256:2a66fdfb34e05b705620dd567f5a03f239a088d5a3f321e7b6ac3239d22aa286 \
+    --hash=sha256:2e307eb9bd99801f82789b44bb45e9f541961831c7311521b13a6c85afc09767 \
+    --hash=sha256:2e407cb4bd5a13984a6c2c0fe1845e4e41e96f183e5e5cd4d77a857d9693494c \
+    --hash=sha256:2f5e60fabb7343a836360c4f0919b8cd0d6dbf08ad2ca6b9cf90bf0c76a3c4f6 \
+    --hash=sha256:36dafec3d6d6088d34e2de6b85f9d8e2324eb734162fba59d2ba9ed7a2043d5b \
+    --hash=sha256:3fe20f63c9ecee44560d0e7f116b3a747a5d7203376abeea292ab3152334d004 \
+    --hash=sha256:41dae968a94b1ef1897cb322b39360a0812661dba7c682aa45098eb8e193dbdf \
+    --hash=sha256:4bd472dbe5e136f96a4b18f295d159d7f26fd399136f5b17b08c4e5f498cd494 \
+    --hash=sha256:4ea39b0ccc4f5d803e3337dd46bcce60b702be4d86fd0b3d7531ef10fd99a1ac \
+    --hash=sha256:5853eb494c71e267912275e5586fe281444eb5e722de4e131cddf9d442615626 \
+    --hash=sha256:5bce61af018b0cb2055e0e72e7d65290d822d3feee430b7b8203d8a855e78766 \
+    --hash=sha256:6295ecd49304dcf3bfbfa45d9a081c96509e95f4b9d0eb7ee4ec0530c4a96514 \
+    --hash=sha256:62ac9cc684da4cf1778d07a89bf5f81b35834cb96ca523d3a7fb32509380cbf6 \
+    --hash=sha256:70e7c2e7b750585569564e2e5ca9845acfaa5da56ac46df68414f29fea97be9f \
+    --hash=sha256:7577c1987baa3adc4b3c62c33bd1118c3ef5c8ddef36f0f2c950ae0b199e100d \
+    --hash=sha256:75facbe9606748f43428fc91a43edb46c7ff68889b91fa31f53b58894503a191 \
+    --hash=sha256:787518a6789009c159453da4d6b683f468ef7a65bbde796bcea803ccf191058d \
+    --hash=sha256:78d6601aed50c74e0ef02f4204da1816147a6d3fbdc8b3872d263338a9052c51 \
+    --hash=sha256:7c43e1e1206cd421cd92e6b3280d4385d41d7166b3ed577ac20444b6995a445f \
+    --hash=sha256:81e38381b782cc7e1e46c4e14cd997ee6040768101aefc8fa3c24a4cc58e98f8 \
+    --hash=sha256:841293b17ad704d70c578f1f0013c890e219952169ce8a24ebc063eecf775454 \
+    --hash=sha256:872b8ca05c40d309ed13eb2e582cab0c5a05e81e987ab9c521bf05ad1d5cf5cb \
+    --hash=sha256:877272cf6b4b7e94c9614f9b10140e198d2186363728ed0f701c6eee1baec1da \
+    --hash=sha256:8c808594c88a025d4e322d5bb549282c93c8e1ba71b790f539567932722d7bd8 \
+    --hash=sha256:8ed58b8acf29798b036d347791141767ccf65eee7f26bde03a71c944449e53de \
+    --hash=sha256:91672bacaa030f92fc2f43b620d7b337fd9a5af28b0d6ed3f77afc43c4a64b5a \
+    --hash=sha256:968f44fdbf6dd757d12920d63b566eeb4d5b395fd2d00d29d7ef00a00582aac9 \
+    --hash=sha256:9f85003f5dfa867e86d53fac6f7e6f30c045673fa27b603c397753bebadc3008 \
+    --hash=sha256:a553dadda40fef6bfa1456dc4be49b113aa92c2a9a9e8711e955618cd69622e3 \
+    --hash=sha256:a68b62a02953b9841730db7797422f983935aeefceb1679f0fc85cbfbd311c32 \
+    --hash=sha256:abbe9fa13da955feb8202e215c4018f4bb57469b1b78c7a4c5c7b93001699938 \
+    --hash=sha256:ad881edc7ccb9d65b0224f4e4d05a1e85cf62d73aab798943df6d48ab0cd79a1 \
+    --hash=sha256:b1792d939ec70abe76f5054d3f36ed5656021dcad1322d1cc996d4e54165cef9 \
+    --hash=sha256:b428ef021242344340460fa4c9185d0b1f66fbdbfecc6c63eff4b7c29fad429d \
+    --hash=sha256:b533558eae785e33e8c148a8d9921692a9fe5aa516efbdff8606e7d87b9d5824 \
+    --hash=sha256:ba59c92039ec0a66103b1d5fe588fa546373587a7d68f5c96f743c3396afc04b \
+    --hash=sha256:bc8d3bd6c72b2dd9decf16ce70e20abcb3274ba01b4e1c96031e0c4067d1e7cd \
+    --hash=sha256:bc9db8a3efb3e403e4ecc6cd9489ea2bac94244f80c78e27c31dcc00d2790ac2 \
+    --hash=sha256:bf7d9fce9bcc4752ca4a1b80aabd38f6d19009ea5cbda0e0856983cf6d0023f5 \
+    --hash=sha256:c2dbb44c3f7e6c4d3487b31037b1bdbf424d97687c1747ce4ff2895795c9bf69 \
+    --hash=sha256:c79ebe8f3676a4c6630fd3f777f3cfecf9289666c84e775a67d1d358578dc2e3 \
+    --hash=sha256:c97528e64cb9ebeff9701e7938653a9951922f2a38bd847787d4a8e498cc83ae \
+    --hash=sha256:d0611a0a2a518464c05ddd5a3a1a0e856ccc10e67079bb17f265ad19ab3c7597 \
+    --hash=sha256:d06adcfa62a4431d404c31216f0f8ac97397d799cd53800e9d3efc2fbb3cf14e \
+    --hash=sha256:d41997519fcba4a1e46eb4a2fe31bc12f0ff957b2b81bac28db24744f333e955 \
+    --hash=sha256:d5b61785a9ce44e5a4b880272baa7cf6c8f48a5180c3e81c59553ba0cb0821ca \
+    --hash=sha256:da152d8cdcab0e56e4f45eb08b9aea6455845ec83172092f09b0e077ece2cf7a \
+    --hash=sha256:da7e547706e69e45d95e116e6939488d62174e033b763ab1496b4c29b76fabea \
+    --hash=sha256:db5283d90da4174865d520e7366801a93777201e91e79bacbac6e6927cbceede \
+    --hash=sha256:db608a6757adabb32f1cfe6066e39b3706d8c3aa69bbc353a5b61edad36a5cb4 \
+    --hash=sha256:e0ea21f66820452a3f5d1655f8704a60d66ba1191359b96541eaf457710a5fc6 \
+    --hash=sha256:e7da3fec7408813a7cebc9e4ec55afed2d0fd65c4754bc376bf03498d4e92686 \
+    --hash=sha256:e92a513161077b53447160b9bd8f522edfbed4bd9759e4c18ab05d7ef7e49408 \
+    --hash=sha256:ecb1fa0db7bf4cff9dac752abb19505a233c7f16684c5826d1f11ebd9472b871 \
+    --hash=sha256:efda5fc8cc1c61e4f639b8067d118e742b812c930f708e6667a5ce0d13499e29 \
+    --hash=sha256:f0a1dbdb5ecbef0d34eb77e56fcb3e95bbd7e50835d9782a45df81cc46949750 \
+    --hash=sha256:f0a71d85ecdd570ded8ac3d1c0f480842f49a40beb423bb8014539a9f32a5897 \
+    --hash=sha256:f4f270de01dd3e129a72efad823da90cc4d6aafb64c410c9033aba70db9f1ff0 \
+    --hash=sha256:f6cb459eea32a4e2cf18ba5fcece2dbdf496384413bc1bae15583f19e567f3b2 \
+    --hash=sha256:f8ad8285b01b0d4695102546b342b493b3ccc6781fc28c8c6a1bb63e95d22f09 \
+    --hash=sha256:f9f39e2f049db33a908319cf46624a569b36983c7c78318e9726a4cb8923b26c
+    # via matplotlib
+kornia==0.6.8 \
+    --hash=sha256:0985e02453c0ab4f030e8d22a3a7554dab312ffa8f8a54ec872190e6f0b58c56 \
+    --hash=sha256:0d6d69330b4fd24da742337b8134da0ce01b4d7da66770db5498d58e8b4a0832
+    # via k-diffusion
+llvmlite==0.39.1 \
+    --hash=sha256:03aee0ccd81735696474dc4f8b6be60774892a2929d6c05d093d17392c237f32 \
+    --hash=sha256:1578f5000fdce513712e99543c50e93758a954297575610f48cb1fd71b27c08a \
+    --hash=sha256:16f56eb1eec3cda3a5c526bc3f63594fc24e0c8d219375afeb336f289764c6c7 \
+    --hash=sha256:1ec3d70b3e507515936e475d9811305f52d049281eaa6c8273448a61c9b5b7e2 \
+    --hash=sha256:22d36591cd5d02038912321d9ab8e4668e53ae2211da5523f454e992b5e13c36 \
+    --hash=sha256:3803f11ad5f6f6c3d2b545a303d68d9fabb1d50e06a8d6418e6fcd2d0df00959 \
+    --hash=sha256:39dc2160aed36e989610fc403487f11b8764b6650017ff367e45384dff88ffbf \
+    --hash=sha256:3fc14e757bc07a919221f0cbaacb512704ce5774d7fcada793f1996d6bc75f2a \
+    --hash=sha256:4c6ebace910410daf0bebda09c1859504fc2f33d122e9a971c4c349c89cca630 \
+    --hash=sha256:50aea09a2b933dab7c9df92361b1844ad3145bfb8dd2deb9cd8b8917d59306fb \
+    --hash=sha256:60f8dd1e76f47b3dbdee4b38d9189f3e020d22a173c00f930b52131001d801f9 \
+    --hash=sha256:62c0ea22e0b9dffb020601bb65cb11dd967a095a488be73f07d8867f4e327ca5 \
+    --hash=sha256:6546bed4e02a1c3d53a22a0bced254b3b6894693318b16c16c8e43e29d6befb6 \
+    --hash=sha256:6717c7a6e93c9d2c3d07c07113ec80ae24af45cde536b34363d4bcd9188091d9 \
+    --hash=sha256:7ebf1eb9badc2a397d4f6a6c8717447c81ac011db00064a00408bc83c923c0e4 \
+    --hash=sha256:9ffc84ade195abd4abcf0bd3b827b9140ae9ef90999429b9ea84d5df69c9058c \
+    --hash=sha256:a3f331a323d0f0ada6b10d60182ef06c20a2f01be21699999d204c5750ffd0b4 \
+    --hash=sha256:b1a0bbdb274fb683f993198775b957d29a6f07b45d184c571ef2a721ce4388cf \
+    --hash=sha256:b43abd7c82e805261c425d50335be9a6c4f84264e34d6d6e475207300005d572 \
+    --hash=sha256:c0f158e4708dda6367d21cf15afc58de4ebce979c7a1aa2f6b977aae737e2a54 \
+    --hash=sha256:d0bfd18c324549c0fec2c5dc610fd024689de6f27c6cc67e4e24a07541d6e49b \
+    --hash=sha256:ddab526c5a2c4ccb8c9ec4821fcea7606933dc53f510e2a6eebb45a418d3488a \
+    --hash=sha256:e172c73fccf7d6db4bd6f7de963dedded900d1a5c6778733241d878ba613980e \
+    --hash=sha256:e2c00ff204afa721b0bb9835b5bf1ba7fba210eefcec5552a9e05a63219ba0dc \
+    --hash=sha256:e31f4b799d530255aaf0566e3da2df5bfc35d3cd9d6d5a3dcc251663656c27b1 \
+    --hash=sha256:e4f212c018db951da3e1dc25c2651abc688221934739721f2dad5ff1dd5f90e7 \
+    --hash=sha256:fa9b26939ae553bf30a9f5c4c754db0fb2d2677327f2511e674aa2f5df941789 \
+    --hash=sha256:fb62fc7016b592435d3e3a8f680e3ea8897c3c9e62e6e6cc58011e7a4801439e
+    # via numba
+lmdb==1.3.0 \
+    --hash=sha256:008243762decf8f6c90430a9bced56290ebbcdb5e877d90e42343bb97033e494 \
+    --hash=sha256:08f4b5129f4683802569b02581142e415c8dcc0ff07605983ec1b07804cecbad \
+    --hash=sha256:17215a42a4b9814c383deabecb160581e4fb75d00198eef0e3cea54f230ffbea \
+    --hash=sha256:18c69fabdaf04efaf246587739cc1062b3e57c6ef0743f5c418df89e5e7e7b9b \
+    --hash=sha256:2cfa4aa9c67f8aee89b23005e98d1f3f32490b6b905fd1cb604b207cbd5755ab \
+    --hash=sha256:2df38115dd9428a54d59ae7c712a4c7cce0d6b1d66056de4b1a8c38718066106 \
+    --hash=sha256:394df860c3f93cfd92b6f4caba785f38208cc9614c18b3803f83a2cc1695042f \
+    --hash=sha256:41318717ab5d15ad2d6d263d34fbf614a045210f64b25e59ce734bb2105e421f \
+    --hash=sha256:4172fba19417d7b29409beca7d73c067b54e5d8ab1fb9b51d7b4c1445d20a167 \
+    --hash=sha256:5a14aca2651c3af6f0d0a6b9168200eea0c8f2d27c40b01a442f33329a6e8dff \
+    --hash=sha256:5ddd590e1c7fcb395931aa3782fb89b9db4550ab2d81d006ecd239e0d462bc41 \
+    --hash=sha256:60a11efc21aaf009d06518996360eed346f6000bfc9de05114374230879f992e \
+    --hash=sha256:6260a526e4ad85b1f374a5ba9475bf369fb07e7728ea6ec57226b02c40d1976b \
+    --hash=sha256:62ab28e3593bdc318ea2f2fa1574e5fca3b6d1f264686d773ba54a637d4f563b \
+    --hash=sha256:63cb73fe7ce9eb93d992d632c85a0476b4332670d9e6a2802b5062f603b7809f \
+    --hash=sha256:65334eafa5d430b18d81ebd5362559a41483c362e1931f6e1b15bab2ecb7d75d \
+    --hash=sha256:7da05d70fcc6561ac6b09e9fb1bf64b7ca294652c64c8a2889273970cee796b9 \
+    --hash=sha256:abbc439cd9fe60ffd6197009087ea885ac150017dc85384093b1d376f83f0ec4 \
+    --hash=sha256:c6adbd6f7f9048e97f31a069e652eb51020a81e80a0ce92dbb9810d21da2409a \
+    --hash=sha256:d6a816954d212f40fd15007cd81ab7a6bebb77436d949a6a9ae04af57fc127f3 \
+    --hash=sha256:d9103aa4908f0bca43c5911ca067d4e3d01f682dff0c0381a1239bd2bd757984 \
+    --hash=sha256:df2724bad7820114a205472994091097d0fa65a3e5fff5a8e688d123fb8c6326 \
+    --hash=sha256:e568ae0887ae196340947d9800136e90feaed6b86a261ef01f01b2ba65fc8106 \
+    --hash=sha256:e6a704b3baced9182836c7f77b769f23856f3a8f62d0282b1bc1feaf81a86712 \
+    --hash=sha256:eefb392f6b5cd43aada49258c5a79be11cb2c8cd3fc3e2d9319a1e0b9f906458 \
+    --hash=sha256:f291e3f561f58dddf63a92a5a6a4b8af3a0920b6705d35e2f80e52e86ee238a2 \
+    --hash=sha256:fa6439356e591d3249ab0e1778a6f8d8408e993f66dc911914c78208f5310309
+    # via
+    #   basicsr
+    #   gfpgan
+markdown==3.4.1 \
+    --hash=sha256:08fb8465cffd03d10b9dd34a5c3fea908e20391a2a90b88d66362cb05beed186 \
+    --hash=sha256:3b809086bb6efad416156e00a0da66fe47618a5d6918dd688f53f40c8e4cfeff
+    # via
+    #   tb-nightly
+    #   tensorboard
+markupsafe==2.1.1 \
+    --hash=sha256:0212a68688482dc52b2d45013df70d169f542b7394fc744c02a57374a4207003 \
+    --hash=sha256:089cf3dbf0cd6c100f02945abeb18484bd1ee57a079aefd52cffd17fba910b88 \
+    --hash=sha256:10c1bfff05d95783da83491be968e8fe789263689c02724e0c691933c52994f5 \
+    --hash=sha256:33b74d289bd2f5e527beadcaa3f401e0df0a89927c1559c8566c066fa4248ab7 \
+    --hash=sha256:3799351e2336dc91ea70b034983ee71cf2f9533cdff7c14c90ea126bfd95d65a \
+    --hash=sha256:3ce11ee3f23f79dbd06fb3d63e2f6af7b12db1d46932fe7bd8afa259a5996603 \
+    --hash=sha256:421be9fbf0ffe9ffd7a378aafebbf6f4602d564d34be190fc19a193232fd12b1 \
+    --hash=sha256:43093fb83d8343aac0b1baa75516da6092f58f41200907ef92448ecab8825135 \
+    --hash=sha256:46d00d6cfecdde84d40e572d63735ef81423ad31184100411e6e3388d405e247 \
+    --hash=sha256:4a33dea2b688b3190ee12bd7cfa29d39c9ed176bda40bfa11099a3ce5d3a7ac6 \
+    --hash=sha256:4b9fe39a2ccc108a4accc2676e77da025ce383c108593d65cc909add5c3bd601 \
+    --hash=sha256:56442863ed2b06d19c37f94d999035e15ee982988920e12a5b4ba29b62ad1f77 \
+    --hash=sha256:671cd1187ed5e62818414afe79ed29da836dde67166a9fac6d435873c44fdd02 \
+    --hash=sha256:694deca8d702d5db21ec83983ce0bb4b26a578e71fbdbd4fdcd387daa90e4d5e \
+    --hash=sha256:6a074d34ee7a5ce3effbc526b7083ec9731bb3cbf921bbe1d3005d4d2bdb3a63 \
+    --hash=sha256:6d0072fea50feec76a4c418096652f2c3238eaa014b2f94aeb1d56a66b41403f \
+    --hash=sha256:6fbf47b5d3728c6aea2abb0589b5d30459e369baa772e0f37a0320185e87c980 \
+    --hash=sha256:7f91197cc9e48f989d12e4e6fbc46495c446636dfc81b9ccf50bb0ec74b91d4b \
+    --hash=sha256:86b1f75c4e7c2ac2ccdaec2b9022845dbb81880ca318bb7a0a01fbf7813e3812 \
+    --hash=sha256:8dc1c72a69aa7e082593c4a203dcf94ddb74bb5c8a731e4e1eb68d031e8498ff \
+    --hash=sha256:8e3dcf21f367459434c18e71b2a9532d96547aef8a871872a5bd69a715c15f96 \
+    --hash=sha256:8e576a51ad59e4bfaac456023a78f6b5e6e7651dcd383bcc3e18d06f9b55d6d1 \
+    --hash=sha256:96e37a3dc86e80bf81758c152fe66dbf60ed5eca3d26305edf01892257049925 \
+    --hash=sha256:97a68e6ada378df82bc9f16b800ab77cbf4b2fada0081794318520138c088e4a \
+    --hash=sha256:99a2a507ed3ac881b975a2976d59f38c19386d128e7a9a18b7df6fff1fd4c1d6 \
+    --hash=sha256:a49907dd8420c5685cfa064a1335b6754b74541bbb3706c259c02ed65b644b3e \
+    --hash=sha256:b09bf97215625a311f669476f44b8b318b075847b49316d3e28c08e41a7a573f \
+    --hash=sha256:b7bd98b796e2b6553da7225aeb61f447f80a1ca64f41d83612e6139ca5213aa4 \
+    --hash=sha256:b87db4360013327109564f0e591bd2a3b318547bcef31b468a92ee504d07ae4f \
+    --hash=sha256:bcb3ed405ed3222f9904899563d6fc492ff75cce56cba05e32eff40e6acbeaa3 \
+    --hash=sha256:d4306c36ca495956b6d568d276ac11fdd9c30a36f1b6eb928070dc5360b22e1c \
+    --hash=sha256:d5ee4f386140395a2c818d149221149c54849dfcfcb9f1debfe07a8b8bd63f9a \
+    --hash=sha256:dda30ba7e87fbbb7eab1ec9f58678558fd9a6b8b853530e176eabd064da81417 \
+    --hash=sha256:e04e26803c9c3851c931eac40c695602c6295b8d432cbe78609649ad9bd2da8a \
+    --hash=sha256:e1c0b87e09fa55a220f058d1d49d3fb8df88fbfab58558f1198e08c1e1de842a \
+    --hash=sha256:e72591e9ecd94d7feb70c1cbd7be7b3ebea3f548870aa91e2732960fa4d57a37 \
+    --hash=sha256:e8c843bbcda3a2f1e3c2ab25913c80a3c5376cd00c6e8c4a86a89a28c8dc5452 \
+    --hash=sha256:efc1913fd2ca4f334418481c7e595c00aad186563bbc1ec76067848c7ca0a933 \
+    --hash=sha256:f121a1420d4e173a5d96e47e9a0c0dcff965afdf1626d28de1460815f7c4ee7a \
+    --hash=sha256:fc7b548b17d238737688817ab67deebb30e8073c95749d55538ed473130ec0c7
+    # via
+    #   jinja2
+    #   werkzeug
+matplotlib==3.6.2 \
+    --hash=sha256:0844523dfaaff566e39dbfa74e6f6dc42e92f7a365ce80929c5030b84caa563a \
+    --hash=sha256:0eda9d1b43f265da91fb9ae10d6922b5a986e2234470a524e6b18f14095b20d2 \
+    --hash=sha256:168093410b99f647ba61361b208f7b0d64dde1172b5b1796d765cd243cadb501 \
+    --hash=sha256:1836f366272b1557a613f8265db220eb8dd883202bbbabe01bad5a4eadfd0c95 \
+    --hash=sha256:19d61ee6414c44a04addbe33005ab1f87539d9f395e25afcbe9a3c50ce77c65c \
+    --hash=sha256:252957e208c23db72ca9918cb33e160c7833faebf295aaedb43f5b083832a267 \
+    --hash=sha256:32d29c8c26362169c80c5718ce367e8c64f4dd068a424e7110df1dd2ed7bd428 \
+    --hash=sha256:380d48c15ec41102a2b70858ab1dedfa33eb77b2c0982cb65a200ae67a48e9cb \
+    --hash=sha256:3964934731fd7a289a91d315919cf757f293969a4244941ab10513d2351b4e83 \
+    --hash=sha256:3cef89888a466228fc4e4b2954e740ce8e9afde7c4315fdd18caa1b8de58ca17 \
+    --hash=sha256:4426c74761790bff46e3d906c14c7aab727543293eed5a924300a952e1a3a3c1 \
+    --hash=sha256:5024b8ed83d7f8809982d095d8ab0b179bebc07616a9713f86d30cf4944acb73 \
+    --hash=sha256:52c2bdd7cd0bf9d5ccdf9c1816568fd4ccd51a4d82419cc5480f548981b47dd0 \
+    --hash=sha256:54fa9fe27f5466b86126ff38123261188bed568c1019e4716af01f97a12fe812 \
+    --hash=sha256:5ba73aa3aca35d2981e0b31230d58abb7b5d7ca104e543ae49709208d8ce706a \
+    --hash=sha256:5e16dcaecffd55b955aa5e2b8a804379789c15987e8ebd2f32f01398a81e975b \
+    --hash=sha256:5ecfc6559132116dedfc482d0ad9df8a89dc5909eebffd22f3deb684132d002f \
+    --hash=sha256:74153008bd24366cf099d1f1e83808d179d618c4e32edb0d489d526523a94d9f \
+    --hash=sha256:78ec3c3412cf277e6252764ee4acbdbec6920cc87ad65862272aaa0e24381eee \
+    --hash=sha256:795ad83940732b45d39b82571f87af0081c120feff2b12e748d96bb191169e33 \
+    --hash=sha256:7f716b6af94dc1b6b97c46401774472f0867e44595990fe80a8ba390f7a0a028 \
+    --hash=sha256:83dc89c5fd728fdb03b76f122f43b4dcee8c61f1489e232d9ad0f58020523e1c \
+    --hash=sha256:8a0ae37576ed444fe853709bdceb2be4c7df6f7acae17b8378765bd28e61b3ae \
+    --hash=sha256:8a8dbe2cb7f33ff54b16bb5c500673502a35f18ac1ed48625e997d40c922f9cc \
+    --hash=sha256:8a9d899953c722b9afd7e88dbefd8fb276c686c3116a43c577cfabf636180558 \
+    --hash=sha256:8d0068e40837c1d0df6e3abf1cdc9a34a6d2611d90e29610fa1d2455aeb4e2e5 \
+    --hash=sha256:9347cc6822f38db2b1d1ce992f375289670e595a2d1c15961aacbe0977407dfc \
+    --hash=sha256:9f335e5625feb90e323d7e3868ec337f7b9ad88b5d633f876e3b778813021dab \
+    --hash=sha256:b03fd10a1709d0101c054883b550f7c4c5e974f751e2680318759af005964990 \
+    --hash=sha256:b0ca2c60d3966dfd6608f5f8c49b8a0fcf76de6654f2eda55fc6ef038d5a6f27 \
+    --hash=sha256:b2604c6450f9dd2c42e223b1f5dca9643a23cfecc9fde4a94bb38e0d2693b136 \
+    --hash=sha256:ca0e7a658fbafcddcaefaa07ba8dae9384be2343468a8e011061791588d839fa \
+    --hash=sha256:d0e9ac04065a814d4cf2c6791a2ad563f739ae3ae830d716d54245c2b96fead6 \
+    --hash=sha256:d50e8c1e571ee39b5dfbc295c11ad65988879f68009dd281a6e1edbc2ff6c18c \
+    --hash=sha256:d840adcad7354be6f2ec28d0706528b0026e4c3934cc6566b84eac18633eab1b \
+    --hash=sha256:e0bbee6c2a5bf2a0017a9b5e397babb88f230e6f07c3cdff4a4c4bc75ed7c617 \
+    --hash=sha256:e5afe0a7ea0e3a7a257907060bee6724a6002b7eec55d0db16fd32409795f3e1 \
+    --hash=sha256:e68be81cd8c22b029924b6d0ee814c337c0e706b8d88495a617319e5dd5441c3 \
+    --hash=sha256:ec9be0f4826cdb3a3a517509dcc5f87f370251b76362051ab59e42b6b765f8c4 \
+    --hash=sha256:f04f97797df35e442ed09f529ad1235d1f1c0f30878e2fe09a2676b71a8801e0 \
+    --hash=sha256:f41e57ad63d336fe50d3a67bb8eaa26c09f6dda6a59f76777a99b8ccd8e26aec
+    # via filterpy
+multidict==6.0.2 \
+    --hash=sha256:0327292e745a880459ef71be14e709aaea2f783f3537588fb4ed09b6c01bca60 \
+    --hash=sha256:041b81a5f6b38244b34dc18c7b6aba91f9cdaf854d9a39e5ff0b58e2b5773b9c \
+    --hash=sha256:0556a1d4ea2d949efe5fd76a09b4a82e3a4a30700553a6725535098d8d9fb672 \
+    --hash=sha256:05f6949d6169878a03e607a21e3b862eaf8e356590e8bdae4227eedadacf6e51 \
+    --hash=sha256:07a017cfa00c9890011628eab2503bee5872f27144936a52eaab449be5eaf032 \
+    --hash=sha256:0b9e95a740109c6047602f4db4da9949e6c5945cefbad34a1299775ddc9a62e2 \
+    --hash=sha256:19adcfc2a7197cdc3987044e3f415168fc5dc1f720c932eb1ef4f71a2067e08b \
+    --hash=sha256:19d9bad105dfb34eb539c97b132057a4e709919ec4dd883ece5838bcbf262b80 \
+    --hash=sha256:225383a6603c086e6cef0f2f05564acb4f4d5f019a4e3e983f572b8530f70c88 \
+    --hash=sha256:23b616fdc3c74c9fe01d76ce0d1ce872d2d396d8fa8e4899398ad64fb5aa214a \
+    --hash=sha256:2957489cba47c2539a8eb7ab32ff49101439ccf78eab724c828c1a54ff3ff98d \
+    --hash=sha256:2d36e929d7f6a16d4eb11b250719c39560dd70545356365b494249e2186bc389 \
+    --hash=sha256:2e4a0785b84fb59e43c18a015ffc575ba93f7d1dbd272b4cdad9f5134b8a006c \
+    --hash=sha256:3368bf2398b0e0fcbf46d85795adc4c259299fec50c1416d0f77c0a843a3eed9 \
+    --hash=sha256:373ba9d1d061c76462d74e7de1c0c8e267e9791ee8cfefcf6b0b2495762c370c \
+    --hash=sha256:4070613ea2227da2bfb2c35a6041e4371b0af6b0be57f424fe2318b42a748516 \
+    --hash=sha256:45183c96ddf61bf96d2684d9fbaf6f3564d86b34cb125761f9a0ef9e36c1d55b \
+    --hash=sha256:4571f1beddff25f3e925eea34268422622963cd8dc395bb8778eb28418248e43 \
+    --hash=sha256:47e6a7e923e9cada7c139531feac59448f1f47727a79076c0b1ee80274cd8eee \
+    --hash=sha256:47fbeedbf94bed6547d3aa632075d804867a352d86688c04e606971595460227 \
+    --hash=sha256:497988d6b6ec6ed6f87030ec03280b696ca47dbf0648045e4e1d28b80346560d \
+    --hash=sha256:4bae31803d708f6f15fd98be6a6ac0b6958fcf68fda3c77a048a4f9073704aae \
+    --hash=sha256:50bd442726e288e884f7be9071016c15a8742eb689a593a0cac49ea093eef0a7 \
+    --hash=sha256:514fe2b8d750d6cdb4712346a2c5084a80220821a3e91f3f71eec11cf8d28fd4 \
+    --hash=sha256:5774d9218d77befa7b70d836004a768fb9aa4fdb53c97498f4d8d3f67bb9cfa9 \
+    --hash=sha256:5fdda29a3c7e76a064f2477c9aab1ba96fd94e02e386f1e665bca1807fc5386f \
+    --hash=sha256:5ff3bd75f38e4c43f1f470f2df7a4d430b821c4ce22be384e1459cb57d6bb013 \
+    --hash=sha256:626fe10ac87851f4cffecee161fc6f8f9853f0f6f1035b59337a51d29ff3b4f9 \
+    --hash=sha256:6701bf8a5d03a43375909ac91b6980aea74b0f5402fbe9428fc3f6edf5d9677e \
+    --hash=sha256:684133b1e1fe91eda8fa7447f137c9490a064c6b7f392aa857bba83a28cfb693 \
+    --hash=sha256:6f3cdef8a247d1eafa649085812f8a310e728bdf3900ff6c434eafb2d443b23a \
+    --hash=sha256:75bdf08716edde767b09e76829db8c1e5ca9d8bb0a8d4bd94ae1eafe3dac5e15 \
+    --hash=sha256:7c40b7bbece294ae3a87c1bc2abff0ff9beef41d14188cda94ada7bcea99b0fb \
+    --hash=sha256:8004dca28e15b86d1b1372515f32eb6f814bdf6f00952699bdeb541691091f96 \
+    --hash=sha256:8064b7c6f0af936a741ea1efd18690bacfbae4078c0c385d7c3f611d11f0cf87 \
+    --hash=sha256:89171b2c769e03a953d5969b2f272efa931426355b6c0cb508022976a17fd376 \
+    --hash=sha256:8cbf0132f3de7cc6c6ce00147cc78e6439ea736cee6bca4f068bcf892b0fd658 \
+    --hash=sha256:9cc57c68cb9139c7cd6fc39f211b02198e69fb90ce4bc4a094cf5fe0d20fd8b0 \
+    --hash=sha256:a007b1638e148c3cfb6bf0bdc4f82776cef0ac487191d093cdc316905e504071 \
+    --hash=sha256:a2c34a93e1d2aa35fbf1485e5010337c72c6791407d03aa5f4eed920343dd360 \
+    --hash=sha256:a45e1135cb07086833ce969555df39149680e5471c04dfd6a915abd2fc3f6dbc \
+    --hash=sha256:ac0e27844758d7177989ce406acc6a83c16ed4524ebc363c1f748cba184d89d3 \
+    --hash=sha256:aef9cc3d9c7d63d924adac329c33835e0243b5052a6dfcbf7732a921c6e918ba \
+    --hash=sha256:b9d153e7f1f9ba0b23ad1568b3b9e17301e23b042c23870f9ee0522dc5cc79e8 \
+    --hash=sha256:bfba7c6d5d7c9099ba21f84662b037a0ffd4a5e6b26ac07d19e423e6fdf965a9 \
+    --hash=sha256:c207fff63adcdf5a485969131dc70e4b194327666b7e8a87a97fbc4fd80a53b2 \
+    --hash=sha256:d0509e469d48940147e1235d994cd849a8f8195e0bca65f8f5439c56e17872a3 \
+    --hash=sha256:d16cce709ebfadc91278a1c005e3c17dd5f71f5098bfae1035149785ea6e9c68 \
+    --hash=sha256:d48b8ee1d4068561ce8033d2c344cf5232cb29ee1a0206a7b828c79cbc5982b8 \
+    --hash=sha256:de989b195c3d636ba000ee4281cd03bb1234635b124bf4cd89eeee9ca8fcb09d \
+    --hash=sha256:e07c8e79d6e6fd37b42f3250dba122053fddb319e84b55dd3a8d6446e1a7ee49 \
+    --hash=sha256:e2c2e459f7050aeb7c1b1276763364884595d47000c1cddb51764c0d8976e608 \
+    --hash=sha256:e5b20e9599ba74391ca0cfbd7b328fcc20976823ba19bc573983a25b32e92b57 \
+    --hash=sha256:e875b6086e325bab7e680e4316d667fc0e5e174bb5611eb16b3ea121c8951b86 \
+    --hash=sha256:f4f052ee022928d34fe1f4d2bc743f32609fb79ed9c49a1710a5ad6b2198db20 \
+    --hash=sha256:fcb91630817aa8b9bc4a74023e4198480587269c272c58b3279875ed7235c293 \
+    --hash=sha256:fd9fc9c4849a07f3635ccffa895d57abce554b467d611a5009ba4f39b78a8849 \
+    --hash=sha256:feba80698173761cddd814fa22e88b0661e98cb810f9f986c54aa34d281e4937 \
+    --hash=sha256:feea820722e69451743a3d56ad74948b68bf456984d63c1a92e8347b7b88452d
+    # via
+    #   aiohttp
+    #   yarl
+networkx==2.8.8 \
+    --hash=sha256:230d388117af870fce5647a3c52401fcf753e94720e6ea6b4197a5355648885e \
+    --hash=sha256:e435dfa75b1d7195c7b8378c3859f0445cd88c6b0375c181ed66823a9ceb7524
+    # via scikit-image
+numba==0.56.4 \
+    --hash=sha256:0240f9026b015e336069329839208ebd70ec34ae5bfbf402e4fcc8e06197528e \
+    --hash=sha256:03634579d10a6129181129de293dd6b5eaabee86881369d24d63f8fe352dd6cb \
+    --hash=sha256:03fe94cd31e96185cce2fae005334a8cc712fc2ba7756e52dff8c9400718173f \
+    --hash=sha256:0611e6d3eebe4cb903f1a836ffdb2bda8d18482bcd0a0dcc56e79e2aa3fefef5 \
+    --hash=sha256:0da583c532cd72feefd8e551435747e0e0fbb3c0530357e6845fcc11e38d6aea \
+    --hash=sha256:14dbbabf6ffcd96ee2ac827389afa59a70ffa9f089576500434c34abf9b054a4 \
+    --hash=sha256:32d9fef412c81483d7efe0ceb6cf4d3310fde8b624a9cecca00f790573ac96ee \
+    --hash=sha256:3a993349b90569518739009d8f4b523dfedd7e0049e6838c0e17435c3e70dcc4 \
+    --hash=sha256:3cb1a07a082a61df80a468f232e452d818f5ae254b40c26390054e4e868556e0 \
+    --hash=sha256:42f9e1be942b215df7e6cc9948cf9c15bb8170acc8286c063a9e57994ef82fd1 \
+    --hash=sha256:4373da9757049db7c90591e9ec55a2e97b2b36ba7ae3bf9c956a513374077470 \
+    --hash=sha256:4e08e203b163ace08bad500b0c16f6092b1eb34fd1fce4feaf31a67a3a5ecf3b \
+    --hash=sha256:553da2ce74e8862e18a72a209ed3b6d2924403bdd0fb341fa891c6455545ba7c \
+    --hash=sha256:720886b852a2d62619ae3900fe71f1852c62db4f287d0c275a60219e1643fc04 \
+    --hash=sha256:85dbaed7a05ff96492b69a8900c5ba605551afb9b27774f7f10511095451137c \
+    --hash=sha256:8a95ca9cc77ea4571081f6594e08bd272b66060634b8324e99cd1843020364f9 \
+    --hash=sha256:91f021145a8081f881996818474ef737800bcc613ffb1e618a655725a0f9e246 \
+    --hash=sha256:9f62672145f8669ec08762895fe85f4cf0ead08ce3164667f2b94b2f62ab23c3 \
+    --hash=sha256:a12ef323c0f2101529d455cfde7f4135eaa147bad17afe10b48634f796d96abd \
+    --hash=sha256:c602d015478b7958408d788ba00a50272649c5186ea8baa6cf71d4a1c761bba1 \
+    --hash=sha256:c75e8a5f810ce80a0cfad6e74ee94f9fde9b40c81312949bf356b7304ef20740 \
+    --hash=sha256:d0ae9270a7a5cc0ede63cd234b4ff1ce166c7a749b91dbbf45e0000c56d3eade \
+    --hash=sha256:d69ad934e13c15684e7887100a8f5f0f61d7a8e57e0fd29d9993210089a5b531 \
+    --hash=sha256:dbcc847bac2d225265d054993a7f910fda66e73d6662fe7156452cac0325b073 \
+    --hash=sha256:e64d338b504c9394a4a34942df4627e1e6cb07396ee3b49fe7b8d6420aa5104f \
+    --hash=sha256:f4cfc3a19d1e26448032049c79fc60331b104f694cf570a9e94f4e2c9d0932bb \
+    --hash=sha256:fbfb45e7b297749029cb28694abf437a78695a100e7c2033983d69f0ba2698d4 \
+    --hash=sha256:fcdf84ba3ed8124eb7234adfbb8792f311991cbf8aed1cad4b1b1a7ee08380c1
+    # via facexlib
+numpy==1.23.4 \
+    --hash=sha256:0fe563fc8ed9dc4474cbf70742673fc4391d70f4363f917599a7fa99f042d5a8 \
+    --hash=sha256:12ac457b63ec8ded85d85c1e17d85efd3c2b0967ca39560b307a35a6703a4735 \
+    --hash=sha256:2341f4ab6dba0834b685cce16dad5f9b6606ea8a00e6da154f5dbded70fdc4dd \
+    --hash=sha256:296d17aed51161dbad3c67ed6d164e51fcd18dbcd5dd4f9d0a9c6055dce30810 \
+    --hash=sha256:488a66cb667359534bc70028d653ba1cf307bae88eab5929cd707c761ff037db \
+    --hash=sha256:4d52914c88b4930dafb6c48ba5115a96cbab40f45740239d9f4159c4ba779962 \
+    --hash=sha256:5e13030f8793e9ee42f9c7d5777465a560eb78fa7e11b1c053427f2ccab90c79 \
+    --hash=sha256:61be02e3bf810b60ab74e81d6d0d36246dbfb644a462458bb53b595791251911 \
+    --hash=sha256:7607b598217745cc40f751da38ffd03512d33ec06f3523fb0b5f82e09f6f676d \
+    --hash=sha256:7a70a7d3ce4c0e9284e92285cba91a4a3f5214d87ee0e95928f3614a256a1488 \
+    --hash=sha256:7ab46e4e7ec63c8a5e6dbf5c1b9e1c92ba23a7ebecc86c336cb7bf3bd2fb10e5 \
+    --hash=sha256:8981d9b5619569899666170c7c9748920f4a5005bf79c72c07d08c8a035757b0 \
+    --hash=sha256:8c053d7557a8f022ec823196d242464b6955a7e7e5015b719e76003f63f82d0f \
+    --hash=sha256:926db372bc4ac1edf81cfb6c59e2a881606b409ddc0d0920b988174b2e2a767f \
+    --hash=sha256:95d79ada05005f6f4f337d3bb9de8a7774f259341c70bc88047a1f7b96a4bcb2 \
+    --hash=sha256:95de7dc7dc47a312f6feddd3da2500826defdccbc41608d0031276a24181a2c0 \
+    --hash=sha256:a0882323e0ca4245eb0a3d0a74f88ce581cc33aedcfa396e415e5bba7bf05f68 \
+    --hash=sha256:a8365b942f9c1a7d0f0dc974747d99dd0a0cdfc5949a33119caf05cb314682d3 \
+    --hash=sha256:a8aae2fb3180940011b4862b2dd3756616841c53db9734b27bb93813cd79fce6 \
+    --hash=sha256:c237129f0e732885c9a6076a537e974160482eab8f10db6292e92154d4c67d71 \
+    --hash=sha256:c67b833dbccefe97cdd3f52798d430b9d3430396af7cdb2a0c32954c3ef73894 \
+    --hash=sha256:ce03305dd694c4873b9429274fd41fc7eb4e0e4dea07e0af97a933b079a5814f \
+    --hash=sha256:d331afac87c92373826af83d2b2b435f57b17a5c74e6268b79355b970626e329 \
+    --hash=sha256:dada341ebb79619fe00a291185bba370c9803b1e1d7051610e01ed809ef3a4ba \
+    --hash=sha256:ed2cc92af0efad20198638c69bb0fc2870a58dabfba6eb722c933b48556c686c \
+    --hash=sha256:f260da502d7441a45695199b4e7fd8ca87db659ba1c78f2bbf31f934fe76ae0e \
+    --hash=sha256:f2f390aa4da44454db40a1f0201401f9036e8d578a25f01a6e237cea238337ef \
+    --hash=sha256:f76025acc8e2114bb664294a07ede0727aa75d63a06d2fae96bf29a81747e4a7
+    # via
+    #   accelerate
+    #   albumentations
+    #   altair
+    #   basicsr
+    #   clean-fid
+    #   contourpy
+    #   diffusers
+    #   facexlib
+    #   filterpy
+    #   gfpgan
+    #   imageio
+    #   matplotlib
+    #   numba
+    #   opencv-python
+    #   opencv-python-headless
+    #   pandas
+    #   pyarrow
+    #   pydeck
+    #   pytorch-lightning
+    #   pywavelets
+    #   qudida
+    #   realesrgan
+    #   scikit-image
+    #   scikit-learn
+    #   scipy
+    #   streamlit
+    #   taming-transformers-rom1504
+    #   tb-nightly
+    #   tensorboard
+    #   test-tube
+    #   tifffile
+    #   torch-fidelity
+    #   torchmetrics
+    #   torchvision
+    #   transformers
+oauthlib==3.2.2 \
+    --hash=sha256:8139f29aac13e25d502680e9e19963e83f16838d48a0d71c287fe40e7067fbca \
+    --hash=sha256:9859c40929662bec5d64f34d01c99e093149682a3f38915dc0655d5a633dd918
+    # via requests-oauthlib
+omegaconf==2.2.3 \
+    --hash=sha256:59ff9fba864ffbb5fb710b64e8a9ba37c68fa339a2e2bb4f1b648d6901552523 \
+    --hash=sha256:d6f2cbf79a992899eb76c6cb1aedfcf0fe7456a8654382edd5ee0c1b199c0657
+    # via taming-transformers-rom1504
+opencv-python==4.6.0.66 \
+    --hash=sha256:0dc82a3d8630c099d2f3ac1b1aabee164e8188db54a786abb7a4e27eba309440 \
+    --hash=sha256:5af8ba35a4fcb8913ffb86e92403e9a656a4bff4a645d196987468f0f8947875 \
+    --hash=sha256:6e32af22e3202748bd233ed8f538741876191863882eba44e332d1a34993165b \
+    --hash=sha256:c5bfae41ad4031e66bb10ec4a0a2ffd3e514d092652781e8b1ac98d1b59f1158 \
+    --hash=sha256:dbdc84a9b4ea2cbae33861652d25093944b9959279200b7ae0badd32439f74de \
+    --hash=sha256:e6e448b62afc95c5b58f97e87ef84699e6607fe5c58730a03301c52496005cae \
+    --hash=sha256:f482e78de6e7b0b060ff994ffd859bddc3f7f382bb2019ef157b0ea8ca8712f5
+    # via
+    #   basicsr
+    #   facexlib
+    #   gfpgan
+    #   realesrgan
+opencv-python-headless==4.6.0.66 \
+    --hash=sha256:21e70f8b0c04098cdf466d27184fe6c3820aaef944a22548db95099959c95889 \
+    --hash=sha256:2c032c373e447c3fc2a670bca20e2918a1205a6e72854df60425fd3f82c78c32 \
+    --hash=sha256:3bacd806cce1f1988e58f3d6f761538e0215d6621d316de94c009dc0acbd6ad3 \
+    --hash=sha256:d5291d7e10aa2c19cab6fd86f0d61af8617290ecd2d7ffcb051e446868d04cc5 \
+    --hash=sha256:e6c069bc963d7e8fcec21b3e33e594d35948badd63eccb2e80f88b0a12102c03 \
+    --hash=sha256:eec6281054346103d6af93f173b7c6a206beb2663d3adc04aa3ddc66e85093df \
+    --hash=sha256:ffbf26fcd697af996408440a93bc69c49c05a36845771f984156dfbeaa95d497
+    # via
+    #   albumentations
+    #   qudida
+packaging==21.3 \
+    --hash=sha256:dd47c42927d89ab911e606518907cc2d3a1f38bbd026385970643f9c5b8ecfeb \
+    --hash=sha256:ef103e05f519cdc783ae24ea4e2e0f508a9c99b2d4969652eed6a2e1ea5bd522
+    # via
+    #   accelerate
+    #   huggingface-hub
+    #   kornia
+    #   matplotlib
+    #   pytorch-lightning
+    #   scikit-image
+    #   streamlit
+    #   torchmetrics
+    #   transformers
+pandas==1.5.1 \
+    --hash=sha256:04e51b01d5192499390c0015630975f57836cc95c7411415b499b599b05c0c96 \
+    --hash=sha256:05c527c64ee02a47a24031c880ee0ded05af0623163494173204c5b72ddce658 \
+    --hash=sha256:0a78e05ec09731c5b3bd7a9805927ea631fe6f6cb06f0e7c63191a9a778d52b4 \
+    --hash=sha256:17da7035d9e6f9ea9cdc3a513161f8739b8f8489d31dc932bc5a29a27243f93d \
+    --hash=sha256:249cec5f2a5b22096440bd85c33106b6102e0672204abd2d5c014106459804ee \
+    --hash=sha256:2c25e5c16ee5c0feb6cf9d982b869eec94a22ddfda9aa2fbed00842cbb697624 \
+    --hash=sha256:32e3d9f65606b3f6e76555bfd1d0b68d94aff0929d82010b791b6254bf5a4b96 \
+    --hash=sha256:36aa1f8f680d7584e9b572c3203b20d22d697c31b71189322f16811d4ecfecd3 \
+    --hash=sha256:5b0c970e2215572197b42f1cff58a908d734503ea54b326412c70d4692256391 \
+    --hash=sha256:5cee0c74e93ed4f9d39007e439debcaadc519d7ea5c0afc3d590a3a7b2edf060 \
+    --hash=sha256:669c8605dba6c798c1863157aefde959c1796671ffb342b80fcb80a4c0bc4c26 \
+    --hash=sha256:66a1ad667b56e679e06ba73bb88c7309b3f48a4c279bd3afea29f65a766e9036 \
+    --hash=sha256:683779e5728ac9138406c59a11e09cd98c7d2c12f0a5fc2b9c5eecdbb4a00075 \
+    --hash=sha256:6bb391659a747cf4f181a227c3e64b6d197100d53da98dcd766cc158bdd9ec68 \
+    --hash=sha256:81f0674fa50b38b6793cd84fae5d67f58f74c2d974d2cb4e476d26eee33343d0 \
+    --hash=sha256:927e59c694e039c75d7023465d311277a1fc29ed7236b5746e9dddf180393113 \
+    --hash=sha256:932d2d7d3cab44cfa275601c982f30c2d874722ef6396bb539e41e4dc4618ed4 \
+    --hash=sha256:a52419d9ba5906db516109660b114faf791136c94c1a636ed6b29cbfff9187ee \
+    --hash=sha256:b156a971bc451c68c9e1f97567c94fd44155f073e3bceb1b0d195fd98ed12048 \
+    --hash=sha256:bcf1a82b770b8f8c1e495b19a20d8296f875a796c4fe6e91da5ef107f18c5ecb \
+    --hash=sha256:cb2a9cf1150302d69bb99861c5cddc9c25aceacb0a4ef5299785d0f5389a3209 \
+    --hash=sha256:d8c709f4700573deb2036d240d140934df7e852520f4a584b2a8d5443b71f54d \
+    --hash=sha256:db45b94885000981522fb92349e6b76f5aee0924cc5315881239c7859883117d \
+    --hash=sha256:ddf46b940ef815af4e542697eaf071f0531449407a7607dd731bf23d156e20a7 \
+    --hash=sha256:e675f8fe9aa6c418dc8d3aac0087b5294c1a4527f1eacf9fe5ea671685285454 \
+    --hash=sha256:eb7e8cf2cf11a2580088009b43de84cabbf6f5dae94ceb489f28dba01a17cb77 \
+    --hash=sha256:f340331a3f411910adfb4bbe46c2ed5872d9e473a783d7f14ecf49bc0869c594
+    # via
+    #   altair
+    #   streamlit
+    #   test-tube
+pathtools==0.1.2 \
+    --hash=sha256:7c35c5421a39bb82e58018febd90e3b6e5db34c5443aaaf742b3f33d4655f1c0
+    # via wandb
+pillow==9.3.0 \
+    --hash=sha256:03150abd92771742d4a8cd6f2fa6246d847dcd2e332a18d0c15cc75bf6703040 \
+    --hash=sha256:073adb2ae23431d3b9bcbcff3fe698b62ed47211d0716b067385538a1b0f28b8 \
+    --hash=sha256:0b07fffc13f474264c336298d1b4ce01d9c5a011415b79d4ee5527bb69ae6f65 \
+    --hash=sha256:0b7257127d646ff8676ec8a15520013a698d1fdc48bc2a79ba4e53df792526f2 \
+    --hash=sha256:12ce4932caf2ddf3e41d17fc9c02d67126935a44b86df6a206cf0d7161548627 \
+    --hash=sha256:15c42fb9dea42465dfd902fb0ecf584b8848ceb28b41ee2b58f866411be33f07 \
+    --hash=sha256:18498994b29e1cf86d505edcb7edbe814d133d2232d256db8c7a8ceb34d18cef \
+    --hash=sha256:1c7c8ae3864846fc95f4611c78129301e203aaa2af813b703c55d10cc1628535 \
+    --hash=sha256:22b012ea2d065fd163ca096f4e37e47cd8b59cf4b0fd47bfca6abb93df70b34c \
+    --hash=sha256:276a5ca930c913f714e372b2591a22c4bd3b81a418c0f6635ba832daec1cbcfc \
+    --hash=sha256:2e0918e03aa0c72ea56edbb00d4d664294815aa11291a11504a377ea018330d3 \
+    --hash=sha256:3033fbe1feb1b59394615a1cafaee85e49d01b51d54de0cbf6aa8e64182518a1 \
+    --hash=sha256:3168434d303babf495d4ba58fc22d6604f6e2afb97adc6a423e917dab828939c \
+    --hash=sha256:32a44128c4bdca7f31de5be641187367fe2a450ad83b833ef78910397db491aa \
+    --hash=sha256:3dd6caf940756101205dffc5367babf288a30043d35f80936f9bfb37f8355b32 \
+    --hash=sha256:40e1ce476a7804b0fb74bcfa80b0a2206ea6a882938eaba917f7a0f004b42502 \
+    --hash=sha256:41e0051336807468be450d52b8edd12ac60bebaa97fe10c8b660f116e50b30e4 \
+    --hash=sha256:4390e9ce199fc1951fcfa65795f239a8a4944117b5935a9317fb320e7767b40f \
+    --hash=sha256:502526a2cbfa431d9fc2a079bdd9061a2397b842bb6bc4239bb176da00993812 \
+    --hash=sha256:51e0e543a33ed92db9f5ef69a0356e0b1a7a6b6a71b80df99f1d181ae5875636 \
+    --hash=sha256:57751894f6618fd4308ed8e0c36c333e2f5469744c34729a27532b3db106ee20 \
+    --hash=sha256:5d77adcd56a42d00cc1be30843d3426aa4e660cab4a61021dc84467123f7a00c \
+    --hash=sha256:655a83b0058ba47c7c52e4e2df5ecf484c1b0b0349805896dd350cbc416bdd91 \
+    --hash=sha256:68943d632f1f9e3dce98908e873b3a090f6cba1cbb1b892a9e8d97c938871fbe \
+    --hash=sha256:6c738585d7a9961d8c2821a1eb3dcb978d14e238be3d70f0a706f7fa9316946b \
+    --hash=sha256:73bd195e43f3fadecfc50c682f5055ec32ee2c933243cafbfdec69ab1aa87cad \
+    --hash=sha256:772a91fc0e03eaf922c63badeca75e91baa80fe2f5f87bdaed4280662aad25c9 \
+    --hash=sha256:77ec3e7be99629898c9a6d24a09de089fa5356ee408cdffffe62d67bb75fdd72 \
+    --hash=sha256:7db8b751ad307d7cf238f02101e8e36a128a6cb199326e867d1398067381bff4 \
+    --hash=sha256:801ec82e4188e935c7f5e22e006d01611d6b41661bba9fe45b60e7ac1a8f84de \
+    --hash=sha256:82409ffe29d70fd733ff3c1025a602abb3e67405d41b9403b00b01debc4c9a29 \
+    --hash=sha256:828989c45c245518065a110434246c44a56a8b2b2f6347d1409c787e6e4651ee \
+    --hash=sha256:829f97c8e258593b9daa80638aee3789b7df9da5cf1336035016d76f03b8860c \
+    --hash=sha256:871b72c3643e516db4ecf20efe735deb27fe30ca17800e661d769faab45a18d7 \
+    --hash=sha256:89dca0ce00a2b49024df6325925555d406b14aa3efc2f752dbb5940c52c56b11 \
+    --hash=sha256:90fb88843d3902fe7c9586d439d1e8c05258f41da473952aa8b328d8b907498c \
+    --hash=sha256:97aabc5c50312afa5e0a2b07c17d4ac5e865b250986f8afe2b02d772567a380c \
+    --hash=sha256:9aaa107275d8527e9d6e7670b64aabaaa36e5b6bd71a1015ddd21da0d4e06448 \
+    --hash=sha256:9f47eabcd2ded7698106b05c2c338672d16a6f2a485e74481f524e2a23c2794b \
+    --hash=sha256:a0a06a052c5f37b4ed81c613a455a81f9a3a69429b4fd7bb913c3fa98abefc20 \
+    --hash=sha256:ab388aaa3f6ce52ac1cb8e122c4bd46657c15905904b3120a6248b5b8b0bc228 \
+    --hash=sha256:ad58d27a5b0262c0c19b47d54c5802db9b34d38bbf886665b626aff83c74bacd \
+    --hash=sha256:ae5331c23ce118c53b172fa64a4c037eb83c9165aba3a7ba9ddd3ec9fa64a699 \
+    --hash=sha256:af0372acb5d3598f36ec0914deed2a63f6bcdb7b606da04dc19a88d31bf0c05b \
+    --hash=sha256:afa4107d1b306cdf8953edde0534562607fe8811b6c4d9a486298ad31de733b2 \
+    --hash=sha256:b03ae6f1a1878233ac620c98f3459f79fd77c7e3c2b20d460284e1fb370557d4 \
+    --hash=sha256:b0915e734b33a474d76c28e07292f196cdf2a590a0d25bcc06e64e545f2d146c \
+    --hash=sha256:b4012d06c846dc2b80651b120e2cdd787b013deb39c09f407727ba90015c684f \
+    --hash=sha256:b472b5ea442148d1c3e2209f20f1e0bb0eb556538690fa70b5e1f79fa0ba8dc2 \
+    --hash=sha256:b59430236b8e58840a0dfb4099a0e8717ffb779c952426a69ae435ca1f57210c \
+    --hash=sha256:b90f7616ea170e92820775ed47e136208e04c967271c9ef615b6fbd08d9af0e3 \
+    --hash=sha256:b9a65733d103311331875c1dca05cb4606997fd33d6acfed695b1232ba1df193 \
+    --hash=sha256:bac18ab8d2d1e6b4ce25e3424f709aceef668347db8637c2296bcf41acb7cf48 \
+    --hash=sha256:bca31dd6014cb8b0b2db1e46081b0ca7d936f856da3b39744aef499db5d84d02 \
+    --hash=sha256:be55f8457cd1eac957af0c3f5ece7bc3f033f89b114ef30f710882717670b2a8 \
+    --hash=sha256:c7025dce65566eb6e89f56c9509d4f628fddcedb131d9465cacd3d8bac337e7e \
+    --hash=sha256:c935a22a557a560108d780f9a0fc426dd7459940dc54faa49d83249c8d3e760f \
+    --hash=sha256:dbb8e7f2abee51cef77673be97760abff1674ed32847ce04b4af90f610144c7b \
+    --hash=sha256:e6ea6b856a74d560d9326c0f5895ef8050126acfdc7ca08ad703eb0081e82b74 \
+    --hash=sha256:ebf2029c1f464c59b8bdbe5143c79fa2045a581ac53679733d3a91d400ff9efb \
+    --hash=sha256:f1ff2ee69f10f13a9596480335f406dd1f70c3650349e2be67ca3139280cade0
+    # via
+    #   basicsr
+    #   clean-fid
+    #   diffusers
+    #   facexlib
+    #   imageio
+    #   k-diffusion
+    #   matplotlib
+    #   realesrgan
+    #   scikit-image
+    #   streamlit
+    #   torch-fidelity
+    #   torchvision
+promise==2.3 \
+    --hash=sha256:dfd18337c523ba4b6a58801c164c1904a9d4d1b1747c7d5dbf45b693a49d93d0
+    # via wandb
+protobuf==3.19.6 \
+    --hash=sha256:010be24d5a44be7b0613750ab40bc8b8cedc796db468eae6c779b395f50d1fa1 \
+    --hash=sha256:0469bc66160180165e4e29de7f445e57a34ab68f49357392c5b2f54c656ab25e \
+    --hash=sha256:0c0714b025ec057b5a7600cb66ce7c693815f897cfda6d6efb58201c472e3437 \
+    --hash=sha256:11478547958c2dfea921920617eb457bc26867b0d1aa065ab05f35080c5d9eb6 \
+    --hash=sha256:14082457dc02be946f60b15aad35e9f5c69e738f80ebbc0900a19bc83734a5a4 \
+    --hash=sha256:2b2d2913bcda0e0ec9a784d194bc490f5dc3d9d71d322d070b11a0ade32ff6ba \
+    --hash=sha256:30a15015d86b9c3b8d6bf78d5b8c7749f2512c29f168ca259c9d7727604d0e39 \
+    --hash=sha256:30f5370d50295b246eaa0296533403961f7e64b03ea12265d6dfce3a391d8992 \
+    --hash=sha256:347b393d4dd06fb93a77620781e11c058b3b0a5289262f094379ada2920a3730 \
+    --hash=sha256:4bc98de3cdccfb5cd769620d5785b92c662b6bfad03a202b83799b6ed3fa1fa7 \
+    --hash=sha256:5057c64052a1f1dd7d4450e9aac25af6bf36cfbfb3a1cd89d16393a036c49157 \
+    --hash=sha256:559670e006e3173308c9254d63facb2c03865818f22204037ab76f7a0ff70b5f \
+    --hash=sha256:5a0d7539a1b1fb7e76bf5faa0b44b30f812758e989e59c40f77a7dab320e79b9 \
+    --hash=sha256:5f5540d57a43042389e87661c6eaa50f47c19c6176e8cf1c4f287aeefeccb5c4 \
+    --hash=sha256:7a552af4dc34793803f4e735aabe97ffc45962dfd3a237bdde242bff5a3de684 \
+    --hash=sha256:84a04134866861b11556a82dd91ea6daf1f4925746b992f277b84013a7cc1229 \
+    --hash=sha256:878b4cd080a21ddda6ac6d1e163403ec6eea2e206cf225982ae04567d39be7b0 \
+    --hash=sha256:90b0d02163c4e67279ddb6dc25e063db0130fc299aefabb5d481053509fae5c8 \
+    --hash=sha256:91d5f1e139ff92c37e0ff07f391101df77e55ebb97f46bbc1535298d72019462 \
+    --hash=sha256:a8ce5ae0de28b51dff886fb922012dad885e66176663950cb2344c0439ecb473 \
+    --hash=sha256:aa3b82ca1f24ab5326dcf4ea00fcbda703e986b22f3d27541654f749564d778b \
+    --hash=sha256:bb6776bd18f01ffe9920e78e03a8676530a5d6c5911934c6a1ac6eb78973ecb6 \
+    --hash=sha256:bbf5cea5048272e1c60d235c7bd12ce1b14b8a16e76917f371c718bd3005f045 \
+    --hash=sha256:c0ccd3f940fe7f3b35a261b1dd1b4fc850c8fde9f74207015431f174be5976b3 \
+    --hash=sha256:d0b635cefebd7a8a0f92020562dead912f81f401af7e71f16bf9506ff3bdbb38
+    # via
+    #   streamlit
+    #   tb-nightly
+    #   tensorboard
+    #   wandb
+psutil==5.9.3 \
+    --hash=sha256:07d880053c6461c9b89cd5d4808f3b8336665fa3acdefd6777662c5ed73a851a \
+    --hash=sha256:12500d761ac091f2426567f19f95fd3f15a197d96befb44a5c1e3cbe6db5752c \
+    --hash=sha256:1b540599481c73408f6b392cdffef5b01e8ff7a2ac8caae0a91b8222e88e8f1e \
+    --hash=sha256:35feafe232d1aaf35d51bd42790cbccb882456f9f18cdc411532902370d660df \
+    --hash=sha256:3a7826e68b0cf4ce2c1ee385d64eab7d70e3133171376cac53d7c1790357ec8f \
+    --hash=sha256:46907fa62acaac364fff0b8a9da7b360265d217e4fdeaca0a2397a6883dffba2 \
+    --hash=sha256:4bd4854f0c83aa84a5a40d3b5d0eb1f3c128f4146371e03baed4589fe4f3c931 \
+    --hash=sha256:538fcf6ae856b5e12d13d7da25ad67f02113c96f5989e6ad44422cb5994ca7fc \
+    --hash=sha256:547ebb02031fdada635452250ff39942db8310b5c4a8102dfe9384ee5791e650 \
+    --hash=sha256:5e8b50241dd3c2ed498507f87a6602825073c07f3b7e9560c58411c14fe1e1c9 \
+    --hash=sha256:5fa88e3d5d0b480602553d362c4b33a63e0c40bfea7312a7bf78799e01e0810b \
+    --hash=sha256:68fa227c32240c52982cb931801c5707a7f96dd8927f9102d6c7771ea1ff5698 \
+    --hash=sha256:6ced1ad823ecfa7d3ce26fe8aa4996e2e53fb49b7fed8ad81c80958501ec0619 \
+    --hash=sha256:71b1206e7909792d16933a0d2c1c7f04ae196186c51ba8567abae1d041f06dcb \
+    --hash=sha256:767ef4fa33acda16703725c0473a91e1832d296c37c63896c7153ba81698f1ab \
+    --hash=sha256:7ccfcdfea4fc4b0a02ca2c31de7fcd186beb9cff8207800e14ab66f79c773af6 \
+    --hash=sha256:7e4939ff75149b67aef77980409f156f0082fa36accc475d45c705bb00c6c16a \
+    --hash=sha256:828c9dc9478b34ab96be75c81942d8df0c2bb49edbb481f597314d92b6441d89 \
+    --hash=sha256:8a4e07611997acf178ad13b842377e3d8e9d0a5bac43ece9bfc22a96735d9a4f \
+    --hash=sha256:941a6c2c591da455d760121b44097781bc970be40e0e43081b9139da485ad5b7 \
+    --hash=sha256:9a4af6ed1094f867834f5f07acd1250605a0874169a5fcadbcec864aec2496a6 \
+    --hash=sha256:9ec296f565191f89c48f33d9544d8d82b0d2af7dd7d2d4e6319f27a818f8d1cc \
+    --hash=sha256:9ec95df684583b5596c82bb380c53a603bb051cf019d5c849c47e117c5064395 \
+    --hash=sha256:a04a1836894c8279e5e0a0127c0db8e198ca133d28be8a2a72b4db16f6cf99c1 \
+    --hash=sha256:a3d81165b8474087bb90ec4f333a638ccfd1d69d34a9b4a1a7eaac06648f9fbe \
+    --hash=sha256:b4a247cd3feaae39bb6085fcebf35b3b8ecd9b022db796d89c8f05067ca28e71 \
+    --hash=sha256:ba38cf9984d5462b506e239cf4bc24e84ead4b1d71a3be35e66dad0d13ded7c1 \
+    --hash=sha256:beb57d8a1ca0ae0eb3d08ccaceb77e1a6d93606f0e1754f0d60a6ebd5c288837 \
+    --hash=sha256:d266cd05bd4a95ca1c2b9b5aac50d249cf7c94a542f47e0b22928ddf8b80d1ef \
+    --hash=sha256:d8c3cc6bb76492133474e130a12351a325336c01c96a24aae731abf5a47fe088 \
+    --hash=sha256:db8e62016add2235cc87fb7ea000ede9e4ca0aa1f221b40cef049d02d5d2593d \
+    --hash=sha256:e7507f6c7b0262d3e7b0eeda15045bf5881f4ada70473b87bc7b7c93b992a7d7 \
+    --hash=sha256:ed15edb14f52925869250b1375f0ff58ca5c4fa8adefe4883cfb0737d32f5c02 \
+    --hash=sha256:f57d63a2b5beaf797b87024d018772439f9d3103a395627b77d17a8d72009543 \
+    --hash=sha256:fa5e32c7d9b60b2528108ade2929b115167fe98d59f89555574715054f50fa31 \
+    --hash=sha256:fe79b4ad4836e3da6c4650cb85a663b3a51aef22e1a829c384e18fae87e5e727
+    # via
+    #   accelerate
+    #   wandb
+pyarrow==10.0.0 \
+    --hash=sha256:10e031794d019425d34406edffe7e32157359e9455f9edb97a1732f8dabf802f \
+    --hash=sha256:25f51dca780fc22cfd7ac30f6bdfe70eb99145aee9acfda987f2c49955d66ed9 \
+    --hash=sha256:2d326a9d47ac237d81b8c4337e9d30a0b361835b536fc7ea53991455ce761fbd \
+    --hash=sha256:3d2694f08c8d4482d14e3798ff036dbd81ae6b1c47948f52515e1aa90fbec3f0 \
+    --hash=sha256:4051664d354b14939b5da35cfa77821ade594bc1cf56dd2032b3068c96697d74 \
+    --hash=sha256:511735040b83f2993f78d7fb615e7b88253d75f41500e87e587c40156ff88120 \
+    --hash=sha256:65d4a312f3ced318423704355acaccc7f7bdfe242472e59bdd54aa0f8837adf8 \
+    --hash=sha256:68ccb82c04c0f7abf7a95541d5e9d9d94290fc66a2d36d3f6ea0777f40c15654 \
+    --hash=sha256:69b8a1fd99201178799b02f18498633847109b701856ec762f314352a431b7d0 \
+    --hash=sha256:758284e1ebd3f2a9abb30544bfec28d151a398bb7c0f2578cbca5ee5b000364a \
+    --hash=sha256:7be7f42f713068293308c989a4a3a2de03b70199bdbe753901c6595ff8640c64 \
+    --hash=sha256:7ce026274cd5d9934cd3694e89edecde4e036018bbc6cb735fd33b9e967e7d47 \
+    --hash=sha256:7e6b837cc44cd62a0e280c8fc4de94ebce503d6d1190e6e94157ab49a8bea67b \
+    --hash=sha256:b153b05765393557716e3729cf988442b3ae4f5567364ded40d58c07feed27c2 \
+    --hash=sha256:b3e3148468d3eed3779d68241f1d13ed4ee7cca4c6dbc7c07e5062b93ad4da33 \
+    --hash=sha256:b45f969ed924282e9d4ede38f3430630d809c36dbff65452cabce03141943d28 \
+    --hash=sha256:b9f63ceb8346aac0bcb487fafe9faca642ad448ca649fcf66a027c6e120cbc12 \
+    --hash=sha256:c79300e1a3e23f2bf4defcf0d70ff5ea25ef6ebf6f121d8670ee14bb662bb7ca \
+    --hash=sha256:d45a59e2f47826544c0ca70bc0f7ed8ffa5ad23f93b0458230c7e983bcad1acf \
+    --hash=sha256:e4c6da9f9e1ff96781ee1478f7cc0860e66c23584887b8e297c4b9905c3c9066 \
+    --hash=sha256:f329951d56b3b943c353f7b27c894e02367a7efbb9fef7979c6b24e02dbfcf55 \
+    --hash=sha256:f76157d9579571c865860e5fd004537c03e21139db76692d96fd8a186adab1f2
+    # via streamlit
+pyasn1==0.4.8 \
+    --hash=sha256:39c7e2ec30515947ff4e87fb6f456dfc6e84857d34be479c9d4a4ba4bf46aa5d \
+    --hash=sha256:aef77c9fb94a3ac588e87841208bdec464471d9871bd5050a287cc9a475cd0ba
+    # via
+    #   pyasn1-modules
+    #   rsa
+pyasn1-modules==0.2.8 \
+    --hash=sha256:905f84c712230b2c592c19470d3ca8d552de726050d1d1716282a1f6146be65e \
+    --hash=sha256:a50b808ffeb97cb3601dd25981f6b016cbb3d31fbf57a8b8a87428e6158d0c74
+    # via google-auth
+pydeck==0.8.0 \
+    --hash=sha256:07edde833f7cfcef6749124351195aa7dcd24663d4909fd7898dbd0b6fbc01ec \
+    --hash=sha256:a8fa7757c6f24bba033af39db3147cb020eef44012ba7e60d954de187f9ed4d5
+    # via streamlit
+pydeprecate==0.3.2 \
+    --hash=sha256:d481116cc5d7f6c473e7c4be820efdd9b90a16b594b350276e9e66a6cb5bdd29 \
+    --hash=sha256:ed86b68ed837e6465245904a3de2f59bf9eef78ac7a2502ee280533d04802457
+    # via pytorch-lightning
+pygments==2.13.0 \
+    --hash=sha256:56a8508ae95f98e2b9bdf93a6be5ae3f7d8af858b43e02c5a2ff083726be40c1 \
+    --hash=sha256:f643f331ab57ba3c9d89212ee4a2dabc6e94f117cf4eefde99a0574720d14c42
+    # via rich
+pympler==1.0.1 \
+    --hash=sha256:993f1a3599ca3f4fcd7160c7545ad06310c9e12f70174ae7ae8d4e25f6c5d3fa \
+    --hash=sha256:d260dda9ae781e1eab6ea15bacb84015849833ba5555f141d2d9b7b7473b307d
+    # via streamlit
+pyparsing==3.0.9 \
+    --hash=sha256:2b020ecf7d21b687f219b71ecad3631f644a47f01403fa1d1036b0c6416d70fb \
+    --hash=sha256:5026bae9a10eeaefb61dab2f09052b9f4307d44aee4eda64b309723d8d206bbc
+    # via
+    #   matplotlib
+    #   packaging
+pyreadline3==3.4.1 \
+    --hash=sha256:6f3d1f7b8a31ba32b73917cefc1f28cc660562f39aea8646d30bd6eff21f7bae \
+    --hash=sha256:b0efb6516fd4fb07b45949053826a62fa4cb353db5be2bbb4a7aa1fdd1e345fb
+    # via -r requirements.in
+pyrsistent==0.19.2 \
+    --hash=sha256:055ab45d5911d7cae397dc418808d8802fb95262751872c841c170b0dbf51eed \
+    --hash=sha256:111156137b2e71f3a9936baf27cb322e8024dac3dc54ec7fb9f0bcf3249e68bb \
+    --hash=sha256:187d5730b0507d9285a96fca9716310d572e5464cadd19f22b63a6976254d77a \
+    --hash=sha256:21455e2b16000440e896ab99e8304617151981ed40c29e9507ef1c2e4314ee95 \
+    --hash=sha256:2aede922a488861de0ad00c7630a6e2d57e8023e4be72d9d7147a9fcd2d30712 \
+    --hash=sha256:3ba4134a3ff0fc7ad225b6b457d1309f4698108fb6b35532d015dca8f5abed73 \
+    --hash=sha256:456cb30ca8bff00596519f2c53e42c245c09e1a4543945703acd4312949bfd41 \
+    --hash=sha256:71d332b0320642b3261e9fee47ab9e65872c2bd90260e5d225dabeed93cbd42b \
+    --hash=sha256:879b4c2f4d41585c42df4d7654ddffff1239dc4065bc88b745f0341828b83e78 \
+    --hash=sha256:9cd3e9978d12b5d99cbdc727a3022da0430ad007dacf33d0bf554b96427f33ab \
+    --hash=sha256:a178209e2df710e3f142cbd05313ba0c5ebed0a55d78d9945ac7a4e09d923308 \
+    --hash=sha256:b39725209e06759217d1ac5fcdb510e98670af9e37223985f330b611f62e7425 \
+    --hash=sha256:bfa0351be89c9fcbcb8c9879b826f4353be10f58f8a677efab0c017bf7137ec2 \
+    --hash=sha256:bfd880614c6237243ff53a0539f1cb26987a6dc8ac6e66e0c5a40617296a045e \
+    --hash=sha256:c43bec251bbd10e3cb58ced80609c5c1eb238da9ca78b964aea410fb820d00d6 \
+    --hash=sha256:d690b18ac4b3e3cab73b0b7aa7dbe65978a172ff94970ff98d82f2031f8971c2 \
+    --hash=sha256:d6982b5a0237e1b7d876b60265564648a69b14017f3b5f908c5be2de3f9abb7a \
+    --hash=sha256:dec3eac7549869365fe263831f576c8457f6c833937c68542d08fde73457d291 \
+    --hash=sha256:e371b844cec09d8dc424d940e54bba8f67a03ebea20ff7b7b0d56f526c71d584 \
+    --hash=sha256:e5d8f84d81e3729c3b506657dddfe46e8ba9c330bf1858ee33108f8bb2adb38a \
+    --hash=sha256:ea6b79a02a28550c98b6ca9c35b9f492beaa54d7c5c9e9949555893c8a9234d0 \
+    --hash=sha256:f1258f4e6c42ad0b20f9cfcc3ada5bd6b83374516cd01c0960e3cb75fdca6770
+    # via jsonschema
+python-dateutil==2.8.2 \
+    --hash=sha256:0123cacc1627ae19ddf3c27a5de5bd67ee4586fbdd6440d9748f8abb483d3e86 \
+    --hash=sha256:961d03dc3453ebbc59dbdea9e4e11c5651520a876d0f4db161e8674aae935da9
+    # via
+    #   matplotlib
+    #   pandas
+    #   streamlit
+python-engineio==4.3.4 \
+    --hash=sha256:7454314a529bba20e745928601ffeaf101c1b5aca9a6c4e48ad397803d10ea0c \
+    --hash=sha256:d8d8b072799c36cadcdcc2b40d2a560ce09797ab3d2d596b2ad519a5e4df19ae
+    # via python-socketio
+python-socketio==5.7.2 \
+    --hash=sha256:92395062d9db3c13d30e7cdedaa0e1330bba78505645db695415f9a3c628d097 \
+    --hash=sha256:d9a9f047e6fdd306c852fbac36516f4b495c2096f8ad9ceb8803b8e5ff5622e3
+    # via flask-socketio
+pytorch-lightning==1.7.7 \
+    --hash=sha256:27c2dd01a18db2415168e3fa3775ccb5a1fa1e2961a50439ad9365507fe9d4ae \
+    --hash=sha256:4438b8284d7f7fdb06cf3566a7b5b6f102ac8971cf7bb6d3f1b1de64628241f3
+    # via taming-transformers-rom1504
+pytz==2022.6 \
+    --hash=sha256:222439474e9c98fced559f1709d89e6c9cbf8d79c794ff3eb9f8800064291427 \
+    --hash=sha256:e89512406b793ca39f5971bc999cc538ce125c0e51c27941bef4568b460095e2
+    # via pandas
+pytz-deprecation-shim==0.1.0.post0 \
+    --hash=sha256:8314c9692a636c8eb3bda879b9f119e350e93223ae83e70e80c31675a0fdc1a6 \
+    --hash=sha256:af097bae1b616dde5c5744441e2ddc69e74dfdcb0c263129610d85b87445a59d
+    # via tzlocal
+pywavelets==1.4.1 \
+    --hash=sha256:030670a213ee8fefa56f6387b0c8e7d970c7f7ad6850dc048bd7c89364771b9b \
+    --hash=sha256:058b46434eac4c04dd89aeef6fa39e4b6496a951d78c500b6641fd5b2cc2f9f4 \
+    --hash=sha256:231b0e0b1cdc1112f4af3c24eea7bf181c418d37922a67670e9bf6cfa2d544d4 \
+    --hash=sha256:23bafd60350b2b868076d976bdd92f950b3944f119b4754b1d7ff22b7acbf6c6 \
+    --hash=sha256:3f19327f2129fb7977bc59b966b4974dfd72879c093e44a7287500a7032695de \
+    --hash=sha256:47cac4fa25bed76a45bc781a293c26ac63e8eaae9eb8f9be961758d22b58649c \
+    --hash=sha256:578af438a02a86b70f1975b546f68aaaf38f28fb082a61ceb799816049ed18aa \
+    --hash=sha256:6437af3ddf083118c26d8f97ab43b0724b956c9f958e9ea788659f6a2834ba93 \
+    --hash=sha256:64c6bac6204327321db30b775060fbe8e8642316e6bff17f06b9f34936f88875 \
+    --hash=sha256:67a0d28a08909f21400cb09ff62ba94c064882ffd9e3a6b27880a111211d59bd \
+    --hash=sha256:71ab30f51ee4470741bb55fc6b197b4a2b612232e30f6ac069106f0156342356 \
+    --hash=sha256:7231461d7a8eb3bdc7aa2d97d9f67ea5a9f8902522818e7e2ead9c2b3408eeb1 \
+    --hash=sha256:754fa5085768227c4f4a26c1e0c78bc509a266d9ebd0eb69a278be7e3ece943c \
+    --hash=sha256:7ab8d9db0fe549ab2ee0bea61f614e658dd2df419d5b75fba47baa761e95f8f2 \
+    --hash=sha256:875d4d620eee655346e3589a16a73790cf9f8917abba062234439b594e706784 \
+    --hash=sha256:88aa5449e109d8f5e7f0adef85f7f73b1ab086102865be64421a3a3d02d277f4 \
+    --hash=sha256:91d3d393cffa634f0e550d88c0e3f217c96cfb9e32781f2960876f1808d9b45b \
+    --hash=sha256:9cb5ca8d11d3f98e89e65796a2125be98424d22e5ada360a0dbabff659fca0fc \
+    --hash=sha256:ab7da0a17822cd2f6545626946d3b82d1a8e106afc4b50e3387719ba01c7b966 \
+    --hash=sha256:ad987748f60418d5f4138db89d82ba0cb49b086e0cbb8fd5c3ed4a814cfb705e \
+    --hash=sha256:d0e56cd7a53aed3cceca91a04d62feb3a0aca6725b1912d29546c26f6ea90426 \
+    --hash=sha256:d854411eb5ee9cb4bc5d0e66e3634aeb8f594210f6a1bed96dbed57ec70f181c \
+    --hash=sha256:da7b9c006171be1f9ddb12cc6e0d3d703b95f7f43cb5e2c6f5f15d3233fcf202 \
+    --hash=sha256:daf0aa79842b571308d7c31a9c43bc99a30b6328e6aea3f50388cd8f69ba7dbc \
+    --hash=sha256:de7cd61a88a982edfec01ea755b0740e94766e00a1ceceeafef3ed4c85c605cd
+    # via scikit-image
+pyyaml==6.0 \
+    --hash=sha256:01b45c0191e6d66c470b6cf1b9531a771a83c1c4208272ead47a3ae4f2f603bf \
+    --hash=sha256:0283c35a6a9fbf047493e3a0ce8d79ef5030852c51e9d911a27badfde0605293 \
+    --hash=sha256:055d937d65826939cb044fc8c9b08889e8c743fdc6a32b33e2390f66013e449b \
+    --hash=sha256:07751360502caac1c067a8132d150cf3d61339af5691fe9e87803040dbc5db57 \
+    --hash=sha256:0b4624f379dab24d3725ffde76559cff63d9ec94e1736b556dacdfebe5ab6d4b \
+    --hash=sha256:0ce82d761c532fe4ec3f87fc45688bdd3a4c1dc5e0b4a19814b9009a29baefd4 \
+    --hash=sha256:1e4747bc279b4f613a09eb64bba2ba602d8a6664c6ce6396a4d0cd413a50ce07 \
+    --hash=sha256:213c60cd50106436cc818accf5baa1aba61c0189ff610f64f4a3e8c6726218ba \
+    --hash=sha256:231710d57adfd809ef5d34183b8ed1eeae3f76459c18fb4a0b373ad56bedcdd9 \
+    --hash=sha256:277a0ef2981ca40581a47093e9e2d13b3f1fbbeffae064c1d21bfceba2030287 \
+    --hash=sha256:2cd5df3de48857ed0544b34e2d40e9fac445930039f3cfe4bcc592a1f836d513 \
+    --hash=sha256:40527857252b61eacd1d9af500c3337ba8deb8fc298940291486c465c8b46ec0 \
+    --hash=sha256:432557aa2c09802be39460360ddffd48156e30721f5e8d917f01d31694216782 \
+    --hash=sha256:473f9edb243cb1935ab5a084eb238d842fb8f404ed2193a915d1784b5a6b5fc0 \
+    --hash=sha256:48c346915c114f5fdb3ead70312bd042a953a8ce5c7106d5bfb1a5254e47da92 \
+    --hash=sha256:50602afada6d6cbfad699b0c7bb50d5ccffa7e46a3d738092afddc1f9758427f \
+    --hash=sha256:68fb519c14306fec9720a2a5b45bc9f0c8d1b9c72adf45c37baedfcd949c35a2 \
+    --hash=sha256:77f396e6ef4c73fdc33a9157446466f1cff553d979bd00ecb64385760c6babdc \
+    --hash=sha256:81957921f441d50af23654aa6c5e5eaf9b06aba7f0a19c18a538dc7ef291c5a1 \
+    --hash=sha256:819b3830a1543db06c4d4b865e70ded25be52a2e0631ccd2f6a47a2822f2fd7c \
+    --hash=sha256:897b80890765f037df3403d22bab41627ca8811ae55e9a722fd0392850ec4d86 \
+    --hash=sha256:98c4d36e99714e55cfbaaee6dd5badbc9a1ec339ebfc3b1f52e293aee6bb71a4 \
+    --hash=sha256:9df7ed3b3d2e0ecfe09e14741b857df43adb5a3ddadc919a2d94fbdf78fea53c \
+    --hash=sha256:9fa600030013c4de8165339db93d182b9431076eb98eb40ee068700c9c813e34 \
+    --hash=sha256:a80a78046a72361de73f8f395f1f1e49f956c6be882eed58505a15f3e430962b \
+    --hash=sha256:afa17f5bc4d1b10afd4466fd3a44dc0e245382deca5b3c353d8b757f9e3ecb8d \
+    --hash=sha256:b3d267842bf12586ba6c734f89d1f5b871df0273157918b0ccefa29deb05c21c \
+    --hash=sha256:b5b9eccad747aabaaffbc6064800670f0c297e52c12754eb1d976c57e4f74dcb \
+    --hash=sha256:bfaef573a63ba8923503d27530362590ff4f576c626d86a9fed95822a8255fd7 \
+    --hash=sha256:c5687b8d43cf58545ade1fe3e055f70eac7a5a1a0bf42824308d868289a95737 \
+    --hash=sha256:cba8c411ef271aa037d7357a2bc8f9ee8b58b9965831d9e51baf703280dc73d3 \
+    --hash=sha256:d15a181d1ecd0d4270dc32edb46f7cb7733c7c508857278d3d378d14d606db2d \
+    --hash=sha256:d4b0ba9512519522b118090257be113b9468d804b19d63c71dbcf4a48fa32358 \
+    --hash=sha256:d4db7c7aef085872ef65a8fd7d6d09a14ae91f691dec3e87ee5ee0539d516f53 \
+    --hash=sha256:d4eccecf9adf6fbcc6861a38015c2a64f38b9d94838ac1810a9023a0609e1b78 \
+    --hash=sha256:d67d839ede4ed1b28a4e8909735fc992a923cdb84e618544973d7dfc71540803 \
+    --hash=sha256:daf496c58a8c52083df09b80c860005194014c3698698d1a57cbcfa182142a3a \
+    --hash=sha256:dbad0e9d368bb989f4515da330b88a057617d16b6a8245084f1b05400f24609f \
+    --hash=sha256:e61ceaab6f49fb8bdfaa0f92c4b57bcfbea54c09277b1b4f7ac376bfb7a7c174 \
+    --hash=sha256:f84fbc98b019fef2ee9a1cb3ce93e3187a6df0b2538a651bfb890254ba9f90b5
+    # via
+    #   accelerate
+    #   albumentations
+    #   basicsr
+    #   gfpgan
+    #   huggingface-hub
+    #   omegaconf
+    #   pytorch-lightning
+    #   transformers
+    #   wandb
+qudida==0.0.4 \
+    --hash=sha256:4519714c40cd0f2e6c51e1735edae8f8b19f4efe1f33be13e9d644ca5f736dd6 \
+    --hash=sha256:db198e2887ab0c9aa0023e565afbff41dfb76b361f85fd5e13f780d75ba18cc8
+    # via albumentations
+realesrgan==0.3.0 \
+    --hash=sha256:0d36da96ab9f447071606e91f502ccdfb08f80cc82ee4f8caf720c7745ccec7e \
+    --hash=sha256:59336c16c30dd5130eff350dd27424acb9b7281d18a6810130e265606c9a6088
+    # via -r requirements.in
+regex==2022.10.31 \
+    --hash=sha256:052b670fafbe30966bbe5d025e90b2a491f85dfe5b2583a163b5e60a85a321ad \
+    --hash=sha256:0653d012b3bf45f194e5e6a41df9258811ac8fc395579fa82958a8b76286bea4 \
+    --hash=sha256:0a069c8483466806ab94ea9068c34b200b8bfc66b6762f45a831c4baaa9e8cdd \
+    --hash=sha256:0cf0da36a212978be2c2e2e2d04bdff46f850108fccc1851332bcae51c8907cc \
+    --hash=sha256:131d4be09bea7ce2577f9623e415cab287a3c8e0624f778c1d955ec7c281bd4d \
+    --hash=sha256:144486e029793a733e43b2e37df16a16df4ceb62102636ff3db6033994711066 \
+    --hash=sha256:1ddf14031a3882f684b8642cb74eea3af93a2be68893901b2b387c5fd92a03ec \
+    --hash=sha256:1eba476b1b242620c266edf6325b443a2e22b633217a9835a52d8da2b5c051f9 \
+    --hash=sha256:20f61c9944f0be2dc2b75689ba409938c14876c19d02f7585af4460b6a21403e \
+    --hash=sha256:22960019a842777a9fa5134c2364efaed5fbf9610ddc5c904bd3a400973b0eb8 \
+    --hash=sha256:22e7ebc231d28393dfdc19b185d97e14a0f178bedd78e85aad660e93b646604e \
+    --hash=sha256:23cbb932cc53a86ebde0fb72e7e645f9a5eec1a5af7aa9ce333e46286caef783 \
+    --hash=sha256:29c04741b9ae13d1e94cf93fca257730b97ce6ea64cfe1eba11cf9ac4e85afb6 \
+    --hash=sha256:2bde29cc44fa81c0a0c8686992c3080b37c488df167a371500b2a43ce9f026d1 \
+    --hash=sha256:2cdc55ca07b4e70dda898d2ab7150ecf17c990076d3acd7a5f3b25cb23a69f1c \
+    --hash=sha256:370f6e97d02bf2dd20d7468ce4f38e173a124e769762d00beadec3bc2f4b3bc4 \
+    --hash=sha256:395161bbdbd04a8333b9ff9763a05e9ceb4fe210e3c7690f5e68cedd3d65d8e1 \
+    --hash=sha256:44136355e2f5e06bf6b23d337a75386371ba742ffa771440b85bed367c1318d1 \
+    --hash=sha256:44a6c2f6374e0033873e9ed577a54a3602b4f609867794c1a3ebba65e4c93ee7 \
+    --hash=sha256:4919899577ba37f505aaebdf6e7dc812d55e8f097331312db7f1aab18767cce8 \
+    --hash=sha256:4b4b1fe58cd102d75ef0552cf17242705ce0759f9695334a56644ad2d83903fe \
+    --hash=sha256:4bdd56ee719a8f751cf5a593476a441c4e56c9b64dc1f0f30902858c4ef8771d \
+    --hash=sha256:4bf41b8b0a80708f7e0384519795e80dcb44d7199a35d52c15cc674d10b3081b \
+    --hash=sha256:4cac3405d8dda8bc6ed499557625585544dd5cbf32072dcc72b5a176cb1271c8 \
+    --hash=sha256:4fe7fda2fe7c8890d454f2cbc91d6c01baf206fbc96d89a80241a02985118c0c \
+    --hash=sha256:50921c140561d3db2ab9f5b11c5184846cde686bb5a9dc64cae442926e86f3af \
+    --hash=sha256:5217c25229b6a85049416a5c1e6451e9060a1edcf988641e309dbe3ab26d3e49 \
+    --hash=sha256:5352bea8a8f84b89d45ccc503f390a6be77917932b1c98c4cdc3565137acc714 \
+    --hash=sha256:542e3e306d1669b25936b64917285cdffcd4f5c6f0247636fec037187bd93542 \
+    --hash=sha256:543883e3496c8b6d58bd036c99486c3c8387c2fc01f7a342b760c1ea3158a318 \
+    --hash=sha256:586b36ebda81e6c1a9c5a5d0bfdc236399ba6595e1397842fd4a45648c30f35e \
+    --hash=sha256:597f899f4ed42a38df7b0e46714880fb4e19a25c2f66e5c908805466721760f5 \
+    --hash=sha256:5a260758454580f11dd8743fa98319bb046037dfab4f7828008909d0aa5292bc \
+    --hash=sha256:5aefb84a301327ad115e9d346c8e2760009131d9d4b4c6b213648d02e2abe144 \
+    --hash=sha256:5e6a5567078b3eaed93558842346c9d678e116ab0135e22eb72db8325e90b453 \
+    --hash=sha256:5ff525698de226c0ca743bfa71fc6b378cda2ddcf0d22d7c37b1cc925c9650a5 \
+    --hash=sha256:61edbca89aa3f5ef7ecac8c23d975fe7261c12665f1d90a6b1af527bba86ce61 \
+    --hash=sha256:659175b2144d199560d99a8d13b2228b85e6019b6e09e556209dfb8c37b78a11 \
+    --hash=sha256:6a9a19bea8495bb419dc5d38c4519567781cd8d571c72efc6aa959473d10221a \
+    --hash=sha256:6b30bddd61d2a3261f025ad0f9ee2586988c6a00c780a2fb0a92cea2aa702c54 \
+    --hash=sha256:6ffd55b5aedc6f25fd8d9f905c9376ca44fcf768673ffb9d160dd6f409bfda73 \
+    --hash=sha256:702d8fc6f25bbf412ee706bd73019da5e44a8400861dfff7ff31eb5b4a1276dc \
+    --hash=sha256:74bcab50a13960f2a610cdcd066e25f1fd59e23b69637c92ad470784a51b1347 \
+    --hash=sha256:75f591b2055523fc02a4bbe598aa867df9e953255f0b7f7715d2a36a9c30065c \
+    --hash=sha256:763b64853b0a8f4f9cfb41a76a4a85a9bcda7fdda5cb057016e7706fde928e66 \
+    --hash=sha256:76c598ca73ec73a2f568e2a72ba46c3b6c8690ad9a07092b18e48ceb936e9f0c \
+    --hash=sha256:78d680ef3e4d405f36f0d6d1ea54e740366f061645930072d39bca16a10d8c93 \
+    --hash=sha256:7b280948d00bd3973c1998f92e22aa3ecb76682e3a4255f33e1020bd32adf443 \
+    --hash=sha256:7db345956ecce0c99b97b042b4ca7326feeec6b75facd8390af73b18e2650ffc \
+    --hash=sha256:7dbdce0c534bbf52274b94768b3498abdf675a691fec5f751b6057b3030f34c1 \
+    --hash=sha256:7ef6b5942e6bfc5706301a18a62300c60db9af7f6368042227ccb7eeb22d0892 \
+    --hash=sha256:7f5a3ffc731494f1a57bd91c47dc483a1e10048131ffb52d901bfe2beb6102e8 \
+    --hash=sha256:8a45b6514861916c429e6059a55cf7db74670eaed2052a648e3e4d04f070e001 \
+    --hash=sha256:8ad241da7fac963d7573cc67a064c57c58766b62a9a20c452ca1f21050868dfa \
+    --hash=sha256:8b0886885f7323beea6f552c28bff62cbe0983b9fbb94126531693ea6c5ebb90 \
+    --hash=sha256:8ca88da1bd78990b536c4a7765f719803eb4f8f9971cc22d6ca965c10a7f2c4c \
+    --hash=sha256:8e0caeff18b96ea90fc0eb6e3bdb2b10ab5b01a95128dfeccb64a7238decf5f0 \
+    --hash=sha256:957403a978e10fb3ca42572a23e6f7badff39aa1ce2f4ade68ee452dc6807692 \
+    --hash=sha256:9af69f6746120998cd9c355e9c3c6aec7dff70d47247188feb4f829502be8ab4 \
+    --hash=sha256:9c94f7cc91ab16b36ba5ce476f1904c91d6c92441f01cd61a8e2729442d6fcf5 \
+    --hash=sha256:a37d51fa9a00d265cf73f3de3930fa9c41548177ba4f0faf76e61d512c774690 \
+    --hash=sha256:a3a98921da9a1bf8457aeee6a551948a83601689e5ecdd736894ea9bbec77e83 \
+    --hash=sha256:a3c1ebd4ed8e76e886507c9eddb1a891673686c813adf889b864a17fafcf6d66 \
+    --hash=sha256:a5f9505efd574d1e5b4a76ac9dd92a12acb2b309551e9aa874c13c11caefbe4f \
+    --hash=sha256:a8ff454ef0bb061e37df03557afda9d785c905dab15584860f982e88be73015f \
+    --hash=sha256:a9d0b68ac1743964755ae2d89772c7e6fb0118acd4d0b7464eaf3921c6b49dd4 \
+    --hash=sha256:aa62a07ac93b7cb6b7d0389d8ef57ffc321d78f60c037b19dfa78d6b17c928ee \
+    --hash=sha256:ac741bf78b9bb432e2d314439275235f41656e189856b11fb4e774d9f7246d81 \
+    --hash=sha256:ae1e96785696b543394a4e3f15f3f225d44f3c55dafe3f206493031419fedf95 \
+    --hash=sha256:b683e5fd7f74fb66e89a1ed16076dbab3f8e9f34c18b1979ded614fe10cdc4d9 \
+    --hash=sha256:b7a8b43ee64ca8f4befa2bea4083f7c52c92864d8518244bfa6e88c751fa8fff \
+    --hash=sha256:b8e38472739028e5f2c3a4aded0ab7eadc447f0d84f310c7a8bb697ec417229e \
+    --hash=sha256:bfff48c7bd23c6e2aec6454aaf6edc44444b229e94743b34bdcdda2e35126cf5 \
+    --hash=sha256:c14b63c9d7bab795d17392c7c1f9aaabbffd4cf4387725a0ac69109fb3b550c6 \
+    --hash=sha256:c27cc1e4b197092e50ddbf0118c788d9977f3f8f35bfbbd3e76c1846a3443df7 \
+    --hash=sha256:c28d3309ebd6d6b2cf82969b5179bed5fefe6142c70f354ece94324fa11bf6a1 \
+    --hash=sha256:c670f4773f2f6f1957ff8a3962c7dd12e4be54d05839b216cb7fd70b5a1df394 \
+    --hash=sha256:ce6910b56b700bea7be82c54ddf2e0ed792a577dfaa4a76b9af07d550af435c6 \
+    --hash=sha256:d0213671691e341f6849bf33cd9fad21f7b1cb88b89e024f33370733fec58742 \
+    --hash=sha256:d03fe67b2325cb3f09be029fd5da8df9e6974f0cde2c2ac6a79d2634e791dd57 \
+    --hash=sha256:d0e5af9a9effb88535a472e19169e09ce750c3d442fb222254a276d77808620b \
+    --hash=sha256:d243b36fbf3d73c25e48014961e83c19c9cc92530516ce3c43050ea6276a2ab7 \
+    --hash=sha256:d26166acf62f731f50bdd885b04b38828436d74e8e362bfcb8df221d868b5d9b \
+    --hash=sha256:d403d781b0e06d2922435ce3b8d2376579f0c217ae491e273bab8d092727d244 \
+    --hash=sha256:d8716f82502997b3d0895d1c64c3b834181b1eaca28f3f6336a71777e437c2af \
+    --hash=sha256:e4f781ffedd17b0b834c8731b75cce2639d5a8afe961c1e58ee7f1f20b3af185 \
+    --hash=sha256:e613a98ead2005c4ce037c7b061f2409a1a4e45099edb0ef3200ee26ed2a69a8 \
+    --hash=sha256:ef4163770525257876f10e8ece1cf25b71468316f61451ded1a6f44273eedeb5
+    # via
+    #   clip
+    #   diffusers
+    #   transformers
+requests==2.25.1 \
+    --hash=sha256:27973dd4a904a4f13b263a19c866c13b92a39ed1c964655f025f3f8d3d75b804 \
+    --hash=sha256:c210084e36a42ae6b9219e00e48287def368a26d03a048ddad7bfee44f75871e
+    # via
+    #   basicsr
+    #   clean-fid
+    #   diffusers
+    #   fsspec
+    #   huggingface-hub
+    #   requests-oauthlib
+    #   streamlit
+    #   tb-nightly
+    #   tensorboard
+    #   torchvision
+    #   transformers
+    #   wandb
+requests-oauthlib==1.3.1 \
+    --hash=sha256:2577c501a2fb8d05a304c09d090d6e47c306fef15809d102b327cf8364bddab5 \
+    --hash=sha256:75beac4a47881eeb94d5ea5d6ad31ef88856affe2332b9aafb52c6452ccf0d7a
+    # via google-auth-oauthlib
+resize-right==0.0.2 \
+    --hash=sha256:78351ca3eda0872208fcbc90861b45de559f90fb4027ce41825fdeb9b995005c \
+    --hash=sha256:7dc35b72ce4012b77f7cc9049835163793ab98a58ab8893610fb119fe59af520
+    # via k-diffusion
+rich==12.6.0 \
+    --hash=sha256:a4eb26484f2c82589bd9a17c73d32a010b1e29d89f1604cd9bf3a2097b81bb5e \
+    --hash=sha256:ba3a3775974105c221d31141f2c116f4fd65c5ceb0698657a11e9f295ec93fd0
+    # via streamlit
+rsa==4.9 \
+    --hash=sha256:90260d9058e514786967344d0ef75fa8727eed8a7d2e43ce9f4bcf1b536174f7 \
+    --hash=sha256:e38464a49c6c85d7f1351b0126661487a7e0a14a50f1675ec50eb34d4f20ef21
+    # via google-auth
+scikit-image==0.19.3 \
+    --hash=sha256:03779a7e1736fdf89d83c0ba67d44110496edd736a3bfce61a2b5177a1c8a099 \
+    --hash=sha256:0b0a199157ce8487c77de4fde0edc0b42d6d42818881c11f459262351d678b2d \
+    --hash=sha256:19a21a101a20c587a3b611a2cf6f86c35aae9f8d9563279b987e83ee1c9a9790 \
+    --hash=sha256:24b5367de1762da6ee126dd8f30cc4e7efda474e0d7d70685433f0e3aa2ec450 \
+    --hash=sha256:2a02d1bd0e2b53e36b952bd5fd6118d9ccc3ee51de35705d63d8eb1f2e86adef \
+    --hash=sha256:2f50b923f8099c1045fcde7418d86b206c87e333e43da980f41d8577b9605245 \
+    --hash=sha256:32fb88cc36203b99c9672fb972c9ef98635deaa5fc889fe969f3e11c44f22919 \
+    --hash=sha256:33dfd463ee6cc509defa279b963829f2230c9e0639ccd3931045be055878eea6 \
+    --hash=sha256:3a01372ae4bca223873304b0bff79b9d92446ac6d6177f73d89b45561e2d09d8 \
+    --hash=sha256:651de1c2ce1fbee834753b46b8e7d81cb12a5594898babba63ac82b30ddad49d \
+    --hash=sha256:6b6a8f98f2ac9bb73706461fd1dec875f6a5141759ed526850a5a49e90003d19 \
+    --hash=sha256:7f9f8a1387afc6c70f2bed007c3854a2d7489f9f7713c242f16f32ee05934bc2 \
+    --hash=sha256:84baa3179f3ae983c3a5d81c1e404bc92dcf7daeb41bfe9369badcda3fb22b92 \
+    --hash=sha256:8d8917fcf85b987b1f287f823f3a1a7dac38b70aaca759bc0200f3bc292d5ced \
+    --hash=sha256:9439e5294de3f18d6e82ec8eee2c46590231cf9c690da80545e83a0733b7a69e \
+    --hash=sha256:9fb0923a3bfa99457c5e17888f27b3b8a83a3600b4fef317992e7b7234764732 \
+    --hash=sha256:a7c3985c68bfe05f7571167ee021d14f5b8d1a4a250c91f0b13be7fb07e6af34 \
+    --hash=sha256:a8714348ddd671f819457a797c97d4c672166f093def66d66c3254cbd1d43f83 \
+    --hash=sha256:ad5d8000207a264d1a55681a9276e6a739d3f05cf4429004ad00d61d1892235f \
+    --hash=sha256:cc24177de3fdceca5d04807ad9c87d665f0bf01032ed94a9055cd1ed2b3f33e9 \
+    --hash=sha256:ce3d2207f253b8eb2c824e30d145a9f07a34a14212d57f3beca9f7e03c383cbe \
+    --hash=sha256:cfbb073f23deb48e0e60c47f8741d8089121d89cc78629ea8c5b51096efc5be7 \
+    --hash=sha256:e207c6ce5ce121d7d9b9d2b61b9adca57d1abed112c902d8ffbfdc20fb42c12b \
+    --hash=sha256:fd9dd3994bb6f9f7a35f228323f3c4dc44b3cf2ff15fd72d895216e9333550c6 \
+    --hash=sha256:fdf48d9b1f13af69e4e2c78e05067e322e9c8c97463c315cd0ecb47a94e259fc \
+    --hash=sha256:ff3b1025356508d41f4fe48528e509d95f9e4015e90cf158cd58c56dc63e0ac5
+    # via
+    #   albumentations
+    #   basicsr
+    #   k-diffusion
+scikit-learn==1.1.3 \
+    --hash=sha256:23fb9e74b813cc2528b5167d82ed08950b11106ccf50297161875e45152fb311 \
+    --hash=sha256:250da993701da88bf475e7c5746abf1285ea0ae47e4d0917cd13afd6600bb162 \
+    --hash=sha256:28b2bd6a1419acd522ff45d282c8ba23dbccb5338802ab0ee12baa4ade0aba4c \
+    --hash=sha256:2ee2c649f2231b68511aabb0dc827edd8936aad682acc6263c34aed11bc95dac \
+    --hash=sha256:30e27721adc308e8fd9f419f43068e43490005f911edf4476a9e585059fa8a83 \
+    --hash=sha256:38814f66285318f2e241305cca545eaa9b4126c65aa5dd78c69371f235f78e2b \
+    --hash=sha256:4d3a19166d4e1cdfcab975c68f471e046ce01e74c42a9a33fa89a14c2fcedf60 \
+    --hash=sha256:5699cded6c0685426433c7e5afe0fecad80ec831ec7fa264940e50c796775cc5 \
+    --hash=sha256:6785b8a3093329bf90ac01801be5525551728ae73edb11baa175df660820add4 \
+    --hash=sha256:6d1c1394e38a3319ace620381f6f23cc807d8780e9915c152449a86fc8f1db21 \
+    --hash=sha256:701181792a28c82fecae12adb5d15d0ecf57bffab7cf4bdbb52c7b3fd428d540 \
+    --hash=sha256:748f2bd632d6993e8918d43f1a26c380aeda4e122a88840d4c3a9af99d4239fe \
+    --hash=sha256:8e9dd76c7274055d1acf4526b8efb16a3531c26dcda714a0c16da99bf9d41900 \
+    --hash=sha256:bef51978a51ec19977700fe7b86aecea49c825884f3811756b74a3b152bb4e35 \
+    --hash=sha256:cd55c6fbef7608dbce1f22baf289dfcc6eb323247daa3c3542f73d389c724786 \
+    --hash=sha256:da5a2e95fef9805b1750e4abda4e834bf8835d26fc709a391543b53feee7bd0e \
+    --hash=sha256:ee47f68d973cee7009f06edb956f2f5588a0f230f24a2a70175fd0ecf36e2653 \
+    --hash=sha256:f4931f2a6c06e02c6c17a05f8ae397e2545965bc7a0a6cb38c8cd7d4fba8624d \
+    --hash=sha256:f5644663987ee221f5d1f47a593271b966c271c236fe05634e6bdc06041b5a2b \
+    --hash=sha256:f5d4231af7199531e77da1b78a4cc6b3d960a00b1ec672578ac818aae2b9c35d \
+    --hash=sha256:fd3ee69d36d42a7dcbb17e355a5653af5fd241a7dfd9133080b3dde8d9e2aafb
+    # via qudida
+scipy==1.9.3 \
+    --hash=sha256:06d2e1b4c491dc7d8eacea139a1b0b295f74e1a1a0f704c375028f8320d16e31 \
+    --hash=sha256:0d54222d7a3ba6022fdf5773931b5d7c56efe41ede7f7128c7b1637700409108 \
+    --hash=sha256:1884b66a54887e21addf9c16fb588720a8309a57b2e258ae1c7986d4444d3bc0 \
+    --hash=sha256:1a72d885fa44247f92743fc20732ae55564ff2a519e8302fb7e18717c5355a8b \
+    --hash=sha256:2318bef588acc7a574f5bfdff9c172d0b1bf2c8143d9582e05f878e580a3781e \
+    --hash=sha256:4db5b30849606a95dcf519763dd3ab6fe9bd91df49eba517359e450a7d80ce2e \
+    --hash=sha256:545c83ffb518094d8c9d83cce216c0c32f8c04aaf28b92cc8283eda0685162d5 \
+    --hash=sha256:5a04cd7d0d3eff6ea4719371cbc44df31411862b9646db617c99718ff68d4840 \
+    --hash=sha256:5b88e6d91ad9d59478fafe92a7c757d00c59e3bdc3331be8ada76a4f8d683f58 \
+    --hash=sha256:68239b6aa6f9c593da8be1509a05cb7f9efe98b80f43a5861cd24c7557e98523 \
+    --hash=sha256:83b89e9586c62e787f5012e8475fbb12185bafb996a03257e9675cd73d3736dd \
+    --hash=sha256:83c06e62a390a9167da60bedd4575a14c1f58ca9dfde59830fc42e5197283dab \
+    --hash=sha256:90453d2b93ea82a9f434e4e1cba043e779ff67b92f7a0e85d05d286a3625df3c \
+    --hash=sha256:abaf921531b5aeaafced90157db505e10345e45038c39e5d9b6c7922d68085cb \
+    --hash=sha256:b41bc822679ad1c9a5f023bc93f6d0543129ca0f37c1ce294dd9d386f0a21096 \
+    --hash=sha256:c68db6b290cbd4049012990d7fe71a2abd9ffbe82c0056ebe0f01df8be5436b0 \
+    --hash=sha256:cff3a5295234037e39500d35316a4c5794739433528310e117b8a9a0c76d20fc \
+    --hash=sha256:d01e1dd7b15bd2449c8bfc6b7cc67d630700ed655654f0dfcf121600bad205c9 \
+    --hash=sha256:d644a64e174c16cb4b2e41dfea6af722053e83d066da7343f333a54dae9bc31c \
+    --hash=sha256:da8245491d73ed0a994ed9c2e380fd058ce2fa8a18da204681f2fe1f57f98f95 \
+    --hash=sha256:fbc5c05c85c1a02be77b1ff591087c83bc44579c6d2bd9fb798bb64ea5e1a027
+    # via
+    #   albumentations
+    #   basicsr
+    #   clean-fid
+    #   facexlib
+    #   filterpy
+    #   gfpgan
+    #   k-diffusion
+    #   scikit-image
+    #   scikit-learn
+    #   torch-fidelity
+    #   torchdiffeq
+semver==2.13.0 \
+    --hash=sha256:ced8b23dceb22134307c1b8abfa523da14198793d9787ac838e70e29e77458d4 \
+    --hash=sha256:fa0fe2722ee1c3f57eac478820c3a5ae2f624af8264cbdf9000c980ff7f75e3f
+    # via streamlit
+send2trash==1.8.0 \
+    --hash=sha256:d2c24762fd3759860a0aff155e45871447ea58d2be6bdd39b5c8f966a0c99c2d \
+    --hash=sha256:f20eaadfdb517eaca5ce077640cb261c7d2698385a6a0f072a4a5447fd49fa08
+    # via -r requirements.in
+sentry-sdk==1.10.1 \
+    --hash=sha256:06c0fa9ccfdc80d7e3b5d2021978d6eb9351fa49db9b5847cf4d1f2a473414ad \
+    --hash=sha256:105faf7bd7b7fa25653404619ee261527266b14103fe1389e0ce077bd23a9691
+    # via wandb
+setproctitle==1.3.2 \
+    --hash=sha256:1c5d5dad7c28bdd1ec4187d818e43796f58a845aa892bb4481587010dc4d362b \
+    --hash=sha256:1c8d9650154afaa86a44ff195b7b10d683c73509d085339d174e394a22cccbb9 \
+    --hash=sha256:1f0cde41857a644b7353a0060b5f94f7ba7cf593ebde5a1094da1be581ac9a31 \
+    --hash=sha256:1f29b75e86260b0ab59adb12661ef9f113d2f93a59951373eb6d68a852b13e83 \
+    --hash=sha256:1fa1a0fbee72b47dc339c87c890d3c03a72ea65c061ade3204f285582f2da30f \
+    --hash=sha256:1ff863a20d1ff6ba2c24e22436a3daa3cd80be1dfb26891aae73f61b54b04aca \
+    --hash=sha256:265ecbe2c6eafe82e104f994ddd7c811520acdd0647b73f65c24f51374cf9494 \
+    --hash=sha256:288943dec88e178bb2fd868adf491197cc0fc8b6810416b1c6775e686bab87fe \
+    --hash=sha256:2e3ac25bfc4a0f29d2409650c7532d5ddfdbf29f16f8a256fc31c47d0dc05172 \
+    --hash=sha256:2fbd8187948284293f43533c150cd69a0e4192c83c377da837dbcd29f6b83084 \
+    --hash=sha256:4058564195b975ddc3f0462375c533cce310ccdd41b80ac9aed641c296c3eff4 \
+    --hash=sha256:4749a2b0c9ac52f864d13cee94546606f92b981b50e46226f7f830a56a9dc8e1 \
+    --hash=sha256:4d8938249a7cea45ab7e1e48b77685d0f2bab1ebfa9dde23e94ab97968996a7c \
+    --hash=sha256:5194b4969f82ea842a4f6af2f82cd16ebdc3f1771fb2771796e6add9835c1973 \
+    --hash=sha256:55ce1e9925ce1765865442ede9dca0ba9bde10593fcd570b1f0fa25d3ec6b31c \
+    --hash=sha256:589be87172b238f839e19f146b9ea47c71e413e951ef0dc6db4218ddacf3c202 \
+    --hash=sha256:5b932c3041aa924163f4aab970c2f0e6b4d9d773f4d50326e0ea1cd69240e5c5 \
+    --hash=sha256:5fb4f769c02f63fac90989711a3fee83919f47ae9afd4758ced5d86596318c65 \
+    --hash=sha256:630f6fe5e24a619ccf970c78e084319ee8be5be253ecc9b5b216b0f474f5ef18 \
+    --hash=sha256:65d884e22037b23fa25b2baf1a3316602ed5c5971eb3e9d771a38c3a69ce6e13 \
+    --hash=sha256:6c877691b90026670e5a70adfbcc735460a9f4c274d35ec5e8a43ce3f8443005 \
+    --hash=sha256:710e16fa3bade3b026907e4a5e841124983620046166f355bbb84be364bf2a02 \
+    --hash=sha256:7a55fe05f15c10e8c705038777656fe45e3bd676d49ad9ac8370b75c66dd7cd7 \
+    --hash=sha256:7aa0aac1711fadffc1d51e9d00a3bea61f68443d6ac0241a224e4d622489d665 \
+    --hash=sha256:7f0bed90a216ef28b9d227d8d73e28a8c9b88c0f48a082d13ab3fa83c581488f \
+    --hash=sha256:7f2719a398e1a2c01c2a63bf30377a34d0b6ef61946ab9cf4d550733af8f1ef1 \
+    --hash=sha256:7fe9df7aeb8c64db6c34fc3b13271a363475d77bc157d3f00275a53910cb1989 \
+    --hash=sha256:8ff3c8cb26afaed25e8bca7b9dd0c1e36de71f35a3a0706b5c0d5172587a3827 \
+    --hash=sha256:9124bedd8006b0e04d4e8a71a0945da9b67e7a4ab88fdad7b1440dc5b6122c42 \
+    --hash=sha256:92c626edc66169a1b09e9541b9c0c9f10488447d8a2b1d87c8f0672e771bc927 \
+    --hash=sha256:a149a5f7f2c5a065d4e63cb0d7a4b6d3b66e6e80f12e3f8827c4f63974cbf122 \
+    --hash=sha256:a47d97a75fd2d10c37410b180f67a5835cb1d8fdea2648fd7f359d4277f180b9 \
+    --hash=sha256:a499fff50387c1520c085a07578a000123f519e5f3eee61dd68e1d301659651f \
+    --hash=sha256:ab45146c71ca6592c9cc8b354a2cc9cc4843c33efcbe1d245d7d37ce9696552d \
+    --hash=sha256:b2c9cb2705fc84cb8798f1ba74194f4c080aaef19d9dae843591c09b97678e98 \
+    --hash=sha256:b34baef93bfb20a8ecb930e395ccd2ae3268050d8cf4fe187de5e2bd806fd796 \
+    --hash=sha256:b617f12c9be61e8f4b2857be4a4319754756845dbbbd9c3718f468bbb1e17bcb \
+    --hash=sha256:b9fb97907c830d260fa0658ed58afd48a86b2b88aac521135c352ff7fd3477fd \
+    --hash=sha256:bae283e85fc084b18ffeb92e061ff7ac5af9e183c9d1345c93e178c3e5069cbe \
+    --hash=sha256:c2c46200656280a064073447ebd363937562debef329482fd7e570c8d498f806 \
+    --hash=sha256:c8a09d570b39517de10ee5b718730e171251ce63bbb890c430c725c8c53d4484 \
+    --hash=sha256:c91b9bc8985d00239f7dc08a49927a7ca1ca8a6af2c3890feec3ed9665b6f91e \
+    --hash=sha256:dad42e676c5261eb50fdb16bdf3e2771cf8f99a79ef69ba88729aeb3472d8575 \
+    --hash=sha256:de3a540cd1817ede31f530d20e6a4935bbc1b145fd8f8cf393903b1e02f1ae76 \
+    --hash=sha256:e00c9d5c541a2713ba0e657e0303bf96ddddc412ef4761676adc35df35d7c246 \
+    --hash=sha256:e1aafc91cbdacc9e5fe712c52077369168e6b6c346f3a9d51bf600b53eae56bb \
+    --hash=sha256:e425be62524dc0c593985da794ee73eb8a17abb10fe692ee43bb39e201d7a099 \
+    --hash=sha256:e43f315c68aa61cbdef522a2272c5a5b9b8fd03c301d3167b5e1343ef50c676c \
+    --hash=sha256:e49ae693306d7624015f31cb3e82708916759d592c2e5f72a35c8f4cc8aef258 \
+    --hash=sha256:e5c50e164cd2459bc5137c15288a9ef57160fd5cbf293265ea3c45efe7870865 \
+    --hash=sha256:e8579a43eafd246e285eb3a5b939e7158073d5087aacdd2308f23200eac2458b \
+    --hash=sha256:e85e50b9c67854f89635a86247412f3ad66b132a4d8534ac017547197c88f27d \
+    --hash=sha256:f0452282258dfcc01697026a8841258dd2057c4438b43914b611bccbcd048f10 \
+    --hash=sha256:f4bfc89bd33ebb8e4c0e9846a09b1f5a4a86f5cb7a317e75cc42fee1131b4f4f \
+    --hash=sha256:fa2f50678f04fda7a75d0fe5dd02bbdd3b13cbe6ed4cf626e4472a7ccf47ae94 \
+    --hash=sha256:faec934cfe5fd6ac1151c02e67156c3f526e82f96b24d550b5d51efa4a5527c6 \
+    --hash=sha256:fcd3cf4286a60fdc95451d8d14e0389a6b4f5cebe02c7f2609325eb016535963 \
+    --hash=sha256:fe8a988c7220c002c45347430993830666e55bc350179d91fcee0feafe64e1d4 \
+    --hash=sha256:fed18e44711c5af4b681c2b3b18f85e6f0f1b2370a28854c645d636d5305ccd8 \
+    --hash=sha256:ffc61a388a5834a97953d6444a2888c24a05f2e333f9ed49f977a87bb1ad4761
+    # via wandb
+shortuuid==1.0.9 \
+    --hash=sha256:459f12fa1acc34ff213b1371467c0325169645a31ed989e268872339af7563d5 \
+    --hash=sha256:b2bb9eb7773170e253bb7ba25971023acb473517a8b76803d9618668cb1dd46f
+    # via wandb
+six==1.16.0 \
+    --hash=sha256:1e61c37477a1626458e36f7b1d82aa5c9b094fa4802892072e49de9c60c4c926 \
+    --hash=sha256:8abb2f1d86890a2dfb989f9a77cfcfd3e47c2a354b01111771326f8aa26e0254
+    # via
+    #   docker-pycreds
+    #   eventlet
+    #   flask-cors
+    #   google-auth
+    #   grpcio
+    #   promise
+    #   python-dateutil
+    #   validators
+    #   wandb
+smmap==5.0.0 \
+    --hash=sha256:2aba19d6a040e78d8b09de5c57e96207b09ed71d8e55ce0959eeee6c8e190d94 \
+    --hash=sha256:c840e62059cd3be204b0c9c9f74be2c09d5648eddd4580d9314c3ecde0b30936
+    # via gitdb
+streamlit==1.14.0 \
+    --hash=sha256:62556d873567e1b3427bcd118a57ee6946619f363bd6bba38df2d1f8225ecba0 \
+    --hash=sha256:e078b8143d150ba721bdb9194218e311c5fe1d6d4156473a2dea6cc848a6c9fc
+    # via -r requirements.in
+taming-transformers-rom1504==0.0.6 \
+    --hash=sha256:051b5804c58caa247bcd51d17ddb525b4d5f892a29d42dc460f40e3e9e34e5d8 \
+    --hash=sha256:73fe5fc1108accee4236ee6976e0987ab236afad0af06cb9f037641a908d2c32
+    # via -r requirements.in
+tb-nightly==2.11.0a20221106 \
+    --hash=sha256:8940457ee42db92f01da8bcdbbea1a476735eda559dde5976f5728919960af4a
+    # via
+    #   basicsr
+    #   gfpgan
+tensorboard==2.10.1 \
+    --hash=sha256:fb9222c1750e2fa35ef170d998a1e229f626eeced3004494a8849c88c15d8c1c
+    # via
+    #   pytorch-lightning
+    #   test-tube
+tensorboard-data-server==0.6.1 \
+    --hash=sha256:809fe9887682d35c1f7d1f54f0f40f98bb1f771b14265b453ca051e2ce58fca7 \
+    --hash=sha256:d8237580755e58eff68d1f3abefb5b1e39ae5c8b127cc40920f9c4fb33f4b98a \
+    --hash=sha256:fa8cef9be4fcae2f2363c88176638baf2da19c5ec90addb49b1cde05c95c88ee
+    # via
+    #   tb-nightly
+    #   tensorboard
+tensorboard-plugin-wit==1.8.1 \
+    --hash=sha256:ff26bdd583d155aa951ee3b152b3d0cffae8005dc697f72b44a8e8c2a77a8cbe
+    # via
+    #   tb-nightly
+    #   tensorboard
+test-tube==0.7.5 \
+    --hash=sha256:1379c33eb8cde3e9b36610f87da0f16c2e06496b1cfebac473df4e7be2faa124
+    # via -r requirements.in
+threadpoolctl==3.1.0 \
+    --hash=sha256:8b99adda265feb6773280df41eece7b2e6561b772d21ffd52e372f999024907b \
+    --hash=sha256:a335baacfaa4400ae1f0d8e3a58d6674d2f8828e3716bb2802c44955ad391380
+    # via scikit-learn
+tifffile==2022.10.10 \
+    --hash=sha256:50b61ba943b866d191295bc38a00191c9fdab23ece063544c7f1a264e3f6aa8e \
+    --hash=sha256:87f3aee8a0d06b74655269a105de75c1958a24653e1930d523eb516100043503
+    # via scikit-image
+tokenizers==0.13.1 \
+    --hash=sha256:0a3412830ad66a643723d6b0fc3202e64e9e299bd9c9eda04b2914b5b1e0ddb0 \
+    --hash=sha256:126bcb18a77cf65961ece04f54bd10ef3713412195e543d9d3eda2f0e4fd677c \
+    --hash=sha256:16434c61c5eb72f6692b9bc52052b07ca92d3eba9dd72a1bc371890e1bdc3f07 \
+    --hash=sha256:1d4acfdb6e7ef974677bb8445462db7fed240185fdc0f5b061db357d4ef8d85d \
+    --hash=sha256:3333d1cee5c8f47c96362ea0abc1f81c77c9b92c6c3d11cbf1d01985f0d5cf1d \
+    --hash=sha256:3acf3cae4c4739fc9ec49fa0e6cce224c1aa0fabc8f8d1358fd7de5c7d49cdca \
+    --hash=sha256:3ba43b3f6f6b41c97c1d041785342cd72ba142999f6c4605d628e8e937398f20 \
+    --hash=sha256:3c69a8389fd88bc32115e99db70f63bef577ba5c54f40a632580038a49612856 \
+    --hash=sha256:3de653a551cc616a442a123da21706cb3a3163cf6919973f978f0921eee1bdf0 \
+    --hash=sha256:4b3be8af87b357340b9b049d28067287b5e5e296e3120b6e4875d3b8469b67e6 \
+    --hash=sha256:680bc0e357b7da6d0d01634bffbd002e866fdaccde303e1d1af58f32464cf308 \
+    --hash=sha256:70de69681a264a5808d39f4bb6331be9a4dec51fd48cd1b959a94da76c4939cc \
+    --hash=sha256:73198cda6e1d991c583ed798514863e16763aa600eb7aa6df7722373290575b2 \
+    --hash=sha256:80864f456f715829f901ad5bb85af97e9ae52fc902270944804f6476ab8c6699 \
+    --hash=sha256:80b9552295fdce0a2513dcb795a3f8591eca1a8dcf8afe0de3214209e6924ad1 \
+    --hash=sha256:84fa41b58a8d3b7363ecdf3397d4b38f345fcf7d4dd14565b4360e7bffc9cae0 \
+    --hash=sha256:890d2139100b5c8ac6d585438d5e145ede1d7b32b4090a6c078db6db0ef1daea \
+    --hash=sha256:8b3f97041f7716998e474d3c7ffd19ac6941f117616696aef2b5ba927bf091e3 \
+    --hash=sha256:910479e92d5fbdf91e8106b4c658fd43d418893d7cfd5fb11983c54a1ff53869 \
+    --hash=sha256:96a1beef1e64d44597627f4e29d794047a66ad4d7474d93daf5a0ee27928e012 \
+    --hash=sha256:98bef54cf51ac335fda1408112df7ff3e584107633bd9066616033e12b0bd519 \
+    --hash=sha256:afcb1bd6d9ed59d5c8e633765589cab12f98aae09804f156b5965b4463b8b8e3 \
+    --hash=sha256:b72dec85488af3e1e8d58fb4b86b5dbe5171c176002b5e098ea6d52a70004bb5 \
+    --hash=sha256:c3109ba62bea56c68c7c2a976250b040afee61b5f86fc791f17afaa2a09fce94 \
+    --hash=sha256:c73b9e6c107e980e65077b89c54311d8d645f6a9efdde82990777fa43c0a8cae \
+    --hash=sha256:d8fca8b492a4697b0182e0c40b164cb0c44a9669d9c98033fec2f88174605eb0 \
+    --hash=sha256:db6872294339bf35c158219fc65bad939ba87d14c936ae7a33a3ca2d1532c5b1 \
+    --hash=sha256:e1a90bc97f53600f52e902f3ae097424de712d8ae0e42d957efc7ed908573a20 \
+    --hash=sha256:f75f476fe183c03c515a0f0f5d195cb05d93fcdc76e31fe3c9753d01f3ee990b \
+    --hash=sha256:fd17b14f84bec0b171869abd17ca0d9bfc564aa1e7f3451f44da526949a911c1 \
+    --hash=sha256:fea71780b66f8c278ebae7221c8959404cf7343b8d2f4b7308aa668cf6f02364
+    # via transformers
+toml==0.10.2 \
+    --hash=sha256:806143ae5bfb6a3c6e736a764057db0e6a0e05e338b5630894a5f779cabb4f9b \
+    --hash=sha256:b3bda1d108d5dd99f4a20d24d9c348e91c4db7ab1b749200bded2f839ccbe68f
+    # via streamlit
+toolz==0.12.0 \
+    --hash=sha256:2059bd4148deb1884bb0eb770a3cde70e7f954cfbbdc2285f1f2de01fd21eb6f \
+    --hash=sha256:88c570861c440ee3f2f6037c4654613228ff40c93a6c25e0eba70d17282c6194
+    # via altair
+torch==1.13.0 \
+    --hash=sha256:0fdd38c96230947b1ed870fed4a560252f8d23c3a2bf4dab9d2d42b18f2e67c8 \
+    --hash=sha256:220325d0f4e69ee9edf00c04208244ef7cf22ebce083815ce272c7491f0603f5 \
+    --hash=sha256:43db0723fc66ad6486f86dc4890c497937f7cd27429f28f73fb7e4d74b7482e2 \
+    --hash=sha256:47fe6228386bff6d74319a2ffe9d4ed943e6e85473d78e80502518c607d644d2 \
+    --hash=sha256:49a949b8136b32b2ec0724cbf4c6678b54e974b7d68f19f1231eea21cde5c23b \
+    --hash=sha256:4a378f5091307381abfb30eb821174e12986f39b1cf7c4522bf99155256819eb \
+    --hash=sha256:635dbb99d981a6483ca533b3dc7be18ef08dd9e1e96fb0bb0e6a99d79e85a130 \
+    --hash=sha256:6c227c16626e4ce766cca5351cc62a2358a11e8e466410a298487b9dff159eb1 \
+    --hash=sha256:857c7d5b1624c5fd979f66d2b074765733dba3f5e1cc97b7d6909155a2aae3ce \
+    --hash=sha256:9197ec216833b836b67e4d68e513d31fb38d9789d7cd998a08fba5b499c38454 \
+    --hash=sha256:922a4910613b310fbeb87707f00cb76fec328eb60cc1349ed2173e7c9b6edcd8 \
+    --hash=sha256:9ac382cedaf2f70afea41380ad8e7c06acef6b5b7e2aef3971cdad666ca6e185 \
+    --hash=sha256:bb33a911460475d1594a8c8cb73f58c08293211760796d99cae8c2509b86d7f1 \
+    --hash=sha256:cd1e67db6575e1b173a626077a54e4911133178557aac50683db03a34e2b636a \
+    --hash=sha256:d2d2753519415d154de4d3e64d2eaaeefdba6b6fd7d69d5ffaef595988117700 \
+    --hash=sha256:e20df14d874b024851c58e8bb3846249cb120e677f7463f60c986e3661f88680 \
+    --hash=sha256:e643ac8d086706e82f77b5d4dfcf145a9dd37b69e03e64177fc23821754d2ed7 \
+    --hash=sha256:ef934a21da6f6a516d0a9c712a80d09c56128abdc6af8dc151bee5199b4c3b4e \
+    --hash=sha256:f01a9ae0d4b69d2fc4145e8beab45b7877342dddbd4838a7d3c11ca7f6680745 \
+    --hash=sha256:f68edfea71ade3862039ba66bcedf954190a2db03b0c41a9b79afd72210abd97 \
+    --hash=sha256:fa768432ce4b8ffa29184c79a3376ab3de4a57b302cdf3c026a6be4c5a8ab75b
+    # via
+    #   accelerate
+    #   basicsr
+    #   clean-fid
+    #   clip
+    #   facexlib
+    #   gfpgan
+    #   k-diffusion
+    #   kornia
+    #   pytorch-lightning
+    #   realesrgan
+    #   taming-transformers-rom1504
+    #   test-tube
+    #   torch-fidelity
+    #   torchdiffeq
+    #   torchmetrics
+    #   torchvision
+torch-fidelity==0.3.0 \
+    --hash=sha256:3d3e33db98919759cc4f3f24cb27e1e74bdc7c905d90a780630e4e1c18492b66 \
+    --hash=sha256:d01284825595feb7dc3eae3dc9a0d8ced02be764813a3483f109bc142b52a1d3
+    # via -r requirements.in
+torchdiffeq==0.2.3 \
+    --hash=sha256:b5b01ec1294a2d8d5f77e567bf17c5de1237c0573cb94deefa88326f0e18c338 \
+    --hash=sha256:fe75f434b9090ac0c27702e02bed21472b0f87035be6581f51edc5d4013ea31a
+    # via k-diffusion
+torchmetrics==0.10.2 \
+    --hash=sha256:43757d82266969906fc74b6e80766fcb2a0d52d6c3d09e3b7c98cf3b733fd20c \
+    --hash=sha256:daa29d96bff5cff04d80eec5b9f5076993d6ac9c2d2163e88b6b31f8d38f7c25
+    # via pytorch-lightning
+torchvision==0.13.1 ; platform_system == "Darwin" \
+    --hash=sha256:0298bae3b09ac361866088434008d82b99d6458fe8888c8df90720ef4b347d44 \
+    --hash=sha256:08f592ea61836ebeceb5c97f4d7a813b9d7dc651bbf7ce4401563ccfae6a21fc \
+    --hash=sha256:099874088df104d54d8008f2a28539ca0117b512daed8bf3c2bbfa2b7ccb187a \
+    --hash=sha256:0e77706cc90462653620e336bb90daf03d7bf1b88c3a9a3037df8d111823a56e \
+    --hash=sha256:19286a733c69dcbd417b86793df807bd227db5786ed787c17297741a9b0d0fc7 \
+    --hash=sha256:3567fb3def829229ec217c1e38f08c5128ff7fb65854cac17ebac358ff7aa309 \
+    --hash=sha256:4d8bf321c4380854ef04613935fdd415dce29d1088a7ff99e06e113f0efe9203 \
+    --hash=sha256:5e631241bee3661de64f83616656224af2e3512eb2580da7c08e08b8c965a8ac \
+    --hash=sha256:7552e80fa222252b8b217a951c85e172a710ea4cad0ae0c06fbb67addece7871 \
+    --hash=sha256:7cb789ceefe6dcd0dc8eeda37bfc45efb7cf34770eac9533861d51ca508eb5b3 \
+    --hash=sha256:83e9e2457f23110fd53b0177e1bc621518d6ea2108f570e853b768ce36b7c679 \
+    --hash=sha256:87c137f343197769a51333076e66bfcd576301d2cd8614b06657187c71b06c4f \
+    --hash=sha256:899eec0b9f3b99b96d6f85b9aa58c002db41c672437677b553015b9135b3be7e \
+    --hash=sha256:8e4d02e4d8a203e0c09c10dfb478214c224d080d31efc0dbf36d9c4051f7f3c6 \
+    --hash=sha256:b167934a5943242da7b1e59318f911d2d253feeca0d13ad5d832b58eed943401 \
+    --hash=sha256:c5ed609c8bc88c575226400b2232e0309094477c82af38952e0373edef0003fd \
+    --hash=sha256:e9a563894f9fa40692e24d1aa58c3ef040450017cfed3598ff9637f404f3fe3b \
+    --hash=sha256:ef5fe3ec1848123cd0ec74c07658192b3147dcd38e507308c790d5943e87b88c \
+    --hash=sha256:f230a1a40ed70d51e463ce43df243ec520902f8725de2502e485efc5eea9d864
+    # via
+    #   -r requirements.in
+    #   basicsr
+    #   clean-fid
+    #   clip
+    #   facexlib
+    #   gfpgan
+    #   k-diffusion
+    #   realesrgan
+    #   taming-transformers-rom1504
+    #   torch-fidelity
+tornado==6.2 \
+    --hash=sha256:1d54d13ab8414ed44de07efecb97d4ef7c39f7438cf5e976ccd356bebb1b5fca \
+    --hash=sha256:20f638fd8cc85f3cbae3c732326e96addff0a15e22d80f049e00121651e82e72 \
+    --hash=sha256:5c87076709343557ef8032934ce5f637dbb552efa7b21d08e89ae7619ed0eb23 \
+    --hash=sha256:5f8c52d219d4995388119af7ccaa0bcec289535747620116a58d830e7c25d8a8 \
+    --hash=sha256:6fdfabffd8dfcb6cf887428849d30cf19a3ea34c2c248461e1f7d718ad30b66b \
+    --hash=sha256:87dcafae3e884462f90c90ecc200defe5e580a7fbbb4365eda7c7c1eb809ebc9 \
+    --hash=sha256:9b630419bde84ec666bfd7ea0a4cb2a8a651c2d5cccdbdd1972a0c859dfc3c13 \
+    --hash=sha256:b8150f721c101abdef99073bf66d3903e292d851bee51910839831caba341a75 \
+    --hash=sha256:ba09ef14ca9893954244fd872798b4ccb2367c165946ce2dd7376aebdde8e3ac \
+    --hash=sha256:d3a2f5999215a3a06a4fc218026cd84c61b8b2b40ac5296a6db1f1451ef04c1e \
+    --hash=sha256:e5f923aa6a47e133d1cf87d60700889d7eae68988704e20c75fb2d65677a8e4b
+    # via streamlit
+tqdm==4.64.1 \
+    --hash=sha256:5f4f682a004951c1b450bc753c710e9280c5746ce6ffedee253ddbcbf54cf1e4 \
+    --hash=sha256:6fee160d6ffcd1b1c68c65f14c829c22832bc401726335ce92c52d395944a6a1
+    # via
+    #   basicsr
+    #   clean-fid
+    #   clip
+    #   facexlib
+    #   gfpgan
+    #   huggingface-hub
+    #   k-diffusion
+    #   pytorch-lightning
+    #   realesrgan
+    #   taming-transformers-rom1504
+    #   torch-fidelity
+    #   transformers
+transformers==4.24.0 \
+    --hash=sha256:486f353a8e594002e48be0e2aba723d96eda839e63bfe274702a4b5eda85559b \
+    --hash=sha256:b7ab50039ef9bf817eff14ab974f306fd20a72350bdc9df3a858fd009419322e
+    # via -r requirements.in
+typing-extensions==4.4.0 \
+    --hash=sha256:1511434bb92bf8dd198c12b1cc812e800d4181cfcb867674e0f8279cc93087aa \
+    --hash=sha256:16fa4864408f655d35ec496218b85f79b3437c829e93320c7c9215ccfd92489e
+    # via
+    #   huggingface-hub
+    #   pytorch-lightning
+    #   qudida
+    #   streamlit
+    #   torch
+    #   torchvision
+tzdata==2022.6 \
+    --hash=sha256:04a680bdc5b15750c39c12a448885a51134a27ec9af83667663f0b3a1bf3f342 \
+    --hash=sha256:91f11db4503385928c15598c98573e3af07e7229181bee5375bd30f1695ddcae
+    # via
+    #   pytz-deprecation-shim
+    #   tzlocal
+tzlocal==4.2 \
+    --hash=sha256:89885494684c929d9191c57aa27502afc87a579be5cdd3225c77c463ea043745 \
+    --hash=sha256:ee5842fa3a795f023514ac2d801c4a81d1743bbe642e3940143326b3a00addd7
+    # via streamlit
+urllib3==1.26.12 \
+    --hash=sha256:3fa96cf423e6987997fc326ae8df396db2a8b7c667747d47ddd8ecba91f4a74e \
+    --hash=sha256:b930dd878d5a8afb066a637fbb35144fe7901e3b209d1cd4f524bd0e9deee997
+    # via
+    #   requests
+    #   sentry-sdk
+validators==0.18.2 \
+    --hash=sha256:0143dcca8a386498edaf5780cbd5960da1a4c85e0719f3ee5c9b41249c4fefbd \
+    --hash=sha256:37cd9a9213278538ad09b5b9f9134266e7c226ab1fede1d500e29e0a8fbb9ea6
+    # via streamlit
+wandb==0.13.5 \
+    --hash=sha256:11f30a22e30abaa9c187e8b6aa4c12d76160b40bbe98a6f14b0dde9297bbfbe2 \
+    --hash=sha256:60d5bcc524b8a314c8e072c03f7702dbd5406261b00a4ce75e7556b805fdc765
+    # via k-diffusion
+wcwidth==0.2.5 \
+    --hash=sha256:beb4802a9cebb9144e99086eff703a642a13d6a0052920003a230f3294bbe784 \
+    --hash=sha256:c4d647b99872929fdb7bdcaa4fbe7f01413ed3d98077df798530e5b04f116c83
+    # via ftfy
+werkzeug==2.2.2 \
+    --hash=sha256:7ea2d48322cc7c0f8b3a215ed73eabd7b5d75d0b50e31ab006286ccff9e00b8f \
+    --hash=sha256:f979ab81f58d7318e064e99c4506445d60135ac5cd2e177a2de0089bfd4c9bd5
+    # via
+    #   flask
+    #   tb-nightly
+    #   tensorboard
+wheel==0.38.2 \
+    --hash=sha256:3d492ef22379a156ec923d2a77051cedfd4df4b667864e9e41ba83f0b70b7149 \
+    --hash=sha256:7a5a3095dceca97a3cac869b8fef4e89b83fafde21b6688f47b6fda7600eb441
+    # via
+    #   tb-nightly
+    #   tensorboard
+whichcraft==0.6.1 \
+    --hash=sha256:acdbb91b63d6a15efbd6430d1d7b2d36e44a71697e93e19b7ded477afd9fce87 \
+    --hash=sha256:deda9266fbb22b8c64fd3ee45c050d61139cd87419765f588e37c8d23e236dd9
+    # via flaskwebgui
+yapf==0.32.0 \
+    --hash=sha256:8fea849025584e486fd06d6ba2bed717f396080fd3cc236ba10cb97c4c51cf32 \
+    --hash=sha256:a3f5085d37ef7e3e004c4ba9f9b3e40c54ff1901cd111f05145ae313a7c67d1b
+    # via
+    #   basicsr
+    #   gfpgan
+yarl==1.8.1 \
+    --hash=sha256:076eede537ab978b605f41db79a56cad2e7efeea2aa6e0fa8f05a26c24a034fb \
+    --hash=sha256:07b21e274de4c637f3e3b7104694e53260b5fc10d51fb3ec5fed1da8e0f754e3 \
+    --hash=sha256:0ab5a138211c1c366404d912824bdcf5545ccba5b3ff52c42c4af4cbdc2c5035 \
+    --hash=sha256:0c03f456522d1ec815893d85fccb5def01ffaa74c1b16ff30f8aaa03eb21e453 \
+    --hash=sha256:12768232751689c1a89b0376a96a32bc7633c08da45ad985d0c49ede691f5c0d \
+    --hash=sha256:19cd801d6f983918a3f3a39f3a45b553c015c5aac92ccd1fac619bd74beece4a \
+    --hash=sha256:1ca7e596c55bd675432b11320b4eacc62310c2145d6801a1f8e9ad160685a231 \
+    --hash=sha256:1e4808f996ca39a6463f45182e2af2fae55e2560be586d447ce8016f389f626f \
+    --hash=sha256:205904cffd69ae972a1707a1bd3ea7cded594b1d773a0ce66714edf17833cdae \
+    --hash=sha256:20df6ff4089bc86e4a66e3b1380460f864df3dd9dccaf88d6b3385d24405893b \
+    --hash=sha256:21ac44b763e0eec15746a3d440f5e09ad2ecc8b5f6dcd3ea8cb4773d6d4703e3 \
+    --hash=sha256:29e256649f42771829974e742061c3501cc50cf16e63f91ed8d1bf98242e5507 \
+    --hash=sha256:2d800b9c2eaf0684c08be5f50e52bfa2aa920e7163c2ea43f4f431e829b4f0fd \
+    --hash=sha256:2d93a049d29df172f48bcb09acf9226318e712ce67374f893b460b42cc1380ae \
+    --hash=sha256:31a9a04ecccd6b03e2b0e12e82131f1488dea5555a13a4d32f064e22a6003cfe \
+    --hash=sha256:3d1a50e461615747dd93c099f297c1994d472b0f4d2db8a64e55b1edf704ec1c \
+    --hash=sha256:449c957ffc6bc2309e1fbe67ab7d2c1efca89d3f4912baeb8ead207bb3cc1cd4 \
+    --hash=sha256:4a88510731cd8d4befaba5fbd734a7dd914de5ab8132a5b3dde0bbd6c9476c64 \
+    --hash=sha256:4c322cbaa4ed78a8aac89b2174a6df398faf50e5fc12c4c191c40c59d5e28357 \
+    --hash=sha256:5395da939ffa959974577eff2cbfc24b004a2fb6c346918f39966a5786874e54 \
+    --hash=sha256:5587bba41399854703212b87071c6d8638fa6e61656385875f8c6dff92b2e461 \
+    --hash=sha256:56c11efb0a89700987d05597b08a1efcd78d74c52febe530126785e1b1a285f4 \
+    --hash=sha256:5999c4662631cb798496535afbd837a102859568adc67d75d2045e31ec3ac497 \
+    --hash=sha256:59ddd85a1214862ce7c7c66457f05543b6a275b70a65de366030d56159a979f0 \
+    --hash=sha256:6347f1a58e658b97b0a0d1ff7658a03cb79bdbda0331603bed24dd7054a6dea1 \
+    --hash=sha256:6628d750041550c5d9da50bb40b5cf28a2e63b9388bac10fedd4f19236ef4957 \
+    --hash=sha256:6afb336e23a793cd3b6476c30f030a0d4c7539cd81649683b5e0c1b0ab0bf350 \
+    --hash=sha256:6c8148e0b52bf9535c40c48faebb00cb294ee577ca069d21bd5c48d302a83780 \
+    --hash=sha256:76577f13333b4fe345c3704811ac7509b31499132ff0181f25ee26619de2c843 \
+    --hash=sha256:7c0da7e44d0c9108d8b98469338705e07f4bb7dab96dbd8fa4e91b337db42548 \
+    --hash=sha256:7de89c8456525650ffa2bb56a3eee6af891e98f498babd43ae307bd42dca98f6 \
+    --hash=sha256:7ec362167e2c9fd178f82f252b6d97669d7245695dc057ee182118042026da40 \
+    --hash=sha256:7fce6cbc6c170ede0221cc8c91b285f7f3c8b9fe28283b51885ff621bbe0f8ee \
+    --hash=sha256:85cba594433915d5c9a0d14b24cfba0339f57a2fff203a5d4fd070e593307d0b \
+    --hash=sha256:8b0af1cf36b93cee99a31a545fe91d08223e64390c5ecc5e94c39511832a4bb6 \
+    --hash=sha256:9130ddf1ae9978abe63808b6b60a897e41fccb834408cde79522feb37fb72fb0 \
+    --hash=sha256:99449cd5366fe4608e7226c6cae80873296dfa0cde45d9b498fefa1de315a09e \
+    --hash=sha256:9de955d98e02fab288c7718662afb33aab64212ecb368c5dc866d9a57bf48880 \
+    --hash=sha256:a0fb2cb4204ddb456a8e32381f9a90000429489a25f64e817e6ff94879d432fc \
+    --hash=sha256:a165442348c211b5dea67c0206fc61366212d7082ba8118c8c5c1c853ea4d82e \
+    --hash=sha256:ab2a60d57ca88e1d4ca34a10e9fb4ab2ac5ad315543351de3a612bbb0560bead \
+    --hash=sha256:abc06b97407868ef38f3d172762f4069323de52f2b70d133d096a48d72215d28 \
+    --hash=sha256:af887845b8c2e060eb5605ff72b6f2dd2aab7a761379373fd89d314f4752abbf \
+    --hash=sha256:b19255dde4b4f4c32e012038f2c169bb72e7f081552bea4641cab4d88bc409dd \
+    --hash=sha256:b3ded839a5c5608eec8b6f9ae9a62cb22cd037ea97c627f38ae0841a48f09eae \
+    --hash=sha256:c1445a0c562ed561d06d8cbc5c8916c6008a31c60bc3655cdd2de1d3bf5174a0 \
+    --hash=sha256:d0272228fabe78ce00a3365ffffd6f643f57a91043e119c289aaba202f4095b0 \
+    --hash=sha256:d0b51530877d3ad7a8d47b2fff0c8df3b8f3b8deddf057379ba50b13df2a5eae \
+    --hash=sha256:d0f77539733e0ec2475ddcd4e26777d08996f8cd55d2aef82ec4d3896687abda \
+    --hash=sha256:d2b8f245dad9e331540c350285910b20dd913dc86d4ee410c11d48523c4fd546 \
+    --hash=sha256:dd032e8422a52e5a4860e062eb84ac94ea08861d334a4bcaf142a63ce8ad4802 \
+    --hash=sha256:de49d77e968de6626ba7ef4472323f9d2e5a56c1d85b7c0e2a190b2173d3b9be \
+    --hash=sha256:de839c3a1826a909fdbfe05f6fe2167c4ab033f1133757b5936efe2f84904c07 \
+    --hash=sha256:e80ed5a9939ceb6fda42811542f31c8602be336b1fb977bccb012e83da7e4936 \
+    --hash=sha256:ea30a42dc94d42f2ba4d0f7c0ffb4f4f9baa1b23045910c0c32df9c9902cb272 \
+    --hash=sha256:ea513a25976d21733bff523e0ca836ef1679630ef4ad22d46987d04b372d57fc \
+    --hash=sha256:ed19b74e81b10b592084a5ad1e70f845f0aacb57577018d31de064e71ffa267a \
+    --hash=sha256:f5af52738e225fcc526ae64071b7e5342abe03f42e0e8918227b38c9aa711e28 \
+    --hash=sha256:fae37373155f5ef9b403ab48af5136ae9851151f7aacd9926251ab26b953118b
+    # via aiohttp
+zipp==3.10.0 \
+    --hash=sha256:4fcb6f278987a6605757302a6e40e896257570d11c51628968ccb2a47e80c6c1 \
+    --hash=sha256:7a7262fd930bd3e36c50b9a64897aec3fafff3dfdeec9623ae22b40e93f99bb8
+    # via importlib-metadata
+
+# The following packages are considered to be unsafe in a requirements file:
+setuptools==65.5.1 \
+    --hash=sha256:d0b9a8433464d5800cbe05094acf5c6d52a91bfac9b52bcfc4d41382be5d5d31 \
+    --hash=sha256:e197a19aa8ec9722928f2206f8de752def0e4c9fc6953527360d1c36d94ddb2f
+    # via
+    #   numba
+    #   tb-nightly
+    #   tensorboard
+    #   wandb
diff --git a/installer/py3.10-linux-x86_64-cuda-reqs.txt b/installer/py3.10-linux-x86_64-cuda-reqs.txt
new file mode 100644
index 0000000000..acb31ffa3f
--- /dev/null
+++ b/installer/py3.10-linux-x86_64-cuda-reqs.txt
@@ -0,0 +1,2066 @@
+#
+# This file is autogenerated by pip-compile with python 3.10
+# To update, run:
+#
+#    pip-compile --allow-unsafe --generate-hashes --output-file=installer/py3.10-linux-x86_64-cuda-reqs.txt requirements.in
+#
+--extra-index-url https://download.pytorch.org/whl/cu116
+--trusted-host https
+
+absl-py==1.3.0 \
+    --hash=sha256:34995df9bd7a09b3b8749e230408f5a2a2dd7a68a0d33c12a3d0cb15a041a507 \
+    --hash=sha256:463c38a08d2e4cef6c498b76ba5bd4858e4c6ef51da1a5a1f27139a022e20248
+    # via
+    #   tb-nightly
+    #   tensorboard
+accelerate==0.13.2 \
+    --hash=sha256:dd6f08b010077f252dda5a7699d87b02885335c456770939c536e65ff07ed760 \
+    --hash=sha256:e22180d7094e4c1bfb05a2b078297c222f6b4fa595fde8916946c3f377cdf019
+    # via k-diffusion
+addict==2.4.0 \
+    --hash=sha256:249bb56bbfd3cdc2a004ea0ff4c2b6ddc84d53bc2194761636eb314d5cfa5dfc \
+    --hash=sha256:b3b2210e0e067a281f5646c8c5db92e99b7231ea8b0eb5f74dbdf9e259d4e494
+    # via basicsr
+aiohttp==3.8.3 \
+    --hash=sha256:02f9a2c72fc95d59b881cf38a4b2be9381b9527f9d328771e90f72ac76f31ad8 \
+    --hash=sha256:059a91e88f2c00fe40aed9031b3606c3f311414f86a90d696dd982e7aec48142 \
+    --hash=sha256:05a3c31c6d7cd08c149e50dc7aa2568317f5844acd745621983380597f027a18 \
+    --hash=sha256:08c78317e950e0762c2983f4dd58dc5e6c9ff75c8a0efeae299d363d439c8e34 \
+    --hash=sha256:09e28f572b21642128ef31f4e8372adb6888846f32fecb288c8b0457597ba61a \
+    --hash=sha256:0d2c6d8c6872df4a6ec37d2ede71eff62395b9e337b4e18efd2177de883a5033 \
+    --hash=sha256:16c121ba0b1ec2b44b73e3a8a171c4f999b33929cd2397124a8c7fcfc8cd9e06 \
+    --hash=sha256:1d90043c1882067f1bd26196d5d2db9aa6d268def3293ed5fb317e13c9413ea4 \
+    --hash=sha256:1e56b9cafcd6531bab5d9b2e890bb4937f4165109fe98e2b98ef0dcfcb06ee9d \
+    --hash=sha256:20acae4f268317bb975671e375493dbdbc67cddb5f6c71eebdb85b34444ac46b \
+    --hash=sha256:21b30885a63c3f4ff5b77a5d6caf008b037cb521a5f33eab445dc566f6d092cc \
+    --hash=sha256:21d69797eb951f155026651f7e9362877334508d39c2fc37bd04ff55b2007091 \
+    --hash=sha256:256deb4b29fe5e47893fa32e1de2d73c3afe7407738bd3c63829874661d4822d \
+    --hash=sha256:25892c92bee6d9449ffac82c2fe257f3a6f297792cdb18ad784737d61e7a9a85 \
+    --hash=sha256:2ca9af5f8f5812d475c5259393f52d712f6d5f0d7fdad9acdb1107dd9e3cb7eb \
+    --hash=sha256:2d252771fc85e0cf8da0b823157962d70639e63cb9b578b1dec9868dd1f4f937 \
+    --hash=sha256:2dea10edfa1a54098703cb7acaa665c07b4e7568472a47f4e64e6319d3821ccf \
+    --hash=sha256:2df5f139233060578d8c2c975128fb231a89ca0a462b35d4b5fcf7c501ebdbe1 \
+    --hash=sha256:2feebbb6074cdbd1ac276dbd737b40e890a1361b3cc30b74ac2f5e24aab41f7b \
+    --hash=sha256:309aa21c1d54b8ef0723181d430347d7452daaff93e8e2363db8e75c72c2fb2d \
+    --hash=sha256:3828fb41b7203176b82fe5d699e0d845435f2374750a44b480ea6b930f6be269 \
+    --hash=sha256:398701865e7a9565d49189f6c90868efaca21be65c725fc87fc305906be915da \
+    --hash=sha256:43046a319664a04b146f81b40e1545d4c8ac7b7dd04c47e40bf09f65f2437346 \
+    --hash=sha256:437399385f2abcd634865705bdc180c8314124b98299d54fe1d4c8990f2f9494 \
+    --hash=sha256:45d88b016c849d74ebc6f2b6e8bc17cabf26e7e40c0661ddd8fae4c00f015697 \
+    --hash=sha256:47841407cc89a4b80b0c52276f3cc8138bbbfba4b179ee3acbd7d77ae33f7ac4 \
+    --hash=sha256:4a4fbc769ea9b6bd97f4ad0b430a6807f92f0e5eb020f1e42ece59f3ecfc4585 \
+    --hash=sha256:4ab94426ddb1ecc6a0b601d832d5d9d421820989b8caa929114811369673235c \
+    --hash=sha256:4b0f30372cef3fdc262f33d06e7b411cd59058ce9174ef159ad938c4a34a89da \
+    --hash=sha256:4e3a23ec214e95c9fe85a58470b660efe6534b83e6cbe38b3ed52b053d7cb6ad \
+    --hash=sha256:512bd5ab136b8dc0ffe3fdf2dfb0c4b4f49c8577f6cae55dca862cd37a4564e2 \
+    --hash=sha256:527b3b87b24844ea7865284aabfab08eb0faf599b385b03c2aa91fc6edd6e4b6 \
+    --hash=sha256:54d107c89a3ebcd13228278d68f1436d3f33f2dd2af5415e3feaeb1156e1a62c \
+    --hash=sha256:5835f258ca9f7c455493a57ee707b76d2d9634d84d5d7f62e77be984ea80b849 \
+    --hash=sha256:598adde339d2cf7d67beaccda3f2ce7c57b3b412702f29c946708f69cf8222aa \
+    --hash=sha256:599418aaaf88a6d02a8c515e656f6faf3d10618d3dd95866eb4436520096c84b \
+    --hash=sha256:5bf651afd22d5f0c4be16cf39d0482ea494f5c88f03e75e5fef3a85177fecdeb \
+    --hash=sha256:5c59fcd80b9049b49acd29bd3598cada4afc8d8d69bd4160cd613246912535d7 \
+    --hash=sha256:653acc3880459f82a65e27bd6526e47ddf19e643457d36a2250b85b41a564715 \
+    --hash=sha256:66bd5f950344fb2b3dbdd421aaa4e84f4411a1a13fca3aeb2bcbe667f80c9f76 \
+    --hash=sha256:6f3553510abdbec67c043ca85727396ceed1272eef029b050677046d3387be8d \
+    --hash=sha256:7018ecc5fe97027214556afbc7c502fbd718d0740e87eb1217b17efd05b3d276 \
+    --hash=sha256:713d22cd9643ba9025d33c4af43943c7a1eb8547729228de18d3e02e278472b6 \
+    --hash=sha256:73a4131962e6d91109bca6536416aa067cf6c4efb871975df734f8d2fd821b37 \
+    --hash=sha256:75880ed07be39beff1881d81e4a907cafb802f306efd6d2d15f2b3c69935f6fb \
+    --hash=sha256:75e14eac916f024305db517e00a9252714fce0abcb10ad327fb6dcdc0d060f1d \
+    --hash=sha256:8135fa153a20d82ffb64f70a1b5c2738684afa197839b34cc3e3c72fa88d302c \
+    --hash=sha256:84b14f36e85295fe69c6b9789b51a0903b774046d5f7df538176516c3e422446 \
+    --hash=sha256:86fc24e58ecb32aee09f864cb11bb91bc4c1086615001647dbfc4dc8c32f4008 \
+    --hash=sha256:87f44875f2804bc0511a69ce44a9595d5944837a62caecc8490bbdb0e18b1342 \
+    --hash=sha256:88c70ed9da9963d5496d38320160e8eb7e5f1886f9290475a881db12f351ab5d \
+    --hash=sha256:88e5be56c231981428f4f506c68b6a46fa25c4123a2e86d156c58a8369d31ab7 \
+    --hash=sha256:89d2e02167fa95172c017732ed7725bc8523c598757f08d13c5acca308e1a061 \
+    --hash=sha256:8d6aaa4e7155afaf994d7924eb290abbe81a6905b303d8cb61310a2aba1c68ba \
+    --hash=sha256:92a2964319d359f494f16011e23434f6f8ef0434acd3cf154a6b7bec511e2fb7 \
+    --hash=sha256:96372fc29471646b9b106ee918c8eeb4cca423fcbf9a34daa1b93767a88a2290 \
+    --hash=sha256:978b046ca728073070e9abc074b6299ebf3501e8dee5e26efacb13cec2b2dea0 \
+    --hash=sha256:9c7149272fb5834fc186328e2c1fa01dda3e1fa940ce18fded6d412e8f2cf76d \
+    --hash=sha256:a0239da9fbafd9ff82fd67c16704a7d1bccf0d107a300e790587ad05547681c8 \
+    --hash=sha256:ad5383a67514e8e76906a06741febd9126fc7c7ff0f599d6fcce3e82b80d026f \
+    --hash=sha256:ad61a9639792fd790523ba072c0555cd6be5a0baf03a49a5dd8cfcf20d56df48 \
+    --hash=sha256:b29bfd650ed8e148f9c515474a6ef0ba1090b7a8faeee26b74a8ff3b33617502 \
+    --hash=sha256:b97decbb3372d4b69e4d4c8117f44632551c692bb1361b356a02b97b69e18a62 \
+    --hash=sha256:ba71c9b4dcbb16212f334126cc3d8beb6af377f6703d9dc2d9fb3874fd667ee9 \
+    --hash=sha256:c37c5cce780349d4d51739ae682dec63573847a2a8dcb44381b174c3d9c8d403 \
+    --hash=sha256:c971bf3786b5fad82ce5ad570dc6ee420f5b12527157929e830f51c55dc8af77 \
+    --hash=sha256:d1fde0f44029e02d02d3993ad55ce93ead9bb9b15c6b7ccd580f90bd7e3de476 \
+    --hash=sha256:d24b8bb40d5c61ef2d9b6a8f4528c2f17f1c5d2d31fed62ec860f6006142e83e \
+    --hash=sha256:d5ba88df9aa5e2f806650fcbeedbe4f6e8736e92fc0e73b0400538fd25a4dd96 \
+    --hash=sha256:d6f76310355e9fae637c3162936e9504b4767d5c52ca268331e2756e54fd4ca5 \
+    --hash=sha256:d737fc67b9a970f3234754974531dc9afeea11c70791dcb7db53b0cf81b79784 \
+    --hash=sha256:da22885266bbfb3f78218dc40205fed2671909fbd0720aedba39b4515c038091 \
+    --hash=sha256:da37dcfbf4b7f45d80ee386a5f81122501ec75672f475da34784196690762f4b \
+    --hash=sha256:db19d60d846283ee275d0416e2a23493f4e6b6028825b51290ac05afc87a6f97 \
+    --hash=sha256:db4c979b0b3e0fa7e9e69ecd11b2b3174c6963cebadeecfb7ad24532ffcdd11a \
+    --hash=sha256:e164e0a98e92d06da343d17d4e9c4da4654f4a4588a20d6c73548a29f176abe2 \
+    --hash=sha256:e168a7560b7c61342ae0412997b069753f27ac4862ec7867eff74f0fe4ea2ad9 \
+    --hash=sha256:e381581b37db1db7597b62a2e6b8b57c3deec95d93b6d6407c5b61ddc98aca6d \
+    --hash=sha256:e65bc19919c910127c06759a63747ebe14f386cda573d95bcc62b427ca1afc73 \
+    --hash=sha256:e7b8813be97cab8cb52b1375f41f8e6804f6507fe4660152e8ca5c48f0436017 \
+    --hash=sha256:e8a78079d9a39ca9ca99a8b0ac2fdc0c4d25fc80c8a8a82e5c8211509c523363 \
+    --hash=sha256:ebf909ea0a3fc9596e40d55d8000702a85e27fd578ff41a5500f68f20fd32e6c \
+    --hash=sha256:ec40170327d4a404b0d91855d41bfe1fe4b699222b2b93e3d833a27330a87a6d \
+    --hash=sha256:f178d2aadf0166be4df834c4953da2d7eef24719e8aec9a65289483eeea9d618 \
+    --hash=sha256:f88df3a83cf9df566f171adba39d5bd52814ac0b94778d2448652fc77f9eb491 \
+    --hash=sha256:f973157ffeab5459eefe7b97a804987876dd0a55570b8fa56b4e1954bf11329b \
+    --hash=sha256:ff25f48fc8e623d95eca0670b8cc1469a83783c924a602e0fbd47363bb54aaca
+    # via fsspec
+aiosignal==1.2.0 \
+    --hash=sha256:26e62109036cd181df6e6ad646f91f0dcfd05fe16d0cb924138ff2ab75d64e3a \
+    --hash=sha256:78ed67db6c7b7ced4f98e495e572106d5c432a93e1ddd1bf475e1dc05f5b7df2
+    # via aiohttp
+albumentations==1.3.0 \
+    --hash=sha256:294165d87d03bc8323e484927f0a5c1a3c64b0e7b9c32a979582a6c93c363bdf \
+    --hash=sha256:be1af36832c8893314f2a5550e8ac19801e04770734c1b70fa3c996b41f37bed
+    # via -r requirements.in
+altair==4.2.0 \
+    --hash=sha256:0c724848ae53410c13fa28be2b3b9a9dcb7b5caa1a70f7f217bd663bb419935a \
+    --hash=sha256:d87d9372e63b48cd96b2a6415f0cf9457f50162ab79dc7a31cd7e024dd840026
+    # via streamlit
+antlr4-python3-runtime==4.9.3 \
+    --hash=sha256:f224469b4168294902bb1efa80a8bf7855f24c99aef99cbefc1bcd3cce77881b
+    # via omegaconf
+async-timeout==4.0.2 \
+    --hash=sha256:2163e1640ddb52b7a8c80d0a67a08587e5d245cc9c553a74a847056bc2976b15 \
+    --hash=sha256:8ca1e4fcf50d07413d66d1a5e416e42cfdf5851c981d679a09851a6853383b3c
+    # via aiohttp
+attrs==22.1.0 \
+    --hash=sha256:29adc2665447e5191d0e7c568fde78b21f9672d344281d0c6e1ab085429b22b6 \
+    --hash=sha256:86efa402f67bf2df34f51a335487cf46b1ec130d02b8d39fd248abfd30da551c
+    # via
+    #   aiohttp
+    #   jsonschema
+basicsr==1.4.2 \
+    --hash=sha256:b89b595a87ef964cda9913b4d99380ddb6554c965577c0c10cb7b78e31301e87
+    # via
+    #   gfpgan
+    #   realesrgan
+bidict==0.22.0 \
+    --hash=sha256:415126d23a0c81e1a8c584a8fb1f6905ea090c772571803aeee0a2242e8e7ba0 \
+    --hash=sha256:5c826b3e15e97cc6e615de295756847c282a79b79c5430d3bfc909b1ac9f5bd8
+    # via python-socketio
+blinker==1.5 \
+    --hash=sha256:1eb563df6fdbc39eeddc177d953203f99f097e9bf0e2b8f9f3cf18b6ca425e36 \
+    --hash=sha256:923e5e2f69c155f2cc42dafbbd70e16e3fde24d2d4aa2ab72fbe386238892462
+    # via streamlit
+cachetools==5.2.0 \
+    --hash=sha256:6a94c6402995a99c3970cc7e4884bb60b4a8639938157eeed436098bf9831757 \
+    --hash=sha256:f9f17d2aec496a9aa6b76f53e3b614c965223c061982d434d160f930c698a9db
+    # via
+    #   google-auth
+    #   streamlit
+certifi==2022.9.24 \
+    --hash=sha256:0d9c601124e5a6ba9712dbc60d9c53c21e34f5f641fe83002317394311bdce14 \
+    --hash=sha256:90c1a32f1d68f940488354e36370f6cca89f0f106db09518524c88d6ed83f382
+    # via
+    #   requests
+    #   sentry-sdk
+chardet==4.0.0 \
+    --hash=sha256:0d6f53a15db4120f2b08c94f11e7d93d2c911ee118b6b30a04ec3ee8310179fa \
+    --hash=sha256:f864054d66fd9118f2e67044ac8981a54775ec5b67aed0441892edb553d21da5
+    # via requests
+charset-normalizer==2.1.1 \
+    --hash=sha256:5a3d016c7c547f69d6f81fb0db9449ce888b418b5b9952cc5e6e66843e9dd845 \
+    --hash=sha256:83e9a75d1911279afd89352c68b45348559d1fc0506b054b346651b5e7fee29f
+    # via aiohttp
+clean-fid==0.1.34 \
+    --hash=sha256:2997f85a67a28c95adaae7899a33fc10537164fef4cdd424e3257bffad79a901
+    # via k-diffusion
+click==8.1.3 \
+    --hash=sha256:7682dc8afb30297001674575ea00d1814d808d6a36af415a82bd481d37ba7b8e \
+    --hash=sha256:bb4d8133cb15a609f44e8213d9b391b0809795062913b383c62be0ee95b1db48
+    # via
+    #   flask
+    #   streamlit
+    #   wandb
+clip @ https://github.com/openai/CLIP/archive/d50d76daa670286dd6cacf3bcd80b5e4823fc8e1.zip \
+    --hash=sha256:b5842c25da441d6c581b53a5c60e0c2127ebafe0f746f8e15561a006c6c3be6a
+    # via -r requirements.in
+commonmark==0.9.1 \
+    --hash=sha256:452f9dc859be7f06631ddcb328b6919c67984aca654e5fefb3914d54691aed60 \
+    --hash=sha256:da2f38c92590f83de410ba1a3cbceafbc74fee9def35f9251ba9a971d6d66fd9
+    # via rich
+contourpy==1.0.6 \
+    --hash=sha256:0236875c5a0784215b49d00ebbe80c5b6b5d5244b3655a36dda88105334dea17 \
+    --hash=sha256:03d1b9c6b44a9e30d554654c72be89af94fab7510b4b9f62356c64c81cec8b7d \
+    --hash=sha256:0537cc1195245bbe24f2913d1f9211b8f04eb203de9044630abd3664c6cc339c \
+    --hash=sha256:06ca79e1efbbe2df795822df2fa173d1a2b38b6e0f047a0ec7903fbca1d1847e \
+    --hash=sha256:08e8d09d96219ace6cb596506fb9b64ea5f270b2fb9121158b976d88871fcfd1 \
+    --hash=sha256:0b1e66346acfb17694d46175a0cea7d9036f12ed0c31dfe86f0f405eedde2bdd \
+    --hash=sha256:0b97454ed5b1368b66ed414c754cba15b9750ce69938fc6153679787402e4cdf \
+    --hash=sha256:0e4854cc02006ad6684ce092bdadab6f0912d131f91c2450ce6dbdea78ee3c0b \
+    --hash=sha256:12a7dc8439544ed05c6553bf026d5e8fa7fad48d63958a95d61698df0e00092b \
+    --hash=sha256:1b1ee48a130da4dd0eb8055bbab34abf3f6262957832fd575e0cab4979a15a41 \
+    --hash=sha256:1c0e1308307a75e07d1f1b5f0f56b5af84538a5e9027109a7bcf6cb47c434e72 \
+    --hash=sha256:1dedf4c64185a216c35eb488e6f433297c660321275734401760dafaeb0ad5c2 \
+    --hash=sha256:208bc904889c910d95aafcf7be9e677726df9ef71e216780170dbb7e37d118fa \
+    --hash=sha256:211dfe2bd43bf5791d23afbe23a7952e8ac8b67591d24be3638cabb648b3a6eb \
+    --hash=sha256:341330ed19074f956cb20877ad8d2ae50e458884bfa6a6df3ae28487cc76c768 \
+    --hash=sha256:344cb3badf6fc7316ad51835f56ac387bdf86c8e1b670904f18f437d70da4183 \
+    --hash=sha256:358f6364e4873f4d73360b35da30066f40387dd3c427a3e5432c6b28dd24a8fa \
+    --hash=sha256:371f6570a81dfdddbb837ba432293a63b4babb942a9eb7aaa699997adfb53278 \
+    --hash=sha256:375d81366afd547b8558c4720337218345148bc2fcffa3a9870cab82b29667f2 \
+    --hash=sha256:3a1917d3941dd58732c449c810fa7ce46cc305ce9325a11261d740118b85e6f3 \
+    --hash=sha256:4081918147fc4c29fad328d5066cfc751da100a1098398742f9f364be63803fc \
+    --hash=sha256:444fb776f58f4906d8d354eb6f6ce59d0a60f7b6a720da6c1ccb839db7c80eb9 \
+    --hash=sha256:46deb310a276cc5c1fd27958e358cce68b1e8a515fa5a574c670a504c3a3fe30 \
+    --hash=sha256:494efed2c761f0f37262815f9e3c4bb9917c5c69806abdee1d1cb6611a7174a0 \
+    --hash=sha256:50627bf76abb6ba291ad08db583161939c2c5fab38c38181b7833423ab9c7de3 \
+    --hash=sha256:5641927cc5ae66155d0c80195dc35726eae060e7defc18b7ab27600f39dd1fe7 \
+    --hash=sha256:5b117d29433fc8393b18a696d794961464e37afb34a6eeb8b2c37b5f4128a83e \
+    --hash=sha256:613c665529899b5d9fade7e5d1760111a0b011231277a0d36c49f0d3d6914bd6 \
+    --hash=sha256:6e459ebb8bb5ee4c22c19cc000174f8059981971a33ce11e17dddf6aca97a142 \
+    --hash=sha256:6f56515e7c6fae4529b731f6c117752247bef9cdad2b12fc5ddf8ca6a50965a5 \
+    --hash=sha256:730c27978a0003b47b359935478b7d63fd8386dbb2dcd36c1e8de88cbfc1e9de \
+    --hash=sha256:75a2e638042118118ab39d337da4c7908c1af74a8464cad59f19fbc5bbafec9b \
+    --hash=sha256:78ced51807ccb2f45d4ea73aca339756d75d021069604c2fccd05390dc3c28eb \
+    --hash=sha256:7ee394502026d68652c2824348a40bf50f31351a668977b51437131a90d777ea \
+    --hash=sha256:8468b40528fa1e15181cccec4198623b55dcd58306f8815a793803f51f6c474a \
+    --hash=sha256:84c593aeff7a0171f639da92cb86d24954bbb61f8a1b530f74eb750a14685832 \
+    --hash=sha256:913bac9d064cff033cf3719e855d4f1db9f1c179e0ecf3ba9fdef21c21c6a16a \
+    --hash=sha256:9447c45df407d3ecb717d837af3b70cfef432138530712263730783b3d016512 \
+    --hash=sha256:9b0e7fe7f949fb719b206548e5cde2518ffb29936afa4303d8a1c4db43dcb675 \
+    --hash=sha256:9bc407a6af672da20da74823443707e38ece8b93a04009dca25856c2d9adadb1 \
+    --hash=sha256:9e8e686a6db92a46111a1ee0ee6f7fbfae4048f0019de207149f43ac1812cf95 \
+    --hash=sha256:9fc4e7973ed0e1fe689435842a6e6b330eb7ccc696080dda9a97b1a1b78e41db \
+    --hash=sha256:a457ee72d9032e86730f62c5eeddf402e732fdf5ca8b13b41772aa8ae13a4563 \
+    --hash=sha256:a628bba09ba72e472bf7b31018b6281fd4cc903f0888049a3724afba13b6e0b8 \
+    --hash=sha256:a79d239fc22c3b8d9d3de492aa0c245533f4f4c7608e5749af866949c0f1b1b9 \
+    --hash=sha256:aa4674cf3fa2bd9c322982644967f01eed0c91bb890f624e0e0daf7a5c3383e9 \
+    --hash=sha256:acd2bd02f1a7adff3a1f33e431eb96ab6d7987b039d2946a9b39fe6fb16a1036 \
+    --hash=sha256:b3b1bd7577c530eaf9d2bc52d1a93fef50ac516a8b1062c3d1b9bcec9ebe329b \
+    --hash=sha256:b48d94386f1994db7c70c76b5808c12e23ed7a4ee13693c2fc5ab109d60243c0 \
+    --hash=sha256:b64f747e92af7da3b85631a55d68c45a2d728b4036b03cdaba4bd94bcc85bd6f \
+    --hash=sha256:b98c820608e2dca6442e786817f646d11057c09a23b68d2b3737e6dcb6e4a49b \
+    --hash=sha256:c1baa49ab9fedbf19d40d93163b7d3e735d9cd8d5efe4cce9907902a6dad391f \
+    --hash=sha256:c38c6536c2d71ca2f7e418acaf5bca30a3af7f2a2fa106083c7d738337848dbe \
+    --hash=sha256:c78bfbc1a7bff053baf7e508449d2765964d67735c909b583204e3240a2aca45 \
+    --hash=sha256:cd2bc0c8f2e8de7dd89a7f1c10b8844e291bca17d359373203ef2e6100819edd \
+    --hash=sha256:d2eff2af97ea0b61381828b1ad6cd249bbd41d280e53aea5cccd7b2b31b8225c \
+    --hash=sha256:d8834c14b8c3dd849005e06703469db9bf96ba2d66a3f88ecc539c9a8982e0ee \
+    --hash=sha256:d912f0154a20a80ea449daada904a7eb6941c83281a9fab95de50529bfc3a1da \
+    --hash=sha256:da1ef35fd79be2926ba80fbb36327463e3656c02526e9b5b4c2b366588b74d9a \
+    --hash=sha256:dbe6fe7a1166b1ddd7b6d887ea6fa8389d3f28b5ed3f73a8f40ece1fc5a3d340 \
+    --hash=sha256:dcd556c8fc37a342dd636d7eef150b1399f823a4462f8c968e11e1ebeabee769 \
+    --hash=sha256:e13b31d1b4b68db60b3b29f8e337908f328c7f05b9add4b1b5c74e0691180109 \
+    --hash=sha256:e1739496c2f0108013629aa095cc32a8c6363444361960c07493818d0dea2da4 \
+    --hash=sha256:e43255a83835a129ef98f75d13d643844d8c646b258bebd11e4a0975203e018f \
+    --hash=sha256:e626cefff8491bce356221c22af5a3ea528b0b41fbabc719c00ae233819ea0bf \
+    --hash=sha256:eadad75bf91897f922e0fb3dca1b322a58b1726a953f98c2e5f0606bd8408621 \
+    --hash=sha256:f33da6b5d19ad1bb5e7ad38bb8ba5c426d2178928bc2b2c44e8823ea0ecb6ff3 \
+    --hash=sha256:f4052a8a4926d4468416fc7d4b2a7b2a3e35f25b39f4061a7e2a3a2748c4fc48 \
+    --hash=sha256:f6ca38dd8d988eca8f07305125dec6f54ac1c518f1aaddcc14d08c01aebb6efc
+    # via matplotlib
+cycler==0.11.0 \
+    --hash=sha256:3a27e95f763a428a739d2add979fa7494c912a32c17c4c38c4d5f082cad165a3 \
+    --hash=sha256:9c87405839a19696e837b3b818fed3f5f69f16f1eec1a1ad77e043dcea9c772f
+    # via matplotlib
+decorator==5.1.1 \
+    --hash=sha256:637996211036b6385ef91435e4fae22989472f9d571faba8927ba8253acbc330 \
+    --hash=sha256:b8c3f85900b9dc423225913c5aace94729fe1fa9763b38939a95226f02d37186
+    # via validators
+diffusers==0.7.2 \
+    --hash=sha256:4a5f8b3a5fbd936bba7d459611cb35ec62875030367be32b232f9e19543e25a9 \
+    --hash=sha256:fb814ffd150cc6f470380b8c6a521181a77beb2f44134d2aad2e4cd8aa2ced0e
+    # via -r requirements.in
+dnspython==2.2.1 \
+    --hash=sha256:0f7569a4a6ff151958b64304071d370daa3243d15941a7beedf0c9fe5105603e \
+    --hash=sha256:a851e51367fb93e9e1361732c1d60dab63eff98712e503ea7d92e6eccb109b4f
+    # via eventlet
+docker-pycreds==0.4.0 \
+    --hash=sha256:6ce3270bcaf404cc4c3e27e4b6c70d3521deae82fb508767870fdbf772d584d4 \
+    --hash=sha256:7266112468627868005106ec19cd0d722702d2b7d5912a28e19b826c3d37af49
+    # via wandb
+einops==0.5.0 \
+    --hash=sha256:055de7eeb3cb9e9710ef3085a811090c6b52e809b7044e8785824ed185f486d1 \
+    --hash=sha256:8b7a83cffc1ea88e306df099b7cbb9c3ba5003bd84d05ae44be5655864abb8d3
+    # via k-diffusion
+entrypoints==0.4 \
+    --hash=sha256:b706eddaa9218a19ebcd67b56818f05bb27589b1ca9e8d797b74affad4ccacd4 \
+    --hash=sha256:f174b5ff827504fd3cd97cc3f8649f3693f51538c7e4bdf3ef002c8429d42f9f
+    # via altair
+eventlet==0.33.1 \
+    --hash=sha256:a085922698e5029f820cf311a648ac324d73cec0e4792877609d978a4b5bbf31 \
+    --hash=sha256:afbe17f06a58491e9aebd7a4a03e70b0b63fd4cf76d8307bae07f280479b1515
+    # via -r requirements.in
+facexlib==0.2.5 \
+    --hash=sha256:31e20cc4ed5d63562d380e4564bae14ac0d5d1899a079bad87621e13564567e4 \
+    --hash=sha256:cc7ceb56c5424319c47223cf75eef6828c34c66082707c6eb35b95d39779f02d
+    # via
+    #   gfpgan
+    #   realesrgan
+filelock==3.8.0 \
+    --hash=sha256:55447caa666f2198c5b6b13a26d2084d26fa5b115c00d065664b2124680c4edc \
+    --hash=sha256:617eb4e5eedc82fc5f47b6d61e4d11cb837c56cb4544e39081099fa17ad109d4
+    # via
+    #   diffusers
+    #   huggingface-hub
+    #   transformers
+filterpy==1.4.5 \
+    --hash=sha256:4f2a4d39e4ea601b9ab42b2db08b5918a9538c168cff1c6895ae26646f3d73b1
+    # via facexlib
+flask==2.2.2 \
+    --hash=sha256:642c450d19c4ad482f96729bd2a8f6d32554aa1e231f4f6b4e7e5264b16cca2b \
+    --hash=sha256:b9c46cc36662a7949f34b52d8ec7bb59c0d74ba08ba6cb9ce9adc1d8676d9526
+    # via
+    #   flask-cors
+    #   flask-socketio
+flask-cors==3.0.10 \
+    --hash=sha256:74efc975af1194fc7891ff5cd85b0f7478be4f7f59fe158102e91abb72bb4438 \
+    --hash=sha256:b60839393f3b84a0f3746f6cdca56c1ad7426aa738b70d6c61375857823181de
+    # via -r requirements.in
+flask-socketio==5.3.1 \
+    --hash=sha256:fd0ed0fc1341671d92d5f5b2f5503916deb7aa7e2940e6636cfa2c087c828bf9 \
+    --hash=sha256:ff0c721f20bff1e2cfba77948727a8db48f187e89a72fe50c34478ce6efb3353
+    # via -r requirements.in
+flaskwebgui==0.3.7 \
+    --hash=sha256:4a69955308eaa8bb256ba04a994dc8f58a48dcd6f9599694ab1bcd9f43d88a5d \
+    --hash=sha256:535974ce2672dcc74787c254de24cceed4101be75d96952dae82014dd57f061e
+    # via -r requirements.in
+fonttools==4.38.0 \
+    --hash=sha256:2bb244009f9bf3fa100fc3ead6aeb99febe5985fa20afbfbaa2f8946c2fbdaf1 \
+    --hash=sha256:820466f43c8be8c3009aef8b87e785014133508f0de64ec469e4efb643ae54fb
+    # via matplotlib
+frozenlist==1.3.1 \
+    --hash=sha256:022178b277cb9277d7d3b3f2762d294f15e85cd2534047e68a118c2bb0058f3e \
+    --hash=sha256:086ca1ac0a40e722d6833d4ce74f5bf1aba2c77cbfdc0cd83722ffea6da52a04 \
+    --hash=sha256:0bc75692fb3770cf2b5856a6c2c9de967ca744863c5e89595df64e252e4b3944 \
+    --hash=sha256:0dde791b9b97f189874d654c55c24bf7b6782343e14909c84beebd28b7217845 \
+    --hash=sha256:12607804084d2244a7bd4685c9d0dca5df17a6a926d4f1967aa7978b1028f89f \
+    --hash=sha256:19127f8dcbc157ccb14c30e6f00392f372ddb64a6ffa7106b26ff2196477ee9f \
+    --hash=sha256:1b51eb355e7f813bcda00276b0114c4172872dc5fb30e3fea059b9367c18fbcb \
+    --hash=sha256:1e1cf7bc8cbbe6ce3881863671bac258b7d6bfc3706c600008925fb799a256e2 \
+    --hash=sha256:219a9676e2eae91cb5cc695a78b4cb43d8123e4160441d2b6ce8d2c70c60e2f3 \
+    --hash=sha256:2743bb63095ef306041c8f8ea22bd6e4d91adabf41887b1ad7886c4c1eb43d5f \
+    --hash=sha256:2af6f7a4e93f5d08ee3f9152bce41a6015b5cf87546cb63872cc19b45476e98a \
+    --hash=sha256:31b44f1feb3630146cffe56344704b730c33e042ffc78d21f2125a6a91168131 \
+    --hash=sha256:31bf9539284f39ff9398deabf5561c2b0da5bb475590b4e13dd8b268d7a3c5c1 \
+    --hash=sha256:35c3d79b81908579beb1fb4e7fcd802b7b4921f1b66055af2578ff7734711cfa \
+    --hash=sha256:3a735e4211a04ccfa3f4833547acdf5d2f863bfeb01cfd3edaffbc251f15cec8 \
+    --hash=sha256:42719a8bd3792744c9b523674b752091a7962d0d2d117f0b417a3eba97d1164b \
+    --hash=sha256:49459f193324fbd6413e8e03bd65789e5198a9fa3095e03f3620dee2f2dabff2 \
+    --hash=sha256:4c0c99e31491a1d92cde8648f2e7ccad0e9abb181f6ac3ddb9fc48b63301808e \
+    --hash=sha256:52137f0aea43e1993264a5180c467a08a3e372ca9d378244c2d86133f948b26b \
+    --hash=sha256:526d5f20e954d103b1d47232e3839f3453c02077b74203e43407b962ab131e7b \
+    --hash=sha256:53b2b45052e7149ee8b96067793db8ecc1ae1111f2f96fe1f88ea5ad5fd92d10 \
+    --hash=sha256:572ce381e9fe027ad5e055f143763637dcbac2542cfe27f1d688846baeef5170 \
+    --hash=sha256:58fb94a01414cddcdc6839807db77ae8057d02ddafc94a42faee6004e46c9ba8 \
+    --hash=sha256:5e77a8bd41e54b05e4fb2708dc6ce28ee70325f8c6f50f3df86a44ecb1d7a19b \
+    --hash=sha256:5f271c93f001748fc26ddea409241312a75e13466b06c94798d1a341cf0e6989 \
+    --hash=sha256:5f63c308f82a7954bf8263a6e6de0adc67c48a8b484fab18ff87f349af356efd \
+    --hash=sha256:61d7857950a3139bce035ad0b0945f839532987dfb4c06cfe160254f4d19df03 \
+    --hash=sha256:61e8cb51fba9f1f33887e22488bad1e28dd8325b72425f04517a4d285a04c519 \
+    --hash=sha256:625d8472c67f2d96f9a4302a947f92a7adbc1e20bedb6aff8dbc8ff039ca6189 \
+    --hash=sha256:6e19add867cebfb249b4e7beac382d33215d6d54476bb6be46b01f8cafb4878b \
+    --hash=sha256:717470bfafbb9d9be624da7780c4296aa7935294bd43a075139c3d55659038ca \
+    --hash=sha256:74140933d45271c1a1283f708c35187f94e1256079b3c43f0c2267f9db5845ff \
+    --hash=sha256:74e6b2b456f21fc93ce1aff2b9728049f1464428ee2c9752a4b4f61e98c4db96 \
+    --hash=sha256:9494122bf39da6422b0972c4579e248867b6b1b50c9b05df7e04a3f30b9a413d \
+    --hash=sha256:94e680aeedc7fd3b892b6fa8395b7b7cc4b344046c065ed4e7a1e390084e8cb5 \
+    --hash=sha256:97d9e00f3ac7c18e685320601f91468ec06c58acc185d18bb8e511f196c8d4b2 \
+    --hash=sha256:9c6ef8014b842f01f5d2b55315f1af5cbfde284eb184075c189fd657c2fd8204 \
+    --hash=sha256:a027f8f723d07c3f21963caa7d585dcc9b089335565dabe9c814b5f70c52705a \
+    --hash=sha256:a718b427ff781c4f4e975525edb092ee2cdef6a9e7bc49e15063b088961806f8 \
+    --hash=sha256:ab386503f53bbbc64d1ad4b6865bf001414930841a870fc97f1546d4d133f141 \
+    --hash=sha256:ab6fa8c7871877810e1b4e9392c187a60611fbf0226a9e0b11b7b92f5ac72792 \
+    --hash=sha256:b47d64cdd973aede3dd71a9364742c542587db214e63b7529fbb487ed67cddd9 \
+    --hash=sha256:b499c6abe62a7a8d023e2c4b2834fce78a6115856ae95522f2f974139814538c \
+    --hash=sha256:bbb1a71b1784e68870800b1bc9f3313918edc63dbb8f29fbd2e767ce5821696c \
+    --hash=sha256:c3b31180b82c519b8926e629bf9f19952c743e089c41380ddca5db556817b221 \
+    --hash=sha256:c56c299602c70bc1bb5d1e75f7d8c007ca40c9d7aebaf6e4ba52925d88ef826d \
+    --hash=sha256:c92deb5d9acce226a501b77307b3b60b264ca21862bd7d3e0c1f3594022f01bc \
+    --hash=sha256:cc2f3e368ee5242a2cbe28323a866656006382872c40869b49b265add546703f \
+    --hash=sha256:d82bed73544e91fb081ab93e3725e45dd8515c675c0e9926b4e1f420a93a6ab9 \
+    --hash=sha256:da1cdfa96425cbe51f8afa43e392366ed0b36ce398f08b60de6b97e3ed4affef \
+    --hash=sha256:da5ba7b59d954f1f214d352308d1d86994d713b13edd4b24a556bcc43d2ddbc3 \
+    --hash=sha256:e0c8c803f2f8db7217898d11657cb6042b9b0553a997c4a0601f48a691480fab \
+    --hash=sha256:ee4c5120ddf7d4dd1eaf079af3af7102b56d919fa13ad55600a4e0ebe532779b \
+    --hash=sha256:eee0c5ecb58296580fc495ac99b003f64f82a74f9576a244d04978a7e97166db \
+    --hash=sha256:f5abc8b4d0c5b556ed8cd41490b606fe99293175a82b98e652c3f2711b452988 \
+    --hash=sha256:f810e764617b0748b49a731ffaa525d9bb36ff38332411704c2400125af859a6 \
+    --hash=sha256:f89139662cc4e65a4813f4babb9ca9544e42bddb823d2ec434e18dad582543bc \
+    --hash=sha256:fa47319a10e0a076709644a0efbcaab9e91902c8bd8ef74c6adb19d320f69b83 \
+    --hash=sha256:fabb953ab913dadc1ff9dcc3a7a7d3dc6a92efab3a0373989b8063347f8705be
+    # via
+    #   aiohttp
+    #   aiosignal
+fsspec[http]==2022.10.0 \
+    --hash=sha256:6b7c6ab3b476cdf17efcfeccde7fca28ef5a48f73a71010aaceec5fc15bf9ebf \
+    --hash=sha256:cb6092474e90487a51de768170f3afa50ca8982c26150a59072b16433879ff1d
+    # via pytorch-lightning
+ftfy==6.1.1 \
+    --hash=sha256:0ffd33fce16b54cccaec78d6ec73d95ad370e5df5a25255c8966a6147bd667ca \
+    --hash=sha256:bfc2019f84fcd851419152320a6375604a0f1459c281b5b199b2cd0d2e727f8f
+    # via clip
+future==0.18.2 \
+    --hash=sha256:b1bead90b70cf6ec3f0710ae53a525360fa360d306a86583adc6bf83a4db537d
+    # via
+    #   basicsr
+    #   test-tube
+getpass-asterisk==1.0.1 \
+    --hash=sha256:20d45cafda0066d761961e0919728526baf7bb5151fbf48a7d5ea4034127d857 \
+    --hash=sha256:7cc357a924cf62fa4e15b73cb4e5e30685c9084e464ffdc3fd9000a2b54ea9e9
+    # via -r requirements.in
+gfpgan @ https://github.com/TencentARC/GFPGAN/archive/2eac2033893ca7f427f4035d80fe95b92649ac56.zip \
+    --hash=sha256:79e6d71c8f1df7c7ccb0ac6b9a2ccb615ad5cde818c8b6f285a8711c05aebf85
+    # via
+    #   -r requirements.in
+    #   realesrgan
+gitdb==4.0.9 \
+    --hash=sha256:8033ad4e853066ba6ca92050b9df2f89301b8fc8bf7e9324d412a63f8bf1a8fd \
+    --hash=sha256:bac2fd45c0a1c9cf619e63a90d62bdc63892ef92387424b855792a6cabe789aa
+    # via gitpython
+gitpython==3.1.29 \
+    --hash=sha256:41eea0deec2deea139b459ac03656f0dd28fc4a3387240ec1d3c259a2c47850f \
+    --hash=sha256:cc36bfc4a3f913e66805a28e84703e419d9c264c1077e537b54f0e1af85dbefd
+    # via
+    #   streamlit
+    #   wandb
+google-auth==2.14.0 \
+    --hash=sha256:1ad5b0e6eba5f69645971abb3d2c197537d5914070a8c6d30299dfdb07c5c700 \
+    --hash=sha256:cf24817855d874ede2efd071aa22125445f555de1685b739a9782fcf408c2a3d
+    # via
+    #   google-auth-oauthlib
+    #   tb-nightly
+    #   tensorboard
+google-auth-oauthlib==0.4.6 \
+    --hash=sha256:3f2a6e802eebbb6fb736a370fbf3b055edcb6b52878bf2f26330b5e041316c73 \
+    --hash=sha256:a90a072f6993f2c327067bf65270046384cda5a8ecb20b94ea9a687f1f233a7a
+    # via
+    #   tb-nightly
+    #   tensorboard
+greenlet==2.0.0.post0 \
+    --hash=sha256:00ebdaf0fa51c284fd2172837d751731a15971e0c20d1a9163cfbdf620ce8b49 \
+    --hash=sha256:029ca674b3a7e8427db8f5c65d5ed4e24a7417af2a415a5958598aefd71980c4 \
+    --hash=sha256:02bdb1e373b275bd705c43b249426e776c4f8a8ff2afaf8ec5ea0dde487d8a14 \
+    --hash=sha256:08dc04f49ed1ea5e6772bb5e8cf2a77d1b1744566f4eca471a55b35af1278b31 \
+    --hash=sha256:08f44e938d142271b954405afb6570e0be48a9f556b6bf4d42d2e3ae6a251fad \
+    --hash=sha256:0a5c03e2a68ec2ff1cba74ceaed899ec8cd353285f4f985c30c8cfbef9d3a3be \
+    --hash=sha256:0fee3240093b745efc857392f09379514ad84db4ca324514594bbdf6380016c8 \
+    --hash=sha256:118e708dd7bc88beaeeaa5a8601a7743b8835b7bbaf7c8f23ffa78f8bc8faf28 \
+    --hash=sha256:13d492a807a5c7334b5931e9b6d9b181991ccc6a40555a7b177f189feff59b4b \
+    --hash=sha256:1cac9e9895aeff26434325404558783ee54f4ff3aec8daa56b8706796f7b01a0 \
+    --hash=sha256:2146d15429b4eeb412428737594acb5660a5bc0fdd1488d8a2a74a5ee32391fa \
+    --hash=sha256:21ee1ae26d072b195edea764218623f6c15eba4ae06816908f33c82e0af018d3 \
+    --hash=sha256:22eca421e3f2f3c18f4f54c0ff525aa9d397c6f116fce9ebd37b420174dbc027 \
+    --hash=sha256:2bab49783858cf724fff6868395cbeb81d1188cba23616b53e79de0beda29f42 \
+    --hash=sha256:2fbdec204ca40b3d0c0992a19c1ba627441c17983ac4ffc45baec7f5f53e20ca \
+    --hash=sha256:30ce47525f9a1515566429ac7de6b1ae76d32c3ccede256e3517a1a6419cf659 \
+    --hash=sha256:335dcf676d5e4122e4006c16ae11eda2467af5461b949c265ce120b6b959ffe2 \
+    --hash=sha256:3407b843b05da71fef0f1dd666059c08ad0e0f4afc3b9c93c998a2e53fac95e5 \
+    --hash=sha256:35827f98fd0d768862b8f15777e6dbb03fe6ac6e7bd1bee3f3ded4536f350347 \
+    --hash=sha256:3a22e5988f9d66b3e9ae9583bf9d8ef792b09f23afeb78707e6a4f47ab57cc5e \
+    --hash=sha256:3c3327da2bdab61078e42e695307465c425671a5a9251e6c29ee130d51943f28 \
+    --hash=sha256:3ca723dfc2789c1fb991809822811896b198ecf0909dbccea4a07170d18c3e1b \
+    --hash=sha256:46156ae88ee71c37b6c4f7af63fff5d3ab8f45ef72e1a660bcf6386c1647f106 \
+    --hash=sha256:4bbe2d074292e3646704371eb640ee52c386d633ed72ff223dadcd3fe8ecd8f9 \
+    --hash=sha256:4c4310f0e42154995d92810f27b44ab7116a4a696feb0ff141ae2de59196efd7 \
+    --hash=sha256:4cfa629de5b2dea27c81b334c4536463e9a49ac0877e2008a276d58d4c72868a \
+    --hash=sha256:4e144ab0de56b4d2a2cf0d2fb9d568b59fce49aab3e129badf17c12b0252047d \
+    --hash=sha256:4ea67f303cec384b148774667c7e3cf02311e7026fc02bdcdcd206dfe4ea4fc9 \
+    --hash=sha256:538c9e8f65a32413ace426f8117ef019021adf8175f7c491fed65f5fe2083e0c \
+    --hash=sha256:56565ac9ab4ff3dd473bfe959e0bf2a5062aabb89b7c94cabb417beb162c9fff \
+    --hash=sha256:5e22485256bb1c60bbcc6f8509b1a11042358a2462d5ecdb9a82dc472d2fdd60 \
+    --hash=sha256:602a69c24f1a9755dd1760b3b31bdfc495c4613260c876a01b7e6d5eb9bcae1b \
+    --hash=sha256:6393ec3cecda53b20241e88bc33d87cbd8126cc10870fc69fa16ca2e20a5ac1b \
+    --hash=sha256:6442bbfb047dc1e47658954b72e1589f2bc4e12e67d51bbad0059a626180daa1 \
+    --hash=sha256:666d2a0b269a68cd4fe0976544ab97970c5334d35d0e47ae9be1723f734d8204 \
+    --hash=sha256:697cfbfc19815c40213badcfe5f076418e0f9100cd25a66f513f32c1026b8bf4 \
+    --hash=sha256:6a1a6745c5dce202aa3f29a1736c53cf2179e9c3b280dc62cea9cb8c69977c83 \
+    --hash=sha256:6fc73fc8dd81d9efa842a55033b6b4cb233b134a0270e127c6874d053ef2049b \
+    --hash=sha256:7e9e0d4c5c618b0442396715ffe6c2f84a60d593dad7e0184388aed36d568a65 \
+    --hash=sha256:81fdcf7c0c2df46a99ca421a552c4370117851c5e4dbd6cb53d569b896d62322 \
+    --hash=sha256:8b26932be686f3582df039d79fe96f7ca13d63b39468162f816f9ff29584b9a4 \
+    --hash=sha256:8b7e5191b974fb66fcbac1818ba494d3512da9cf6eaef7acd952f9862eaaa20c \
+    --hash=sha256:8c80e9c41a83d8c90399af8c7dcdeae0c03c48b40b9d0ab84457533d5d7882bf \
+    --hash=sha256:9f2f110b9cc325f6543e0e3f4ab8008c272a59052f9464047c29d4be4511ce05 \
+    --hash=sha256:a339e510a079dc8372e39ce1c7629414db51966235c9670c58d529def79243a2 \
+    --hash=sha256:ad9abc3e4d2370cecb524421cc5c8a664006aa11d5c1cb3c9250e3bf65ab546e \
+    --hash=sha256:b043782c8f6cccc8fae3a16db397eca1d36a41b0706cbf6f514aea1e1a260bab \
+    --hash=sha256:b31de27313abbb567c528ed123380fcf18a5dfd03134570dfd12227e21ac1184 \
+    --hash=sha256:b75e5644cc353328cd57ec8dafaaf5f81b2c3ecf7c4b278b907e99ad53ba7839 \
+    --hash=sha256:b8cfc8fc944bd7b704691bc28225a2635e377e92dc413459845868d3f7724982 \
+    --hash=sha256:c2055c52260808d87622293b57df1c68aeb12ddd8a0cfc0223fb57a5f629e202 \
+    --hash=sha256:c416106b3b8e905b6ab0e84ec90047a6401021aa023f9aa93978e57cd8f8189f \
+    --hash=sha256:d0e210e17a6181a3fd3f8dce957043a4e74177ffa9f295514984b2b633940dce \
+    --hash=sha256:d9453135e48cd631e3e9f06d9da9100d17c9f662e4a6d8b552c29be6c834a6b9 \
+    --hash=sha256:dd0198006278291d9469309d655093df1f5e5107c0261e242b5f390baee32199 \
+    --hash=sha256:e1781bda1e787d3ad33788cc3be47f6e47a9581676d02670c15ee36c9460adfe \
+    --hash=sha256:e56a5a9f303e3ac011ba445a6d84f05d08666bf8db094afafcec5228622c30f5 \
+    --hash=sha256:e93ae35f0fd3caf75e58c76a1cab71e6ece169aaa1b281782ef9efde0a6b83f2 \
+    --hash=sha256:eb36b6570646227a63eda03916f1cc6f3744ee96d28f7a0a5629c59267a8055f \
+    --hash=sha256:f8c425a130e04d5404edaf6f5906e5ab12f3aa1168a1828aba6dfadac5910469
+    # via eventlet
+grpcio==1.50.0 \
+    --hash=sha256:05f7c248e440f538aaad13eee78ef35f0541e73498dd6f832fe284542ac4b298 \
+    --hash=sha256:080b66253f29e1646ac53ef288c12944b131a2829488ac3bac8f52abb4413c0d \
+    --hash=sha256:12b479839a5e753580b5e6053571de14006157f2ef9b71f38c56dc9b23b95ad6 \
+    --hash=sha256:156f8009e36780fab48c979c5605eda646065d4695deea4cfcbcfdd06627ddb6 \
+    --hash=sha256:15f9e6d7f564e8f0776770e6ef32dac172c6f9960c478616c366862933fa08b4 \
+    --hash=sha256:177afaa7dba3ab5bfc211a71b90da1b887d441df33732e94e26860b3321434d9 \
+    --hash=sha256:1a4cd8cb09d1bc70b3ea37802be484c5ae5a576108bad14728f2516279165dd7 \
+    --hash=sha256:1d8d02dbb616c0a9260ce587eb751c9c7dc689bc39efa6a88cc4fa3e9c138a7b \
+    --hash=sha256:2b71916fa8f9eb2abd93151fafe12e18cebb302686b924bd4ec39266211da525 \
+    --hash=sha256:2d9fd6e38b16c4d286a01e1776fdf6c7a4123d99ae8d6b3f0b4a03a34bf6ce45 \
+    --hash=sha256:3b611b3de3dfd2c47549ca01abfa9bbb95937eb0ea546ea1d762a335739887be \
+    --hash=sha256:3e4244c09cc1b65c286d709658c061f12c61c814be0b7030a2d9966ff02611e0 \
+    --hash=sha256:40838061e24f960b853d7bce85086c8e1b81c6342b1f4c47ff0edd44bbae2722 \
+    --hash=sha256:4b123fbb7a777a2fedec684ca0b723d85e1d2379b6032a9a9b7851829ed3ca9a \
+    --hash=sha256:531f8b46f3d3db91d9ef285191825d108090856b3bc86a75b7c3930f16ce432f \
+    --hash=sha256:67dd41a31f6fc5c7db097a5c14a3fa588af54736ffc174af4411d34c4f306f68 \
+    --hash=sha256:7489dbb901f4fdf7aec8d3753eadd40839c9085967737606d2c35b43074eea24 \
+    --hash=sha256:8d4c8e73bf20fb53fe5a7318e768b9734cf122fe671fcce75654b98ba12dfb75 \
+    --hash=sha256:8e69aa4e9b7f065f01d3fdcecbe0397895a772d99954bb82eefbb1682d274518 \
+    --hash=sha256:8e8999a097ad89b30d584c034929f7c0be280cd7851ac23e9067111167dcbf55 \
+    --hash=sha256:906f4d1beb83b3496be91684c47a5d870ee628715227d5d7c54b04a8de802974 \
+    --hash=sha256:92d7635d1059d40d2ec29c8bf5ec58900120b3ce5150ef7414119430a4b2dd5c \
+    --hash=sha256:931e746d0f75b2a5cff0a1197d21827a3a2f400c06bace036762110f19d3d507 \
+    --hash=sha256:95ce51f7a09491fb3da8cf3935005bff19983b77c4e9437ef77235d787b06842 \
+    --hash=sha256:9eea18a878cffc804506d39c6682d71f6b42ec1c151d21865a95fae743fda500 \
+    --hash=sha256:a23d47f2fc7111869f0ff547f771733661ff2818562b04b9ed674fa208e261f4 \
+    --hash=sha256:a4c23e54f58e016761b576976da6a34d876420b993f45f66a2bfb00363ecc1f9 \
+    --hash=sha256:a50a1be449b9e238b9bd43d3857d40edf65df9416dea988929891d92a9f8a778 \
+    --hash=sha256:ab5d0e3590f0a16cb88de4a3fa78d10eb66a84ca80901eb2c17c1d2c308c230f \
+    --hash=sha256:ae23daa7eda93c1c49a9ecc316e027ceb99adbad750fbd3a56fa9e4a2ffd5ae0 \
+    --hash=sha256:af98d49e56605a2912cf330b4627e5286243242706c3a9fa0bcec6e6f68646fc \
+    --hash=sha256:b2f77a90ba7b85bfb31329f8eab9d9540da2cf8a302128fb1241d7ea239a5469 \
+    --hash=sha256:baab51dcc4f2aecabf4ed1e2f57bceab240987c8b03533f1cef90890e6502067 \
+    --hash=sha256:ca8a2254ab88482936ce941485c1c20cdeaef0efa71a61dbad171ab6758ec998 \
+    --hash=sha256:cb11464f480e6103c59d558a3875bd84eed6723f0921290325ebe97262ae1347 \
+    --hash=sha256:ce8513aee0af9c159319692bfbf488b718d1793d764798c3d5cff827a09e25ef \
+    --hash=sha256:cf151f97f5f381163912e8952eb5b3afe89dec9ed723d1561d59cabf1e219a35 \
+    --hash=sha256:d144ad10eeca4c1d1ce930faa105899f86f5d99cecfe0d7224f3c4c76265c15e \
+    --hash=sha256:d534d169673dd5e6e12fb57cc67664c2641361e1a0885545495e65a7b761b0f4 \
+    --hash=sha256:d75061367a69808ab2e84c960e9dce54749bcc1e44ad3f85deee3a6c75b4ede9 \
+    --hash=sha256:d84d04dec64cc4ed726d07c5d17b73c343c8ddcd6b59c7199c801d6bbb9d9ed1 \
+    --hash=sha256:de411d2b030134b642c092e986d21aefb9d26a28bf5a18c47dd08ded411a3bc5 \
+    --hash=sha256:e07fe0d7ae395897981d16be61f0db9791f482f03fee7d1851fe20ddb4f69c03 \
+    --hash=sha256:ea8ccf95e4c7e20419b7827aa5b6da6f02720270686ac63bd3493a651830235c \
+    --hash=sha256:f7025930039a011ed7d7e7ef95a1cb5f516e23c5a6ecc7947259b67bea8e06ca
+    # via
+    #   tb-nightly
+    #   tensorboard
+huggingface-hub==0.10.1 \
+    --hash=sha256:5c188d5b16bec4b78449f8681f9975ff9d321c16046cc29bcf0d7e464ff29276 \
+    --hash=sha256:dc3b0e9a663fe6cad6a8522055c02a9d8673dbd527223288e2442bc028c253db
+    # via
+    #   diffusers
+    #   transformers
+idna==2.10 \
+    --hash=sha256:b307872f855b18632ce0c21c5e45be78c0ea7ae4c15c828c20788b26921eb3f6 \
+    --hash=sha256:b97d804b1e9b523befed77c48dacec60e6dcb0b5391d57af6a65a312a90648c0
+    # via
+    #   requests
+    #   yarl
+imageio==2.22.3 \
+    --hash=sha256:63f007b7f2a082306e36922b3fd529a7aa305d2b78f46195bab8e22bbfe866e9 \
+    --hash=sha256:a4b88f9f3d428b8c0ceeb7e297df8c346a642bb7e3111743eb85717d60b26f6f
+    # via
+    #   scikit-image
+    #   test-tube
+imageio-ffmpeg==0.4.7 \
+    --hash=sha256:27b48c32becae1658aa81c3a6b922538e4099edf5fbcbdb4ff5dbc84b8ffd3d3 \
+    --hash=sha256:6514f1380daf42815bc8c83aad63f33e0b8b47133421ddafe7b410cd8dfbbea5 \
+    --hash=sha256:6aba52ddf0a64442ffcb8d30ac6afb668186acec99ecbc7ae5bd171c4f500bbc \
+    --hash=sha256:7a08838f97f363e37ca41821b864fd3fdc99ab1fe2421040c78eb5f56a9e723e \
+    --hash=sha256:8e724d12dfe83e2a6eb39619e820243ca96c81c47c2648e66e05f7ee24e14312 \
+    --hash=sha256:fc60686ef03c2d0f842901b206223c30051a6a120384458761390104470846fd
+    # via -r requirements.in
+importlib-metadata==5.0.0 \
+    --hash=sha256:da31db32b304314d044d3c12c79bd59e307889b287ad12ff387b3500835fc2ab \
+    --hash=sha256:ddb0e35065e8938f867ed4928d0ae5bf2a53b7773871bfe6bcc7e4fcdc7dea43
+    # via
+    #   diffusers
+    #   streamlit
+itsdangerous==2.1.2 \
+    --hash=sha256:2c2349112351b88699d8d4b6b075022c0808887cb7ad10069318a8b0bc88db44 \
+    --hash=sha256:5dbbc68b317e5e42f327f9021763545dc3fc3bfe22e6deb96aaf1fc38874156a
+    # via flask
+jinja2==3.1.2 \
+    --hash=sha256:31351a702a408a9e7595a8fc6150fc3f43bb6bf7e319770cbc0db9df9437e852 \
+    --hash=sha256:6088930bfe239f0e6710546ab9c19c9ef35e29792895fed6e6e31a023a182a61
+    # via
+    #   altair
+    #   flask
+    #   pydeck
+joblib==1.2.0 \
+    --hash=sha256:091138ed78f800342968c523bdde947e7a305b8594b910a0fea2ab83c3c6d385 \
+    --hash=sha256:e1cee4a79e4af22881164f218d4311f60074197fb707e082e803b61f6d137018
+    # via scikit-learn
+jsonmerge==1.9.0 \
+    --hash=sha256:a2d1f80021c5c1d70a49e31f862b5f068f9db066080d8561e80654de74a3584d
+    # via k-diffusion
+jsonschema==4.17.0 \
+    --hash=sha256:5bfcf2bca16a087ade17e02b282d34af7ccd749ef76241e7f9bd7c0cb8a9424d \
+    --hash=sha256:f660066c3966db7d6daeaea8a75e0b68237a48e51cf49882087757bb59916248
+    # via
+    #   altair
+    #   jsonmerge
+k-diffusion @ https://github.com/invoke-ai/k-diffusion/archive/7f16b2c33411f26b3eae78d10648d625cb0c1095.zip \
+    --hash=sha256:c3f2c84036aa98c3abf4552fafab04df5ca472aa639982795e05bb1db43ce5e4
+    # via -r requirements.in
+kiwisolver==1.4.4 \
+    --hash=sha256:02f79693ec433cb4b5f51694e8477ae83b3205768a6fb48ffba60549080e295b \
+    --hash=sha256:03baab2d6b4a54ddbb43bba1a3a2d1627e82d205c5cf8f4c924dc49284b87166 \
+    --hash=sha256:1041feb4cda8708ce73bb4dcb9ce1ccf49d553bf87c3954bdfa46f0c3f77252c \
+    --hash=sha256:10ee06759482c78bdb864f4109886dff7b8a56529bc1609d4f1112b93fe6423c \
+    --hash=sha256:1d1573129aa0fd901076e2bfb4275a35f5b7aa60fbfb984499d661ec950320b0 \
+    --hash=sha256:283dffbf061a4ec60391d51e6155e372a1f7a4f5b15d59c8505339454f8989e4 \
+    --hash=sha256:28bc5b299f48150b5f822ce68624e445040595a4ac3d59251703779836eceff9 \
+    --hash=sha256:2a66fdfb34e05b705620dd567f5a03f239a088d5a3f321e7b6ac3239d22aa286 \
+    --hash=sha256:2e307eb9bd99801f82789b44bb45e9f541961831c7311521b13a6c85afc09767 \
+    --hash=sha256:2e407cb4bd5a13984a6c2c0fe1845e4e41e96f183e5e5cd4d77a857d9693494c \
+    --hash=sha256:2f5e60fabb7343a836360c4f0919b8cd0d6dbf08ad2ca6b9cf90bf0c76a3c4f6 \
+    --hash=sha256:36dafec3d6d6088d34e2de6b85f9d8e2324eb734162fba59d2ba9ed7a2043d5b \
+    --hash=sha256:3fe20f63c9ecee44560d0e7f116b3a747a5d7203376abeea292ab3152334d004 \
+    --hash=sha256:41dae968a94b1ef1897cb322b39360a0812661dba7c682aa45098eb8e193dbdf \
+    --hash=sha256:4bd472dbe5e136f96a4b18f295d159d7f26fd399136f5b17b08c4e5f498cd494 \
+    --hash=sha256:4ea39b0ccc4f5d803e3337dd46bcce60b702be4d86fd0b3d7531ef10fd99a1ac \
+    --hash=sha256:5853eb494c71e267912275e5586fe281444eb5e722de4e131cddf9d442615626 \
+    --hash=sha256:5bce61af018b0cb2055e0e72e7d65290d822d3feee430b7b8203d8a855e78766 \
+    --hash=sha256:6295ecd49304dcf3bfbfa45d9a081c96509e95f4b9d0eb7ee4ec0530c4a96514 \
+    --hash=sha256:62ac9cc684da4cf1778d07a89bf5f81b35834cb96ca523d3a7fb32509380cbf6 \
+    --hash=sha256:70e7c2e7b750585569564e2e5ca9845acfaa5da56ac46df68414f29fea97be9f \
+    --hash=sha256:7577c1987baa3adc4b3c62c33bd1118c3ef5c8ddef36f0f2c950ae0b199e100d \
+    --hash=sha256:75facbe9606748f43428fc91a43edb46c7ff68889b91fa31f53b58894503a191 \
+    --hash=sha256:787518a6789009c159453da4d6b683f468ef7a65bbde796bcea803ccf191058d \
+    --hash=sha256:78d6601aed50c74e0ef02f4204da1816147a6d3fbdc8b3872d263338a9052c51 \
+    --hash=sha256:7c43e1e1206cd421cd92e6b3280d4385d41d7166b3ed577ac20444b6995a445f \
+    --hash=sha256:81e38381b782cc7e1e46c4e14cd997ee6040768101aefc8fa3c24a4cc58e98f8 \
+    --hash=sha256:841293b17ad704d70c578f1f0013c890e219952169ce8a24ebc063eecf775454 \
+    --hash=sha256:872b8ca05c40d309ed13eb2e582cab0c5a05e81e987ab9c521bf05ad1d5cf5cb \
+    --hash=sha256:877272cf6b4b7e94c9614f9b10140e198d2186363728ed0f701c6eee1baec1da \
+    --hash=sha256:8c808594c88a025d4e322d5bb549282c93c8e1ba71b790f539567932722d7bd8 \
+    --hash=sha256:8ed58b8acf29798b036d347791141767ccf65eee7f26bde03a71c944449e53de \
+    --hash=sha256:91672bacaa030f92fc2f43b620d7b337fd9a5af28b0d6ed3f77afc43c4a64b5a \
+    --hash=sha256:968f44fdbf6dd757d12920d63b566eeb4d5b395fd2d00d29d7ef00a00582aac9 \
+    --hash=sha256:9f85003f5dfa867e86d53fac6f7e6f30c045673fa27b603c397753bebadc3008 \
+    --hash=sha256:a553dadda40fef6bfa1456dc4be49b113aa92c2a9a9e8711e955618cd69622e3 \
+    --hash=sha256:a68b62a02953b9841730db7797422f983935aeefceb1679f0fc85cbfbd311c32 \
+    --hash=sha256:abbe9fa13da955feb8202e215c4018f4bb57469b1b78c7a4c5c7b93001699938 \
+    --hash=sha256:ad881edc7ccb9d65b0224f4e4d05a1e85cf62d73aab798943df6d48ab0cd79a1 \
+    --hash=sha256:b1792d939ec70abe76f5054d3f36ed5656021dcad1322d1cc996d4e54165cef9 \
+    --hash=sha256:b428ef021242344340460fa4c9185d0b1f66fbdbfecc6c63eff4b7c29fad429d \
+    --hash=sha256:b533558eae785e33e8c148a8d9921692a9fe5aa516efbdff8606e7d87b9d5824 \
+    --hash=sha256:ba59c92039ec0a66103b1d5fe588fa546373587a7d68f5c96f743c3396afc04b \
+    --hash=sha256:bc8d3bd6c72b2dd9decf16ce70e20abcb3274ba01b4e1c96031e0c4067d1e7cd \
+    --hash=sha256:bc9db8a3efb3e403e4ecc6cd9489ea2bac94244f80c78e27c31dcc00d2790ac2 \
+    --hash=sha256:bf7d9fce9bcc4752ca4a1b80aabd38f6d19009ea5cbda0e0856983cf6d0023f5 \
+    --hash=sha256:c2dbb44c3f7e6c4d3487b31037b1bdbf424d97687c1747ce4ff2895795c9bf69 \
+    --hash=sha256:c79ebe8f3676a4c6630fd3f777f3cfecf9289666c84e775a67d1d358578dc2e3 \
+    --hash=sha256:c97528e64cb9ebeff9701e7938653a9951922f2a38bd847787d4a8e498cc83ae \
+    --hash=sha256:d0611a0a2a518464c05ddd5a3a1a0e856ccc10e67079bb17f265ad19ab3c7597 \
+    --hash=sha256:d06adcfa62a4431d404c31216f0f8ac97397d799cd53800e9d3efc2fbb3cf14e \
+    --hash=sha256:d41997519fcba4a1e46eb4a2fe31bc12f0ff957b2b81bac28db24744f333e955 \
+    --hash=sha256:d5b61785a9ce44e5a4b880272baa7cf6c8f48a5180c3e81c59553ba0cb0821ca \
+    --hash=sha256:da152d8cdcab0e56e4f45eb08b9aea6455845ec83172092f09b0e077ece2cf7a \
+    --hash=sha256:da7e547706e69e45d95e116e6939488d62174e033b763ab1496b4c29b76fabea \
+    --hash=sha256:db5283d90da4174865d520e7366801a93777201e91e79bacbac6e6927cbceede \
+    --hash=sha256:db608a6757adabb32f1cfe6066e39b3706d8c3aa69bbc353a5b61edad36a5cb4 \
+    --hash=sha256:e0ea21f66820452a3f5d1655f8704a60d66ba1191359b96541eaf457710a5fc6 \
+    --hash=sha256:e7da3fec7408813a7cebc9e4ec55afed2d0fd65c4754bc376bf03498d4e92686 \
+    --hash=sha256:e92a513161077b53447160b9bd8f522edfbed4bd9759e4c18ab05d7ef7e49408 \
+    --hash=sha256:ecb1fa0db7bf4cff9dac752abb19505a233c7f16684c5826d1f11ebd9472b871 \
+    --hash=sha256:efda5fc8cc1c61e4f639b8067d118e742b812c930f708e6667a5ce0d13499e29 \
+    --hash=sha256:f0a1dbdb5ecbef0d34eb77e56fcb3e95bbd7e50835d9782a45df81cc46949750 \
+    --hash=sha256:f0a71d85ecdd570ded8ac3d1c0f480842f49a40beb423bb8014539a9f32a5897 \
+    --hash=sha256:f4f270de01dd3e129a72efad823da90cc4d6aafb64c410c9033aba70db9f1ff0 \
+    --hash=sha256:f6cb459eea32a4e2cf18ba5fcece2dbdf496384413bc1bae15583f19e567f3b2 \
+    --hash=sha256:f8ad8285b01b0d4695102546b342b493b3ccc6781fc28c8c6a1bb63e95d22f09 \
+    --hash=sha256:f9f39e2f049db33a908319cf46624a569b36983c7c78318e9726a4cb8923b26c
+    # via matplotlib
+kornia==0.6.8 \
+    --hash=sha256:0985e02453c0ab4f030e8d22a3a7554dab312ffa8f8a54ec872190e6f0b58c56 \
+    --hash=sha256:0d6d69330b4fd24da742337b8134da0ce01b4d7da66770db5498d58e8b4a0832
+    # via k-diffusion
+llvmlite==0.39.1 \
+    --hash=sha256:03aee0ccd81735696474dc4f8b6be60774892a2929d6c05d093d17392c237f32 \
+    --hash=sha256:1578f5000fdce513712e99543c50e93758a954297575610f48cb1fd71b27c08a \
+    --hash=sha256:16f56eb1eec3cda3a5c526bc3f63594fc24e0c8d219375afeb336f289764c6c7 \
+    --hash=sha256:1ec3d70b3e507515936e475d9811305f52d049281eaa6c8273448a61c9b5b7e2 \
+    --hash=sha256:22d36591cd5d02038912321d9ab8e4668e53ae2211da5523f454e992b5e13c36 \
+    --hash=sha256:3803f11ad5f6f6c3d2b545a303d68d9fabb1d50e06a8d6418e6fcd2d0df00959 \
+    --hash=sha256:39dc2160aed36e989610fc403487f11b8764b6650017ff367e45384dff88ffbf \
+    --hash=sha256:3fc14e757bc07a919221f0cbaacb512704ce5774d7fcada793f1996d6bc75f2a \
+    --hash=sha256:4c6ebace910410daf0bebda09c1859504fc2f33d122e9a971c4c349c89cca630 \
+    --hash=sha256:50aea09a2b933dab7c9df92361b1844ad3145bfb8dd2deb9cd8b8917d59306fb \
+    --hash=sha256:60f8dd1e76f47b3dbdee4b38d9189f3e020d22a173c00f930b52131001d801f9 \
+    --hash=sha256:62c0ea22e0b9dffb020601bb65cb11dd967a095a488be73f07d8867f4e327ca5 \
+    --hash=sha256:6546bed4e02a1c3d53a22a0bced254b3b6894693318b16c16c8e43e29d6befb6 \
+    --hash=sha256:6717c7a6e93c9d2c3d07c07113ec80ae24af45cde536b34363d4bcd9188091d9 \
+    --hash=sha256:7ebf1eb9badc2a397d4f6a6c8717447c81ac011db00064a00408bc83c923c0e4 \
+    --hash=sha256:9ffc84ade195abd4abcf0bd3b827b9140ae9ef90999429b9ea84d5df69c9058c \
+    --hash=sha256:a3f331a323d0f0ada6b10d60182ef06c20a2f01be21699999d204c5750ffd0b4 \
+    --hash=sha256:b1a0bbdb274fb683f993198775b957d29a6f07b45d184c571ef2a721ce4388cf \
+    --hash=sha256:b43abd7c82e805261c425d50335be9a6c4f84264e34d6d6e475207300005d572 \
+    --hash=sha256:c0f158e4708dda6367d21cf15afc58de4ebce979c7a1aa2f6b977aae737e2a54 \
+    --hash=sha256:d0bfd18c324549c0fec2c5dc610fd024689de6f27c6cc67e4e24a07541d6e49b \
+    --hash=sha256:ddab526c5a2c4ccb8c9ec4821fcea7606933dc53f510e2a6eebb45a418d3488a \
+    --hash=sha256:e172c73fccf7d6db4bd6f7de963dedded900d1a5c6778733241d878ba613980e \
+    --hash=sha256:e2c00ff204afa721b0bb9835b5bf1ba7fba210eefcec5552a9e05a63219ba0dc \
+    --hash=sha256:e31f4b799d530255aaf0566e3da2df5bfc35d3cd9d6d5a3dcc251663656c27b1 \
+    --hash=sha256:e4f212c018db951da3e1dc25c2651abc688221934739721f2dad5ff1dd5f90e7 \
+    --hash=sha256:fa9b26939ae553bf30a9f5c4c754db0fb2d2677327f2511e674aa2f5df941789 \
+    --hash=sha256:fb62fc7016b592435d3e3a8f680e3ea8897c3c9e62e6e6cc58011e7a4801439e
+    # via numba
+lmdb==1.3.0 \
+    --hash=sha256:008243762decf8f6c90430a9bced56290ebbcdb5e877d90e42343bb97033e494 \
+    --hash=sha256:08f4b5129f4683802569b02581142e415c8dcc0ff07605983ec1b07804cecbad \
+    --hash=sha256:17215a42a4b9814c383deabecb160581e4fb75d00198eef0e3cea54f230ffbea \
+    --hash=sha256:18c69fabdaf04efaf246587739cc1062b3e57c6ef0743f5c418df89e5e7e7b9b \
+    --hash=sha256:2cfa4aa9c67f8aee89b23005e98d1f3f32490b6b905fd1cb604b207cbd5755ab \
+    --hash=sha256:2df38115dd9428a54d59ae7c712a4c7cce0d6b1d66056de4b1a8c38718066106 \
+    --hash=sha256:394df860c3f93cfd92b6f4caba785f38208cc9614c18b3803f83a2cc1695042f \
+    --hash=sha256:41318717ab5d15ad2d6d263d34fbf614a045210f64b25e59ce734bb2105e421f \
+    --hash=sha256:4172fba19417d7b29409beca7d73c067b54e5d8ab1fb9b51d7b4c1445d20a167 \
+    --hash=sha256:5a14aca2651c3af6f0d0a6b9168200eea0c8f2d27c40b01a442f33329a6e8dff \
+    --hash=sha256:5ddd590e1c7fcb395931aa3782fb89b9db4550ab2d81d006ecd239e0d462bc41 \
+    --hash=sha256:60a11efc21aaf009d06518996360eed346f6000bfc9de05114374230879f992e \
+    --hash=sha256:6260a526e4ad85b1f374a5ba9475bf369fb07e7728ea6ec57226b02c40d1976b \
+    --hash=sha256:62ab28e3593bdc318ea2f2fa1574e5fca3b6d1f264686d773ba54a637d4f563b \
+    --hash=sha256:63cb73fe7ce9eb93d992d632c85a0476b4332670d9e6a2802b5062f603b7809f \
+    --hash=sha256:65334eafa5d430b18d81ebd5362559a41483c362e1931f6e1b15bab2ecb7d75d \
+    --hash=sha256:7da05d70fcc6561ac6b09e9fb1bf64b7ca294652c64c8a2889273970cee796b9 \
+    --hash=sha256:abbc439cd9fe60ffd6197009087ea885ac150017dc85384093b1d376f83f0ec4 \
+    --hash=sha256:c6adbd6f7f9048e97f31a069e652eb51020a81e80a0ce92dbb9810d21da2409a \
+    --hash=sha256:d6a816954d212f40fd15007cd81ab7a6bebb77436d949a6a9ae04af57fc127f3 \
+    --hash=sha256:d9103aa4908f0bca43c5911ca067d4e3d01f682dff0c0381a1239bd2bd757984 \
+    --hash=sha256:df2724bad7820114a205472994091097d0fa65a3e5fff5a8e688d123fb8c6326 \
+    --hash=sha256:e568ae0887ae196340947d9800136e90feaed6b86a261ef01f01b2ba65fc8106 \
+    --hash=sha256:e6a704b3baced9182836c7f77b769f23856f3a8f62d0282b1bc1feaf81a86712 \
+    --hash=sha256:eefb392f6b5cd43aada49258c5a79be11cb2c8cd3fc3e2d9319a1e0b9f906458 \
+    --hash=sha256:f291e3f561f58dddf63a92a5a6a4b8af3a0920b6705d35e2f80e52e86ee238a2 \
+    --hash=sha256:fa6439356e591d3249ab0e1778a6f8d8408e993f66dc911914c78208f5310309
+    # via
+    #   basicsr
+    #   gfpgan
+markdown==3.4.1 \
+    --hash=sha256:08fb8465cffd03d10b9dd34a5c3fea908e20391a2a90b88d66362cb05beed186 \
+    --hash=sha256:3b809086bb6efad416156e00a0da66fe47618a5d6918dd688f53f40c8e4cfeff
+    # via
+    #   tb-nightly
+    #   tensorboard
+markupsafe==2.1.1 \
+    --hash=sha256:0212a68688482dc52b2d45013df70d169f542b7394fc744c02a57374a4207003 \
+    --hash=sha256:089cf3dbf0cd6c100f02945abeb18484bd1ee57a079aefd52cffd17fba910b88 \
+    --hash=sha256:10c1bfff05d95783da83491be968e8fe789263689c02724e0c691933c52994f5 \
+    --hash=sha256:33b74d289bd2f5e527beadcaa3f401e0df0a89927c1559c8566c066fa4248ab7 \
+    --hash=sha256:3799351e2336dc91ea70b034983ee71cf2f9533cdff7c14c90ea126bfd95d65a \
+    --hash=sha256:3ce11ee3f23f79dbd06fb3d63e2f6af7b12db1d46932fe7bd8afa259a5996603 \
+    --hash=sha256:421be9fbf0ffe9ffd7a378aafebbf6f4602d564d34be190fc19a193232fd12b1 \
+    --hash=sha256:43093fb83d8343aac0b1baa75516da6092f58f41200907ef92448ecab8825135 \
+    --hash=sha256:46d00d6cfecdde84d40e572d63735ef81423ad31184100411e6e3388d405e247 \
+    --hash=sha256:4a33dea2b688b3190ee12bd7cfa29d39c9ed176bda40bfa11099a3ce5d3a7ac6 \
+    --hash=sha256:4b9fe39a2ccc108a4accc2676e77da025ce383c108593d65cc909add5c3bd601 \
+    --hash=sha256:56442863ed2b06d19c37f94d999035e15ee982988920e12a5b4ba29b62ad1f77 \
+    --hash=sha256:671cd1187ed5e62818414afe79ed29da836dde67166a9fac6d435873c44fdd02 \
+    --hash=sha256:694deca8d702d5db21ec83983ce0bb4b26a578e71fbdbd4fdcd387daa90e4d5e \
+    --hash=sha256:6a074d34ee7a5ce3effbc526b7083ec9731bb3cbf921bbe1d3005d4d2bdb3a63 \
+    --hash=sha256:6d0072fea50feec76a4c418096652f2c3238eaa014b2f94aeb1d56a66b41403f \
+    --hash=sha256:6fbf47b5d3728c6aea2abb0589b5d30459e369baa772e0f37a0320185e87c980 \
+    --hash=sha256:7f91197cc9e48f989d12e4e6fbc46495c446636dfc81b9ccf50bb0ec74b91d4b \
+    --hash=sha256:86b1f75c4e7c2ac2ccdaec2b9022845dbb81880ca318bb7a0a01fbf7813e3812 \
+    --hash=sha256:8dc1c72a69aa7e082593c4a203dcf94ddb74bb5c8a731e4e1eb68d031e8498ff \
+    --hash=sha256:8e3dcf21f367459434c18e71b2a9532d96547aef8a871872a5bd69a715c15f96 \
+    --hash=sha256:8e576a51ad59e4bfaac456023a78f6b5e6e7651dcd383bcc3e18d06f9b55d6d1 \
+    --hash=sha256:96e37a3dc86e80bf81758c152fe66dbf60ed5eca3d26305edf01892257049925 \
+    --hash=sha256:97a68e6ada378df82bc9f16b800ab77cbf4b2fada0081794318520138c088e4a \
+    --hash=sha256:99a2a507ed3ac881b975a2976d59f38c19386d128e7a9a18b7df6fff1fd4c1d6 \
+    --hash=sha256:a49907dd8420c5685cfa064a1335b6754b74541bbb3706c259c02ed65b644b3e \
+    --hash=sha256:b09bf97215625a311f669476f44b8b318b075847b49316d3e28c08e41a7a573f \
+    --hash=sha256:b7bd98b796e2b6553da7225aeb61f447f80a1ca64f41d83612e6139ca5213aa4 \
+    --hash=sha256:b87db4360013327109564f0e591bd2a3b318547bcef31b468a92ee504d07ae4f \
+    --hash=sha256:bcb3ed405ed3222f9904899563d6fc492ff75cce56cba05e32eff40e6acbeaa3 \
+    --hash=sha256:d4306c36ca495956b6d568d276ac11fdd9c30a36f1b6eb928070dc5360b22e1c \
+    --hash=sha256:d5ee4f386140395a2c818d149221149c54849dfcfcb9f1debfe07a8b8bd63f9a \
+    --hash=sha256:dda30ba7e87fbbb7eab1ec9f58678558fd9a6b8b853530e176eabd064da81417 \
+    --hash=sha256:e04e26803c9c3851c931eac40c695602c6295b8d432cbe78609649ad9bd2da8a \
+    --hash=sha256:e1c0b87e09fa55a220f058d1d49d3fb8df88fbfab58558f1198e08c1e1de842a \
+    --hash=sha256:e72591e9ecd94d7feb70c1cbd7be7b3ebea3f548870aa91e2732960fa4d57a37 \
+    --hash=sha256:e8c843bbcda3a2f1e3c2ab25913c80a3c5376cd00c6e8c4a86a89a28c8dc5452 \
+    --hash=sha256:efc1913fd2ca4f334418481c7e595c00aad186563bbc1ec76067848c7ca0a933 \
+    --hash=sha256:f121a1420d4e173a5d96e47e9a0c0dcff965afdf1626d28de1460815f7c4ee7a \
+    --hash=sha256:fc7b548b17d238737688817ab67deebb30e8073c95749d55538ed473130ec0c7
+    # via
+    #   jinja2
+    #   werkzeug
+matplotlib==3.6.2 \
+    --hash=sha256:0844523dfaaff566e39dbfa74e6f6dc42e92f7a365ce80929c5030b84caa563a \
+    --hash=sha256:0eda9d1b43f265da91fb9ae10d6922b5a986e2234470a524e6b18f14095b20d2 \
+    --hash=sha256:168093410b99f647ba61361b208f7b0d64dde1172b5b1796d765cd243cadb501 \
+    --hash=sha256:1836f366272b1557a613f8265db220eb8dd883202bbbabe01bad5a4eadfd0c95 \
+    --hash=sha256:19d61ee6414c44a04addbe33005ab1f87539d9f395e25afcbe9a3c50ce77c65c \
+    --hash=sha256:252957e208c23db72ca9918cb33e160c7833faebf295aaedb43f5b083832a267 \
+    --hash=sha256:32d29c8c26362169c80c5718ce367e8c64f4dd068a424e7110df1dd2ed7bd428 \
+    --hash=sha256:380d48c15ec41102a2b70858ab1dedfa33eb77b2c0982cb65a200ae67a48e9cb \
+    --hash=sha256:3964934731fd7a289a91d315919cf757f293969a4244941ab10513d2351b4e83 \
+    --hash=sha256:3cef89888a466228fc4e4b2954e740ce8e9afde7c4315fdd18caa1b8de58ca17 \
+    --hash=sha256:4426c74761790bff46e3d906c14c7aab727543293eed5a924300a952e1a3a3c1 \
+    --hash=sha256:5024b8ed83d7f8809982d095d8ab0b179bebc07616a9713f86d30cf4944acb73 \
+    --hash=sha256:52c2bdd7cd0bf9d5ccdf9c1816568fd4ccd51a4d82419cc5480f548981b47dd0 \
+    --hash=sha256:54fa9fe27f5466b86126ff38123261188bed568c1019e4716af01f97a12fe812 \
+    --hash=sha256:5ba73aa3aca35d2981e0b31230d58abb7b5d7ca104e543ae49709208d8ce706a \
+    --hash=sha256:5e16dcaecffd55b955aa5e2b8a804379789c15987e8ebd2f32f01398a81e975b \
+    --hash=sha256:5ecfc6559132116dedfc482d0ad9df8a89dc5909eebffd22f3deb684132d002f \
+    --hash=sha256:74153008bd24366cf099d1f1e83808d179d618c4e32edb0d489d526523a94d9f \
+    --hash=sha256:78ec3c3412cf277e6252764ee4acbdbec6920cc87ad65862272aaa0e24381eee \
+    --hash=sha256:795ad83940732b45d39b82571f87af0081c120feff2b12e748d96bb191169e33 \
+    --hash=sha256:7f716b6af94dc1b6b97c46401774472f0867e44595990fe80a8ba390f7a0a028 \
+    --hash=sha256:83dc89c5fd728fdb03b76f122f43b4dcee8c61f1489e232d9ad0f58020523e1c \
+    --hash=sha256:8a0ae37576ed444fe853709bdceb2be4c7df6f7acae17b8378765bd28e61b3ae \
+    --hash=sha256:8a8dbe2cb7f33ff54b16bb5c500673502a35f18ac1ed48625e997d40c922f9cc \
+    --hash=sha256:8a9d899953c722b9afd7e88dbefd8fb276c686c3116a43c577cfabf636180558 \
+    --hash=sha256:8d0068e40837c1d0df6e3abf1cdc9a34a6d2611d90e29610fa1d2455aeb4e2e5 \
+    --hash=sha256:9347cc6822f38db2b1d1ce992f375289670e595a2d1c15961aacbe0977407dfc \
+    --hash=sha256:9f335e5625feb90e323d7e3868ec337f7b9ad88b5d633f876e3b778813021dab \
+    --hash=sha256:b03fd10a1709d0101c054883b550f7c4c5e974f751e2680318759af005964990 \
+    --hash=sha256:b0ca2c60d3966dfd6608f5f8c49b8a0fcf76de6654f2eda55fc6ef038d5a6f27 \
+    --hash=sha256:b2604c6450f9dd2c42e223b1f5dca9643a23cfecc9fde4a94bb38e0d2693b136 \
+    --hash=sha256:ca0e7a658fbafcddcaefaa07ba8dae9384be2343468a8e011061791588d839fa \
+    --hash=sha256:d0e9ac04065a814d4cf2c6791a2ad563f739ae3ae830d716d54245c2b96fead6 \
+    --hash=sha256:d50e8c1e571ee39b5dfbc295c11ad65988879f68009dd281a6e1edbc2ff6c18c \
+    --hash=sha256:d840adcad7354be6f2ec28d0706528b0026e4c3934cc6566b84eac18633eab1b \
+    --hash=sha256:e0bbee6c2a5bf2a0017a9b5e397babb88f230e6f07c3cdff4a4c4bc75ed7c617 \
+    --hash=sha256:e5afe0a7ea0e3a7a257907060bee6724a6002b7eec55d0db16fd32409795f3e1 \
+    --hash=sha256:e68be81cd8c22b029924b6d0ee814c337c0e706b8d88495a617319e5dd5441c3 \
+    --hash=sha256:ec9be0f4826cdb3a3a517509dcc5f87f370251b76362051ab59e42b6b765f8c4 \
+    --hash=sha256:f04f97797df35e442ed09f529ad1235d1f1c0f30878e2fe09a2676b71a8801e0 \
+    --hash=sha256:f41e57ad63d336fe50d3a67bb8eaa26c09f6dda6a59f76777a99b8ccd8e26aec
+    # via filterpy
+multidict==6.0.2 \
+    --hash=sha256:0327292e745a880459ef71be14e709aaea2f783f3537588fb4ed09b6c01bca60 \
+    --hash=sha256:041b81a5f6b38244b34dc18c7b6aba91f9cdaf854d9a39e5ff0b58e2b5773b9c \
+    --hash=sha256:0556a1d4ea2d949efe5fd76a09b4a82e3a4a30700553a6725535098d8d9fb672 \
+    --hash=sha256:05f6949d6169878a03e607a21e3b862eaf8e356590e8bdae4227eedadacf6e51 \
+    --hash=sha256:07a017cfa00c9890011628eab2503bee5872f27144936a52eaab449be5eaf032 \
+    --hash=sha256:0b9e95a740109c6047602f4db4da9949e6c5945cefbad34a1299775ddc9a62e2 \
+    --hash=sha256:19adcfc2a7197cdc3987044e3f415168fc5dc1f720c932eb1ef4f71a2067e08b \
+    --hash=sha256:19d9bad105dfb34eb539c97b132057a4e709919ec4dd883ece5838bcbf262b80 \
+    --hash=sha256:225383a6603c086e6cef0f2f05564acb4f4d5f019a4e3e983f572b8530f70c88 \
+    --hash=sha256:23b616fdc3c74c9fe01d76ce0d1ce872d2d396d8fa8e4899398ad64fb5aa214a \
+    --hash=sha256:2957489cba47c2539a8eb7ab32ff49101439ccf78eab724c828c1a54ff3ff98d \
+    --hash=sha256:2d36e929d7f6a16d4eb11b250719c39560dd70545356365b494249e2186bc389 \
+    --hash=sha256:2e4a0785b84fb59e43c18a015ffc575ba93f7d1dbd272b4cdad9f5134b8a006c \
+    --hash=sha256:3368bf2398b0e0fcbf46d85795adc4c259299fec50c1416d0f77c0a843a3eed9 \
+    --hash=sha256:373ba9d1d061c76462d74e7de1c0c8e267e9791ee8cfefcf6b0b2495762c370c \
+    --hash=sha256:4070613ea2227da2bfb2c35a6041e4371b0af6b0be57f424fe2318b42a748516 \
+    --hash=sha256:45183c96ddf61bf96d2684d9fbaf6f3564d86b34cb125761f9a0ef9e36c1d55b \
+    --hash=sha256:4571f1beddff25f3e925eea34268422622963cd8dc395bb8778eb28418248e43 \
+    --hash=sha256:47e6a7e923e9cada7c139531feac59448f1f47727a79076c0b1ee80274cd8eee \
+    --hash=sha256:47fbeedbf94bed6547d3aa632075d804867a352d86688c04e606971595460227 \
+    --hash=sha256:497988d6b6ec6ed6f87030ec03280b696ca47dbf0648045e4e1d28b80346560d \
+    --hash=sha256:4bae31803d708f6f15fd98be6a6ac0b6958fcf68fda3c77a048a4f9073704aae \
+    --hash=sha256:50bd442726e288e884f7be9071016c15a8742eb689a593a0cac49ea093eef0a7 \
+    --hash=sha256:514fe2b8d750d6cdb4712346a2c5084a80220821a3e91f3f71eec11cf8d28fd4 \
+    --hash=sha256:5774d9218d77befa7b70d836004a768fb9aa4fdb53c97498f4d8d3f67bb9cfa9 \
+    --hash=sha256:5fdda29a3c7e76a064f2477c9aab1ba96fd94e02e386f1e665bca1807fc5386f \
+    --hash=sha256:5ff3bd75f38e4c43f1f470f2df7a4d430b821c4ce22be384e1459cb57d6bb013 \
+    --hash=sha256:626fe10ac87851f4cffecee161fc6f8f9853f0f6f1035b59337a51d29ff3b4f9 \
+    --hash=sha256:6701bf8a5d03a43375909ac91b6980aea74b0f5402fbe9428fc3f6edf5d9677e \
+    --hash=sha256:684133b1e1fe91eda8fa7447f137c9490a064c6b7f392aa857bba83a28cfb693 \
+    --hash=sha256:6f3cdef8a247d1eafa649085812f8a310e728bdf3900ff6c434eafb2d443b23a \
+    --hash=sha256:75bdf08716edde767b09e76829db8c1e5ca9d8bb0a8d4bd94ae1eafe3dac5e15 \
+    --hash=sha256:7c40b7bbece294ae3a87c1bc2abff0ff9beef41d14188cda94ada7bcea99b0fb \
+    --hash=sha256:8004dca28e15b86d1b1372515f32eb6f814bdf6f00952699bdeb541691091f96 \
+    --hash=sha256:8064b7c6f0af936a741ea1efd18690bacfbae4078c0c385d7c3f611d11f0cf87 \
+    --hash=sha256:89171b2c769e03a953d5969b2f272efa931426355b6c0cb508022976a17fd376 \
+    --hash=sha256:8cbf0132f3de7cc6c6ce00147cc78e6439ea736cee6bca4f068bcf892b0fd658 \
+    --hash=sha256:9cc57c68cb9139c7cd6fc39f211b02198e69fb90ce4bc4a094cf5fe0d20fd8b0 \
+    --hash=sha256:a007b1638e148c3cfb6bf0bdc4f82776cef0ac487191d093cdc316905e504071 \
+    --hash=sha256:a2c34a93e1d2aa35fbf1485e5010337c72c6791407d03aa5f4eed920343dd360 \
+    --hash=sha256:a45e1135cb07086833ce969555df39149680e5471c04dfd6a915abd2fc3f6dbc \
+    --hash=sha256:ac0e27844758d7177989ce406acc6a83c16ed4524ebc363c1f748cba184d89d3 \
+    --hash=sha256:aef9cc3d9c7d63d924adac329c33835e0243b5052a6dfcbf7732a921c6e918ba \
+    --hash=sha256:b9d153e7f1f9ba0b23ad1568b3b9e17301e23b042c23870f9ee0522dc5cc79e8 \
+    --hash=sha256:bfba7c6d5d7c9099ba21f84662b037a0ffd4a5e6b26ac07d19e423e6fdf965a9 \
+    --hash=sha256:c207fff63adcdf5a485969131dc70e4b194327666b7e8a87a97fbc4fd80a53b2 \
+    --hash=sha256:d0509e469d48940147e1235d994cd849a8f8195e0bca65f8f5439c56e17872a3 \
+    --hash=sha256:d16cce709ebfadc91278a1c005e3c17dd5f71f5098bfae1035149785ea6e9c68 \
+    --hash=sha256:d48b8ee1d4068561ce8033d2c344cf5232cb29ee1a0206a7b828c79cbc5982b8 \
+    --hash=sha256:de989b195c3d636ba000ee4281cd03bb1234635b124bf4cd89eeee9ca8fcb09d \
+    --hash=sha256:e07c8e79d6e6fd37b42f3250dba122053fddb319e84b55dd3a8d6446e1a7ee49 \
+    --hash=sha256:e2c2e459f7050aeb7c1b1276763364884595d47000c1cddb51764c0d8976e608 \
+    --hash=sha256:e5b20e9599ba74391ca0cfbd7b328fcc20976823ba19bc573983a25b32e92b57 \
+    --hash=sha256:e875b6086e325bab7e680e4316d667fc0e5e174bb5611eb16b3ea121c8951b86 \
+    --hash=sha256:f4f052ee022928d34fe1f4d2bc743f32609fb79ed9c49a1710a5ad6b2198db20 \
+    --hash=sha256:fcb91630817aa8b9bc4a74023e4198480587269c272c58b3279875ed7235c293 \
+    --hash=sha256:fd9fc9c4849a07f3635ccffa895d57abce554b467d611a5009ba4f39b78a8849 \
+    --hash=sha256:feba80698173761cddd814fa22e88b0661e98cb810f9f986c54aa34d281e4937 \
+    --hash=sha256:feea820722e69451743a3d56ad74948b68bf456984d63c1a92e8347b7b88452d
+    # via
+    #   aiohttp
+    #   yarl
+networkx==2.8.8 \
+    --hash=sha256:230d388117af870fce5647a3c52401fcf753e94720e6ea6b4197a5355648885e \
+    --hash=sha256:e435dfa75b1d7195c7b8378c3859f0445cd88c6b0375c181ed66823a9ceb7524
+    # via scikit-image
+numba==0.56.4 \
+    --hash=sha256:0240f9026b015e336069329839208ebd70ec34ae5bfbf402e4fcc8e06197528e \
+    --hash=sha256:03634579d10a6129181129de293dd6b5eaabee86881369d24d63f8fe352dd6cb \
+    --hash=sha256:03fe94cd31e96185cce2fae005334a8cc712fc2ba7756e52dff8c9400718173f \
+    --hash=sha256:0611e6d3eebe4cb903f1a836ffdb2bda8d18482bcd0a0dcc56e79e2aa3fefef5 \
+    --hash=sha256:0da583c532cd72feefd8e551435747e0e0fbb3c0530357e6845fcc11e38d6aea \
+    --hash=sha256:14dbbabf6ffcd96ee2ac827389afa59a70ffa9f089576500434c34abf9b054a4 \
+    --hash=sha256:32d9fef412c81483d7efe0ceb6cf4d3310fde8b624a9cecca00f790573ac96ee \
+    --hash=sha256:3a993349b90569518739009d8f4b523dfedd7e0049e6838c0e17435c3e70dcc4 \
+    --hash=sha256:3cb1a07a082a61df80a468f232e452d818f5ae254b40c26390054e4e868556e0 \
+    --hash=sha256:42f9e1be942b215df7e6cc9948cf9c15bb8170acc8286c063a9e57994ef82fd1 \
+    --hash=sha256:4373da9757049db7c90591e9ec55a2e97b2b36ba7ae3bf9c956a513374077470 \
+    --hash=sha256:4e08e203b163ace08bad500b0c16f6092b1eb34fd1fce4feaf31a67a3a5ecf3b \
+    --hash=sha256:553da2ce74e8862e18a72a209ed3b6d2924403bdd0fb341fa891c6455545ba7c \
+    --hash=sha256:720886b852a2d62619ae3900fe71f1852c62db4f287d0c275a60219e1643fc04 \
+    --hash=sha256:85dbaed7a05ff96492b69a8900c5ba605551afb9b27774f7f10511095451137c \
+    --hash=sha256:8a95ca9cc77ea4571081f6594e08bd272b66060634b8324e99cd1843020364f9 \
+    --hash=sha256:91f021145a8081f881996818474ef737800bcc613ffb1e618a655725a0f9e246 \
+    --hash=sha256:9f62672145f8669ec08762895fe85f4cf0ead08ce3164667f2b94b2f62ab23c3 \
+    --hash=sha256:a12ef323c0f2101529d455cfde7f4135eaa147bad17afe10b48634f796d96abd \
+    --hash=sha256:c602d015478b7958408d788ba00a50272649c5186ea8baa6cf71d4a1c761bba1 \
+    --hash=sha256:c75e8a5f810ce80a0cfad6e74ee94f9fde9b40c81312949bf356b7304ef20740 \
+    --hash=sha256:d0ae9270a7a5cc0ede63cd234b4ff1ce166c7a749b91dbbf45e0000c56d3eade \
+    --hash=sha256:d69ad934e13c15684e7887100a8f5f0f61d7a8e57e0fd29d9993210089a5b531 \
+    --hash=sha256:dbcc847bac2d225265d054993a7f910fda66e73d6662fe7156452cac0325b073 \
+    --hash=sha256:e64d338b504c9394a4a34942df4627e1e6cb07396ee3b49fe7b8d6420aa5104f \
+    --hash=sha256:f4cfc3a19d1e26448032049c79fc60331b104f694cf570a9e94f4e2c9d0932bb \
+    --hash=sha256:fbfb45e7b297749029cb28694abf437a78695a100e7c2033983d69f0ba2698d4 \
+    --hash=sha256:fcdf84ba3ed8124eb7234adfbb8792f311991cbf8aed1cad4b1b1a7ee08380c1
+    # via facexlib
+numpy==1.23.4 \
+    --hash=sha256:0fe563fc8ed9dc4474cbf70742673fc4391d70f4363f917599a7fa99f042d5a8 \
+    --hash=sha256:12ac457b63ec8ded85d85c1e17d85efd3c2b0967ca39560b307a35a6703a4735 \
+    --hash=sha256:2341f4ab6dba0834b685cce16dad5f9b6606ea8a00e6da154f5dbded70fdc4dd \
+    --hash=sha256:296d17aed51161dbad3c67ed6d164e51fcd18dbcd5dd4f9d0a9c6055dce30810 \
+    --hash=sha256:488a66cb667359534bc70028d653ba1cf307bae88eab5929cd707c761ff037db \
+    --hash=sha256:4d52914c88b4930dafb6c48ba5115a96cbab40f45740239d9f4159c4ba779962 \
+    --hash=sha256:5e13030f8793e9ee42f9c7d5777465a560eb78fa7e11b1c053427f2ccab90c79 \
+    --hash=sha256:61be02e3bf810b60ab74e81d6d0d36246dbfb644a462458bb53b595791251911 \
+    --hash=sha256:7607b598217745cc40f751da38ffd03512d33ec06f3523fb0b5f82e09f6f676d \
+    --hash=sha256:7a70a7d3ce4c0e9284e92285cba91a4a3f5214d87ee0e95928f3614a256a1488 \
+    --hash=sha256:7ab46e4e7ec63c8a5e6dbf5c1b9e1c92ba23a7ebecc86c336cb7bf3bd2fb10e5 \
+    --hash=sha256:8981d9b5619569899666170c7c9748920f4a5005bf79c72c07d08c8a035757b0 \
+    --hash=sha256:8c053d7557a8f022ec823196d242464b6955a7e7e5015b719e76003f63f82d0f \
+    --hash=sha256:926db372bc4ac1edf81cfb6c59e2a881606b409ddc0d0920b988174b2e2a767f \
+    --hash=sha256:95d79ada05005f6f4f337d3bb9de8a7774f259341c70bc88047a1f7b96a4bcb2 \
+    --hash=sha256:95de7dc7dc47a312f6feddd3da2500826defdccbc41608d0031276a24181a2c0 \
+    --hash=sha256:a0882323e0ca4245eb0a3d0a74f88ce581cc33aedcfa396e415e5bba7bf05f68 \
+    --hash=sha256:a8365b942f9c1a7d0f0dc974747d99dd0a0cdfc5949a33119caf05cb314682d3 \
+    --hash=sha256:a8aae2fb3180940011b4862b2dd3756616841c53db9734b27bb93813cd79fce6 \
+    --hash=sha256:c237129f0e732885c9a6076a537e974160482eab8f10db6292e92154d4c67d71 \
+    --hash=sha256:c67b833dbccefe97cdd3f52798d430b9d3430396af7cdb2a0c32954c3ef73894 \
+    --hash=sha256:ce03305dd694c4873b9429274fd41fc7eb4e0e4dea07e0af97a933b079a5814f \
+    --hash=sha256:d331afac87c92373826af83d2b2b435f57b17a5c74e6268b79355b970626e329 \
+    --hash=sha256:dada341ebb79619fe00a291185bba370c9803b1e1d7051610e01ed809ef3a4ba \
+    --hash=sha256:ed2cc92af0efad20198638c69bb0fc2870a58dabfba6eb722c933b48556c686c \
+    --hash=sha256:f260da502d7441a45695199b4e7fd8ca87db659ba1c78f2bbf31f934fe76ae0e \
+    --hash=sha256:f2f390aa4da44454db40a1f0201401f9036e8d578a25f01a6e237cea238337ef \
+    --hash=sha256:f76025acc8e2114bb664294a07ede0727aa75d63a06d2fae96bf29a81747e4a7
+    # via
+    #   accelerate
+    #   albumentations
+    #   altair
+    #   basicsr
+    #   clean-fid
+    #   contourpy
+    #   diffusers
+    #   facexlib
+    #   filterpy
+    #   gfpgan
+    #   imageio
+    #   matplotlib
+    #   numba
+    #   opencv-python
+    #   opencv-python-headless
+    #   pandas
+    #   pyarrow
+    #   pydeck
+    #   pytorch-lightning
+    #   pywavelets
+    #   qudida
+    #   realesrgan
+    #   scikit-image
+    #   scikit-learn
+    #   scipy
+    #   streamlit
+    #   taming-transformers-rom1504
+    #   tb-nightly
+    #   tensorboard
+    #   test-tube
+    #   tifffile
+    #   torch-fidelity
+    #   torchmetrics
+    #   torchvision
+    #   transformers
+oauthlib==3.2.2 \
+    --hash=sha256:8139f29aac13e25d502680e9e19963e83f16838d48a0d71c287fe40e7067fbca \
+    --hash=sha256:9859c40929662bec5d64f34d01c99e093149682a3f38915dc0655d5a633dd918
+    # via requests-oauthlib
+omegaconf==2.2.3 \
+    --hash=sha256:59ff9fba864ffbb5fb710b64e8a9ba37c68fa339a2e2bb4f1b648d6901552523 \
+    --hash=sha256:d6f2cbf79a992899eb76c6cb1aedfcf0fe7456a8654382edd5ee0c1b199c0657
+    # via taming-transformers-rom1504
+opencv-python==4.6.0.66 \
+    --hash=sha256:0dc82a3d8630c099d2f3ac1b1aabee164e8188db54a786abb7a4e27eba309440 \
+    --hash=sha256:5af8ba35a4fcb8913ffb86e92403e9a656a4bff4a645d196987468f0f8947875 \
+    --hash=sha256:6e32af22e3202748bd233ed8f538741876191863882eba44e332d1a34993165b \
+    --hash=sha256:c5bfae41ad4031e66bb10ec4a0a2ffd3e514d092652781e8b1ac98d1b59f1158 \
+    --hash=sha256:dbdc84a9b4ea2cbae33861652d25093944b9959279200b7ae0badd32439f74de \
+    --hash=sha256:e6e448b62afc95c5b58f97e87ef84699e6607fe5c58730a03301c52496005cae \
+    --hash=sha256:f482e78de6e7b0b060ff994ffd859bddc3f7f382bb2019ef157b0ea8ca8712f5
+    # via
+    #   basicsr
+    #   facexlib
+    #   gfpgan
+    #   realesrgan
+opencv-python-headless==4.6.0.66 \
+    --hash=sha256:21e70f8b0c04098cdf466d27184fe6c3820aaef944a22548db95099959c95889 \
+    --hash=sha256:2c032c373e447c3fc2a670bca20e2918a1205a6e72854df60425fd3f82c78c32 \
+    --hash=sha256:3bacd806cce1f1988e58f3d6f761538e0215d6621d316de94c009dc0acbd6ad3 \
+    --hash=sha256:d5291d7e10aa2c19cab6fd86f0d61af8617290ecd2d7ffcb051e446868d04cc5 \
+    --hash=sha256:e6c069bc963d7e8fcec21b3e33e594d35948badd63eccb2e80f88b0a12102c03 \
+    --hash=sha256:eec6281054346103d6af93f173b7c6a206beb2663d3adc04aa3ddc66e85093df \
+    --hash=sha256:ffbf26fcd697af996408440a93bc69c49c05a36845771f984156dfbeaa95d497
+    # via
+    #   albumentations
+    #   qudida
+packaging==21.3 \
+    --hash=sha256:dd47c42927d89ab911e606518907cc2d3a1f38bbd026385970643f9c5b8ecfeb \
+    --hash=sha256:ef103e05f519cdc783ae24ea4e2e0f508a9c99b2d4969652eed6a2e1ea5bd522
+    # via
+    #   accelerate
+    #   huggingface-hub
+    #   kornia
+    #   matplotlib
+    #   pytorch-lightning
+    #   scikit-image
+    #   streamlit
+    #   torchmetrics
+    #   transformers
+pandas==1.5.1 \
+    --hash=sha256:04e51b01d5192499390c0015630975f57836cc95c7411415b499b599b05c0c96 \
+    --hash=sha256:05c527c64ee02a47a24031c880ee0ded05af0623163494173204c5b72ddce658 \
+    --hash=sha256:0a78e05ec09731c5b3bd7a9805927ea631fe6f6cb06f0e7c63191a9a778d52b4 \
+    --hash=sha256:17da7035d9e6f9ea9cdc3a513161f8739b8f8489d31dc932bc5a29a27243f93d \
+    --hash=sha256:249cec5f2a5b22096440bd85c33106b6102e0672204abd2d5c014106459804ee \
+    --hash=sha256:2c25e5c16ee5c0feb6cf9d982b869eec94a22ddfda9aa2fbed00842cbb697624 \
+    --hash=sha256:32e3d9f65606b3f6e76555bfd1d0b68d94aff0929d82010b791b6254bf5a4b96 \
+    --hash=sha256:36aa1f8f680d7584e9b572c3203b20d22d697c31b71189322f16811d4ecfecd3 \
+    --hash=sha256:5b0c970e2215572197b42f1cff58a908d734503ea54b326412c70d4692256391 \
+    --hash=sha256:5cee0c74e93ed4f9d39007e439debcaadc519d7ea5c0afc3d590a3a7b2edf060 \
+    --hash=sha256:669c8605dba6c798c1863157aefde959c1796671ffb342b80fcb80a4c0bc4c26 \
+    --hash=sha256:66a1ad667b56e679e06ba73bb88c7309b3f48a4c279bd3afea29f65a766e9036 \
+    --hash=sha256:683779e5728ac9138406c59a11e09cd98c7d2c12f0a5fc2b9c5eecdbb4a00075 \
+    --hash=sha256:6bb391659a747cf4f181a227c3e64b6d197100d53da98dcd766cc158bdd9ec68 \
+    --hash=sha256:81f0674fa50b38b6793cd84fae5d67f58f74c2d974d2cb4e476d26eee33343d0 \
+    --hash=sha256:927e59c694e039c75d7023465d311277a1fc29ed7236b5746e9dddf180393113 \
+    --hash=sha256:932d2d7d3cab44cfa275601c982f30c2d874722ef6396bb539e41e4dc4618ed4 \
+    --hash=sha256:a52419d9ba5906db516109660b114faf791136c94c1a636ed6b29cbfff9187ee \
+    --hash=sha256:b156a971bc451c68c9e1f97567c94fd44155f073e3bceb1b0d195fd98ed12048 \
+    --hash=sha256:bcf1a82b770b8f8c1e495b19a20d8296f875a796c4fe6e91da5ef107f18c5ecb \
+    --hash=sha256:cb2a9cf1150302d69bb99861c5cddc9c25aceacb0a4ef5299785d0f5389a3209 \
+    --hash=sha256:d8c709f4700573deb2036d240d140934df7e852520f4a584b2a8d5443b71f54d \
+    --hash=sha256:db45b94885000981522fb92349e6b76f5aee0924cc5315881239c7859883117d \
+    --hash=sha256:ddf46b940ef815af4e542697eaf071f0531449407a7607dd731bf23d156e20a7 \
+    --hash=sha256:e675f8fe9aa6c418dc8d3aac0087b5294c1a4527f1eacf9fe5ea671685285454 \
+    --hash=sha256:eb7e8cf2cf11a2580088009b43de84cabbf6f5dae94ceb489f28dba01a17cb77 \
+    --hash=sha256:f340331a3f411910adfb4bbe46c2ed5872d9e473a783d7f14ecf49bc0869c594
+    # via
+    #   altair
+    #   streamlit
+    #   test-tube
+pathtools==0.1.2 \
+    --hash=sha256:7c35c5421a39bb82e58018febd90e3b6e5db34c5443aaaf742b3f33d4655f1c0
+    # via wandb
+pillow==9.3.0 \
+    --hash=sha256:03150abd92771742d4a8cd6f2fa6246d847dcd2e332a18d0c15cc75bf6703040 \
+    --hash=sha256:073adb2ae23431d3b9bcbcff3fe698b62ed47211d0716b067385538a1b0f28b8 \
+    --hash=sha256:0b07fffc13f474264c336298d1b4ce01d9c5a011415b79d4ee5527bb69ae6f65 \
+    --hash=sha256:0b7257127d646ff8676ec8a15520013a698d1fdc48bc2a79ba4e53df792526f2 \
+    --hash=sha256:12ce4932caf2ddf3e41d17fc9c02d67126935a44b86df6a206cf0d7161548627 \
+    --hash=sha256:15c42fb9dea42465dfd902fb0ecf584b8848ceb28b41ee2b58f866411be33f07 \
+    --hash=sha256:18498994b29e1cf86d505edcb7edbe814d133d2232d256db8c7a8ceb34d18cef \
+    --hash=sha256:1c7c8ae3864846fc95f4611c78129301e203aaa2af813b703c55d10cc1628535 \
+    --hash=sha256:22b012ea2d065fd163ca096f4e37e47cd8b59cf4b0fd47bfca6abb93df70b34c \
+    --hash=sha256:276a5ca930c913f714e372b2591a22c4bd3b81a418c0f6635ba832daec1cbcfc \
+    --hash=sha256:2e0918e03aa0c72ea56edbb00d4d664294815aa11291a11504a377ea018330d3 \
+    --hash=sha256:3033fbe1feb1b59394615a1cafaee85e49d01b51d54de0cbf6aa8e64182518a1 \
+    --hash=sha256:3168434d303babf495d4ba58fc22d6604f6e2afb97adc6a423e917dab828939c \
+    --hash=sha256:32a44128c4bdca7f31de5be641187367fe2a450ad83b833ef78910397db491aa \
+    --hash=sha256:3dd6caf940756101205dffc5367babf288a30043d35f80936f9bfb37f8355b32 \
+    --hash=sha256:40e1ce476a7804b0fb74bcfa80b0a2206ea6a882938eaba917f7a0f004b42502 \
+    --hash=sha256:41e0051336807468be450d52b8edd12ac60bebaa97fe10c8b660f116e50b30e4 \
+    --hash=sha256:4390e9ce199fc1951fcfa65795f239a8a4944117b5935a9317fb320e7767b40f \
+    --hash=sha256:502526a2cbfa431d9fc2a079bdd9061a2397b842bb6bc4239bb176da00993812 \
+    --hash=sha256:51e0e543a33ed92db9f5ef69a0356e0b1a7a6b6a71b80df99f1d181ae5875636 \
+    --hash=sha256:57751894f6618fd4308ed8e0c36c333e2f5469744c34729a27532b3db106ee20 \
+    --hash=sha256:5d77adcd56a42d00cc1be30843d3426aa4e660cab4a61021dc84467123f7a00c \
+    --hash=sha256:655a83b0058ba47c7c52e4e2df5ecf484c1b0b0349805896dd350cbc416bdd91 \
+    --hash=sha256:68943d632f1f9e3dce98908e873b3a090f6cba1cbb1b892a9e8d97c938871fbe \
+    --hash=sha256:6c738585d7a9961d8c2821a1eb3dcb978d14e238be3d70f0a706f7fa9316946b \
+    --hash=sha256:73bd195e43f3fadecfc50c682f5055ec32ee2c933243cafbfdec69ab1aa87cad \
+    --hash=sha256:772a91fc0e03eaf922c63badeca75e91baa80fe2f5f87bdaed4280662aad25c9 \
+    --hash=sha256:77ec3e7be99629898c9a6d24a09de089fa5356ee408cdffffe62d67bb75fdd72 \
+    --hash=sha256:7db8b751ad307d7cf238f02101e8e36a128a6cb199326e867d1398067381bff4 \
+    --hash=sha256:801ec82e4188e935c7f5e22e006d01611d6b41661bba9fe45b60e7ac1a8f84de \
+    --hash=sha256:82409ffe29d70fd733ff3c1025a602abb3e67405d41b9403b00b01debc4c9a29 \
+    --hash=sha256:828989c45c245518065a110434246c44a56a8b2b2f6347d1409c787e6e4651ee \
+    --hash=sha256:829f97c8e258593b9daa80638aee3789b7df9da5cf1336035016d76f03b8860c \
+    --hash=sha256:871b72c3643e516db4ecf20efe735deb27fe30ca17800e661d769faab45a18d7 \
+    --hash=sha256:89dca0ce00a2b49024df6325925555d406b14aa3efc2f752dbb5940c52c56b11 \
+    --hash=sha256:90fb88843d3902fe7c9586d439d1e8c05258f41da473952aa8b328d8b907498c \
+    --hash=sha256:97aabc5c50312afa5e0a2b07c17d4ac5e865b250986f8afe2b02d772567a380c \
+    --hash=sha256:9aaa107275d8527e9d6e7670b64aabaaa36e5b6bd71a1015ddd21da0d4e06448 \
+    --hash=sha256:9f47eabcd2ded7698106b05c2c338672d16a6f2a485e74481f524e2a23c2794b \
+    --hash=sha256:a0a06a052c5f37b4ed81c613a455a81f9a3a69429b4fd7bb913c3fa98abefc20 \
+    --hash=sha256:ab388aaa3f6ce52ac1cb8e122c4bd46657c15905904b3120a6248b5b8b0bc228 \
+    --hash=sha256:ad58d27a5b0262c0c19b47d54c5802db9b34d38bbf886665b626aff83c74bacd \
+    --hash=sha256:ae5331c23ce118c53b172fa64a4c037eb83c9165aba3a7ba9ddd3ec9fa64a699 \
+    --hash=sha256:af0372acb5d3598f36ec0914deed2a63f6bcdb7b606da04dc19a88d31bf0c05b \
+    --hash=sha256:afa4107d1b306cdf8953edde0534562607fe8811b6c4d9a486298ad31de733b2 \
+    --hash=sha256:b03ae6f1a1878233ac620c98f3459f79fd77c7e3c2b20d460284e1fb370557d4 \
+    --hash=sha256:b0915e734b33a474d76c28e07292f196cdf2a590a0d25bcc06e64e545f2d146c \
+    --hash=sha256:b4012d06c846dc2b80651b120e2cdd787b013deb39c09f407727ba90015c684f \
+    --hash=sha256:b472b5ea442148d1c3e2209f20f1e0bb0eb556538690fa70b5e1f79fa0ba8dc2 \
+    --hash=sha256:b59430236b8e58840a0dfb4099a0e8717ffb779c952426a69ae435ca1f57210c \
+    --hash=sha256:b90f7616ea170e92820775ed47e136208e04c967271c9ef615b6fbd08d9af0e3 \
+    --hash=sha256:b9a65733d103311331875c1dca05cb4606997fd33d6acfed695b1232ba1df193 \
+    --hash=sha256:bac18ab8d2d1e6b4ce25e3424f709aceef668347db8637c2296bcf41acb7cf48 \
+    --hash=sha256:bca31dd6014cb8b0b2db1e46081b0ca7d936f856da3b39744aef499db5d84d02 \
+    --hash=sha256:be55f8457cd1eac957af0c3f5ece7bc3f033f89b114ef30f710882717670b2a8 \
+    --hash=sha256:c7025dce65566eb6e89f56c9509d4f628fddcedb131d9465cacd3d8bac337e7e \
+    --hash=sha256:c935a22a557a560108d780f9a0fc426dd7459940dc54faa49d83249c8d3e760f \
+    --hash=sha256:dbb8e7f2abee51cef77673be97760abff1674ed32847ce04b4af90f610144c7b \
+    --hash=sha256:e6ea6b856a74d560d9326c0f5895ef8050126acfdc7ca08ad703eb0081e82b74 \
+    --hash=sha256:ebf2029c1f464c59b8bdbe5143c79fa2045a581ac53679733d3a91d400ff9efb \
+    --hash=sha256:f1ff2ee69f10f13a9596480335f406dd1f70c3650349e2be67ca3139280cade0
+    # via
+    #   basicsr
+    #   clean-fid
+    #   diffusers
+    #   facexlib
+    #   imageio
+    #   k-diffusion
+    #   matplotlib
+    #   realesrgan
+    #   scikit-image
+    #   streamlit
+    #   torch-fidelity
+    #   torchvision
+promise==2.3 \
+    --hash=sha256:dfd18337c523ba4b6a58801c164c1904a9d4d1b1747c7d5dbf45b693a49d93d0
+    # via wandb
+protobuf==3.19.6 \
+    --hash=sha256:010be24d5a44be7b0613750ab40bc8b8cedc796db468eae6c779b395f50d1fa1 \
+    --hash=sha256:0469bc66160180165e4e29de7f445e57a34ab68f49357392c5b2f54c656ab25e \
+    --hash=sha256:0c0714b025ec057b5a7600cb66ce7c693815f897cfda6d6efb58201c472e3437 \
+    --hash=sha256:11478547958c2dfea921920617eb457bc26867b0d1aa065ab05f35080c5d9eb6 \
+    --hash=sha256:14082457dc02be946f60b15aad35e9f5c69e738f80ebbc0900a19bc83734a5a4 \
+    --hash=sha256:2b2d2913bcda0e0ec9a784d194bc490f5dc3d9d71d322d070b11a0ade32ff6ba \
+    --hash=sha256:30a15015d86b9c3b8d6bf78d5b8c7749f2512c29f168ca259c9d7727604d0e39 \
+    --hash=sha256:30f5370d50295b246eaa0296533403961f7e64b03ea12265d6dfce3a391d8992 \
+    --hash=sha256:347b393d4dd06fb93a77620781e11c058b3b0a5289262f094379ada2920a3730 \
+    --hash=sha256:4bc98de3cdccfb5cd769620d5785b92c662b6bfad03a202b83799b6ed3fa1fa7 \
+    --hash=sha256:5057c64052a1f1dd7d4450e9aac25af6bf36cfbfb3a1cd89d16393a036c49157 \
+    --hash=sha256:559670e006e3173308c9254d63facb2c03865818f22204037ab76f7a0ff70b5f \
+    --hash=sha256:5a0d7539a1b1fb7e76bf5faa0b44b30f812758e989e59c40f77a7dab320e79b9 \
+    --hash=sha256:5f5540d57a43042389e87661c6eaa50f47c19c6176e8cf1c4f287aeefeccb5c4 \
+    --hash=sha256:7a552af4dc34793803f4e735aabe97ffc45962dfd3a237bdde242bff5a3de684 \
+    --hash=sha256:84a04134866861b11556a82dd91ea6daf1f4925746b992f277b84013a7cc1229 \
+    --hash=sha256:878b4cd080a21ddda6ac6d1e163403ec6eea2e206cf225982ae04567d39be7b0 \
+    --hash=sha256:90b0d02163c4e67279ddb6dc25e063db0130fc299aefabb5d481053509fae5c8 \
+    --hash=sha256:91d5f1e139ff92c37e0ff07f391101df77e55ebb97f46bbc1535298d72019462 \
+    --hash=sha256:a8ce5ae0de28b51dff886fb922012dad885e66176663950cb2344c0439ecb473 \
+    --hash=sha256:aa3b82ca1f24ab5326dcf4ea00fcbda703e986b22f3d27541654f749564d778b \
+    --hash=sha256:bb6776bd18f01ffe9920e78e03a8676530a5d6c5911934c6a1ac6eb78973ecb6 \
+    --hash=sha256:bbf5cea5048272e1c60d235c7bd12ce1b14b8a16e76917f371c718bd3005f045 \
+    --hash=sha256:c0ccd3f940fe7f3b35a261b1dd1b4fc850c8fde9f74207015431f174be5976b3 \
+    --hash=sha256:d0b635cefebd7a8a0f92020562dead912f81f401af7e71f16bf9506ff3bdbb38
+    # via
+    #   streamlit
+    #   tb-nightly
+    #   tensorboard
+    #   wandb
+psutil==5.9.3 \
+    --hash=sha256:07d880053c6461c9b89cd5d4808f3b8336665fa3acdefd6777662c5ed73a851a \
+    --hash=sha256:12500d761ac091f2426567f19f95fd3f15a197d96befb44a5c1e3cbe6db5752c \
+    --hash=sha256:1b540599481c73408f6b392cdffef5b01e8ff7a2ac8caae0a91b8222e88e8f1e \
+    --hash=sha256:35feafe232d1aaf35d51bd42790cbccb882456f9f18cdc411532902370d660df \
+    --hash=sha256:3a7826e68b0cf4ce2c1ee385d64eab7d70e3133171376cac53d7c1790357ec8f \
+    --hash=sha256:46907fa62acaac364fff0b8a9da7b360265d217e4fdeaca0a2397a6883dffba2 \
+    --hash=sha256:4bd4854f0c83aa84a5a40d3b5d0eb1f3c128f4146371e03baed4589fe4f3c931 \
+    --hash=sha256:538fcf6ae856b5e12d13d7da25ad67f02113c96f5989e6ad44422cb5994ca7fc \
+    --hash=sha256:547ebb02031fdada635452250ff39942db8310b5c4a8102dfe9384ee5791e650 \
+    --hash=sha256:5e8b50241dd3c2ed498507f87a6602825073c07f3b7e9560c58411c14fe1e1c9 \
+    --hash=sha256:5fa88e3d5d0b480602553d362c4b33a63e0c40bfea7312a7bf78799e01e0810b \
+    --hash=sha256:68fa227c32240c52982cb931801c5707a7f96dd8927f9102d6c7771ea1ff5698 \
+    --hash=sha256:6ced1ad823ecfa7d3ce26fe8aa4996e2e53fb49b7fed8ad81c80958501ec0619 \
+    --hash=sha256:71b1206e7909792d16933a0d2c1c7f04ae196186c51ba8567abae1d041f06dcb \
+    --hash=sha256:767ef4fa33acda16703725c0473a91e1832d296c37c63896c7153ba81698f1ab \
+    --hash=sha256:7ccfcdfea4fc4b0a02ca2c31de7fcd186beb9cff8207800e14ab66f79c773af6 \
+    --hash=sha256:7e4939ff75149b67aef77980409f156f0082fa36accc475d45c705bb00c6c16a \
+    --hash=sha256:828c9dc9478b34ab96be75c81942d8df0c2bb49edbb481f597314d92b6441d89 \
+    --hash=sha256:8a4e07611997acf178ad13b842377e3d8e9d0a5bac43ece9bfc22a96735d9a4f \
+    --hash=sha256:941a6c2c591da455d760121b44097781bc970be40e0e43081b9139da485ad5b7 \
+    --hash=sha256:9a4af6ed1094f867834f5f07acd1250605a0874169a5fcadbcec864aec2496a6 \
+    --hash=sha256:9ec296f565191f89c48f33d9544d8d82b0d2af7dd7d2d4e6319f27a818f8d1cc \
+    --hash=sha256:9ec95df684583b5596c82bb380c53a603bb051cf019d5c849c47e117c5064395 \
+    --hash=sha256:a04a1836894c8279e5e0a0127c0db8e198ca133d28be8a2a72b4db16f6cf99c1 \
+    --hash=sha256:a3d81165b8474087bb90ec4f333a638ccfd1d69d34a9b4a1a7eaac06648f9fbe \
+    --hash=sha256:b4a247cd3feaae39bb6085fcebf35b3b8ecd9b022db796d89c8f05067ca28e71 \
+    --hash=sha256:ba38cf9984d5462b506e239cf4bc24e84ead4b1d71a3be35e66dad0d13ded7c1 \
+    --hash=sha256:beb57d8a1ca0ae0eb3d08ccaceb77e1a6d93606f0e1754f0d60a6ebd5c288837 \
+    --hash=sha256:d266cd05bd4a95ca1c2b9b5aac50d249cf7c94a542f47e0b22928ddf8b80d1ef \
+    --hash=sha256:d8c3cc6bb76492133474e130a12351a325336c01c96a24aae731abf5a47fe088 \
+    --hash=sha256:db8e62016add2235cc87fb7ea000ede9e4ca0aa1f221b40cef049d02d5d2593d \
+    --hash=sha256:e7507f6c7b0262d3e7b0eeda15045bf5881f4ada70473b87bc7b7c93b992a7d7 \
+    --hash=sha256:ed15edb14f52925869250b1375f0ff58ca5c4fa8adefe4883cfb0737d32f5c02 \
+    --hash=sha256:f57d63a2b5beaf797b87024d018772439f9d3103a395627b77d17a8d72009543 \
+    --hash=sha256:fa5e32c7d9b60b2528108ade2929b115167fe98d59f89555574715054f50fa31 \
+    --hash=sha256:fe79b4ad4836e3da6c4650cb85a663b3a51aef22e1a829c384e18fae87e5e727
+    # via
+    #   accelerate
+    #   wandb
+pyarrow==10.0.0 \
+    --hash=sha256:10e031794d019425d34406edffe7e32157359e9455f9edb97a1732f8dabf802f \
+    --hash=sha256:25f51dca780fc22cfd7ac30f6bdfe70eb99145aee9acfda987f2c49955d66ed9 \
+    --hash=sha256:2d326a9d47ac237d81b8c4337e9d30a0b361835b536fc7ea53991455ce761fbd \
+    --hash=sha256:3d2694f08c8d4482d14e3798ff036dbd81ae6b1c47948f52515e1aa90fbec3f0 \
+    --hash=sha256:4051664d354b14939b5da35cfa77821ade594bc1cf56dd2032b3068c96697d74 \
+    --hash=sha256:511735040b83f2993f78d7fb615e7b88253d75f41500e87e587c40156ff88120 \
+    --hash=sha256:65d4a312f3ced318423704355acaccc7f7bdfe242472e59bdd54aa0f8837adf8 \
+    --hash=sha256:68ccb82c04c0f7abf7a95541d5e9d9d94290fc66a2d36d3f6ea0777f40c15654 \
+    --hash=sha256:69b8a1fd99201178799b02f18498633847109b701856ec762f314352a431b7d0 \
+    --hash=sha256:758284e1ebd3f2a9abb30544bfec28d151a398bb7c0f2578cbca5ee5b000364a \
+    --hash=sha256:7be7f42f713068293308c989a4a3a2de03b70199bdbe753901c6595ff8640c64 \
+    --hash=sha256:7ce026274cd5d9934cd3694e89edecde4e036018bbc6cb735fd33b9e967e7d47 \
+    --hash=sha256:7e6b837cc44cd62a0e280c8fc4de94ebce503d6d1190e6e94157ab49a8bea67b \
+    --hash=sha256:b153b05765393557716e3729cf988442b3ae4f5567364ded40d58c07feed27c2 \
+    --hash=sha256:b3e3148468d3eed3779d68241f1d13ed4ee7cca4c6dbc7c07e5062b93ad4da33 \
+    --hash=sha256:b45f969ed924282e9d4ede38f3430630d809c36dbff65452cabce03141943d28 \
+    --hash=sha256:b9f63ceb8346aac0bcb487fafe9faca642ad448ca649fcf66a027c6e120cbc12 \
+    --hash=sha256:c79300e1a3e23f2bf4defcf0d70ff5ea25ef6ebf6f121d8670ee14bb662bb7ca \
+    --hash=sha256:d45a59e2f47826544c0ca70bc0f7ed8ffa5ad23f93b0458230c7e983bcad1acf \
+    --hash=sha256:e4c6da9f9e1ff96781ee1478f7cc0860e66c23584887b8e297c4b9905c3c9066 \
+    --hash=sha256:f329951d56b3b943c353f7b27c894e02367a7efbb9fef7979c6b24e02dbfcf55 \
+    --hash=sha256:f76157d9579571c865860e5fd004537c03e21139db76692d96fd8a186adab1f2
+    # via streamlit
+pyasn1==0.4.8 \
+    --hash=sha256:39c7e2ec30515947ff4e87fb6f456dfc6e84857d34be479c9d4a4ba4bf46aa5d \
+    --hash=sha256:aef77c9fb94a3ac588e87841208bdec464471d9871bd5050a287cc9a475cd0ba
+    # via
+    #   pyasn1-modules
+    #   rsa
+pyasn1-modules==0.2.8 \
+    --hash=sha256:905f84c712230b2c592c19470d3ca8d552de726050d1d1716282a1f6146be65e \
+    --hash=sha256:a50b808ffeb97cb3601dd25981f6b016cbb3d31fbf57a8b8a87428e6158d0c74
+    # via google-auth
+pydeck==0.8.0 \
+    --hash=sha256:07edde833f7cfcef6749124351195aa7dcd24663d4909fd7898dbd0b6fbc01ec \
+    --hash=sha256:a8fa7757c6f24bba033af39db3147cb020eef44012ba7e60d954de187f9ed4d5
+    # via streamlit
+pydeprecate==0.3.2 \
+    --hash=sha256:d481116cc5d7f6c473e7c4be820efdd9b90a16b594b350276e9e66a6cb5bdd29 \
+    --hash=sha256:ed86b68ed837e6465245904a3de2f59bf9eef78ac7a2502ee280533d04802457
+    # via pytorch-lightning
+pygments==2.13.0 \
+    --hash=sha256:56a8508ae95f98e2b9bdf93a6be5ae3f7d8af858b43e02c5a2ff083726be40c1 \
+    --hash=sha256:f643f331ab57ba3c9d89212ee4a2dabc6e94f117cf4eefde99a0574720d14c42
+    # via rich
+pympler==1.0.1 \
+    --hash=sha256:993f1a3599ca3f4fcd7160c7545ad06310c9e12f70174ae7ae8d4e25f6c5d3fa \
+    --hash=sha256:d260dda9ae781e1eab6ea15bacb84015849833ba5555f141d2d9b7b7473b307d
+    # via streamlit
+pyparsing==3.0.9 \
+    --hash=sha256:2b020ecf7d21b687f219b71ecad3631f644a47f01403fa1d1036b0c6416d70fb \
+    --hash=sha256:5026bae9a10eeaefb61dab2f09052b9f4307d44aee4eda64b309723d8d206bbc
+    # via
+    #   matplotlib
+    #   packaging
+pyreadline3==3.4.1 \
+    --hash=sha256:6f3d1f7b8a31ba32b73917cefc1f28cc660562f39aea8646d30bd6eff21f7bae \
+    --hash=sha256:b0efb6516fd4fb07b45949053826a62fa4cb353db5be2bbb4a7aa1fdd1e345fb
+    # via -r requirements.in
+pyrsistent==0.19.2 \
+    --hash=sha256:055ab45d5911d7cae397dc418808d8802fb95262751872c841c170b0dbf51eed \
+    --hash=sha256:111156137b2e71f3a9936baf27cb322e8024dac3dc54ec7fb9f0bcf3249e68bb \
+    --hash=sha256:187d5730b0507d9285a96fca9716310d572e5464cadd19f22b63a6976254d77a \
+    --hash=sha256:21455e2b16000440e896ab99e8304617151981ed40c29e9507ef1c2e4314ee95 \
+    --hash=sha256:2aede922a488861de0ad00c7630a6e2d57e8023e4be72d9d7147a9fcd2d30712 \
+    --hash=sha256:3ba4134a3ff0fc7ad225b6b457d1309f4698108fb6b35532d015dca8f5abed73 \
+    --hash=sha256:456cb30ca8bff00596519f2c53e42c245c09e1a4543945703acd4312949bfd41 \
+    --hash=sha256:71d332b0320642b3261e9fee47ab9e65872c2bd90260e5d225dabeed93cbd42b \
+    --hash=sha256:879b4c2f4d41585c42df4d7654ddffff1239dc4065bc88b745f0341828b83e78 \
+    --hash=sha256:9cd3e9978d12b5d99cbdc727a3022da0430ad007dacf33d0bf554b96427f33ab \
+    --hash=sha256:a178209e2df710e3f142cbd05313ba0c5ebed0a55d78d9945ac7a4e09d923308 \
+    --hash=sha256:b39725209e06759217d1ac5fcdb510e98670af9e37223985f330b611f62e7425 \
+    --hash=sha256:bfa0351be89c9fcbcb8c9879b826f4353be10f58f8a677efab0c017bf7137ec2 \
+    --hash=sha256:bfd880614c6237243ff53a0539f1cb26987a6dc8ac6e66e0c5a40617296a045e \
+    --hash=sha256:c43bec251bbd10e3cb58ced80609c5c1eb238da9ca78b964aea410fb820d00d6 \
+    --hash=sha256:d690b18ac4b3e3cab73b0b7aa7dbe65978a172ff94970ff98d82f2031f8971c2 \
+    --hash=sha256:d6982b5a0237e1b7d876b60265564648a69b14017f3b5f908c5be2de3f9abb7a \
+    --hash=sha256:dec3eac7549869365fe263831f576c8457f6c833937c68542d08fde73457d291 \
+    --hash=sha256:e371b844cec09d8dc424d940e54bba8f67a03ebea20ff7b7b0d56f526c71d584 \
+    --hash=sha256:e5d8f84d81e3729c3b506657dddfe46e8ba9c330bf1858ee33108f8bb2adb38a \
+    --hash=sha256:ea6b79a02a28550c98b6ca9c35b9f492beaa54d7c5c9e9949555893c8a9234d0 \
+    --hash=sha256:f1258f4e6c42ad0b20f9cfcc3ada5bd6b83374516cd01c0960e3cb75fdca6770
+    # via jsonschema
+python-dateutil==2.8.2 \
+    --hash=sha256:0123cacc1627ae19ddf3c27a5de5bd67ee4586fbdd6440d9748f8abb483d3e86 \
+    --hash=sha256:961d03dc3453ebbc59dbdea9e4e11c5651520a876d0f4db161e8674aae935da9
+    # via
+    #   matplotlib
+    #   pandas
+    #   streamlit
+python-engineio==4.3.4 \
+    --hash=sha256:7454314a529bba20e745928601ffeaf101c1b5aca9a6c4e48ad397803d10ea0c \
+    --hash=sha256:d8d8b072799c36cadcdcc2b40d2a560ce09797ab3d2d596b2ad519a5e4df19ae
+    # via python-socketio
+python-socketio==5.7.2 \
+    --hash=sha256:92395062d9db3c13d30e7cdedaa0e1330bba78505645db695415f9a3c628d097 \
+    --hash=sha256:d9a9f047e6fdd306c852fbac36516f4b495c2096f8ad9ceb8803b8e5ff5622e3
+    # via flask-socketio
+pytorch-lightning==1.7.7 \
+    --hash=sha256:27c2dd01a18db2415168e3fa3775ccb5a1fa1e2961a50439ad9365507fe9d4ae \
+    --hash=sha256:4438b8284d7f7fdb06cf3566a7b5b6f102ac8971cf7bb6d3f1b1de64628241f3
+    # via taming-transformers-rom1504
+pytz==2022.6 \
+    --hash=sha256:222439474e9c98fced559f1709d89e6c9cbf8d79c794ff3eb9f8800064291427 \
+    --hash=sha256:e89512406b793ca39f5971bc999cc538ce125c0e51c27941bef4568b460095e2
+    # via pandas
+pytz-deprecation-shim==0.1.0.post0 \
+    --hash=sha256:8314c9692a636c8eb3bda879b9f119e350e93223ae83e70e80c31675a0fdc1a6 \
+    --hash=sha256:af097bae1b616dde5c5744441e2ddc69e74dfdcb0c263129610d85b87445a59d
+    # via tzlocal
+pywavelets==1.4.1 \
+    --hash=sha256:030670a213ee8fefa56f6387b0c8e7d970c7f7ad6850dc048bd7c89364771b9b \
+    --hash=sha256:058b46434eac4c04dd89aeef6fa39e4b6496a951d78c500b6641fd5b2cc2f9f4 \
+    --hash=sha256:231b0e0b1cdc1112f4af3c24eea7bf181c418d37922a67670e9bf6cfa2d544d4 \
+    --hash=sha256:23bafd60350b2b868076d976bdd92f950b3944f119b4754b1d7ff22b7acbf6c6 \
+    --hash=sha256:3f19327f2129fb7977bc59b966b4974dfd72879c093e44a7287500a7032695de \
+    --hash=sha256:47cac4fa25bed76a45bc781a293c26ac63e8eaae9eb8f9be961758d22b58649c \
+    --hash=sha256:578af438a02a86b70f1975b546f68aaaf38f28fb082a61ceb799816049ed18aa \
+    --hash=sha256:6437af3ddf083118c26d8f97ab43b0724b956c9f958e9ea788659f6a2834ba93 \
+    --hash=sha256:64c6bac6204327321db30b775060fbe8e8642316e6bff17f06b9f34936f88875 \
+    --hash=sha256:67a0d28a08909f21400cb09ff62ba94c064882ffd9e3a6b27880a111211d59bd \
+    --hash=sha256:71ab30f51ee4470741bb55fc6b197b4a2b612232e30f6ac069106f0156342356 \
+    --hash=sha256:7231461d7a8eb3bdc7aa2d97d9f67ea5a9f8902522818e7e2ead9c2b3408eeb1 \
+    --hash=sha256:754fa5085768227c4f4a26c1e0c78bc509a266d9ebd0eb69a278be7e3ece943c \
+    --hash=sha256:7ab8d9db0fe549ab2ee0bea61f614e658dd2df419d5b75fba47baa761e95f8f2 \
+    --hash=sha256:875d4d620eee655346e3589a16a73790cf9f8917abba062234439b594e706784 \
+    --hash=sha256:88aa5449e109d8f5e7f0adef85f7f73b1ab086102865be64421a3a3d02d277f4 \
+    --hash=sha256:91d3d393cffa634f0e550d88c0e3f217c96cfb9e32781f2960876f1808d9b45b \
+    --hash=sha256:9cb5ca8d11d3f98e89e65796a2125be98424d22e5ada360a0dbabff659fca0fc \
+    --hash=sha256:ab7da0a17822cd2f6545626946d3b82d1a8e106afc4b50e3387719ba01c7b966 \
+    --hash=sha256:ad987748f60418d5f4138db89d82ba0cb49b086e0cbb8fd5c3ed4a814cfb705e \
+    --hash=sha256:d0e56cd7a53aed3cceca91a04d62feb3a0aca6725b1912d29546c26f6ea90426 \
+    --hash=sha256:d854411eb5ee9cb4bc5d0e66e3634aeb8f594210f6a1bed96dbed57ec70f181c \
+    --hash=sha256:da7b9c006171be1f9ddb12cc6e0d3d703b95f7f43cb5e2c6f5f15d3233fcf202 \
+    --hash=sha256:daf0aa79842b571308d7c31a9c43bc99a30b6328e6aea3f50388cd8f69ba7dbc \
+    --hash=sha256:de7cd61a88a982edfec01ea755b0740e94766e00a1ceceeafef3ed4c85c605cd
+    # via scikit-image
+pyyaml==6.0 \
+    --hash=sha256:01b45c0191e6d66c470b6cf1b9531a771a83c1c4208272ead47a3ae4f2f603bf \
+    --hash=sha256:0283c35a6a9fbf047493e3a0ce8d79ef5030852c51e9d911a27badfde0605293 \
+    --hash=sha256:055d937d65826939cb044fc8c9b08889e8c743fdc6a32b33e2390f66013e449b \
+    --hash=sha256:07751360502caac1c067a8132d150cf3d61339af5691fe9e87803040dbc5db57 \
+    --hash=sha256:0b4624f379dab24d3725ffde76559cff63d9ec94e1736b556dacdfebe5ab6d4b \
+    --hash=sha256:0ce82d761c532fe4ec3f87fc45688bdd3a4c1dc5e0b4a19814b9009a29baefd4 \
+    --hash=sha256:1e4747bc279b4f613a09eb64bba2ba602d8a6664c6ce6396a4d0cd413a50ce07 \
+    --hash=sha256:213c60cd50106436cc818accf5baa1aba61c0189ff610f64f4a3e8c6726218ba \
+    --hash=sha256:231710d57adfd809ef5d34183b8ed1eeae3f76459c18fb4a0b373ad56bedcdd9 \
+    --hash=sha256:277a0ef2981ca40581a47093e9e2d13b3f1fbbeffae064c1d21bfceba2030287 \
+    --hash=sha256:2cd5df3de48857ed0544b34e2d40e9fac445930039f3cfe4bcc592a1f836d513 \
+    --hash=sha256:40527857252b61eacd1d9af500c3337ba8deb8fc298940291486c465c8b46ec0 \
+    --hash=sha256:432557aa2c09802be39460360ddffd48156e30721f5e8d917f01d31694216782 \
+    --hash=sha256:473f9edb243cb1935ab5a084eb238d842fb8f404ed2193a915d1784b5a6b5fc0 \
+    --hash=sha256:48c346915c114f5fdb3ead70312bd042a953a8ce5c7106d5bfb1a5254e47da92 \
+    --hash=sha256:50602afada6d6cbfad699b0c7bb50d5ccffa7e46a3d738092afddc1f9758427f \
+    --hash=sha256:68fb519c14306fec9720a2a5b45bc9f0c8d1b9c72adf45c37baedfcd949c35a2 \
+    --hash=sha256:77f396e6ef4c73fdc33a9157446466f1cff553d979bd00ecb64385760c6babdc \
+    --hash=sha256:81957921f441d50af23654aa6c5e5eaf9b06aba7f0a19c18a538dc7ef291c5a1 \
+    --hash=sha256:819b3830a1543db06c4d4b865e70ded25be52a2e0631ccd2f6a47a2822f2fd7c \
+    --hash=sha256:897b80890765f037df3403d22bab41627ca8811ae55e9a722fd0392850ec4d86 \
+    --hash=sha256:98c4d36e99714e55cfbaaee6dd5badbc9a1ec339ebfc3b1f52e293aee6bb71a4 \
+    --hash=sha256:9df7ed3b3d2e0ecfe09e14741b857df43adb5a3ddadc919a2d94fbdf78fea53c \
+    --hash=sha256:9fa600030013c4de8165339db93d182b9431076eb98eb40ee068700c9c813e34 \
+    --hash=sha256:a80a78046a72361de73f8f395f1f1e49f956c6be882eed58505a15f3e430962b \
+    --hash=sha256:afa17f5bc4d1b10afd4466fd3a44dc0e245382deca5b3c353d8b757f9e3ecb8d \
+    --hash=sha256:b3d267842bf12586ba6c734f89d1f5b871df0273157918b0ccefa29deb05c21c \
+    --hash=sha256:b5b9eccad747aabaaffbc6064800670f0c297e52c12754eb1d976c57e4f74dcb \
+    --hash=sha256:bfaef573a63ba8923503d27530362590ff4f576c626d86a9fed95822a8255fd7 \
+    --hash=sha256:c5687b8d43cf58545ade1fe3e055f70eac7a5a1a0bf42824308d868289a95737 \
+    --hash=sha256:cba8c411ef271aa037d7357a2bc8f9ee8b58b9965831d9e51baf703280dc73d3 \
+    --hash=sha256:d15a181d1ecd0d4270dc32edb46f7cb7733c7c508857278d3d378d14d606db2d \
+    --hash=sha256:d4b0ba9512519522b118090257be113b9468d804b19d63c71dbcf4a48fa32358 \
+    --hash=sha256:d4db7c7aef085872ef65a8fd7d6d09a14ae91f691dec3e87ee5ee0539d516f53 \
+    --hash=sha256:d4eccecf9adf6fbcc6861a38015c2a64f38b9d94838ac1810a9023a0609e1b78 \
+    --hash=sha256:d67d839ede4ed1b28a4e8909735fc992a923cdb84e618544973d7dfc71540803 \
+    --hash=sha256:daf496c58a8c52083df09b80c860005194014c3698698d1a57cbcfa182142a3a \
+    --hash=sha256:dbad0e9d368bb989f4515da330b88a057617d16b6a8245084f1b05400f24609f \
+    --hash=sha256:e61ceaab6f49fb8bdfaa0f92c4b57bcfbea54c09277b1b4f7ac376bfb7a7c174 \
+    --hash=sha256:f84fbc98b019fef2ee9a1cb3ce93e3187a6df0b2538a651bfb890254ba9f90b5
+    # via
+    #   accelerate
+    #   albumentations
+    #   basicsr
+    #   gfpgan
+    #   huggingface-hub
+    #   omegaconf
+    #   pytorch-lightning
+    #   transformers
+    #   wandb
+qudida==0.0.4 \
+    --hash=sha256:4519714c40cd0f2e6c51e1735edae8f8b19f4efe1f33be13e9d644ca5f736dd6 \
+    --hash=sha256:db198e2887ab0c9aa0023e565afbff41dfb76b361f85fd5e13f780d75ba18cc8
+    # via albumentations
+realesrgan==0.3.0 \
+    --hash=sha256:0d36da96ab9f447071606e91f502ccdfb08f80cc82ee4f8caf720c7745ccec7e \
+    --hash=sha256:59336c16c30dd5130eff350dd27424acb9b7281d18a6810130e265606c9a6088
+    # via -r requirements.in
+regex==2022.10.31 \
+    --hash=sha256:052b670fafbe30966bbe5d025e90b2a491f85dfe5b2583a163b5e60a85a321ad \
+    --hash=sha256:0653d012b3bf45f194e5e6a41df9258811ac8fc395579fa82958a8b76286bea4 \
+    --hash=sha256:0a069c8483466806ab94ea9068c34b200b8bfc66b6762f45a831c4baaa9e8cdd \
+    --hash=sha256:0cf0da36a212978be2c2e2e2d04bdff46f850108fccc1851332bcae51c8907cc \
+    --hash=sha256:131d4be09bea7ce2577f9623e415cab287a3c8e0624f778c1d955ec7c281bd4d \
+    --hash=sha256:144486e029793a733e43b2e37df16a16df4ceb62102636ff3db6033994711066 \
+    --hash=sha256:1ddf14031a3882f684b8642cb74eea3af93a2be68893901b2b387c5fd92a03ec \
+    --hash=sha256:1eba476b1b242620c266edf6325b443a2e22b633217a9835a52d8da2b5c051f9 \
+    --hash=sha256:20f61c9944f0be2dc2b75689ba409938c14876c19d02f7585af4460b6a21403e \
+    --hash=sha256:22960019a842777a9fa5134c2364efaed5fbf9610ddc5c904bd3a400973b0eb8 \
+    --hash=sha256:22e7ebc231d28393dfdc19b185d97e14a0f178bedd78e85aad660e93b646604e \
+    --hash=sha256:23cbb932cc53a86ebde0fb72e7e645f9a5eec1a5af7aa9ce333e46286caef783 \
+    --hash=sha256:29c04741b9ae13d1e94cf93fca257730b97ce6ea64cfe1eba11cf9ac4e85afb6 \
+    --hash=sha256:2bde29cc44fa81c0a0c8686992c3080b37c488df167a371500b2a43ce9f026d1 \
+    --hash=sha256:2cdc55ca07b4e70dda898d2ab7150ecf17c990076d3acd7a5f3b25cb23a69f1c \
+    --hash=sha256:370f6e97d02bf2dd20d7468ce4f38e173a124e769762d00beadec3bc2f4b3bc4 \
+    --hash=sha256:395161bbdbd04a8333b9ff9763a05e9ceb4fe210e3c7690f5e68cedd3d65d8e1 \
+    --hash=sha256:44136355e2f5e06bf6b23d337a75386371ba742ffa771440b85bed367c1318d1 \
+    --hash=sha256:44a6c2f6374e0033873e9ed577a54a3602b4f609867794c1a3ebba65e4c93ee7 \
+    --hash=sha256:4919899577ba37f505aaebdf6e7dc812d55e8f097331312db7f1aab18767cce8 \
+    --hash=sha256:4b4b1fe58cd102d75ef0552cf17242705ce0759f9695334a56644ad2d83903fe \
+    --hash=sha256:4bdd56ee719a8f751cf5a593476a441c4e56c9b64dc1f0f30902858c4ef8771d \
+    --hash=sha256:4bf41b8b0a80708f7e0384519795e80dcb44d7199a35d52c15cc674d10b3081b \
+    --hash=sha256:4cac3405d8dda8bc6ed499557625585544dd5cbf32072dcc72b5a176cb1271c8 \
+    --hash=sha256:4fe7fda2fe7c8890d454f2cbc91d6c01baf206fbc96d89a80241a02985118c0c \
+    --hash=sha256:50921c140561d3db2ab9f5b11c5184846cde686bb5a9dc64cae442926e86f3af \
+    --hash=sha256:5217c25229b6a85049416a5c1e6451e9060a1edcf988641e309dbe3ab26d3e49 \
+    --hash=sha256:5352bea8a8f84b89d45ccc503f390a6be77917932b1c98c4cdc3565137acc714 \
+    --hash=sha256:542e3e306d1669b25936b64917285cdffcd4f5c6f0247636fec037187bd93542 \
+    --hash=sha256:543883e3496c8b6d58bd036c99486c3c8387c2fc01f7a342b760c1ea3158a318 \
+    --hash=sha256:586b36ebda81e6c1a9c5a5d0bfdc236399ba6595e1397842fd4a45648c30f35e \
+    --hash=sha256:597f899f4ed42a38df7b0e46714880fb4e19a25c2f66e5c908805466721760f5 \
+    --hash=sha256:5a260758454580f11dd8743fa98319bb046037dfab4f7828008909d0aa5292bc \
+    --hash=sha256:5aefb84a301327ad115e9d346c8e2760009131d9d4b4c6b213648d02e2abe144 \
+    --hash=sha256:5e6a5567078b3eaed93558842346c9d678e116ab0135e22eb72db8325e90b453 \
+    --hash=sha256:5ff525698de226c0ca743bfa71fc6b378cda2ddcf0d22d7c37b1cc925c9650a5 \
+    --hash=sha256:61edbca89aa3f5ef7ecac8c23d975fe7261c12665f1d90a6b1af527bba86ce61 \
+    --hash=sha256:659175b2144d199560d99a8d13b2228b85e6019b6e09e556209dfb8c37b78a11 \
+    --hash=sha256:6a9a19bea8495bb419dc5d38c4519567781cd8d571c72efc6aa959473d10221a \
+    --hash=sha256:6b30bddd61d2a3261f025ad0f9ee2586988c6a00c780a2fb0a92cea2aa702c54 \
+    --hash=sha256:6ffd55b5aedc6f25fd8d9f905c9376ca44fcf768673ffb9d160dd6f409bfda73 \
+    --hash=sha256:702d8fc6f25bbf412ee706bd73019da5e44a8400861dfff7ff31eb5b4a1276dc \
+    --hash=sha256:74bcab50a13960f2a610cdcd066e25f1fd59e23b69637c92ad470784a51b1347 \
+    --hash=sha256:75f591b2055523fc02a4bbe598aa867df9e953255f0b7f7715d2a36a9c30065c \
+    --hash=sha256:763b64853b0a8f4f9cfb41a76a4a85a9bcda7fdda5cb057016e7706fde928e66 \
+    --hash=sha256:76c598ca73ec73a2f568e2a72ba46c3b6c8690ad9a07092b18e48ceb936e9f0c \
+    --hash=sha256:78d680ef3e4d405f36f0d6d1ea54e740366f061645930072d39bca16a10d8c93 \
+    --hash=sha256:7b280948d00bd3973c1998f92e22aa3ecb76682e3a4255f33e1020bd32adf443 \
+    --hash=sha256:7db345956ecce0c99b97b042b4ca7326feeec6b75facd8390af73b18e2650ffc \
+    --hash=sha256:7dbdce0c534bbf52274b94768b3498abdf675a691fec5f751b6057b3030f34c1 \
+    --hash=sha256:7ef6b5942e6bfc5706301a18a62300c60db9af7f6368042227ccb7eeb22d0892 \
+    --hash=sha256:7f5a3ffc731494f1a57bd91c47dc483a1e10048131ffb52d901bfe2beb6102e8 \
+    --hash=sha256:8a45b6514861916c429e6059a55cf7db74670eaed2052a648e3e4d04f070e001 \
+    --hash=sha256:8ad241da7fac963d7573cc67a064c57c58766b62a9a20c452ca1f21050868dfa \
+    --hash=sha256:8b0886885f7323beea6f552c28bff62cbe0983b9fbb94126531693ea6c5ebb90 \
+    --hash=sha256:8ca88da1bd78990b536c4a7765f719803eb4f8f9971cc22d6ca965c10a7f2c4c \
+    --hash=sha256:8e0caeff18b96ea90fc0eb6e3bdb2b10ab5b01a95128dfeccb64a7238decf5f0 \
+    --hash=sha256:957403a978e10fb3ca42572a23e6f7badff39aa1ce2f4ade68ee452dc6807692 \
+    --hash=sha256:9af69f6746120998cd9c355e9c3c6aec7dff70d47247188feb4f829502be8ab4 \
+    --hash=sha256:9c94f7cc91ab16b36ba5ce476f1904c91d6c92441f01cd61a8e2729442d6fcf5 \
+    --hash=sha256:a37d51fa9a00d265cf73f3de3930fa9c41548177ba4f0faf76e61d512c774690 \
+    --hash=sha256:a3a98921da9a1bf8457aeee6a551948a83601689e5ecdd736894ea9bbec77e83 \
+    --hash=sha256:a3c1ebd4ed8e76e886507c9eddb1a891673686c813adf889b864a17fafcf6d66 \
+    --hash=sha256:a5f9505efd574d1e5b4a76ac9dd92a12acb2b309551e9aa874c13c11caefbe4f \
+    --hash=sha256:a8ff454ef0bb061e37df03557afda9d785c905dab15584860f982e88be73015f \
+    --hash=sha256:a9d0b68ac1743964755ae2d89772c7e6fb0118acd4d0b7464eaf3921c6b49dd4 \
+    --hash=sha256:aa62a07ac93b7cb6b7d0389d8ef57ffc321d78f60c037b19dfa78d6b17c928ee \
+    --hash=sha256:ac741bf78b9bb432e2d314439275235f41656e189856b11fb4e774d9f7246d81 \
+    --hash=sha256:ae1e96785696b543394a4e3f15f3f225d44f3c55dafe3f206493031419fedf95 \
+    --hash=sha256:b683e5fd7f74fb66e89a1ed16076dbab3f8e9f34c18b1979ded614fe10cdc4d9 \
+    --hash=sha256:b7a8b43ee64ca8f4befa2bea4083f7c52c92864d8518244bfa6e88c751fa8fff \
+    --hash=sha256:b8e38472739028e5f2c3a4aded0ab7eadc447f0d84f310c7a8bb697ec417229e \
+    --hash=sha256:bfff48c7bd23c6e2aec6454aaf6edc44444b229e94743b34bdcdda2e35126cf5 \
+    --hash=sha256:c14b63c9d7bab795d17392c7c1f9aaabbffd4cf4387725a0ac69109fb3b550c6 \
+    --hash=sha256:c27cc1e4b197092e50ddbf0118c788d9977f3f8f35bfbbd3e76c1846a3443df7 \
+    --hash=sha256:c28d3309ebd6d6b2cf82969b5179bed5fefe6142c70f354ece94324fa11bf6a1 \
+    --hash=sha256:c670f4773f2f6f1957ff8a3962c7dd12e4be54d05839b216cb7fd70b5a1df394 \
+    --hash=sha256:ce6910b56b700bea7be82c54ddf2e0ed792a577dfaa4a76b9af07d550af435c6 \
+    --hash=sha256:d0213671691e341f6849bf33cd9fad21f7b1cb88b89e024f33370733fec58742 \
+    --hash=sha256:d03fe67b2325cb3f09be029fd5da8df9e6974f0cde2c2ac6a79d2634e791dd57 \
+    --hash=sha256:d0e5af9a9effb88535a472e19169e09ce750c3d442fb222254a276d77808620b \
+    --hash=sha256:d243b36fbf3d73c25e48014961e83c19c9cc92530516ce3c43050ea6276a2ab7 \
+    --hash=sha256:d26166acf62f731f50bdd885b04b38828436d74e8e362bfcb8df221d868b5d9b \
+    --hash=sha256:d403d781b0e06d2922435ce3b8d2376579f0c217ae491e273bab8d092727d244 \
+    --hash=sha256:d8716f82502997b3d0895d1c64c3b834181b1eaca28f3f6336a71777e437c2af \
+    --hash=sha256:e4f781ffedd17b0b834c8731b75cce2639d5a8afe961c1e58ee7f1f20b3af185 \
+    --hash=sha256:e613a98ead2005c4ce037c7b061f2409a1a4e45099edb0ef3200ee26ed2a69a8 \
+    --hash=sha256:ef4163770525257876f10e8ece1cf25b71468316f61451ded1a6f44273eedeb5
+    # via
+    #   clip
+    #   diffusers
+    #   transformers
+requests==2.25.1 \
+    --hash=sha256:27973dd4a904a4f13b263a19c866c13b92a39ed1c964655f025f3f8d3d75b804 \
+    --hash=sha256:c210084e36a42ae6b9219e00e48287def368a26d03a048ddad7bfee44f75871e
+    # via
+    #   basicsr
+    #   clean-fid
+    #   diffusers
+    #   fsspec
+    #   huggingface-hub
+    #   requests-oauthlib
+    #   streamlit
+    #   tb-nightly
+    #   tensorboard
+    #   torchvision
+    #   transformers
+    #   wandb
+requests-oauthlib==1.3.1 \
+    --hash=sha256:2577c501a2fb8d05a304c09d090d6e47c306fef15809d102b327cf8364bddab5 \
+    --hash=sha256:75beac4a47881eeb94d5ea5d6ad31ef88856affe2332b9aafb52c6452ccf0d7a
+    # via google-auth-oauthlib
+resize-right==0.0.2 \
+    --hash=sha256:78351ca3eda0872208fcbc90861b45de559f90fb4027ce41825fdeb9b995005c \
+    --hash=sha256:7dc35b72ce4012b77f7cc9049835163793ab98a58ab8893610fb119fe59af520
+    # via k-diffusion
+rich==12.6.0 \
+    --hash=sha256:a4eb26484f2c82589bd9a17c73d32a010b1e29d89f1604cd9bf3a2097b81bb5e \
+    --hash=sha256:ba3a3775974105c221d31141f2c116f4fd65c5ceb0698657a11e9f295ec93fd0
+    # via streamlit
+rsa==4.9 \
+    --hash=sha256:90260d9058e514786967344d0ef75fa8727eed8a7d2e43ce9f4bcf1b536174f7 \
+    --hash=sha256:e38464a49c6c85d7f1351b0126661487a7e0a14a50f1675ec50eb34d4f20ef21
+    # via google-auth
+scikit-image==0.19.3 \
+    --hash=sha256:03779a7e1736fdf89d83c0ba67d44110496edd736a3bfce61a2b5177a1c8a099 \
+    --hash=sha256:0b0a199157ce8487c77de4fde0edc0b42d6d42818881c11f459262351d678b2d \
+    --hash=sha256:19a21a101a20c587a3b611a2cf6f86c35aae9f8d9563279b987e83ee1c9a9790 \
+    --hash=sha256:24b5367de1762da6ee126dd8f30cc4e7efda474e0d7d70685433f0e3aa2ec450 \
+    --hash=sha256:2a02d1bd0e2b53e36b952bd5fd6118d9ccc3ee51de35705d63d8eb1f2e86adef \
+    --hash=sha256:2f50b923f8099c1045fcde7418d86b206c87e333e43da980f41d8577b9605245 \
+    --hash=sha256:32fb88cc36203b99c9672fb972c9ef98635deaa5fc889fe969f3e11c44f22919 \
+    --hash=sha256:33dfd463ee6cc509defa279b963829f2230c9e0639ccd3931045be055878eea6 \
+    --hash=sha256:3a01372ae4bca223873304b0bff79b9d92446ac6d6177f73d89b45561e2d09d8 \
+    --hash=sha256:651de1c2ce1fbee834753b46b8e7d81cb12a5594898babba63ac82b30ddad49d \
+    --hash=sha256:6b6a8f98f2ac9bb73706461fd1dec875f6a5141759ed526850a5a49e90003d19 \
+    --hash=sha256:7f9f8a1387afc6c70f2bed007c3854a2d7489f9f7713c242f16f32ee05934bc2 \
+    --hash=sha256:84baa3179f3ae983c3a5d81c1e404bc92dcf7daeb41bfe9369badcda3fb22b92 \
+    --hash=sha256:8d8917fcf85b987b1f287f823f3a1a7dac38b70aaca759bc0200f3bc292d5ced \
+    --hash=sha256:9439e5294de3f18d6e82ec8eee2c46590231cf9c690da80545e83a0733b7a69e \
+    --hash=sha256:9fb0923a3bfa99457c5e17888f27b3b8a83a3600b4fef317992e7b7234764732 \
+    --hash=sha256:a7c3985c68bfe05f7571167ee021d14f5b8d1a4a250c91f0b13be7fb07e6af34 \
+    --hash=sha256:a8714348ddd671f819457a797c97d4c672166f093def66d66c3254cbd1d43f83 \
+    --hash=sha256:ad5d8000207a264d1a55681a9276e6a739d3f05cf4429004ad00d61d1892235f \
+    --hash=sha256:cc24177de3fdceca5d04807ad9c87d665f0bf01032ed94a9055cd1ed2b3f33e9 \
+    --hash=sha256:ce3d2207f253b8eb2c824e30d145a9f07a34a14212d57f3beca9f7e03c383cbe \
+    --hash=sha256:cfbb073f23deb48e0e60c47f8741d8089121d89cc78629ea8c5b51096efc5be7 \
+    --hash=sha256:e207c6ce5ce121d7d9b9d2b61b9adca57d1abed112c902d8ffbfdc20fb42c12b \
+    --hash=sha256:fd9dd3994bb6f9f7a35f228323f3c4dc44b3cf2ff15fd72d895216e9333550c6 \
+    --hash=sha256:fdf48d9b1f13af69e4e2c78e05067e322e9c8c97463c315cd0ecb47a94e259fc \
+    --hash=sha256:ff3b1025356508d41f4fe48528e509d95f9e4015e90cf158cd58c56dc63e0ac5
+    # via
+    #   albumentations
+    #   basicsr
+    #   k-diffusion
+scikit-learn==1.1.3 \
+    --hash=sha256:23fb9e74b813cc2528b5167d82ed08950b11106ccf50297161875e45152fb311 \
+    --hash=sha256:250da993701da88bf475e7c5746abf1285ea0ae47e4d0917cd13afd6600bb162 \
+    --hash=sha256:28b2bd6a1419acd522ff45d282c8ba23dbccb5338802ab0ee12baa4ade0aba4c \
+    --hash=sha256:2ee2c649f2231b68511aabb0dc827edd8936aad682acc6263c34aed11bc95dac \
+    --hash=sha256:30e27721adc308e8fd9f419f43068e43490005f911edf4476a9e585059fa8a83 \
+    --hash=sha256:38814f66285318f2e241305cca545eaa9b4126c65aa5dd78c69371f235f78e2b \
+    --hash=sha256:4d3a19166d4e1cdfcab975c68f471e046ce01e74c42a9a33fa89a14c2fcedf60 \
+    --hash=sha256:5699cded6c0685426433c7e5afe0fecad80ec831ec7fa264940e50c796775cc5 \
+    --hash=sha256:6785b8a3093329bf90ac01801be5525551728ae73edb11baa175df660820add4 \
+    --hash=sha256:6d1c1394e38a3319ace620381f6f23cc807d8780e9915c152449a86fc8f1db21 \
+    --hash=sha256:701181792a28c82fecae12adb5d15d0ecf57bffab7cf4bdbb52c7b3fd428d540 \
+    --hash=sha256:748f2bd632d6993e8918d43f1a26c380aeda4e122a88840d4c3a9af99d4239fe \
+    --hash=sha256:8e9dd76c7274055d1acf4526b8efb16a3531c26dcda714a0c16da99bf9d41900 \
+    --hash=sha256:bef51978a51ec19977700fe7b86aecea49c825884f3811756b74a3b152bb4e35 \
+    --hash=sha256:cd55c6fbef7608dbce1f22baf289dfcc6eb323247daa3c3542f73d389c724786 \
+    --hash=sha256:da5a2e95fef9805b1750e4abda4e834bf8835d26fc709a391543b53feee7bd0e \
+    --hash=sha256:ee47f68d973cee7009f06edb956f2f5588a0f230f24a2a70175fd0ecf36e2653 \
+    --hash=sha256:f4931f2a6c06e02c6c17a05f8ae397e2545965bc7a0a6cb38c8cd7d4fba8624d \
+    --hash=sha256:f5644663987ee221f5d1f47a593271b966c271c236fe05634e6bdc06041b5a2b \
+    --hash=sha256:f5d4231af7199531e77da1b78a4cc6b3d960a00b1ec672578ac818aae2b9c35d \
+    --hash=sha256:fd3ee69d36d42a7dcbb17e355a5653af5fd241a7dfd9133080b3dde8d9e2aafb
+    # via qudida
+scipy==1.9.3 \
+    --hash=sha256:06d2e1b4c491dc7d8eacea139a1b0b295f74e1a1a0f704c375028f8320d16e31 \
+    --hash=sha256:0d54222d7a3ba6022fdf5773931b5d7c56efe41ede7f7128c7b1637700409108 \
+    --hash=sha256:1884b66a54887e21addf9c16fb588720a8309a57b2e258ae1c7986d4444d3bc0 \
+    --hash=sha256:1a72d885fa44247f92743fc20732ae55564ff2a519e8302fb7e18717c5355a8b \
+    --hash=sha256:2318bef588acc7a574f5bfdff9c172d0b1bf2c8143d9582e05f878e580a3781e \
+    --hash=sha256:4db5b30849606a95dcf519763dd3ab6fe9bd91df49eba517359e450a7d80ce2e \
+    --hash=sha256:545c83ffb518094d8c9d83cce216c0c32f8c04aaf28b92cc8283eda0685162d5 \
+    --hash=sha256:5a04cd7d0d3eff6ea4719371cbc44df31411862b9646db617c99718ff68d4840 \
+    --hash=sha256:5b88e6d91ad9d59478fafe92a7c757d00c59e3bdc3331be8ada76a4f8d683f58 \
+    --hash=sha256:68239b6aa6f9c593da8be1509a05cb7f9efe98b80f43a5861cd24c7557e98523 \
+    --hash=sha256:83b89e9586c62e787f5012e8475fbb12185bafb996a03257e9675cd73d3736dd \
+    --hash=sha256:83c06e62a390a9167da60bedd4575a14c1f58ca9dfde59830fc42e5197283dab \
+    --hash=sha256:90453d2b93ea82a9f434e4e1cba043e779ff67b92f7a0e85d05d286a3625df3c \
+    --hash=sha256:abaf921531b5aeaafced90157db505e10345e45038c39e5d9b6c7922d68085cb \
+    --hash=sha256:b41bc822679ad1c9a5f023bc93f6d0543129ca0f37c1ce294dd9d386f0a21096 \
+    --hash=sha256:c68db6b290cbd4049012990d7fe71a2abd9ffbe82c0056ebe0f01df8be5436b0 \
+    --hash=sha256:cff3a5295234037e39500d35316a4c5794739433528310e117b8a9a0c76d20fc \
+    --hash=sha256:d01e1dd7b15bd2449c8bfc6b7cc67d630700ed655654f0dfcf121600bad205c9 \
+    --hash=sha256:d644a64e174c16cb4b2e41dfea6af722053e83d066da7343f333a54dae9bc31c \
+    --hash=sha256:da8245491d73ed0a994ed9c2e380fd058ce2fa8a18da204681f2fe1f57f98f95 \
+    --hash=sha256:fbc5c05c85c1a02be77b1ff591087c83bc44579c6d2bd9fb798bb64ea5e1a027
+    # via
+    #   albumentations
+    #   basicsr
+    #   clean-fid
+    #   facexlib
+    #   filterpy
+    #   gfpgan
+    #   k-diffusion
+    #   scikit-image
+    #   scikit-learn
+    #   torch-fidelity
+    #   torchdiffeq
+semver==2.13.0 \
+    --hash=sha256:ced8b23dceb22134307c1b8abfa523da14198793d9787ac838e70e29e77458d4 \
+    --hash=sha256:fa0fe2722ee1c3f57eac478820c3a5ae2f624af8264cbdf9000c980ff7f75e3f
+    # via streamlit
+send2trash==1.8.0 \
+    --hash=sha256:d2c24762fd3759860a0aff155e45871447ea58d2be6bdd39b5c8f966a0c99c2d \
+    --hash=sha256:f20eaadfdb517eaca5ce077640cb261c7d2698385a6a0f072a4a5447fd49fa08
+    # via -r requirements.in
+sentry-sdk==1.10.1 \
+    --hash=sha256:06c0fa9ccfdc80d7e3b5d2021978d6eb9351fa49db9b5847cf4d1f2a473414ad \
+    --hash=sha256:105faf7bd7b7fa25653404619ee261527266b14103fe1389e0ce077bd23a9691
+    # via wandb
+setproctitle==1.3.2 \
+    --hash=sha256:1c5d5dad7c28bdd1ec4187d818e43796f58a845aa892bb4481587010dc4d362b \
+    --hash=sha256:1c8d9650154afaa86a44ff195b7b10d683c73509d085339d174e394a22cccbb9 \
+    --hash=sha256:1f0cde41857a644b7353a0060b5f94f7ba7cf593ebde5a1094da1be581ac9a31 \
+    --hash=sha256:1f29b75e86260b0ab59adb12661ef9f113d2f93a59951373eb6d68a852b13e83 \
+    --hash=sha256:1fa1a0fbee72b47dc339c87c890d3c03a72ea65c061ade3204f285582f2da30f \
+    --hash=sha256:1ff863a20d1ff6ba2c24e22436a3daa3cd80be1dfb26891aae73f61b54b04aca \
+    --hash=sha256:265ecbe2c6eafe82e104f994ddd7c811520acdd0647b73f65c24f51374cf9494 \
+    --hash=sha256:288943dec88e178bb2fd868adf491197cc0fc8b6810416b1c6775e686bab87fe \
+    --hash=sha256:2e3ac25bfc4a0f29d2409650c7532d5ddfdbf29f16f8a256fc31c47d0dc05172 \
+    --hash=sha256:2fbd8187948284293f43533c150cd69a0e4192c83c377da837dbcd29f6b83084 \
+    --hash=sha256:4058564195b975ddc3f0462375c533cce310ccdd41b80ac9aed641c296c3eff4 \
+    --hash=sha256:4749a2b0c9ac52f864d13cee94546606f92b981b50e46226f7f830a56a9dc8e1 \
+    --hash=sha256:4d8938249a7cea45ab7e1e48b77685d0f2bab1ebfa9dde23e94ab97968996a7c \
+    --hash=sha256:5194b4969f82ea842a4f6af2f82cd16ebdc3f1771fb2771796e6add9835c1973 \
+    --hash=sha256:55ce1e9925ce1765865442ede9dca0ba9bde10593fcd570b1f0fa25d3ec6b31c \
+    --hash=sha256:589be87172b238f839e19f146b9ea47c71e413e951ef0dc6db4218ddacf3c202 \
+    --hash=sha256:5b932c3041aa924163f4aab970c2f0e6b4d9d773f4d50326e0ea1cd69240e5c5 \
+    --hash=sha256:5fb4f769c02f63fac90989711a3fee83919f47ae9afd4758ced5d86596318c65 \
+    --hash=sha256:630f6fe5e24a619ccf970c78e084319ee8be5be253ecc9b5b216b0f474f5ef18 \
+    --hash=sha256:65d884e22037b23fa25b2baf1a3316602ed5c5971eb3e9d771a38c3a69ce6e13 \
+    --hash=sha256:6c877691b90026670e5a70adfbcc735460a9f4c274d35ec5e8a43ce3f8443005 \
+    --hash=sha256:710e16fa3bade3b026907e4a5e841124983620046166f355bbb84be364bf2a02 \
+    --hash=sha256:7a55fe05f15c10e8c705038777656fe45e3bd676d49ad9ac8370b75c66dd7cd7 \
+    --hash=sha256:7aa0aac1711fadffc1d51e9d00a3bea61f68443d6ac0241a224e4d622489d665 \
+    --hash=sha256:7f0bed90a216ef28b9d227d8d73e28a8c9b88c0f48a082d13ab3fa83c581488f \
+    --hash=sha256:7f2719a398e1a2c01c2a63bf30377a34d0b6ef61946ab9cf4d550733af8f1ef1 \
+    --hash=sha256:7fe9df7aeb8c64db6c34fc3b13271a363475d77bc157d3f00275a53910cb1989 \
+    --hash=sha256:8ff3c8cb26afaed25e8bca7b9dd0c1e36de71f35a3a0706b5c0d5172587a3827 \
+    --hash=sha256:9124bedd8006b0e04d4e8a71a0945da9b67e7a4ab88fdad7b1440dc5b6122c42 \
+    --hash=sha256:92c626edc66169a1b09e9541b9c0c9f10488447d8a2b1d87c8f0672e771bc927 \
+    --hash=sha256:a149a5f7f2c5a065d4e63cb0d7a4b6d3b66e6e80f12e3f8827c4f63974cbf122 \
+    --hash=sha256:a47d97a75fd2d10c37410b180f67a5835cb1d8fdea2648fd7f359d4277f180b9 \
+    --hash=sha256:a499fff50387c1520c085a07578a000123f519e5f3eee61dd68e1d301659651f \
+    --hash=sha256:ab45146c71ca6592c9cc8b354a2cc9cc4843c33efcbe1d245d7d37ce9696552d \
+    --hash=sha256:b2c9cb2705fc84cb8798f1ba74194f4c080aaef19d9dae843591c09b97678e98 \
+    --hash=sha256:b34baef93bfb20a8ecb930e395ccd2ae3268050d8cf4fe187de5e2bd806fd796 \
+    --hash=sha256:b617f12c9be61e8f4b2857be4a4319754756845dbbbd9c3718f468bbb1e17bcb \
+    --hash=sha256:b9fb97907c830d260fa0658ed58afd48a86b2b88aac521135c352ff7fd3477fd \
+    --hash=sha256:bae283e85fc084b18ffeb92e061ff7ac5af9e183c9d1345c93e178c3e5069cbe \
+    --hash=sha256:c2c46200656280a064073447ebd363937562debef329482fd7e570c8d498f806 \
+    --hash=sha256:c8a09d570b39517de10ee5b718730e171251ce63bbb890c430c725c8c53d4484 \
+    --hash=sha256:c91b9bc8985d00239f7dc08a49927a7ca1ca8a6af2c3890feec3ed9665b6f91e \
+    --hash=sha256:dad42e676c5261eb50fdb16bdf3e2771cf8f99a79ef69ba88729aeb3472d8575 \
+    --hash=sha256:de3a540cd1817ede31f530d20e6a4935bbc1b145fd8f8cf393903b1e02f1ae76 \
+    --hash=sha256:e00c9d5c541a2713ba0e657e0303bf96ddddc412ef4761676adc35df35d7c246 \
+    --hash=sha256:e1aafc91cbdacc9e5fe712c52077369168e6b6c346f3a9d51bf600b53eae56bb \
+    --hash=sha256:e425be62524dc0c593985da794ee73eb8a17abb10fe692ee43bb39e201d7a099 \
+    --hash=sha256:e43f315c68aa61cbdef522a2272c5a5b9b8fd03c301d3167b5e1343ef50c676c \
+    --hash=sha256:e49ae693306d7624015f31cb3e82708916759d592c2e5f72a35c8f4cc8aef258 \
+    --hash=sha256:e5c50e164cd2459bc5137c15288a9ef57160fd5cbf293265ea3c45efe7870865 \
+    --hash=sha256:e8579a43eafd246e285eb3a5b939e7158073d5087aacdd2308f23200eac2458b \
+    --hash=sha256:e85e50b9c67854f89635a86247412f3ad66b132a4d8534ac017547197c88f27d \
+    --hash=sha256:f0452282258dfcc01697026a8841258dd2057c4438b43914b611bccbcd048f10 \
+    --hash=sha256:f4bfc89bd33ebb8e4c0e9846a09b1f5a4a86f5cb7a317e75cc42fee1131b4f4f \
+    --hash=sha256:fa2f50678f04fda7a75d0fe5dd02bbdd3b13cbe6ed4cf626e4472a7ccf47ae94 \
+    --hash=sha256:faec934cfe5fd6ac1151c02e67156c3f526e82f96b24d550b5d51efa4a5527c6 \
+    --hash=sha256:fcd3cf4286a60fdc95451d8d14e0389a6b4f5cebe02c7f2609325eb016535963 \
+    --hash=sha256:fe8a988c7220c002c45347430993830666e55bc350179d91fcee0feafe64e1d4 \
+    --hash=sha256:fed18e44711c5af4b681c2b3b18f85e6f0f1b2370a28854c645d636d5305ccd8 \
+    --hash=sha256:ffc61a388a5834a97953d6444a2888c24a05f2e333f9ed49f977a87bb1ad4761
+    # via wandb
+shortuuid==1.0.9 \
+    --hash=sha256:459f12fa1acc34ff213b1371467c0325169645a31ed989e268872339af7563d5 \
+    --hash=sha256:b2bb9eb7773170e253bb7ba25971023acb473517a8b76803d9618668cb1dd46f
+    # via wandb
+six==1.16.0 \
+    --hash=sha256:1e61c37477a1626458e36f7b1d82aa5c9b094fa4802892072e49de9c60c4c926 \
+    --hash=sha256:8abb2f1d86890a2dfb989f9a77cfcfd3e47c2a354b01111771326f8aa26e0254
+    # via
+    #   docker-pycreds
+    #   eventlet
+    #   flask-cors
+    #   google-auth
+    #   grpcio
+    #   promise
+    #   python-dateutil
+    #   validators
+    #   wandb
+smmap==5.0.0 \
+    --hash=sha256:2aba19d6a040e78d8b09de5c57e96207b09ed71d8e55ce0959eeee6c8e190d94 \
+    --hash=sha256:c840e62059cd3be204b0c9c9f74be2c09d5648eddd4580d9314c3ecde0b30936
+    # via gitdb
+streamlit==1.14.0 \
+    --hash=sha256:62556d873567e1b3427bcd118a57ee6946619f363bd6bba38df2d1f8225ecba0 \
+    --hash=sha256:e078b8143d150ba721bdb9194218e311c5fe1d6d4156473a2dea6cc848a6c9fc
+    # via -r requirements.in
+taming-transformers-rom1504==0.0.6 \
+    --hash=sha256:051b5804c58caa247bcd51d17ddb525b4d5f892a29d42dc460f40e3e9e34e5d8 \
+    --hash=sha256:73fe5fc1108accee4236ee6976e0987ab236afad0af06cb9f037641a908d2c32
+    # via -r requirements.in
+tb-nightly==2.11.0a20221106 \
+    --hash=sha256:8940457ee42db92f01da8bcdbbea1a476735eda559dde5976f5728919960af4a
+    # via
+    #   basicsr
+    #   gfpgan
+tensorboard==2.10.1 \
+    --hash=sha256:fb9222c1750e2fa35ef170d998a1e229f626eeced3004494a8849c88c15d8c1c
+    # via
+    #   pytorch-lightning
+    #   test-tube
+tensorboard-data-server==0.6.1 \
+    --hash=sha256:809fe9887682d35c1f7d1f54f0f40f98bb1f771b14265b453ca051e2ce58fca7 \
+    --hash=sha256:d8237580755e58eff68d1f3abefb5b1e39ae5c8b127cc40920f9c4fb33f4b98a \
+    --hash=sha256:fa8cef9be4fcae2f2363c88176638baf2da19c5ec90addb49b1cde05c95c88ee
+    # via
+    #   tb-nightly
+    #   tensorboard
+tensorboard-plugin-wit==1.8.1 \
+    --hash=sha256:ff26bdd583d155aa951ee3b152b3d0cffae8005dc697f72b44a8e8c2a77a8cbe
+    # via
+    #   tb-nightly
+    #   tensorboard
+test-tube==0.7.5 \
+    --hash=sha256:1379c33eb8cde3e9b36610f87da0f16c2e06496b1cfebac473df4e7be2faa124
+    # via -r requirements.in
+threadpoolctl==3.1.0 \
+    --hash=sha256:8b99adda265feb6773280df41eece7b2e6561b772d21ffd52e372f999024907b \
+    --hash=sha256:a335baacfaa4400ae1f0d8e3a58d6674d2f8828e3716bb2802c44955ad391380
+    # via scikit-learn
+tifffile==2022.10.10 \
+    --hash=sha256:50b61ba943b866d191295bc38a00191c9fdab23ece063544c7f1a264e3f6aa8e \
+    --hash=sha256:87f3aee8a0d06b74655269a105de75c1958a24653e1930d523eb516100043503
+    # via scikit-image
+tokenizers==0.13.1 \
+    --hash=sha256:0a3412830ad66a643723d6b0fc3202e64e9e299bd9c9eda04b2914b5b1e0ddb0 \
+    --hash=sha256:126bcb18a77cf65961ece04f54bd10ef3713412195e543d9d3eda2f0e4fd677c \
+    --hash=sha256:16434c61c5eb72f6692b9bc52052b07ca92d3eba9dd72a1bc371890e1bdc3f07 \
+    --hash=sha256:1d4acfdb6e7ef974677bb8445462db7fed240185fdc0f5b061db357d4ef8d85d \
+    --hash=sha256:3333d1cee5c8f47c96362ea0abc1f81c77c9b92c6c3d11cbf1d01985f0d5cf1d \
+    --hash=sha256:3acf3cae4c4739fc9ec49fa0e6cce224c1aa0fabc8f8d1358fd7de5c7d49cdca \
+    --hash=sha256:3ba43b3f6f6b41c97c1d041785342cd72ba142999f6c4605d628e8e937398f20 \
+    --hash=sha256:3c69a8389fd88bc32115e99db70f63bef577ba5c54f40a632580038a49612856 \
+    --hash=sha256:3de653a551cc616a442a123da21706cb3a3163cf6919973f978f0921eee1bdf0 \
+    --hash=sha256:4b3be8af87b357340b9b049d28067287b5e5e296e3120b6e4875d3b8469b67e6 \
+    --hash=sha256:680bc0e357b7da6d0d01634bffbd002e866fdaccde303e1d1af58f32464cf308 \
+    --hash=sha256:70de69681a264a5808d39f4bb6331be9a4dec51fd48cd1b959a94da76c4939cc \
+    --hash=sha256:73198cda6e1d991c583ed798514863e16763aa600eb7aa6df7722373290575b2 \
+    --hash=sha256:80864f456f715829f901ad5bb85af97e9ae52fc902270944804f6476ab8c6699 \
+    --hash=sha256:80b9552295fdce0a2513dcb795a3f8591eca1a8dcf8afe0de3214209e6924ad1 \
+    --hash=sha256:84fa41b58a8d3b7363ecdf3397d4b38f345fcf7d4dd14565b4360e7bffc9cae0 \
+    --hash=sha256:890d2139100b5c8ac6d585438d5e145ede1d7b32b4090a6c078db6db0ef1daea \
+    --hash=sha256:8b3f97041f7716998e474d3c7ffd19ac6941f117616696aef2b5ba927bf091e3 \
+    --hash=sha256:910479e92d5fbdf91e8106b4c658fd43d418893d7cfd5fb11983c54a1ff53869 \
+    --hash=sha256:96a1beef1e64d44597627f4e29d794047a66ad4d7474d93daf5a0ee27928e012 \
+    --hash=sha256:98bef54cf51ac335fda1408112df7ff3e584107633bd9066616033e12b0bd519 \
+    --hash=sha256:afcb1bd6d9ed59d5c8e633765589cab12f98aae09804f156b5965b4463b8b8e3 \
+    --hash=sha256:b72dec85488af3e1e8d58fb4b86b5dbe5171c176002b5e098ea6d52a70004bb5 \
+    --hash=sha256:c3109ba62bea56c68c7c2a976250b040afee61b5f86fc791f17afaa2a09fce94 \
+    --hash=sha256:c73b9e6c107e980e65077b89c54311d8d645f6a9efdde82990777fa43c0a8cae \
+    --hash=sha256:d8fca8b492a4697b0182e0c40b164cb0c44a9669d9c98033fec2f88174605eb0 \
+    --hash=sha256:db6872294339bf35c158219fc65bad939ba87d14c936ae7a33a3ca2d1532c5b1 \
+    --hash=sha256:e1a90bc97f53600f52e902f3ae097424de712d8ae0e42d957efc7ed908573a20 \
+    --hash=sha256:f75f476fe183c03c515a0f0f5d195cb05d93fcdc76e31fe3c9753d01f3ee990b \
+    --hash=sha256:fd17b14f84bec0b171869abd17ca0d9bfc564aa1e7f3451f44da526949a911c1 \
+    --hash=sha256:fea71780b66f8c278ebae7221c8959404cf7343b8d2f4b7308aa668cf6f02364
+    # via transformers
+toml==0.10.2 \
+    --hash=sha256:806143ae5bfb6a3c6e736a764057db0e6a0e05e338b5630894a5f779cabb4f9b \
+    --hash=sha256:b3bda1d108d5dd99f4a20d24d9c348e91c4db7ab1b749200bded2f839ccbe68f
+    # via streamlit
+toolz==0.12.0 \
+    --hash=sha256:2059bd4148deb1884bb0eb770a3cde70e7f954cfbbdc2285f1f2de01fd21eb6f \
+    --hash=sha256:88c570861c440ee3f2f6037c4654613228ff40c93a6c25e0eba70d17282c6194
+    # via altair
+torch==1.12.1+cu116 \
+    --hash=sha256:7725420dabebfcaf44984edce3283eea91f98f0f7d5874bc68c7a164bd8126e3 \
+    --hash=sha256:832effad8b21109700323a5aa137a2e4bdea711dac3d8491ff542f798dab0101 \
+    --hash=sha256:84f031e4ee25d95368d7531aa58e79da9808d3fa53b4b363ea03a2450b6fd0af \
+    --hash=sha256:b6bc31244aa2818929fbb30c483c221df471e9d856e805c5a1ff72b131ae9e7b \
+    --hash=sha256:b8e8906e770bcad12e67c269e1bcdd7661a8abd96519a4ba643e86440bbcc1bf \
+    --hash=sha256:bca5a77071d7eb901beb775648b125e6d9279f231d1f23e56530b5a189df8975 \
+    --hash=sha256:dda312901220895087cc83d3665464a3dc171d04460c61c31af463efbfb54896 \
+    --hash=sha256:fc9b4786ec54be67eaa8b0c7c9999e2f4ae2b89a1c18e41de1515a190440c691
+    # via
+    #   accelerate
+    #   basicsr
+    #   clean-fid
+    #   clip
+    #   facexlib
+    #   gfpgan
+    #   k-diffusion
+    #   kornia
+    #   pytorch-lightning
+    #   realesrgan
+    #   taming-transformers-rom1504
+    #   test-tube
+    #   torch-fidelity
+    #   torchdiffeq
+    #   torchmetrics
+    #   torchvision
+torch-fidelity==0.3.0 \
+    --hash=sha256:3d3e33db98919759cc4f3f24cb27e1e74bdc7c905d90a780630e4e1c18492b66 \
+    --hash=sha256:d01284825595feb7dc3eae3dc9a0d8ced02be764813a3483f109bc142b52a1d3
+    # via -r requirements.in
+torchdiffeq==0.2.3 \
+    --hash=sha256:b5b01ec1294a2d8d5f77e567bf17c5de1237c0573cb94deefa88326f0e18c338 \
+    --hash=sha256:fe75f434b9090ac0c27702e02bed21472b0f87035be6581f51edc5d4013ea31a
+    # via k-diffusion
+torchmetrics==0.10.2 \
+    --hash=sha256:43757d82266969906fc74b6e80766fcb2a0d52d6c3d09e3b7c98cf3b733fd20c \
+    --hash=sha256:daa29d96bff5cff04d80eec5b9f5076993d6ac9c2d2163e88b6b31f8d38f7c25
+    # via pytorch-lightning
+torchvision==0.13.1+cu116 ; platform_system == "Linux" or platform_system == "Windows" \
+    --hash=sha256:0c9a2b605ac30fcf475d60f79ba378af0073a22de585453f8c3dd6c1452ab9bc \
+    --hash=sha256:75986abe572138258eb9795cb4cd73f40b2bdf8374fefa1af6ff6bb0dbc972c6 \
+    --hash=sha256:8a4c395bb72cf51eb4318c6861c9a5ea490d48ec36a3d767220ef182445449cb \
+    --hash=sha256:92e4685c6010b6b1c228ebb5fe93105d0a71e5b586483a942e04529a43e0bb42 \
+    --hash=sha256:9ec5654c56a22fe420dc0af0ff5cd31105f583fdb0240043ff26a7cfed7e05fb \
+    --hash=sha256:ba8b7d3c33f63feb29c7dd8c0db68b735d0c9d924ff4e84121b4b20b17cec7a5 \
+    --hash=sha256:c3ceb2b3f456f0c984af71ef55f8637f178a29dc3e13a66fbb010ceead2891e1 \
+    --hash=sha256:dcf32f6d998493e76ec21a38bbb856b7402295cf7a67fb09ce5bde7e7e725756
+    # via
+    #   -r requirements.in
+    #   basicsr
+    #   clean-fid
+    #   clip
+    #   facexlib
+    #   gfpgan
+    #   k-diffusion
+    #   realesrgan
+    #   taming-transformers-rom1504
+    #   torch-fidelity
+tornado==6.2 \
+    --hash=sha256:1d54d13ab8414ed44de07efecb97d4ef7c39f7438cf5e976ccd356bebb1b5fca \
+    --hash=sha256:20f638fd8cc85f3cbae3c732326e96addff0a15e22d80f049e00121651e82e72 \
+    --hash=sha256:5c87076709343557ef8032934ce5f637dbb552efa7b21d08e89ae7619ed0eb23 \
+    --hash=sha256:5f8c52d219d4995388119af7ccaa0bcec289535747620116a58d830e7c25d8a8 \
+    --hash=sha256:6fdfabffd8dfcb6cf887428849d30cf19a3ea34c2c248461e1f7d718ad30b66b \
+    --hash=sha256:87dcafae3e884462f90c90ecc200defe5e580a7fbbb4365eda7c7c1eb809ebc9 \
+    --hash=sha256:9b630419bde84ec666bfd7ea0a4cb2a8a651c2d5cccdbdd1972a0c859dfc3c13 \
+    --hash=sha256:b8150f721c101abdef99073bf66d3903e292d851bee51910839831caba341a75 \
+    --hash=sha256:ba09ef14ca9893954244fd872798b4ccb2367c165946ce2dd7376aebdde8e3ac \
+    --hash=sha256:d3a2f5999215a3a06a4fc218026cd84c61b8b2b40ac5296a6db1f1451ef04c1e \
+    --hash=sha256:e5f923aa6a47e133d1cf87d60700889d7eae68988704e20c75fb2d65677a8e4b
+    # via streamlit
+tqdm==4.64.1 \
+    --hash=sha256:5f4f682a004951c1b450bc753c710e9280c5746ce6ffedee253ddbcbf54cf1e4 \
+    --hash=sha256:6fee160d6ffcd1b1c68c65f14c829c22832bc401726335ce92c52d395944a6a1
+    # via
+    #   basicsr
+    #   clean-fid
+    #   clip
+    #   facexlib
+    #   gfpgan
+    #   huggingface-hub
+    #   k-diffusion
+    #   pytorch-lightning
+    #   realesrgan
+    #   taming-transformers-rom1504
+    #   torch-fidelity
+    #   transformers
+transformers==4.24.0 \
+    --hash=sha256:486f353a8e594002e48be0e2aba723d96eda839e63bfe274702a4b5eda85559b \
+    --hash=sha256:b7ab50039ef9bf817eff14ab974f306fd20a72350bdc9df3a858fd009419322e
+    # via -r requirements.in
+typing-extensions==4.4.0 \
+    --hash=sha256:1511434bb92bf8dd198c12b1cc812e800d4181cfcb867674e0f8279cc93087aa \
+    --hash=sha256:16fa4864408f655d35ec496218b85f79b3437c829e93320c7c9215ccfd92489e
+    # via
+    #   huggingface-hub
+    #   pytorch-lightning
+    #   qudida
+    #   streamlit
+    #   torch
+    #   torchvision
+tzdata==2022.6 \
+    --hash=sha256:04a680bdc5b15750c39c12a448885a51134a27ec9af83667663f0b3a1bf3f342 \
+    --hash=sha256:91f11db4503385928c15598c98573e3af07e7229181bee5375bd30f1695ddcae
+    # via
+    #   pytz-deprecation-shim
+    #   tzlocal
+tzlocal==4.2 \
+    --hash=sha256:89885494684c929d9191c57aa27502afc87a579be5cdd3225c77c463ea043745 \
+    --hash=sha256:ee5842fa3a795f023514ac2d801c4a81d1743bbe642e3940143326b3a00addd7
+    # via streamlit
+urllib3==1.26.12 \
+    --hash=sha256:3fa96cf423e6987997fc326ae8df396db2a8b7c667747d47ddd8ecba91f4a74e \
+    --hash=sha256:b930dd878d5a8afb066a637fbb35144fe7901e3b209d1cd4f524bd0e9deee997
+    # via
+    #   requests
+    #   sentry-sdk
+validators==0.18.2 \
+    --hash=sha256:0143dcca8a386498edaf5780cbd5960da1a4c85e0719f3ee5c9b41249c4fefbd \
+    --hash=sha256:37cd9a9213278538ad09b5b9f9134266e7c226ab1fede1d500e29e0a8fbb9ea6
+    # via streamlit
+wandb==0.13.5 \
+    --hash=sha256:11f30a22e30abaa9c187e8b6aa4c12d76160b40bbe98a6f14b0dde9297bbfbe2 \
+    --hash=sha256:60d5bcc524b8a314c8e072c03f7702dbd5406261b00a4ce75e7556b805fdc765
+    # via k-diffusion
+watchdog==2.1.9 \
+    --hash=sha256:083171652584e1b8829581f965b9b7723ca5f9a2cd7e20271edf264cfd7c1412 \
+    --hash=sha256:117ffc6ec261639a0209a3252546b12800670d4bf5f84fbd355957a0595fe654 \
+    --hash=sha256:186f6c55abc5e03872ae14c2f294a153ec7292f807af99f57611acc8caa75306 \
+    --hash=sha256:195fc70c6e41237362ba720e9aaf394f8178bfc7fa68207f112d108edef1af33 \
+    --hash=sha256:226b3c6c468ce72051a4c15a4cc2ef317c32590d82ba0b330403cafd98a62cfd \
+    --hash=sha256:247dcf1df956daa24828bfea5a138d0e7a7c98b1a47cf1fa5b0c3c16241fcbb7 \
+    --hash=sha256:255bb5758f7e89b1a13c05a5bceccec2219f8995a3a4c4d6968fe1de6a3b2892 \
+    --hash=sha256:43ce20ebb36a51f21fa376f76d1d4692452b2527ccd601950d69ed36b9e21609 \
+    --hash=sha256:4f4e1c4aa54fb86316a62a87b3378c025e228178d55481d30d857c6c438897d6 \
+    --hash=sha256:5952135968519e2447a01875a6f5fc8c03190b24d14ee52b0f4b1682259520b1 \
+    --hash=sha256:64a27aed691408a6abd83394b38503e8176f69031ca25d64131d8d640a307591 \
+    --hash=sha256:6b17d302850c8d412784d9246cfe8d7e3af6bcd45f958abb2d08a6f8bedf695d \
+    --hash=sha256:70af927aa1613ded6a68089a9262a009fbdf819f46d09c1a908d4b36e1ba2b2d \
+    --hash=sha256:7a833211f49143c3d336729b0020ffd1274078e94b0ae42e22f596999f50279c \
+    --hash=sha256:8250546a98388cbc00c3ee3cc5cf96799b5a595270dfcfa855491a64b86ef8c3 \
+    --hash=sha256:97f9752208f5154e9e7b76acc8c4f5a58801b338de2af14e7e181ee3b28a5d39 \
+    --hash=sha256:9f05a5f7c12452f6a27203f76779ae3f46fa30f1dd833037ea8cbc2887c60213 \
+    --hash=sha256:a735a990a1095f75ca4f36ea2ef2752c99e6ee997c46b0de507ba40a09bf7330 \
+    --hash=sha256:ad576a565260d8f99d97f2e64b0f97a48228317095908568a9d5c786c829d428 \
+    --hash=sha256:b530ae007a5f5d50b7fbba96634c7ee21abec70dc3e7f0233339c81943848dc1 \
+    --hash=sha256:bfc4d351e6348d6ec51df007432e6fe80adb53fd41183716017026af03427846 \
+    --hash=sha256:d3dda00aca282b26194bdd0adec21e4c21e916956d972369359ba63ade616153 \
+    --hash=sha256:d9820fe47c20c13e3c9dd544d3706a2a26c02b2b43c993b62fcd8011bcc0adb3 \
+    --hash=sha256:ed80a1628cee19f5cfc6bb74e173f1b4189eb532e705e2a13e3250312a62e0c9 \
+    --hash=sha256:ee3e38a6cc050a8830089f79cbec8a3878ec2fe5160cdb2dc8ccb6def8552658
+    # via streamlit
+wcwidth==0.2.5 \
+    --hash=sha256:beb4802a9cebb9144e99086eff703a642a13d6a0052920003a230f3294bbe784 \
+    --hash=sha256:c4d647b99872929fdb7bdcaa4fbe7f01413ed3d98077df798530e5b04f116c83
+    # via ftfy
+werkzeug==2.2.2 \
+    --hash=sha256:7ea2d48322cc7c0f8b3a215ed73eabd7b5d75d0b50e31ab006286ccff9e00b8f \
+    --hash=sha256:f979ab81f58d7318e064e99c4506445d60135ac5cd2e177a2de0089bfd4c9bd5
+    # via
+    #   flask
+    #   tb-nightly
+    #   tensorboard
+wheel==0.38.2 \
+    --hash=sha256:3d492ef22379a156ec923d2a77051cedfd4df4b667864e9e41ba83f0b70b7149 \
+    --hash=sha256:7a5a3095dceca97a3cac869b8fef4e89b83fafde21b6688f47b6fda7600eb441
+    # via
+    #   tb-nightly
+    #   tensorboard
+whichcraft==0.6.1 \
+    --hash=sha256:acdbb91b63d6a15efbd6430d1d7b2d36e44a71697e93e19b7ded477afd9fce87 \
+    --hash=sha256:deda9266fbb22b8c64fd3ee45c050d61139cd87419765f588e37c8d23e236dd9
+    # via flaskwebgui
+yapf==0.32.0 \
+    --hash=sha256:8fea849025584e486fd06d6ba2bed717f396080fd3cc236ba10cb97c4c51cf32 \
+    --hash=sha256:a3f5085d37ef7e3e004c4ba9f9b3e40c54ff1901cd111f05145ae313a7c67d1b
+    # via
+    #   basicsr
+    #   gfpgan
+yarl==1.8.1 \
+    --hash=sha256:076eede537ab978b605f41db79a56cad2e7efeea2aa6e0fa8f05a26c24a034fb \
+    --hash=sha256:07b21e274de4c637f3e3b7104694e53260b5fc10d51fb3ec5fed1da8e0f754e3 \
+    --hash=sha256:0ab5a138211c1c366404d912824bdcf5545ccba5b3ff52c42c4af4cbdc2c5035 \
+    --hash=sha256:0c03f456522d1ec815893d85fccb5def01ffaa74c1b16ff30f8aaa03eb21e453 \
+    --hash=sha256:12768232751689c1a89b0376a96a32bc7633c08da45ad985d0c49ede691f5c0d \
+    --hash=sha256:19cd801d6f983918a3f3a39f3a45b553c015c5aac92ccd1fac619bd74beece4a \
+    --hash=sha256:1ca7e596c55bd675432b11320b4eacc62310c2145d6801a1f8e9ad160685a231 \
+    --hash=sha256:1e4808f996ca39a6463f45182e2af2fae55e2560be586d447ce8016f389f626f \
+    --hash=sha256:205904cffd69ae972a1707a1bd3ea7cded594b1d773a0ce66714edf17833cdae \
+    --hash=sha256:20df6ff4089bc86e4a66e3b1380460f864df3dd9dccaf88d6b3385d24405893b \
+    --hash=sha256:21ac44b763e0eec15746a3d440f5e09ad2ecc8b5f6dcd3ea8cb4773d6d4703e3 \
+    --hash=sha256:29e256649f42771829974e742061c3501cc50cf16e63f91ed8d1bf98242e5507 \
+    --hash=sha256:2d800b9c2eaf0684c08be5f50e52bfa2aa920e7163c2ea43f4f431e829b4f0fd \
+    --hash=sha256:2d93a049d29df172f48bcb09acf9226318e712ce67374f893b460b42cc1380ae \
+    --hash=sha256:31a9a04ecccd6b03e2b0e12e82131f1488dea5555a13a4d32f064e22a6003cfe \
+    --hash=sha256:3d1a50e461615747dd93c099f297c1994d472b0f4d2db8a64e55b1edf704ec1c \
+    --hash=sha256:449c957ffc6bc2309e1fbe67ab7d2c1efca89d3f4912baeb8ead207bb3cc1cd4 \
+    --hash=sha256:4a88510731cd8d4befaba5fbd734a7dd914de5ab8132a5b3dde0bbd6c9476c64 \
+    --hash=sha256:4c322cbaa4ed78a8aac89b2174a6df398faf50e5fc12c4c191c40c59d5e28357 \
+    --hash=sha256:5395da939ffa959974577eff2cbfc24b004a2fb6c346918f39966a5786874e54 \
+    --hash=sha256:5587bba41399854703212b87071c6d8638fa6e61656385875f8c6dff92b2e461 \
+    --hash=sha256:56c11efb0a89700987d05597b08a1efcd78d74c52febe530126785e1b1a285f4 \
+    --hash=sha256:5999c4662631cb798496535afbd837a102859568adc67d75d2045e31ec3ac497 \
+    --hash=sha256:59ddd85a1214862ce7c7c66457f05543b6a275b70a65de366030d56159a979f0 \
+    --hash=sha256:6347f1a58e658b97b0a0d1ff7658a03cb79bdbda0331603bed24dd7054a6dea1 \
+    --hash=sha256:6628d750041550c5d9da50bb40b5cf28a2e63b9388bac10fedd4f19236ef4957 \
+    --hash=sha256:6afb336e23a793cd3b6476c30f030a0d4c7539cd81649683b5e0c1b0ab0bf350 \
+    --hash=sha256:6c8148e0b52bf9535c40c48faebb00cb294ee577ca069d21bd5c48d302a83780 \
+    --hash=sha256:76577f13333b4fe345c3704811ac7509b31499132ff0181f25ee26619de2c843 \
+    --hash=sha256:7c0da7e44d0c9108d8b98469338705e07f4bb7dab96dbd8fa4e91b337db42548 \
+    --hash=sha256:7de89c8456525650ffa2bb56a3eee6af891e98f498babd43ae307bd42dca98f6 \
+    --hash=sha256:7ec362167e2c9fd178f82f252b6d97669d7245695dc057ee182118042026da40 \
+    --hash=sha256:7fce6cbc6c170ede0221cc8c91b285f7f3c8b9fe28283b51885ff621bbe0f8ee \
+    --hash=sha256:85cba594433915d5c9a0d14b24cfba0339f57a2fff203a5d4fd070e593307d0b \
+    --hash=sha256:8b0af1cf36b93cee99a31a545fe91d08223e64390c5ecc5e94c39511832a4bb6 \
+    --hash=sha256:9130ddf1ae9978abe63808b6b60a897e41fccb834408cde79522feb37fb72fb0 \
+    --hash=sha256:99449cd5366fe4608e7226c6cae80873296dfa0cde45d9b498fefa1de315a09e \
+    --hash=sha256:9de955d98e02fab288c7718662afb33aab64212ecb368c5dc866d9a57bf48880 \
+    --hash=sha256:a0fb2cb4204ddb456a8e32381f9a90000429489a25f64e817e6ff94879d432fc \
+    --hash=sha256:a165442348c211b5dea67c0206fc61366212d7082ba8118c8c5c1c853ea4d82e \
+    --hash=sha256:ab2a60d57ca88e1d4ca34a10e9fb4ab2ac5ad315543351de3a612bbb0560bead \
+    --hash=sha256:abc06b97407868ef38f3d172762f4069323de52f2b70d133d096a48d72215d28 \
+    --hash=sha256:af887845b8c2e060eb5605ff72b6f2dd2aab7a761379373fd89d314f4752abbf \
+    --hash=sha256:b19255dde4b4f4c32e012038f2c169bb72e7f081552bea4641cab4d88bc409dd \
+    --hash=sha256:b3ded839a5c5608eec8b6f9ae9a62cb22cd037ea97c627f38ae0841a48f09eae \
+    --hash=sha256:c1445a0c562ed561d06d8cbc5c8916c6008a31c60bc3655cdd2de1d3bf5174a0 \
+    --hash=sha256:d0272228fabe78ce00a3365ffffd6f643f57a91043e119c289aaba202f4095b0 \
+    --hash=sha256:d0b51530877d3ad7a8d47b2fff0c8df3b8f3b8deddf057379ba50b13df2a5eae \
+    --hash=sha256:d0f77539733e0ec2475ddcd4e26777d08996f8cd55d2aef82ec4d3896687abda \
+    --hash=sha256:d2b8f245dad9e331540c350285910b20dd913dc86d4ee410c11d48523c4fd546 \
+    --hash=sha256:dd032e8422a52e5a4860e062eb84ac94ea08861d334a4bcaf142a63ce8ad4802 \
+    --hash=sha256:de49d77e968de6626ba7ef4472323f9d2e5a56c1d85b7c0e2a190b2173d3b9be \
+    --hash=sha256:de839c3a1826a909fdbfe05f6fe2167c4ab033f1133757b5936efe2f84904c07 \
+    --hash=sha256:e80ed5a9939ceb6fda42811542f31c8602be336b1fb977bccb012e83da7e4936 \
+    --hash=sha256:ea30a42dc94d42f2ba4d0f7c0ffb4f4f9baa1b23045910c0c32df9c9902cb272 \
+    --hash=sha256:ea513a25976d21733bff523e0ca836ef1679630ef4ad22d46987d04b372d57fc \
+    --hash=sha256:ed19b74e81b10b592084a5ad1e70f845f0aacb57577018d31de064e71ffa267a \
+    --hash=sha256:f5af52738e225fcc526ae64071b7e5342abe03f42e0e8918227b38c9aa711e28 \
+    --hash=sha256:fae37373155f5ef9b403ab48af5136ae9851151f7aacd9926251ab26b953118b
+    # via aiohttp
+zipp==3.10.0 \
+    --hash=sha256:4fcb6f278987a6605757302a6e40e896257570d11c51628968ccb2a47e80c6c1 \
+    --hash=sha256:7a7262fd930bd3e36c50b9a64897aec3fafff3dfdeec9623ae22b40e93f99bb8
+    # via importlib-metadata
+
+# The following packages are considered to be unsafe in a requirements file:
+setuptools==65.5.1 \
+    --hash=sha256:d0b9a8433464d5800cbe05094acf5c6d52a91bfac9b52bcfc4d41382be5d5d31 \
+    --hash=sha256:e197a19aa8ec9722928f2206f8de752def0e4c9fc6953527360d1c36d94ddb2f
+    # via
+    #   numba
+    #   tb-nightly
+    #   tensorboard
+    #   wandb
diff --git a/installer/py3.10-windows-x86_64-cuda-reqs.txt b/installer/py3.10-windows-x86_64-cuda-reqs.txt
new file mode 100644
index 0000000000..9945bcc3dd
--- /dev/null
+++ b/installer/py3.10-windows-x86_64-cuda-reqs.txt
@@ -0,0 +1,2072 @@
+#
+# This file is autogenerated by pip-compile with python 3.10
+# To update, run:
+#
+#    pip-compile --allow-unsafe --generate-hashes --output-file=installer/py3.10-windows-x86_64-cuda-reqs.txt requirements.in
+#
+--extra-index-url https://download.pytorch.org/whl/cu116
+--trusted-host https
+
+absl-py==1.3.0 \
+    --hash=sha256:34995df9bd7a09b3b8749e230408f5a2a2dd7a68a0d33c12a3d0cb15a041a507 \
+    --hash=sha256:463c38a08d2e4cef6c498b76ba5bd4858e4c6ef51da1a5a1f27139a022e20248
+    # via
+    #   tb-nightly
+    #   tensorboard
+accelerate==0.13.2 \
+    --hash=sha256:dd6f08b010077f252dda5a7699d87b02885335c456770939c536e65ff07ed760 \
+    --hash=sha256:e22180d7094e4c1bfb05a2b078297c222f6b4fa595fde8916946c3f377cdf019
+    # via k-diffusion
+addict==2.4.0 \
+    --hash=sha256:249bb56bbfd3cdc2a004ea0ff4c2b6ddc84d53bc2194761636eb314d5cfa5dfc \
+    --hash=sha256:b3b2210e0e067a281f5646c8c5db92e99b7231ea8b0eb5f74dbdf9e259d4e494
+    # via basicsr
+aiohttp==3.8.3 \
+    --hash=sha256:02f9a2c72fc95d59b881cf38a4b2be9381b9527f9d328771e90f72ac76f31ad8 \
+    --hash=sha256:059a91e88f2c00fe40aed9031b3606c3f311414f86a90d696dd982e7aec48142 \
+    --hash=sha256:05a3c31c6d7cd08c149e50dc7aa2568317f5844acd745621983380597f027a18 \
+    --hash=sha256:08c78317e950e0762c2983f4dd58dc5e6c9ff75c8a0efeae299d363d439c8e34 \
+    --hash=sha256:09e28f572b21642128ef31f4e8372adb6888846f32fecb288c8b0457597ba61a \
+    --hash=sha256:0d2c6d8c6872df4a6ec37d2ede71eff62395b9e337b4e18efd2177de883a5033 \
+    --hash=sha256:16c121ba0b1ec2b44b73e3a8a171c4f999b33929cd2397124a8c7fcfc8cd9e06 \
+    --hash=sha256:1d90043c1882067f1bd26196d5d2db9aa6d268def3293ed5fb317e13c9413ea4 \
+    --hash=sha256:1e56b9cafcd6531bab5d9b2e890bb4937f4165109fe98e2b98ef0dcfcb06ee9d \
+    --hash=sha256:20acae4f268317bb975671e375493dbdbc67cddb5f6c71eebdb85b34444ac46b \
+    --hash=sha256:21b30885a63c3f4ff5b77a5d6caf008b037cb521a5f33eab445dc566f6d092cc \
+    --hash=sha256:21d69797eb951f155026651f7e9362877334508d39c2fc37bd04ff55b2007091 \
+    --hash=sha256:256deb4b29fe5e47893fa32e1de2d73c3afe7407738bd3c63829874661d4822d \
+    --hash=sha256:25892c92bee6d9449ffac82c2fe257f3a6f297792cdb18ad784737d61e7a9a85 \
+    --hash=sha256:2ca9af5f8f5812d475c5259393f52d712f6d5f0d7fdad9acdb1107dd9e3cb7eb \
+    --hash=sha256:2d252771fc85e0cf8da0b823157962d70639e63cb9b578b1dec9868dd1f4f937 \
+    --hash=sha256:2dea10edfa1a54098703cb7acaa665c07b4e7568472a47f4e64e6319d3821ccf \
+    --hash=sha256:2df5f139233060578d8c2c975128fb231a89ca0a462b35d4b5fcf7c501ebdbe1 \
+    --hash=sha256:2feebbb6074cdbd1ac276dbd737b40e890a1361b3cc30b74ac2f5e24aab41f7b \
+    --hash=sha256:309aa21c1d54b8ef0723181d430347d7452daaff93e8e2363db8e75c72c2fb2d \
+    --hash=sha256:3828fb41b7203176b82fe5d699e0d845435f2374750a44b480ea6b930f6be269 \
+    --hash=sha256:398701865e7a9565d49189f6c90868efaca21be65c725fc87fc305906be915da \
+    --hash=sha256:43046a319664a04b146f81b40e1545d4c8ac7b7dd04c47e40bf09f65f2437346 \
+    --hash=sha256:437399385f2abcd634865705bdc180c8314124b98299d54fe1d4c8990f2f9494 \
+    --hash=sha256:45d88b016c849d74ebc6f2b6e8bc17cabf26e7e40c0661ddd8fae4c00f015697 \
+    --hash=sha256:47841407cc89a4b80b0c52276f3cc8138bbbfba4b179ee3acbd7d77ae33f7ac4 \
+    --hash=sha256:4a4fbc769ea9b6bd97f4ad0b430a6807f92f0e5eb020f1e42ece59f3ecfc4585 \
+    --hash=sha256:4ab94426ddb1ecc6a0b601d832d5d9d421820989b8caa929114811369673235c \
+    --hash=sha256:4b0f30372cef3fdc262f33d06e7b411cd59058ce9174ef159ad938c4a34a89da \
+    --hash=sha256:4e3a23ec214e95c9fe85a58470b660efe6534b83e6cbe38b3ed52b053d7cb6ad \
+    --hash=sha256:512bd5ab136b8dc0ffe3fdf2dfb0c4b4f49c8577f6cae55dca862cd37a4564e2 \
+    --hash=sha256:527b3b87b24844ea7865284aabfab08eb0faf599b385b03c2aa91fc6edd6e4b6 \
+    --hash=sha256:54d107c89a3ebcd13228278d68f1436d3f33f2dd2af5415e3feaeb1156e1a62c \
+    --hash=sha256:5835f258ca9f7c455493a57ee707b76d2d9634d84d5d7f62e77be984ea80b849 \
+    --hash=sha256:598adde339d2cf7d67beaccda3f2ce7c57b3b412702f29c946708f69cf8222aa \
+    --hash=sha256:599418aaaf88a6d02a8c515e656f6faf3d10618d3dd95866eb4436520096c84b \
+    --hash=sha256:5bf651afd22d5f0c4be16cf39d0482ea494f5c88f03e75e5fef3a85177fecdeb \
+    --hash=sha256:5c59fcd80b9049b49acd29bd3598cada4afc8d8d69bd4160cd613246912535d7 \
+    --hash=sha256:653acc3880459f82a65e27bd6526e47ddf19e643457d36a2250b85b41a564715 \
+    --hash=sha256:66bd5f950344fb2b3dbdd421aaa4e84f4411a1a13fca3aeb2bcbe667f80c9f76 \
+    --hash=sha256:6f3553510abdbec67c043ca85727396ceed1272eef029b050677046d3387be8d \
+    --hash=sha256:7018ecc5fe97027214556afbc7c502fbd718d0740e87eb1217b17efd05b3d276 \
+    --hash=sha256:713d22cd9643ba9025d33c4af43943c7a1eb8547729228de18d3e02e278472b6 \
+    --hash=sha256:73a4131962e6d91109bca6536416aa067cf6c4efb871975df734f8d2fd821b37 \
+    --hash=sha256:75880ed07be39beff1881d81e4a907cafb802f306efd6d2d15f2b3c69935f6fb \
+    --hash=sha256:75e14eac916f024305db517e00a9252714fce0abcb10ad327fb6dcdc0d060f1d \
+    --hash=sha256:8135fa153a20d82ffb64f70a1b5c2738684afa197839b34cc3e3c72fa88d302c \
+    --hash=sha256:84b14f36e85295fe69c6b9789b51a0903b774046d5f7df538176516c3e422446 \
+    --hash=sha256:86fc24e58ecb32aee09f864cb11bb91bc4c1086615001647dbfc4dc8c32f4008 \
+    --hash=sha256:87f44875f2804bc0511a69ce44a9595d5944837a62caecc8490bbdb0e18b1342 \
+    --hash=sha256:88c70ed9da9963d5496d38320160e8eb7e5f1886f9290475a881db12f351ab5d \
+    --hash=sha256:88e5be56c231981428f4f506c68b6a46fa25c4123a2e86d156c58a8369d31ab7 \
+    --hash=sha256:89d2e02167fa95172c017732ed7725bc8523c598757f08d13c5acca308e1a061 \
+    --hash=sha256:8d6aaa4e7155afaf994d7924eb290abbe81a6905b303d8cb61310a2aba1c68ba \
+    --hash=sha256:92a2964319d359f494f16011e23434f6f8ef0434acd3cf154a6b7bec511e2fb7 \
+    --hash=sha256:96372fc29471646b9b106ee918c8eeb4cca423fcbf9a34daa1b93767a88a2290 \
+    --hash=sha256:978b046ca728073070e9abc074b6299ebf3501e8dee5e26efacb13cec2b2dea0 \
+    --hash=sha256:9c7149272fb5834fc186328e2c1fa01dda3e1fa940ce18fded6d412e8f2cf76d \
+    --hash=sha256:a0239da9fbafd9ff82fd67c16704a7d1bccf0d107a300e790587ad05547681c8 \
+    --hash=sha256:ad5383a67514e8e76906a06741febd9126fc7c7ff0f599d6fcce3e82b80d026f \
+    --hash=sha256:ad61a9639792fd790523ba072c0555cd6be5a0baf03a49a5dd8cfcf20d56df48 \
+    --hash=sha256:b29bfd650ed8e148f9c515474a6ef0ba1090b7a8faeee26b74a8ff3b33617502 \
+    --hash=sha256:b97decbb3372d4b69e4d4c8117f44632551c692bb1361b356a02b97b69e18a62 \
+    --hash=sha256:ba71c9b4dcbb16212f334126cc3d8beb6af377f6703d9dc2d9fb3874fd667ee9 \
+    --hash=sha256:c37c5cce780349d4d51739ae682dec63573847a2a8dcb44381b174c3d9c8d403 \
+    --hash=sha256:c971bf3786b5fad82ce5ad570dc6ee420f5b12527157929e830f51c55dc8af77 \
+    --hash=sha256:d1fde0f44029e02d02d3993ad55ce93ead9bb9b15c6b7ccd580f90bd7e3de476 \
+    --hash=sha256:d24b8bb40d5c61ef2d9b6a8f4528c2f17f1c5d2d31fed62ec860f6006142e83e \
+    --hash=sha256:d5ba88df9aa5e2f806650fcbeedbe4f6e8736e92fc0e73b0400538fd25a4dd96 \
+    --hash=sha256:d6f76310355e9fae637c3162936e9504b4767d5c52ca268331e2756e54fd4ca5 \
+    --hash=sha256:d737fc67b9a970f3234754974531dc9afeea11c70791dcb7db53b0cf81b79784 \
+    --hash=sha256:da22885266bbfb3f78218dc40205fed2671909fbd0720aedba39b4515c038091 \
+    --hash=sha256:da37dcfbf4b7f45d80ee386a5f81122501ec75672f475da34784196690762f4b \
+    --hash=sha256:db19d60d846283ee275d0416e2a23493f4e6b6028825b51290ac05afc87a6f97 \
+    --hash=sha256:db4c979b0b3e0fa7e9e69ecd11b2b3174c6963cebadeecfb7ad24532ffcdd11a \
+    --hash=sha256:e164e0a98e92d06da343d17d4e9c4da4654f4a4588a20d6c73548a29f176abe2 \
+    --hash=sha256:e168a7560b7c61342ae0412997b069753f27ac4862ec7867eff74f0fe4ea2ad9 \
+    --hash=sha256:e381581b37db1db7597b62a2e6b8b57c3deec95d93b6d6407c5b61ddc98aca6d \
+    --hash=sha256:e65bc19919c910127c06759a63747ebe14f386cda573d95bcc62b427ca1afc73 \
+    --hash=sha256:e7b8813be97cab8cb52b1375f41f8e6804f6507fe4660152e8ca5c48f0436017 \
+    --hash=sha256:e8a78079d9a39ca9ca99a8b0ac2fdc0c4d25fc80c8a8a82e5c8211509c523363 \
+    --hash=sha256:ebf909ea0a3fc9596e40d55d8000702a85e27fd578ff41a5500f68f20fd32e6c \
+    --hash=sha256:ec40170327d4a404b0d91855d41bfe1fe4b699222b2b93e3d833a27330a87a6d \
+    --hash=sha256:f178d2aadf0166be4df834c4953da2d7eef24719e8aec9a65289483eeea9d618 \
+    --hash=sha256:f88df3a83cf9df566f171adba39d5bd52814ac0b94778d2448652fc77f9eb491 \
+    --hash=sha256:f973157ffeab5459eefe7b97a804987876dd0a55570b8fa56b4e1954bf11329b \
+    --hash=sha256:ff25f48fc8e623d95eca0670b8cc1469a83783c924a602e0fbd47363bb54aaca
+    # via fsspec
+aiosignal==1.2.0 \
+    --hash=sha256:26e62109036cd181df6e6ad646f91f0dcfd05fe16d0cb924138ff2ab75d64e3a \
+    --hash=sha256:78ed67db6c7b7ced4f98e495e572106d5c432a93e1ddd1bf475e1dc05f5b7df2
+    # via aiohttp
+albumentations==1.3.0 \
+    --hash=sha256:294165d87d03bc8323e484927f0a5c1a3c64b0e7b9c32a979582a6c93c363bdf \
+    --hash=sha256:be1af36832c8893314f2a5550e8ac19801e04770734c1b70fa3c996b41f37bed
+    # via -r requirements.in
+altair==4.2.0 \
+    --hash=sha256:0c724848ae53410c13fa28be2b3b9a9dcb7b5caa1a70f7f217bd663bb419935a \
+    --hash=sha256:d87d9372e63b48cd96b2a6415f0cf9457f50162ab79dc7a31cd7e024dd840026
+    # via streamlit
+antlr4-python3-runtime==4.9.3 \
+    --hash=sha256:f224469b4168294902bb1efa80a8bf7855f24c99aef99cbefc1bcd3cce77881b
+    # via omegaconf
+async-timeout==4.0.2 \
+    --hash=sha256:2163e1640ddb52b7a8c80d0a67a08587e5d245cc9c553a74a847056bc2976b15 \
+    --hash=sha256:8ca1e4fcf50d07413d66d1a5e416e42cfdf5851c981d679a09851a6853383b3c
+    # via aiohttp
+attrs==22.1.0 \
+    --hash=sha256:29adc2665447e5191d0e7c568fde78b21f9672d344281d0c6e1ab085429b22b6 \
+    --hash=sha256:86efa402f67bf2df34f51a335487cf46b1ec130d02b8d39fd248abfd30da551c
+    # via
+    #   aiohttp
+    #   jsonschema
+basicsr==1.4.2 \
+    --hash=sha256:b89b595a87ef964cda9913b4d99380ddb6554c965577c0c10cb7b78e31301e87
+    # via
+    #   gfpgan
+    #   realesrgan
+bidict==0.22.0 \
+    --hash=sha256:415126d23a0c81e1a8c584a8fb1f6905ea090c772571803aeee0a2242e8e7ba0 \
+    --hash=sha256:5c826b3e15e97cc6e615de295756847c282a79b79c5430d3bfc909b1ac9f5bd8
+    # via python-socketio
+blinker==1.5 \
+    --hash=sha256:1eb563df6fdbc39eeddc177d953203f99f097e9bf0e2b8f9f3cf18b6ca425e36 \
+    --hash=sha256:923e5e2f69c155f2cc42dafbbd70e16e3fde24d2d4aa2ab72fbe386238892462
+    # via streamlit
+cachetools==5.2.0 \
+    --hash=sha256:6a94c6402995a99c3970cc7e4884bb60b4a8639938157eeed436098bf9831757 \
+    --hash=sha256:f9f17d2aec496a9aa6b76f53e3b614c965223c061982d434d160f930c698a9db
+    # via
+    #   google-auth
+    #   streamlit
+certifi==2022.9.24 \
+    --hash=sha256:0d9c601124e5a6ba9712dbc60d9c53c21e34f5f641fe83002317394311bdce14 \
+    --hash=sha256:90c1a32f1d68f940488354e36370f6cca89f0f106db09518524c88d6ed83f382
+    # via
+    #   requests
+    #   sentry-sdk
+chardet==4.0.0 \
+    --hash=sha256:0d6f53a15db4120f2b08c94f11e7d93d2c911ee118b6b30a04ec3ee8310179fa \
+    --hash=sha256:f864054d66fd9118f2e67044ac8981a54775ec5b67aed0441892edb553d21da5
+    # via requests
+charset-normalizer==2.1.1 \
+    --hash=sha256:5a3d016c7c547f69d6f81fb0db9449ce888b418b5b9952cc5e6e66843e9dd845 \
+    --hash=sha256:83e9a75d1911279afd89352c68b45348559d1fc0506b054b346651b5e7fee29f
+    # via aiohttp
+clean-fid==0.1.34 \
+    --hash=sha256:2997f85a67a28c95adaae7899a33fc10537164fef4cdd424e3257bffad79a901
+    # via k-diffusion
+click==8.1.3 \
+    --hash=sha256:7682dc8afb30297001674575ea00d1814d808d6a36af415a82bd481d37ba7b8e \
+    --hash=sha256:bb4d8133cb15a609f44e8213d9b391b0809795062913b383c62be0ee95b1db48
+    # via
+    #   flask
+    #   streamlit
+    #   wandb
+clip @ https://github.com/openai/CLIP/archive/d50d76daa670286dd6cacf3bcd80b5e4823fc8e1.zip \
+    --hash=sha256:b5842c25da441d6c581b53a5c60e0c2127ebafe0f746f8e15561a006c6c3be6a
+    # via -r requirements.in
+colorama==0.4.6 \
+    --hash=sha256:08695f5cb7ed6e0531a20572697297273c47b8cae5a63ffc6d6ed5c201be6e44 \
+    --hash=sha256:4f1d9991f5acc0ca119f9d443620b77f9d6b33703e51011c16baf57afb285fc6
+    # via
+    #   click
+    #   tqdm
+commonmark==0.9.1 \
+    --hash=sha256:452f9dc859be7f06631ddcb328b6919c67984aca654e5fefb3914d54691aed60 \
+    --hash=sha256:da2f38c92590f83de410ba1a3cbceafbc74fee9def35f9251ba9a971d6d66fd9
+    # via rich
+contourpy==1.0.6 \
+    --hash=sha256:0236875c5a0784215b49d00ebbe80c5b6b5d5244b3655a36dda88105334dea17 \
+    --hash=sha256:03d1b9c6b44a9e30d554654c72be89af94fab7510b4b9f62356c64c81cec8b7d \
+    --hash=sha256:0537cc1195245bbe24f2913d1f9211b8f04eb203de9044630abd3664c6cc339c \
+    --hash=sha256:06ca79e1efbbe2df795822df2fa173d1a2b38b6e0f047a0ec7903fbca1d1847e \
+    --hash=sha256:08e8d09d96219ace6cb596506fb9b64ea5f270b2fb9121158b976d88871fcfd1 \
+    --hash=sha256:0b1e66346acfb17694d46175a0cea7d9036f12ed0c31dfe86f0f405eedde2bdd \
+    --hash=sha256:0b97454ed5b1368b66ed414c754cba15b9750ce69938fc6153679787402e4cdf \
+    --hash=sha256:0e4854cc02006ad6684ce092bdadab6f0912d131f91c2450ce6dbdea78ee3c0b \
+    --hash=sha256:12a7dc8439544ed05c6553bf026d5e8fa7fad48d63958a95d61698df0e00092b \
+    --hash=sha256:1b1ee48a130da4dd0eb8055bbab34abf3f6262957832fd575e0cab4979a15a41 \
+    --hash=sha256:1c0e1308307a75e07d1f1b5f0f56b5af84538a5e9027109a7bcf6cb47c434e72 \
+    --hash=sha256:1dedf4c64185a216c35eb488e6f433297c660321275734401760dafaeb0ad5c2 \
+    --hash=sha256:208bc904889c910d95aafcf7be9e677726df9ef71e216780170dbb7e37d118fa \
+    --hash=sha256:211dfe2bd43bf5791d23afbe23a7952e8ac8b67591d24be3638cabb648b3a6eb \
+    --hash=sha256:341330ed19074f956cb20877ad8d2ae50e458884bfa6a6df3ae28487cc76c768 \
+    --hash=sha256:344cb3badf6fc7316ad51835f56ac387bdf86c8e1b670904f18f437d70da4183 \
+    --hash=sha256:358f6364e4873f4d73360b35da30066f40387dd3c427a3e5432c6b28dd24a8fa \
+    --hash=sha256:371f6570a81dfdddbb837ba432293a63b4babb942a9eb7aaa699997adfb53278 \
+    --hash=sha256:375d81366afd547b8558c4720337218345148bc2fcffa3a9870cab82b29667f2 \
+    --hash=sha256:3a1917d3941dd58732c449c810fa7ce46cc305ce9325a11261d740118b85e6f3 \
+    --hash=sha256:4081918147fc4c29fad328d5066cfc751da100a1098398742f9f364be63803fc \
+    --hash=sha256:444fb776f58f4906d8d354eb6f6ce59d0a60f7b6a720da6c1ccb839db7c80eb9 \
+    --hash=sha256:46deb310a276cc5c1fd27958e358cce68b1e8a515fa5a574c670a504c3a3fe30 \
+    --hash=sha256:494efed2c761f0f37262815f9e3c4bb9917c5c69806abdee1d1cb6611a7174a0 \
+    --hash=sha256:50627bf76abb6ba291ad08db583161939c2c5fab38c38181b7833423ab9c7de3 \
+    --hash=sha256:5641927cc5ae66155d0c80195dc35726eae060e7defc18b7ab27600f39dd1fe7 \
+    --hash=sha256:5b117d29433fc8393b18a696d794961464e37afb34a6eeb8b2c37b5f4128a83e \
+    --hash=sha256:613c665529899b5d9fade7e5d1760111a0b011231277a0d36c49f0d3d6914bd6 \
+    --hash=sha256:6e459ebb8bb5ee4c22c19cc000174f8059981971a33ce11e17dddf6aca97a142 \
+    --hash=sha256:6f56515e7c6fae4529b731f6c117752247bef9cdad2b12fc5ddf8ca6a50965a5 \
+    --hash=sha256:730c27978a0003b47b359935478b7d63fd8386dbb2dcd36c1e8de88cbfc1e9de \
+    --hash=sha256:75a2e638042118118ab39d337da4c7908c1af74a8464cad59f19fbc5bbafec9b \
+    --hash=sha256:78ced51807ccb2f45d4ea73aca339756d75d021069604c2fccd05390dc3c28eb \
+    --hash=sha256:7ee394502026d68652c2824348a40bf50f31351a668977b51437131a90d777ea \
+    --hash=sha256:8468b40528fa1e15181cccec4198623b55dcd58306f8815a793803f51f6c474a \
+    --hash=sha256:84c593aeff7a0171f639da92cb86d24954bbb61f8a1b530f74eb750a14685832 \
+    --hash=sha256:913bac9d064cff033cf3719e855d4f1db9f1c179e0ecf3ba9fdef21c21c6a16a \
+    --hash=sha256:9447c45df407d3ecb717d837af3b70cfef432138530712263730783b3d016512 \
+    --hash=sha256:9b0e7fe7f949fb719b206548e5cde2518ffb29936afa4303d8a1c4db43dcb675 \
+    --hash=sha256:9bc407a6af672da20da74823443707e38ece8b93a04009dca25856c2d9adadb1 \
+    --hash=sha256:9e8e686a6db92a46111a1ee0ee6f7fbfae4048f0019de207149f43ac1812cf95 \
+    --hash=sha256:9fc4e7973ed0e1fe689435842a6e6b330eb7ccc696080dda9a97b1a1b78e41db \
+    --hash=sha256:a457ee72d9032e86730f62c5eeddf402e732fdf5ca8b13b41772aa8ae13a4563 \
+    --hash=sha256:a628bba09ba72e472bf7b31018b6281fd4cc903f0888049a3724afba13b6e0b8 \
+    --hash=sha256:a79d239fc22c3b8d9d3de492aa0c245533f4f4c7608e5749af866949c0f1b1b9 \
+    --hash=sha256:aa4674cf3fa2bd9c322982644967f01eed0c91bb890f624e0e0daf7a5c3383e9 \
+    --hash=sha256:acd2bd02f1a7adff3a1f33e431eb96ab6d7987b039d2946a9b39fe6fb16a1036 \
+    --hash=sha256:b3b1bd7577c530eaf9d2bc52d1a93fef50ac516a8b1062c3d1b9bcec9ebe329b \
+    --hash=sha256:b48d94386f1994db7c70c76b5808c12e23ed7a4ee13693c2fc5ab109d60243c0 \
+    --hash=sha256:b64f747e92af7da3b85631a55d68c45a2d728b4036b03cdaba4bd94bcc85bd6f \
+    --hash=sha256:b98c820608e2dca6442e786817f646d11057c09a23b68d2b3737e6dcb6e4a49b \
+    --hash=sha256:c1baa49ab9fedbf19d40d93163b7d3e735d9cd8d5efe4cce9907902a6dad391f \
+    --hash=sha256:c38c6536c2d71ca2f7e418acaf5bca30a3af7f2a2fa106083c7d738337848dbe \
+    --hash=sha256:c78bfbc1a7bff053baf7e508449d2765964d67735c909b583204e3240a2aca45 \
+    --hash=sha256:cd2bc0c8f2e8de7dd89a7f1c10b8844e291bca17d359373203ef2e6100819edd \
+    --hash=sha256:d2eff2af97ea0b61381828b1ad6cd249bbd41d280e53aea5cccd7b2b31b8225c \
+    --hash=sha256:d8834c14b8c3dd849005e06703469db9bf96ba2d66a3f88ecc539c9a8982e0ee \
+    --hash=sha256:d912f0154a20a80ea449daada904a7eb6941c83281a9fab95de50529bfc3a1da \
+    --hash=sha256:da1ef35fd79be2926ba80fbb36327463e3656c02526e9b5b4c2b366588b74d9a \
+    --hash=sha256:dbe6fe7a1166b1ddd7b6d887ea6fa8389d3f28b5ed3f73a8f40ece1fc5a3d340 \
+    --hash=sha256:dcd556c8fc37a342dd636d7eef150b1399f823a4462f8c968e11e1ebeabee769 \
+    --hash=sha256:e13b31d1b4b68db60b3b29f8e337908f328c7f05b9add4b1b5c74e0691180109 \
+    --hash=sha256:e1739496c2f0108013629aa095cc32a8c6363444361960c07493818d0dea2da4 \
+    --hash=sha256:e43255a83835a129ef98f75d13d643844d8c646b258bebd11e4a0975203e018f \
+    --hash=sha256:e626cefff8491bce356221c22af5a3ea528b0b41fbabc719c00ae233819ea0bf \
+    --hash=sha256:eadad75bf91897f922e0fb3dca1b322a58b1726a953f98c2e5f0606bd8408621 \
+    --hash=sha256:f33da6b5d19ad1bb5e7ad38bb8ba5c426d2178928bc2b2c44e8823ea0ecb6ff3 \
+    --hash=sha256:f4052a8a4926d4468416fc7d4b2a7b2a3e35f25b39f4061a7e2a3a2748c4fc48 \
+    --hash=sha256:f6ca38dd8d988eca8f07305125dec6f54ac1c518f1aaddcc14d08c01aebb6efc
+    # via matplotlib
+cycler==0.11.0 \
+    --hash=sha256:3a27e95f763a428a739d2add979fa7494c912a32c17c4c38c4d5f082cad165a3 \
+    --hash=sha256:9c87405839a19696e837b3b818fed3f5f69f16f1eec1a1ad77e043dcea9c772f
+    # via matplotlib
+decorator==5.1.1 \
+    --hash=sha256:637996211036b6385ef91435e4fae22989472f9d571faba8927ba8253acbc330 \
+    --hash=sha256:b8c3f85900b9dc423225913c5aace94729fe1fa9763b38939a95226f02d37186
+    # via validators
+diffusers==0.7.2 \
+    --hash=sha256:4a5f8b3a5fbd936bba7d459611cb35ec62875030367be32b232f9e19543e25a9 \
+    --hash=sha256:fb814ffd150cc6f470380b8c6a521181a77beb2f44134d2aad2e4cd8aa2ced0e
+    # via -r requirements.in
+dnspython==2.2.1 \
+    --hash=sha256:0f7569a4a6ff151958b64304071d370daa3243d15941a7beedf0c9fe5105603e \
+    --hash=sha256:a851e51367fb93e9e1361732c1d60dab63eff98712e503ea7d92e6eccb109b4f
+    # via eventlet
+docker-pycreds==0.4.0 \
+    --hash=sha256:6ce3270bcaf404cc4c3e27e4b6c70d3521deae82fb508767870fdbf772d584d4 \
+    --hash=sha256:7266112468627868005106ec19cd0d722702d2b7d5912a28e19b826c3d37af49
+    # via wandb
+einops==0.5.0 \
+    --hash=sha256:055de7eeb3cb9e9710ef3085a811090c6b52e809b7044e8785824ed185f486d1 \
+    --hash=sha256:8b7a83cffc1ea88e306df099b7cbb9c3ba5003bd84d05ae44be5655864abb8d3
+    # via k-diffusion
+entrypoints==0.4 \
+    --hash=sha256:b706eddaa9218a19ebcd67b56818f05bb27589b1ca9e8d797b74affad4ccacd4 \
+    --hash=sha256:f174b5ff827504fd3cd97cc3f8649f3693f51538c7e4bdf3ef002c8429d42f9f
+    # via altair
+eventlet==0.33.1 \
+    --hash=sha256:a085922698e5029f820cf311a648ac324d73cec0e4792877609d978a4b5bbf31 \
+    --hash=sha256:afbe17f06a58491e9aebd7a4a03e70b0b63fd4cf76d8307bae07f280479b1515
+    # via -r requirements.in
+facexlib==0.2.5 \
+    --hash=sha256:31e20cc4ed5d63562d380e4564bae14ac0d5d1899a079bad87621e13564567e4 \
+    --hash=sha256:cc7ceb56c5424319c47223cf75eef6828c34c66082707c6eb35b95d39779f02d
+    # via
+    #   gfpgan
+    #   realesrgan
+filelock==3.8.0 \
+    --hash=sha256:55447caa666f2198c5b6b13a26d2084d26fa5b115c00d065664b2124680c4edc \
+    --hash=sha256:617eb4e5eedc82fc5f47b6d61e4d11cb837c56cb4544e39081099fa17ad109d4
+    # via
+    #   diffusers
+    #   huggingface-hub
+    #   transformers
+filterpy==1.4.5 \
+    --hash=sha256:4f2a4d39e4ea601b9ab42b2db08b5918a9538c168cff1c6895ae26646f3d73b1
+    # via facexlib
+flask==2.2.2 \
+    --hash=sha256:642c450d19c4ad482f96729bd2a8f6d32554aa1e231f4f6b4e7e5264b16cca2b \
+    --hash=sha256:b9c46cc36662a7949f34b52d8ec7bb59c0d74ba08ba6cb9ce9adc1d8676d9526
+    # via
+    #   flask-cors
+    #   flask-socketio
+flask-cors==3.0.10 \
+    --hash=sha256:74efc975af1194fc7891ff5cd85b0f7478be4f7f59fe158102e91abb72bb4438 \
+    --hash=sha256:b60839393f3b84a0f3746f6cdca56c1ad7426aa738b70d6c61375857823181de
+    # via -r requirements.in
+flask-socketio==5.3.1 \
+    --hash=sha256:fd0ed0fc1341671d92d5f5b2f5503916deb7aa7e2940e6636cfa2c087c828bf9 \
+    --hash=sha256:ff0c721f20bff1e2cfba77948727a8db48f187e89a72fe50c34478ce6efb3353
+    # via -r requirements.in
+flaskwebgui==0.3.7 \
+    --hash=sha256:4a69955308eaa8bb256ba04a994dc8f58a48dcd6f9599694ab1bcd9f43d88a5d \
+    --hash=sha256:535974ce2672dcc74787c254de24cceed4101be75d96952dae82014dd57f061e
+    # via -r requirements.in
+fonttools==4.38.0 \
+    --hash=sha256:2bb244009f9bf3fa100fc3ead6aeb99febe5985fa20afbfbaa2f8946c2fbdaf1 \
+    --hash=sha256:820466f43c8be8c3009aef8b87e785014133508f0de64ec469e4efb643ae54fb
+    # via matplotlib
+frozenlist==1.3.1 \
+    --hash=sha256:022178b277cb9277d7d3b3f2762d294f15e85cd2534047e68a118c2bb0058f3e \
+    --hash=sha256:086ca1ac0a40e722d6833d4ce74f5bf1aba2c77cbfdc0cd83722ffea6da52a04 \
+    --hash=sha256:0bc75692fb3770cf2b5856a6c2c9de967ca744863c5e89595df64e252e4b3944 \
+    --hash=sha256:0dde791b9b97f189874d654c55c24bf7b6782343e14909c84beebd28b7217845 \
+    --hash=sha256:12607804084d2244a7bd4685c9d0dca5df17a6a926d4f1967aa7978b1028f89f \
+    --hash=sha256:19127f8dcbc157ccb14c30e6f00392f372ddb64a6ffa7106b26ff2196477ee9f \
+    --hash=sha256:1b51eb355e7f813bcda00276b0114c4172872dc5fb30e3fea059b9367c18fbcb \
+    --hash=sha256:1e1cf7bc8cbbe6ce3881863671bac258b7d6bfc3706c600008925fb799a256e2 \
+    --hash=sha256:219a9676e2eae91cb5cc695a78b4cb43d8123e4160441d2b6ce8d2c70c60e2f3 \
+    --hash=sha256:2743bb63095ef306041c8f8ea22bd6e4d91adabf41887b1ad7886c4c1eb43d5f \
+    --hash=sha256:2af6f7a4e93f5d08ee3f9152bce41a6015b5cf87546cb63872cc19b45476e98a \
+    --hash=sha256:31b44f1feb3630146cffe56344704b730c33e042ffc78d21f2125a6a91168131 \
+    --hash=sha256:31bf9539284f39ff9398deabf5561c2b0da5bb475590b4e13dd8b268d7a3c5c1 \
+    --hash=sha256:35c3d79b81908579beb1fb4e7fcd802b7b4921f1b66055af2578ff7734711cfa \
+    --hash=sha256:3a735e4211a04ccfa3f4833547acdf5d2f863bfeb01cfd3edaffbc251f15cec8 \
+    --hash=sha256:42719a8bd3792744c9b523674b752091a7962d0d2d117f0b417a3eba97d1164b \
+    --hash=sha256:49459f193324fbd6413e8e03bd65789e5198a9fa3095e03f3620dee2f2dabff2 \
+    --hash=sha256:4c0c99e31491a1d92cde8648f2e7ccad0e9abb181f6ac3ddb9fc48b63301808e \
+    --hash=sha256:52137f0aea43e1993264a5180c467a08a3e372ca9d378244c2d86133f948b26b \
+    --hash=sha256:526d5f20e954d103b1d47232e3839f3453c02077b74203e43407b962ab131e7b \
+    --hash=sha256:53b2b45052e7149ee8b96067793db8ecc1ae1111f2f96fe1f88ea5ad5fd92d10 \
+    --hash=sha256:572ce381e9fe027ad5e055f143763637dcbac2542cfe27f1d688846baeef5170 \
+    --hash=sha256:58fb94a01414cddcdc6839807db77ae8057d02ddafc94a42faee6004e46c9ba8 \
+    --hash=sha256:5e77a8bd41e54b05e4fb2708dc6ce28ee70325f8c6f50f3df86a44ecb1d7a19b \
+    --hash=sha256:5f271c93f001748fc26ddea409241312a75e13466b06c94798d1a341cf0e6989 \
+    --hash=sha256:5f63c308f82a7954bf8263a6e6de0adc67c48a8b484fab18ff87f349af356efd \
+    --hash=sha256:61d7857950a3139bce035ad0b0945f839532987dfb4c06cfe160254f4d19df03 \
+    --hash=sha256:61e8cb51fba9f1f33887e22488bad1e28dd8325b72425f04517a4d285a04c519 \
+    --hash=sha256:625d8472c67f2d96f9a4302a947f92a7adbc1e20bedb6aff8dbc8ff039ca6189 \
+    --hash=sha256:6e19add867cebfb249b4e7beac382d33215d6d54476bb6be46b01f8cafb4878b \
+    --hash=sha256:717470bfafbb9d9be624da7780c4296aa7935294bd43a075139c3d55659038ca \
+    --hash=sha256:74140933d45271c1a1283f708c35187f94e1256079b3c43f0c2267f9db5845ff \
+    --hash=sha256:74e6b2b456f21fc93ce1aff2b9728049f1464428ee2c9752a4b4f61e98c4db96 \
+    --hash=sha256:9494122bf39da6422b0972c4579e248867b6b1b50c9b05df7e04a3f30b9a413d \
+    --hash=sha256:94e680aeedc7fd3b892b6fa8395b7b7cc4b344046c065ed4e7a1e390084e8cb5 \
+    --hash=sha256:97d9e00f3ac7c18e685320601f91468ec06c58acc185d18bb8e511f196c8d4b2 \
+    --hash=sha256:9c6ef8014b842f01f5d2b55315f1af5cbfde284eb184075c189fd657c2fd8204 \
+    --hash=sha256:a027f8f723d07c3f21963caa7d585dcc9b089335565dabe9c814b5f70c52705a \
+    --hash=sha256:a718b427ff781c4f4e975525edb092ee2cdef6a9e7bc49e15063b088961806f8 \
+    --hash=sha256:ab386503f53bbbc64d1ad4b6865bf001414930841a870fc97f1546d4d133f141 \
+    --hash=sha256:ab6fa8c7871877810e1b4e9392c187a60611fbf0226a9e0b11b7b92f5ac72792 \
+    --hash=sha256:b47d64cdd973aede3dd71a9364742c542587db214e63b7529fbb487ed67cddd9 \
+    --hash=sha256:b499c6abe62a7a8d023e2c4b2834fce78a6115856ae95522f2f974139814538c \
+    --hash=sha256:bbb1a71b1784e68870800b1bc9f3313918edc63dbb8f29fbd2e767ce5821696c \
+    --hash=sha256:c3b31180b82c519b8926e629bf9f19952c743e089c41380ddca5db556817b221 \
+    --hash=sha256:c56c299602c70bc1bb5d1e75f7d8c007ca40c9d7aebaf6e4ba52925d88ef826d \
+    --hash=sha256:c92deb5d9acce226a501b77307b3b60b264ca21862bd7d3e0c1f3594022f01bc \
+    --hash=sha256:cc2f3e368ee5242a2cbe28323a866656006382872c40869b49b265add546703f \
+    --hash=sha256:d82bed73544e91fb081ab93e3725e45dd8515c675c0e9926b4e1f420a93a6ab9 \
+    --hash=sha256:da1cdfa96425cbe51f8afa43e392366ed0b36ce398f08b60de6b97e3ed4affef \
+    --hash=sha256:da5ba7b59d954f1f214d352308d1d86994d713b13edd4b24a556bcc43d2ddbc3 \
+    --hash=sha256:e0c8c803f2f8db7217898d11657cb6042b9b0553a997c4a0601f48a691480fab \
+    --hash=sha256:ee4c5120ddf7d4dd1eaf079af3af7102b56d919fa13ad55600a4e0ebe532779b \
+    --hash=sha256:eee0c5ecb58296580fc495ac99b003f64f82a74f9576a244d04978a7e97166db \
+    --hash=sha256:f5abc8b4d0c5b556ed8cd41490b606fe99293175a82b98e652c3f2711b452988 \
+    --hash=sha256:f810e764617b0748b49a731ffaa525d9bb36ff38332411704c2400125af859a6 \
+    --hash=sha256:f89139662cc4e65a4813f4babb9ca9544e42bddb823d2ec434e18dad582543bc \
+    --hash=sha256:fa47319a10e0a076709644a0efbcaab9e91902c8bd8ef74c6adb19d320f69b83 \
+    --hash=sha256:fabb953ab913dadc1ff9dcc3a7a7d3dc6a92efab3a0373989b8063347f8705be
+    # via
+    #   aiohttp
+    #   aiosignal
+fsspec[http]==2022.10.0 \
+    --hash=sha256:6b7c6ab3b476cdf17efcfeccde7fca28ef5a48f73a71010aaceec5fc15bf9ebf \
+    --hash=sha256:cb6092474e90487a51de768170f3afa50ca8982c26150a59072b16433879ff1d
+    # via pytorch-lightning
+ftfy==6.1.1 \
+    --hash=sha256:0ffd33fce16b54cccaec78d6ec73d95ad370e5df5a25255c8966a6147bd667ca \
+    --hash=sha256:bfc2019f84fcd851419152320a6375604a0f1459c281b5b199b2cd0d2e727f8f
+    # via clip
+future==0.18.2 \
+    --hash=sha256:b1bead90b70cf6ec3f0710ae53a525360fa360d306a86583adc6bf83a4db537d
+    # via
+    #   basicsr
+    #   test-tube
+getpass-asterisk==1.0.1 \
+    --hash=sha256:20d45cafda0066d761961e0919728526baf7bb5151fbf48a7d5ea4034127d857 \
+    --hash=sha256:7cc357a924cf62fa4e15b73cb4e5e30685c9084e464ffdc3fd9000a2b54ea9e9
+    # via -r requirements.in
+gfpgan @ https://github.com/TencentARC/GFPGAN/archive/2eac2033893ca7f427f4035d80fe95b92649ac56.zip \
+    --hash=sha256:79e6d71c8f1df7c7ccb0ac6b9a2ccb615ad5cde818c8b6f285a8711c05aebf85
+    # via
+    #   -r requirements.in
+    #   realesrgan
+gitdb==4.0.9 \
+    --hash=sha256:8033ad4e853066ba6ca92050b9df2f89301b8fc8bf7e9324d412a63f8bf1a8fd \
+    --hash=sha256:bac2fd45c0a1c9cf619e63a90d62bdc63892ef92387424b855792a6cabe789aa
+    # via gitpython
+gitpython==3.1.29 \
+    --hash=sha256:41eea0deec2deea139b459ac03656f0dd28fc4a3387240ec1d3c259a2c47850f \
+    --hash=sha256:cc36bfc4a3f913e66805a28e84703e419d9c264c1077e537b54f0e1af85dbefd
+    # via
+    #   streamlit
+    #   wandb
+google-auth==2.14.0 \
+    --hash=sha256:1ad5b0e6eba5f69645971abb3d2c197537d5914070a8c6d30299dfdb07c5c700 \
+    --hash=sha256:cf24817855d874ede2efd071aa22125445f555de1685b739a9782fcf408c2a3d
+    # via
+    #   google-auth-oauthlib
+    #   tb-nightly
+    #   tensorboard
+google-auth-oauthlib==0.4.6 \
+    --hash=sha256:3f2a6e802eebbb6fb736a370fbf3b055edcb6b52878bf2f26330b5e041316c73 \
+    --hash=sha256:a90a072f6993f2c327067bf65270046384cda5a8ecb20b94ea9a687f1f233a7a
+    # via
+    #   tb-nightly
+    #   tensorboard
+greenlet==2.0.0.post0 \
+    --hash=sha256:00ebdaf0fa51c284fd2172837d751731a15971e0c20d1a9163cfbdf620ce8b49 \
+    --hash=sha256:029ca674b3a7e8427db8f5c65d5ed4e24a7417af2a415a5958598aefd71980c4 \
+    --hash=sha256:02bdb1e373b275bd705c43b249426e776c4f8a8ff2afaf8ec5ea0dde487d8a14 \
+    --hash=sha256:08dc04f49ed1ea5e6772bb5e8cf2a77d1b1744566f4eca471a55b35af1278b31 \
+    --hash=sha256:08f44e938d142271b954405afb6570e0be48a9f556b6bf4d42d2e3ae6a251fad \
+    --hash=sha256:0a5c03e2a68ec2ff1cba74ceaed899ec8cd353285f4f985c30c8cfbef9d3a3be \
+    --hash=sha256:0fee3240093b745efc857392f09379514ad84db4ca324514594bbdf6380016c8 \
+    --hash=sha256:118e708dd7bc88beaeeaa5a8601a7743b8835b7bbaf7c8f23ffa78f8bc8faf28 \
+    --hash=sha256:13d492a807a5c7334b5931e9b6d9b181991ccc6a40555a7b177f189feff59b4b \
+    --hash=sha256:1cac9e9895aeff26434325404558783ee54f4ff3aec8daa56b8706796f7b01a0 \
+    --hash=sha256:2146d15429b4eeb412428737594acb5660a5bc0fdd1488d8a2a74a5ee32391fa \
+    --hash=sha256:21ee1ae26d072b195edea764218623f6c15eba4ae06816908f33c82e0af018d3 \
+    --hash=sha256:22eca421e3f2f3c18f4f54c0ff525aa9d397c6f116fce9ebd37b420174dbc027 \
+    --hash=sha256:2bab49783858cf724fff6868395cbeb81d1188cba23616b53e79de0beda29f42 \
+    --hash=sha256:2fbdec204ca40b3d0c0992a19c1ba627441c17983ac4ffc45baec7f5f53e20ca \
+    --hash=sha256:30ce47525f9a1515566429ac7de6b1ae76d32c3ccede256e3517a1a6419cf659 \
+    --hash=sha256:335dcf676d5e4122e4006c16ae11eda2467af5461b949c265ce120b6b959ffe2 \
+    --hash=sha256:3407b843b05da71fef0f1dd666059c08ad0e0f4afc3b9c93c998a2e53fac95e5 \
+    --hash=sha256:35827f98fd0d768862b8f15777e6dbb03fe6ac6e7bd1bee3f3ded4536f350347 \
+    --hash=sha256:3a22e5988f9d66b3e9ae9583bf9d8ef792b09f23afeb78707e6a4f47ab57cc5e \
+    --hash=sha256:3c3327da2bdab61078e42e695307465c425671a5a9251e6c29ee130d51943f28 \
+    --hash=sha256:3ca723dfc2789c1fb991809822811896b198ecf0909dbccea4a07170d18c3e1b \
+    --hash=sha256:46156ae88ee71c37b6c4f7af63fff5d3ab8f45ef72e1a660bcf6386c1647f106 \
+    --hash=sha256:4bbe2d074292e3646704371eb640ee52c386d633ed72ff223dadcd3fe8ecd8f9 \
+    --hash=sha256:4c4310f0e42154995d92810f27b44ab7116a4a696feb0ff141ae2de59196efd7 \
+    --hash=sha256:4cfa629de5b2dea27c81b334c4536463e9a49ac0877e2008a276d58d4c72868a \
+    --hash=sha256:4e144ab0de56b4d2a2cf0d2fb9d568b59fce49aab3e129badf17c12b0252047d \
+    --hash=sha256:4ea67f303cec384b148774667c7e3cf02311e7026fc02bdcdcd206dfe4ea4fc9 \
+    --hash=sha256:538c9e8f65a32413ace426f8117ef019021adf8175f7c491fed65f5fe2083e0c \
+    --hash=sha256:56565ac9ab4ff3dd473bfe959e0bf2a5062aabb89b7c94cabb417beb162c9fff \
+    --hash=sha256:5e22485256bb1c60bbcc6f8509b1a11042358a2462d5ecdb9a82dc472d2fdd60 \
+    --hash=sha256:602a69c24f1a9755dd1760b3b31bdfc495c4613260c876a01b7e6d5eb9bcae1b \
+    --hash=sha256:6393ec3cecda53b20241e88bc33d87cbd8126cc10870fc69fa16ca2e20a5ac1b \
+    --hash=sha256:6442bbfb047dc1e47658954b72e1589f2bc4e12e67d51bbad0059a626180daa1 \
+    --hash=sha256:666d2a0b269a68cd4fe0976544ab97970c5334d35d0e47ae9be1723f734d8204 \
+    --hash=sha256:697cfbfc19815c40213badcfe5f076418e0f9100cd25a66f513f32c1026b8bf4 \
+    --hash=sha256:6a1a6745c5dce202aa3f29a1736c53cf2179e9c3b280dc62cea9cb8c69977c83 \
+    --hash=sha256:6fc73fc8dd81d9efa842a55033b6b4cb233b134a0270e127c6874d053ef2049b \
+    --hash=sha256:7e9e0d4c5c618b0442396715ffe6c2f84a60d593dad7e0184388aed36d568a65 \
+    --hash=sha256:81fdcf7c0c2df46a99ca421a552c4370117851c5e4dbd6cb53d569b896d62322 \
+    --hash=sha256:8b26932be686f3582df039d79fe96f7ca13d63b39468162f816f9ff29584b9a4 \
+    --hash=sha256:8b7e5191b974fb66fcbac1818ba494d3512da9cf6eaef7acd952f9862eaaa20c \
+    --hash=sha256:8c80e9c41a83d8c90399af8c7dcdeae0c03c48b40b9d0ab84457533d5d7882bf \
+    --hash=sha256:9f2f110b9cc325f6543e0e3f4ab8008c272a59052f9464047c29d4be4511ce05 \
+    --hash=sha256:a339e510a079dc8372e39ce1c7629414db51966235c9670c58d529def79243a2 \
+    --hash=sha256:ad9abc3e4d2370cecb524421cc5c8a664006aa11d5c1cb3c9250e3bf65ab546e \
+    --hash=sha256:b043782c8f6cccc8fae3a16db397eca1d36a41b0706cbf6f514aea1e1a260bab \
+    --hash=sha256:b31de27313abbb567c528ed123380fcf18a5dfd03134570dfd12227e21ac1184 \
+    --hash=sha256:b75e5644cc353328cd57ec8dafaaf5f81b2c3ecf7c4b278b907e99ad53ba7839 \
+    --hash=sha256:b8cfc8fc944bd7b704691bc28225a2635e377e92dc413459845868d3f7724982 \
+    --hash=sha256:c2055c52260808d87622293b57df1c68aeb12ddd8a0cfc0223fb57a5f629e202 \
+    --hash=sha256:c416106b3b8e905b6ab0e84ec90047a6401021aa023f9aa93978e57cd8f8189f \
+    --hash=sha256:d0e210e17a6181a3fd3f8dce957043a4e74177ffa9f295514984b2b633940dce \
+    --hash=sha256:d9453135e48cd631e3e9f06d9da9100d17c9f662e4a6d8b552c29be6c834a6b9 \
+    --hash=sha256:dd0198006278291d9469309d655093df1f5e5107c0261e242b5f390baee32199 \
+    --hash=sha256:e1781bda1e787d3ad33788cc3be47f6e47a9581676d02670c15ee36c9460adfe \
+    --hash=sha256:e56a5a9f303e3ac011ba445a6d84f05d08666bf8db094afafcec5228622c30f5 \
+    --hash=sha256:e93ae35f0fd3caf75e58c76a1cab71e6ece169aaa1b281782ef9efde0a6b83f2 \
+    --hash=sha256:eb36b6570646227a63eda03916f1cc6f3744ee96d28f7a0a5629c59267a8055f \
+    --hash=sha256:f8c425a130e04d5404edaf6f5906e5ab12f3aa1168a1828aba6dfadac5910469
+    # via eventlet
+grpcio==1.50.0 \
+    --hash=sha256:05f7c248e440f538aaad13eee78ef35f0541e73498dd6f832fe284542ac4b298 \
+    --hash=sha256:080b66253f29e1646ac53ef288c12944b131a2829488ac3bac8f52abb4413c0d \
+    --hash=sha256:12b479839a5e753580b5e6053571de14006157f2ef9b71f38c56dc9b23b95ad6 \
+    --hash=sha256:156f8009e36780fab48c979c5605eda646065d4695deea4cfcbcfdd06627ddb6 \
+    --hash=sha256:15f9e6d7f564e8f0776770e6ef32dac172c6f9960c478616c366862933fa08b4 \
+    --hash=sha256:177afaa7dba3ab5bfc211a71b90da1b887d441df33732e94e26860b3321434d9 \
+    --hash=sha256:1a4cd8cb09d1bc70b3ea37802be484c5ae5a576108bad14728f2516279165dd7 \
+    --hash=sha256:1d8d02dbb616c0a9260ce587eb751c9c7dc689bc39efa6a88cc4fa3e9c138a7b \
+    --hash=sha256:2b71916fa8f9eb2abd93151fafe12e18cebb302686b924bd4ec39266211da525 \
+    --hash=sha256:2d9fd6e38b16c4d286a01e1776fdf6c7a4123d99ae8d6b3f0b4a03a34bf6ce45 \
+    --hash=sha256:3b611b3de3dfd2c47549ca01abfa9bbb95937eb0ea546ea1d762a335739887be \
+    --hash=sha256:3e4244c09cc1b65c286d709658c061f12c61c814be0b7030a2d9966ff02611e0 \
+    --hash=sha256:40838061e24f960b853d7bce85086c8e1b81c6342b1f4c47ff0edd44bbae2722 \
+    --hash=sha256:4b123fbb7a777a2fedec684ca0b723d85e1d2379b6032a9a9b7851829ed3ca9a \
+    --hash=sha256:531f8b46f3d3db91d9ef285191825d108090856b3bc86a75b7c3930f16ce432f \
+    --hash=sha256:67dd41a31f6fc5c7db097a5c14a3fa588af54736ffc174af4411d34c4f306f68 \
+    --hash=sha256:7489dbb901f4fdf7aec8d3753eadd40839c9085967737606d2c35b43074eea24 \
+    --hash=sha256:8d4c8e73bf20fb53fe5a7318e768b9734cf122fe671fcce75654b98ba12dfb75 \
+    --hash=sha256:8e69aa4e9b7f065f01d3fdcecbe0397895a772d99954bb82eefbb1682d274518 \
+    --hash=sha256:8e8999a097ad89b30d584c034929f7c0be280cd7851ac23e9067111167dcbf55 \
+    --hash=sha256:906f4d1beb83b3496be91684c47a5d870ee628715227d5d7c54b04a8de802974 \
+    --hash=sha256:92d7635d1059d40d2ec29c8bf5ec58900120b3ce5150ef7414119430a4b2dd5c \
+    --hash=sha256:931e746d0f75b2a5cff0a1197d21827a3a2f400c06bace036762110f19d3d507 \
+    --hash=sha256:95ce51f7a09491fb3da8cf3935005bff19983b77c4e9437ef77235d787b06842 \
+    --hash=sha256:9eea18a878cffc804506d39c6682d71f6b42ec1c151d21865a95fae743fda500 \
+    --hash=sha256:a23d47f2fc7111869f0ff547f771733661ff2818562b04b9ed674fa208e261f4 \
+    --hash=sha256:a4c23e54f58e016761b576976da6a34d876420b993f45f66a2bfb00363ecc1f9 \
+    --hash=sha256:a50a1be449b9e238b9bd43d3857d40edf65df9416dea988929891d92a9f8a778 \
+    --hash=sha256:ab5d0e3590f0a16cb88de4a3fa78d10eb66a84ca80901eb2c17c1d2c308c230f \
+    --hash=sha256:ae23daa7eda93c1c49a9ecc316e027ceb99adbad750fbd3a56fa9e4a2ffd5ae0 \
+    --hash=sha256:af98d49e56605a2912cf330b4627e5286243242706c3a9fa0bcec6e6f68646fc \
+    --hash=sha256:b2f77a90ba7b85bfb31329f8eab9d9540da2cf8a302128fb1241d7ea239a5469 \
+    --hash=sha256:baab51dcc4f2aecabf4ed1e2f57bceab240987c8b03533f1cef90890e6502067 \
+    --hash=sha256:ca8a2254ab88482936ce941485c1c20cdeaef0efa71a61dbad171ab6758ec998 \
+    --hash=sha256:cb11464f480e6103c59d558a3875bd84eed6723f0921290325ebe97262ae1347 \
+    --hash=sha256:ce8513aee0af9c159319692bfbf488b718d1793d764798c3d5cff827a09e25ef \
+    --hash=sha256:cf151f97f5f381163912e8952eb5b3afe89dec9ed723d1561d59cabf1e219a35 \
+    --hash=sha256:d144ad10eeca4c1d1ce930faa105899f86f5d99cecfe0d7224f3c4c76265c15e \
+    --hash=sha256:d534d169673dd5e6e12fb57cc67664c2641361e1a0885545495e65a7b761b0f4 \
+    --hash=sha256:d75061367a69808ab2e84c960e9dce54749bcc1e44ad3f85deee3a6c75b4ede9 \
+    --hash=sha256:d84d04dec64cc4ed726d07c5d17b73c343c8ddcd6b59c7199c801d6bbb9d9ed1 \
+    --hash=sha256:de411d2b030134b642c092e986d21aefb9d26a28bf5a18c47dd08ded411a3bc5 \
+    --hash=sha256:e07fe0d7ae395897981d16be61f0db9791f482f03fee7d1851fe20ddb4f69c03 \
+    --hash=sha256:ea8ccf95e4c7e20419b7827aa5b6da6f02720270686ac63bd3493a651830235c \
+    --hash=sha256:f7025930039a011ed7d7e7ef95a1cb5f516e23c5a6ecc7947259b67bea8e06ca
+    # via
+    #   tb-nightly
+    #   tensorboard
+huggingface-hub==0.10.1 \
+    --hash=sha256:5c188d5b16bec4b78449f8681f9975ff9d321c16046cc29bcf0d7e464ff29276 \
+    --hash=sha256:dc3b0e9a663fe6cad6a8522055c02a9d8673dbd527223288e2442bc028c253db
+    # via
+    #   diffusers
+    #   transformers
+idna==2.10 \
+    --hash=sha256:b307872f855b18632ce0c21c5e45be78c0ea7ae4c15c828c20788b26921eb3f6 \
+    --hash=sha256:b97d804b1e9b523befed77c48dacec60e6dcb0b5391d57af6a65a312a90648c0
+    # via
+    #   requests
+    #   yarl
+imageio==2.22.3 \
+    --hash=sha256:63f007b7f2a082306e36922b3fd529a7aa305d2b78f46195bab8e22bbfe866e9 \
+    --hash=sha256:a4b88f9f3d428b8c0ceeb7e297df8c346a642bb7e3111743eb85717d60b26f6f
+    # via
+    #   scikit-image
+    #   test-tube
+imageio-ffmpeg==0.4.7 \
+    --hash=sha256:27b48c32becae1658aa81c3a6b922538e4099edf5fbcbdb4ff5dbc84b8ffd3d3 \
+    --hash=sha256:6514f1380daf42815bc8c83aad63f33e0b8b47133421ddafe7b410cd8dfbbea5 \
+    --hash=sha256:6aba52ddf0a64442ffcb8d30ac6afb668186acec99ecbc7ae5bd171c4f500bbc \
+    --hash=sha256:7a08838f97f363e37ca41821b864fd3fdc99ab1fe2421040c78eb5f56a9e723e \
+    --hash=sha256:8e724d12dfe83e2a6eb39619e820243ca96c81c47c2648e66e05f7ee24e14312 \
+    --hash=sha256:fc60686ef03c2d0f842901b206223c30051a6a120384458761390104470846fd
+    # via -r requirements.in
+importlib-metadata==5.0.0 \
+    --hash=sha256:da31db32b304314d044d3c12c79bd59e307889b287ad12ff387b3500835fc2ab \
+    --hash=sha256:ddb0e35065e8938f867ed4928d0ae5bf2a53b7773871bfe6bcc7e4fcdc7dea43
+    # via
+    #   diffusers
+    #   streamlit
+itsdangerous==2.1.2 \
+    --hash=sha256:2c2349112351b88699d8d4b6b075022c0808887cb7ad10069318a8b0bc88db44 \
+    --hash=sha256:5dbbc68b317e5e42f327f9021763545dc3fc3bfe22e6deb96aaf1fc38874156a
+    # via flask
+jinja2==3.1.2 \
+    --hash=sha256:31351a702a408a9e7595a8fc6150fc3f43bb6bf7e319770cbc0db9df9437e852 \
+    --hash=sha256:6088930bfe239f0e6710546ab9c19c9ef35e29792895fed6e6e31a023a182a61
+    # via
+    #   altair
+    #   flask
+    #   pydeck
+joblib==1.2.0 \
+    --hash=sha256:091138ed78f800342968c523bdde947e7a305b8594b910a0fea2ab83c3c6d385 \
+    --hash=sha256:e1cee4a79e4af22881164f218d4311f60074197fb707e082e803b61f6d137018
+    # via scikit-learn
+jsonmerge==1.9.0 \
+    --hash=sha256:a2d1f80021c5c1d70a49e31f862b5f068f9db066080d8561e80654de74a3584d
+    # via k-diffusion
+jsonschema==4.17.0 \
+    --hash=sha256:5bfcf2bca16a087ade17e02b282d34af7ccd749ef76241e7f9bd7c0cb8a9424d \
+    --hash=sha256:f660066c3966db7d6daeaea8a75e0b68237a48e51cf49882087757bb59916248
+    # via
+    #   altair
+    #   jsonmerge
+k-diffusion @ https://github.com/invoke-ai/k-diffusion/archive/7f16b2c33411f26b3eae78d10648d625cb0c1095.zip \
+    --hash=sha256:c3f2c84036aa98c3abf4552fafab04df5ca472aa639982795e05bb1db43ce5e4
+    # via -r requirements.in
+kiwisolver==1.4.4 \
+    --hash=sha256:02f79693ec433cb4b5f51694e8477ae83b3205768a6fb48ffba60549080e295b \
+    --hash=sha256:03baab2d6b4a54ddbb43bba1a3a2d1627e82d205c5cf8f4c924dc49284b87166 \
+    --hash=sha256:1041feb4cda8708ce73bb4dcb9ce1ccf49d553bf87c3954bdfa46f0c3f77252c \
+    --hash=sha256:10ee06759482c78bdb864f4109886dff7b8a56529bc1609d4f1112b93fe6423c \
+    --hash=sha256:1d1573129aa0fd901076e2bfb4275a35f5b7aa60fbfb984499d661ec950320b0 \
+    --hash=sha256:283dffbf061a4ec60391d51e6155e372a1f7a4f5b15d59c8505339454f8989e4 \
+    --hash=sha256:28bc5b299f48150b5f822ce68624e445040595a4ac3d59251703779836eceff9 \
+    --hash=sha256:2a66fdfb34e05b705620dd567f5a03f239a088d5a3f321e7b6ac3239d22aa286 \
+    --hash=sha256:2e307eb9bd99801f82789b44bb45e9f541961831c7311521b13a6c85afc09767 \
+    --hash=sha256:2e407cb4bd5a13984a6c2c0fe1845e4e41e96f183e5e5cd4d77a857d9693494c \
+    --hash=sha256:2f5e60fabb7343a836360c4f0919b8cd0d6dbf08ad2ca6b9cf90bf0c76a3c4f6 \
+    --hash=sha256:36dafec3d6d6088d34e2de6b85f9d8e2324eb734162fba59d2ba9ed7a2043d5b \
+    --hash=sha256:3fe20f63c9ecee44560d0e7f116b3a747a5d7203376abeea292ab3152334d004 \
+    --hash=sha256:41dae968a94b1ef1897cb322b39360a0812661dba7c682aa45098eb8e193dbdf \
+    --hash=sha256:4bd472dbe5e136f96a4b18f295d159d7f26fd399136f5b17b08c4e5f498cd494 \
+    --hash=sha256:4ea39b0ccc4f5d803e3337dd46bcce60b702be4d86fd0b3d7531ef10fd99a1ac \
+    --hash=sha256:5853eb494c71e267912275e5586fe281444eb5e722de4e131cddf9d442615626 \
+    --hash=sha256:5bce61af018b0cb2055e0e72e7d65290d822d3feee430b7b8203d8a855e78766 \
+    --hash=sha256:6295ecd49304dcf3bfbfa45d9a081c96509e95f4b9d0eb7ee4ec0530c4a96514 \
+    --hash=sha256:62ac9cc684da4cf1778d07a89bf5f81b35834cb96ca523d3a7fb32509380cbf6 \
+    --hash=sha256:70e7c2e7b750585569564e2e5ca9845acfaa5da56ac46df68414f29fea97be9f \
+    --hash=sha256:7577c1987baa3adc4b3c62c33bd1118c3ef5c8ddef36f0f2c950ae0b199e100d \
+    --hash=sha256:75facbe9606748f43428fc91a43edb46c7ff68889b91fa31f53b58894503a191 \
+    --hash=sha256:787518a6789009c159453da4d6b683f468ef7a65bbde796bcea803ccf191058d \
+    --hash=sha256:78d6601aed50c74e0ef02f4204da1816147a6d3fbdc8b3872d263338a9052c51 \
+    --hash=sha256:7c43e1e1206cd421cd92e6b3280d4385d41d7166b3ed577ac20444b6995a445f \
+    --hash=sha256:81e38381b782cc7e1e46c4e14cd997ee6040768101aefc8fa3c24a4cc58e98f8 \
+    --hash=sha256:841293b17ad704d70c578f1f0013c890e219952169ce8a24ebc063eecf775454 \
+    --hash=sha256:872b8ca05c40d309ed13eb2e582cab0c5a05e81e987ab9c521bf05ad1d5cf5cb \
+    --hash=sha256:877272cf6b4b7e94c9614f9b10140e198d2186363728ed0f701c6eee1baec1da \
+    --hash=sha256:8c808594c88a025d4e322d5bb549282c93c8e1ba71b790f539567932722d7bd8 \
+    --hash=sha256:8ed58b8acf29798b036d347791141767ccf65eee7f26bde03a71c944449e53de \
+    --hash=sha256:91672bacaa030f92fc2f43b620d7b337fd9a5af28b0d6ed3f77afc43c4a64b5a \
+    --hash=sha256:968f44fdbf6dd757d12920d63b566eeb4d5b395fd2d00d29d7ef00a00582aac9 \
+    --hash=sha256:9f85003f5dfa867e86d53fac6f7e6f30c045673fa27b603c397753bebadc3008 \
+    --hash=sha256:a553dadda40fef6bfa1456dc4be49b113aa92c2a9a9e8711e955618cd69622e3 \
+    --hash=sha256:a68b62a02953b9841730db7797422f983935aeefceb1679f0fc85cbfbd311c32 \
+    --hash=sha256:abbe9fa13da955feb8202e215c4018f4bb57469b1b78c7a4c5c7b93001699938 \
+    --hash=sha256:ad881edc7ccb9d65b0224f4e4d05a1e85cf62d73aab798943df6d48ab0cd79a1 \
+    --hash=sha256:b1792d939ec70abe76f5054d3f36ed5656021dcad1322d1cc996d4e54165cef9 \
+    --hash=sha256:b428ef021242344340460fa4c9185d0b1f66fbdbfecc6c63eff4b7c29fad429d \
+    --hash=sha256:b533558eae785e33e8c148a8d9921692a9fe5aa516efbdff8606e7d87b9d5824 \
+    --hash=sha256:ba59c92039ec0a66103b1d5fe588fa546373587a7d68f5c96f743c3396afc04b \
+    --hash=sha256:bc8d3bd6c72b2dd9decf16ce70e20abcb3274ba01b4e1c96031e0c4067d1e7cd \
+    --hash=sha256:bc9db8a3efb3e403e4ecc6cd9489ea2bac94244f80c78e27c31dcc00d2790ac2 \
+    --hash=sha256:bf7d9fce9bcc4752ca4a1b80aabd38f6d19009ea5cbda0e0856983cf6d0023f5 \
+    --hash=sha256:c2dbb44c3f7e6c4d3487b31037b1bdbf424d97687c1747ce4ff2895795c9bf69 \
+    --hash=sha256:c79ebe8f3676a4c6630fd3f777f3cfecf9289666c84e775a67d1d358578dc2e3 \
+    --hash=sha256:c97528e64cb9ebeff9701e7938653a9951922f2a38bd847787d4a8e498cc83ae \
+    --hash=sha256:d0611a0a2a518464c05ddd5a3a1a0e856ccc10e67079bb17f265ad19ab3c7597 \
+    --hash=sha256:d06adcfa62a4431d404c31216f0f8ac97397d799cd53800e9d3efc2fbb3cf14e \
+    --hash=sha256:d41997519fcba4a1e46eb4a2fe31bc12f0ff957b2b81bac28db24744f333e955 \
+    --hash=sha256:d5b61785a9ce44e5a4b880272baa7cf6c8f48a5180c3e81c59553ba0cb0821ca \
+    --hash=sha256:da152d8cdcab0e56e4f45eb08b9aea6455845ec83172092f09b0e077ece2cf7a \
+    --hash=sha256:da7e547706e69e45d95e116e6939488d62174e033b763ab1496b4c29b76fabea \
+    --hash=sha256:db5283d90da4174865d520e7366801a93777201e91e79bacbac6e6927cbceede \
+    --hash=sha256:db608a6757adabb32f1cfe6066e39b3706d8c3aa69bbc353a5b61edad36a5cb4 \
+    --hash=sha256:e0ea21f66820452a3f5d1655f8704a60d66ba1191359b96541eaf457710a5fc6 \
+    --hash=sha256:e7da3fec7408813a7cebc9e4ec55afed2d0fd65c4754bc376bf03498d4e92686 \
+    --hash=sha256:e92a513161077b53447160b9bd8f522edfbed4bd9759e4c18ab05d7ef7e49408 \
+    --hash=sha256:ecb1fa0db7bf4cff9dac752abb19505a233c7f16684c5826d1f11ebd9472b871 \
+    --hash=sha256:efda5fc8cc1c61e4f639b8067d118e742b812c930f708e6667a5ce0d13499e29 \
+    --hash=sha256:f0a1dbdb5ecbef0d34eb77e56fcb3e95bbd7e50835d9782a45df81cc46949750 \
+    --hash=sha256:f0a71d85ecdd570ded8ac3d1c0f480842f49a40beb423bb8014539a9f32a5897 \
+    --hash=sha256:f4f270de01dd3e129a72efad823da90cc4d6aafb64c410c9033aba70db9f1ff0 \
+    --hash=sha256:f6cb459eea32a4e2cf18ba5fcece2dbdf496384413bc1bae15583f19e567f3b2 \
+    --hash=sha256:f8ad8285b01b0d4695102546b342b493b3ccc6781fc28c8c6a1bb63e95d22f09 \
+    --hash=sha256:f9f39e2f049db33a908319cf46624a569b36983c7c78318e9726a4cb8923b26c
+    # via matplotlib
+kornia==0.6.8 \
+    --hash=sha256:0985e02453c0ab4f030e8d22a3a7554dab312ffa8f8a54ec872190e6f0b58c56 \
+    --hash=sha256:0d6d69330b4fd24da742337b8134da0ce01b4d7da66770db5498d58e8b4a0832
+    # via k-diffusion
+llvmlite==0.39.1 \
+    --hash=sha256:03aee0ccd81735696474dc4f8b6be60774892a2929d6c05d093d17392c237f32 \
+    --hash=sha256:1578f5000fdce513712e99543c50e93758a954297575610f48cb1fd71b27c08a \
+    --hash=sha256:16f56eb1eec3cda3a5c526bc3f63594fc24e0c8d219375afeb336f289764c6c7 \
+    --hash=sha256:1ec3d70b3e507515936e475d9811305f52d049281eaa6c8273448a61c9b5b7e2 \
+    --hash=sha256:22d36591cd5d02038912321d9ab8e4668e53ae2211da5523f454e992b5e13c36 \
+    --hash=sha256:3803f11ad5f6f6c3d2b545a303d68d9fabb1d50e06a8d6418e6fcd2d0df00959 \
+    --hash=sha256:39dc2160aed36e989610fc403487f11b8764b6650017ff367e45384dff88ffbf \
+    --hash=sha256:3fc14e757bc07a919221f0cbaacb512704ce5774d7fcada793f1996d6bc75f2a \
+    --hash=sha256:4c6ebace910410daf0bebda09c1859504fc2f33d122e9a971c4c349c89cca630 \
+    --hash=sha256:50aea09a2b933dab7c9df92361b1844ad3145bfb8dd2deb9cd8b8917d59306fb \
+    --hash=sha256:60f8dd1e76f47b3dbdee4b38d9189f3e020d22a173c00f930b52131001d801f9 \
+    --hash=sha256:62c0ea22e0b9dffb020601bb65cb11dd967a095a488be73f07d8867f4e327ca5 \
+    --hash=sha256:6546bed4e02a1c3d53a22a0bced254b3b6894693318b16c16c8e43e29d6befb6 \
+    --hash=sha256:6717c7a6e93c9d2c3d07c07113ec80ae24af45cde536b34363d4bcd9188091d9 \
+    --hash=sha256:7ebf1eb9badc2a397d4f6a6c8717447c81ac011db00064a00408bc83c923c0e4 \
+    --hash=sha256:9ffc84ade195abd4abcf0bd3b827b9140ae9ef90999429b9ea84d5df69c9058c \
+    --hash=sha256:a3f331a323d0f0ada6b10d60182ef06c20a2f01be21699999d204c5750ffd0b4 \
+    --hash=sha256:b1a0bbdb274fb683f993198775b957d29a6f07b45d184c571ef2a721ce4388cf \
+    --hash=sha256:b43abd7c82e805261c425d50335be9a6c4f84264e34d6d6e475207300005d572 \
+    --hash=sha256:c0f158e4708dda6367d21cf15afc58de4ebce979c7a1aa2f6b977aae737e2a54 \
+    --hash=sha256:d0bfd18c324549c0fec2c5dc610fd024689de6f27c6cc67e4e24a07541d6e49b \
+    --hash=sha256:ddab526c5a2c4ccb8c9ec4821fcea7606933dc53f510e2a6eebb45a418d3488a \
+    --hash=sha256:e172c73fccf7d6db4bd6f7de963dedded900d1a5c6778733241d878ba613980e \
+    --hash=sha256:e2c00ff204afa721b0bb9835b5bf1ba7fba210eefcec5552a9e05a63219ba0dc \
+    --hash=sha256:e31f4b799d530255aaf0566e3da2df5bfc35d3cd9d6d5a3dcc251663656c27b1 \
+    --hash=sha256:e4f212c018db951da3e1dc25c2651abc688221934739721f2dad5ff1dd5f90e7 \
+    --hash=sha256:fa9b26939ae553bf30a9f5c4c754db0fb2d2677327f2511e674aa2f5df941789 \
+    --hash=sha256:fb62fc7016b592435d3e3a8f680e3ea8897c3c9e62e6e6cc58011e7a4801439e
+    # via numba
+lmdb==1.3.0 \
+    --hash=sha256:008243762decf8f6c90430a9bced56290ebbcdb5e877d90e42343bb97033e494 \
+    --hash=sha256:08f4b5129f4683802569b02581142e415c8dcc0ff07605983ec1b07804cecbad \
+    --hash=sha256:17215a42a4b9814c383deabecb160581e4fb75d00198eef0e3cea54f230ffbea \
+    --hash=sha256:18c69fabdaf04efaf246587739cc1062b3e57c6ef0743f5c418df89e5e7e7b9b \
+    --hash=sha256:2cfa4aa9c67f8aee89b23005e98d1f3f32490b6b905fd1cb604b207cbd5755ab \
+    --hash=sha256:2df38115dd9428a54d59ae7c712a4c7cce0d6b1d66056de4b1a8c38718066106 \
+    --hash=sha256:394df860c3f93cfd92b6f4caba785f38208cc9614c18b3803f83a2cc1695042f \
+    --hash=sha256:41318717ab5d15ad2d6d263d34fbf614a045210f64b25e59ce734bb2105e421f \
+    --hash=sha256:4172fba19417d7b29409beca7d73c067b54e5d8ab1fb9b51d7b4c1445d20a167 \
+    --hash=sha256:5a14aca2651c3af6f0d0a6b9168200eea0c8f2d27c40b01a442f33329a6e8dff \
+    --hash=sha256:5ddd590e1c7fcb395931aa3782fb89b9db4550ab2d81d006ecd239e0d462bc41 \
+    --hash=sha256:60a11efc21aaf009d06518996360eed346f6000bfc9de05114374230879f992e \
+    --hash=sha256:6260a526e4ad85b1f374a5ba9475bf369fb07e7728ea6ec57226b02c40d1976b \
+    --hash=sha256:62ab28e3593bdc318ea2f2fa1574e5fca3b6d1f264686d773ba54a637d4f563b \
+    --hash=sha256:63cb73fe7ce9eb93d992d632c85a0476b4332670d9e6a2802b5062f603b7809f \
+    --hash=sha256:65334eafa5d430b18d81ebd5362559a41483c362e1931f6e1b15bab2ecb7d75d \
+    --hash=sha256:7da05d70fcc6561ac6b09e9fb1bf64b7ca294652c64c8a2889273970cee796b9 \
+    --hash=sha256:abbc439cd9fe60ffd6197009087ea885ac150017dc85384093b1d376f83f0ec4 \
+    --hash=sha256:c6adbd6f7f9048e97f31a069e652eb51020a81e80a0ce92dbb9810d21da2409a \
+    --hash=sha256:d6a816954d212f40fd15007cd81ab7a6bebb77436d949a6a9ae04af57fc127f3 \
+    --hash=sha256:d9103aa4908f0bca43c5911ca067d4e3d01f682dff0c0381a1239bd2bd757984 \
+    --hash=sha256:df2724bad7820114a205472994091097d0fa65a3e5fff5a8e688d123fb8c6326 \
+    --hash=sha256:e568ae0887ae196340947d9800136e90feaed6b86a261ef01f01b2ba65fc8106 \
+    --hash=sha256:e6a704b3baced9182836c7f77b769f23856f3a8f62d0282b1bc1feaf81a86712 \
+    --hash=sha256:eefb392f6b5cd43aada49258c5a79be11cb2c8cd3fc3e2d9319a1e0b9f906458 \
+    --hash=sha256:f291e3f561f58dddf63a92a5a6a4b8af3a0920b6705d35e2f80e52e86ee238a2 \
+    --hash=sha256:fa6439356e591d3249ab0e1778a6f8d8408e993f66dc911914c78208f5310309
+    # via
+    #   basicsr
+    #   gfpgan
+markdown==3.4.1 \
+    --hash=sha256:08fb8465cffd03d10b9dd34a5c3fea908e20391a2a90b88d66362cb05beed186 \
+    --hash=sha256:3b809086bb6efad416156e00a0da66fe47618a5d6918dd688f53f40c8e4cfeff
+    # via
+    #   tb-nightly
+    #   tensorboard
+markupsafe==2.1.1 \
+    --hash=sha256:0212a68688482dc52b2d45013df70d169f542b7394fc744c02a57374a4207003 \
+    --hash=sha256:089cf3dbf0cd6c100f02945abeb18484bd1ee57a079aefd52cffd17fba910b88 \
+    --hash=sha256:10c1bfff05d95783da83491be968e8fe789263689c02724e0c691933c52994f5 \
+    --hash=sha256:33b74d289bd2f5e527beadcaa3f401e0df0a89927c1559c8566c066fa4248ab7 \
+    --hash=sha256:3799351e2336dc91ea70b034983ee71cf2f9533cdff7c14c90ea126bfd95d65a \
+    --hash=sha256:3ce11ee3f23f79dbd06fb3d63e2f6af7b12db1d46932fe7bd8afa259a5996603 \
+    --hash=sha256:421be9fbf0ffe9ffd7a378aafebbf6f4602d564d34be190fc19a193232fd12b1 \
+    --hash=sha256:43093fb83d8343aac0b1baa75516da6092f58f41200907ef92448ecab8825135 \
+    --hash=sha256:46d00d6cfecdde84d40e572d63735ef81423ad31184100411e6e3388d405e247 \
+    --hash=sha256:4a33dea2b688b3190ee12bd7cfa29d39c9ed176bda40bfa11099a3ce5d3a7ac6 \
+    --hash=sha256:4b9fe39a2ccc108a4accc2676e77da025ce383c108593d65cc909add5c3bd601 \
+    --hash=sha256:56442863ed2b06d19c37f94d999035e15ee982988920e12a5b4ba29b62ad1f77 \
+    --hash=sha256:671cd1187ed5e62818414afe79ed29da836dde67166a9fac6d435873c44fdd02 \
+    --hash=sha256:694deca8d702d5db21ec83983ce0bb4b26a578e71fbdbd4fdcd387daa90e4d5e \
+    --hash=sha256:6a074d34ee7a5ce3effbc526b7083ec9731bb3cbf921bbe1d3005d4d2bdb3a63 \
+    --hash=sha256:6d0072fea50feec76a4c418096652f2c3238eaa014b2f94aeb1d56a66b41403f \
+    --hash=sha256:6fbf47b5d3728c6aea2abb0589b5d30459e369baa772e0f37a0320185e87c980 \
+    --hash=sha256:7f91197cc9e48f989d12e4e6fbc46495c446636dfc81b9ccf50bb0ec74b91d4b \
+    --hash=sha256:86b1f75c4e7c2ac2ccdaec2b9022845dbb81880ca318bb7a0a01fbf7813e3812 \
+    --hash=sha256:8dc1c72a69aa7e082593c4a203dcf94ddb74bb5c8a731e4e1eb68d031e8498ff \
+    --hash=sha256:8e3dcf21f367459434c18e71b2a9532d96547aef8a871872a5bd69a715c15f96 \
+    --hash=sha256:8e576a51ad59e4bfaac456023a78f6b5e6e7651dcd383bcc3e18d06f9b55d6d1 \
+    --hash=sha256:96e37a3dc86e80bf81758c152fe66dbf60ed5eca3d26305edf01892257049925 \
+    --hash=sha256:97a68e6ada378df82bc9f16b800ab77cbf4b2fada0081794318520138c088e4a \
+    --hash=sha256:99a2a507ed3ac881b975a2976d59f38c19386d128e7a9a18b7df6fff1fd4c1d6 \
+    --hash=sha256:a49907dd8420c5685cfa064a1335b6754b74541bbb3706c259c02ed65b644b3e \
+    --hash=sha256:b09bf97215625a311f669476f44b8b318b075847b49316d3e28c08e41a7a573f \
+    --hash=sha256:b7bd98b796e2b6553da7225aeb61f447f80a1ca64f41d83612e6139ca5213aa4 \
+    --hash=sha256:b87db4360013327109564f0e591bd2a3b318547bcef31b468a92ee504d07ae4f \
+    --hash=sha256:bcb3ed405ed3222f9904899563d6fc492ff75cce56cba05e32eff40e6acbeaa3 \
+    --hash=sha256:d4306c36ca495956b6d568d276ac11fdd9c30a36f1b6eb928070dc5360b22e1c \
+    --hash=sha256:d5ee4f386140395a2c818d149221149c54849dfcfcb9f1debfe07a8b8bd63f9a \
+    --hash=sha256:dda30ba7e87fbbb7eab1ec9f58678558fd9a6b8b853530e176eabd064da81417 \
+    --hash=sha256:e04e26803c9c3851c931eac40c695602c6295b8d432cbe78609649ad9bd2da8a \
+    --hash=sha256:e1c0b87e09fa55a220f058d1d49d3fb8df88fbfab58558f1198e08c1e1de842a \
+    --hash=sha256:e72591e9ecd94d7feb70c1cbd7be7b3ebea3f548870aa91e2732960fa4d57a37 \
+    --hash=sha256:e8c843bbcda3a2f1e3c2ab25913c80a3c5376cd00c6e8c4a86a89a28c8dc5452 \
+    --hash=sha256:efc1913fd2ca4f334418481c7e595c00aad186563bbc1ec76067848c7ca0a933 \
+    --hash=sha256:f121a1420d4e173a5d96e47e9a0c0dcff965afdf1626d28de1460815f7c4ee7a \
+    --hash=sha256:fc7b548b17d238737688817ab67deebb30e8073c95749d55538ed473130ec0c7
+    # via
+    #   jinja2
+    #   werkzeug
+matplotlib==3.6.2 \
+    --hash=sha256:0844523dfaaff566e39dbfa74e6f6dc42e92f7a365ce80929c5030b84caa563a \
+    --hash=sha256:0eda9d1b43f265da91fb9ae10d6922b5a986e2234470a524e6b18f14095b20d2 \
+    --hash=sha256:168093410b99f647ba61361b208f7b0d64dde1172b5b1796d765cd243cadb501 \
+    --hash=sha256:1836f366272b1557a613f8265db220eb8dd883202bbbabe01bad5a4eadfd0c95 \
+    --hash=sha256:19d61ee6414c44a04addbe33005ab1f87539d9f395e25afcbe9a3c50ce77c65c \
+    --hash=sha256:252957e208c23db72ca9918cb33e160c7833faebf295aaedb43f5b083832a267 \
+    --hash=sha256:32d29c8c26362169c80c5718ce367e8c64f4dd068a424e7110df1dd2ed7bd428 \
+    --hash=sha256:380d48c15ec41102a2b70858ab1dedfa33eb77b2c0982cb65a200ae67a48e9cb \
+    --hash=sha256:3964934731fd7a289a91d315919cf757f293969a4244941ab10513d2351b4e83 \
+    --hash=sha256:3cef89888a466228fc4e4b2954e740ce8e9afde7c4315fdd18caa1b8de58ca17 \
+    --hash=sha256:4426c74761790bff46e3d906c14c7aab727543293eed5a924300a952e1a3a3c1 \
+    --hash=sha256:5024b8ed83d7f8809982d095d8ab0b179bebc07616a9713f86d30cf4944acb73 \
+    --hash=sha256:52c2bdd7cd0bf9d5ccdf9c1816568fd4ccd51a4d82419cc5480f548981b47dd0 \
+    --hash=sha256:54fa9fe27f5466b86126ff38123261188bed568c1019e4716af01f97a12fe812 \
+    --hash=sha256:5ba73aa3aca35d2981e0b31230d58abb7b5d7ca104e543ae49709208d8ce706a \
+    --hash=sha256:5e16dcaecffd55b955aa5e2b8a804379789c15987e8ebd2f32f01398a81e975b \
+    --hash=sha256:5ecfc6559132116dedfc482d0ad9df8a89dc5909eebffd22f3deb684132d002f \
+    --hash=sha256:74153008bd24366cf099d1f1e83808d179d618c4e32edb0d489d526523a94d9f \
+    --hash=sha256:78ec3c3412cf277e6252764ee4acbdbec6920cc87ad65862272aaa0e24381eee \
+    --hash=sha256:795ad83940732b45d39b82571f87af0081c120feff2b12e748d96bb191169e33 \
+    --hash=sha256:7f716b6af94dc1b6b97c46401774472f0867e44595990fe80a8ba390f7a0a028 \
+    --hash=sha256:83dc89c5fd728fdb03b76f122f43b4dcee8c61f1489e232d9ad0f58020523e1c \
+    --hash=sha256:8a0ae37576ed444fe853709bdceb2be4c7df6f7acae17b8378765bd28e61b3ae \
+    --hash=sha256:8a8dbe2cb7f33ff54b16bb5c500673502a35f18ac1ed48625e997d40c922f9cc \
+    --hash=sha256:8a9d899953c722b9afd7e88dbefd8fb276c686c3116a43c577cfabf636180558 \
+    --hash=sha256:8d0068e40837c1d0df6e3abf1cdc9a34a6d2611d90e29610fa1d2455aeb4e2e5 \
+    --hash=sha256:9347cc6822f38db2b1d1ce992f375289670e595a2d1c15961aacbe0977407dfc \
+    --hash=sha256:9f335e5625feb90e323d7e3868ec337f7b9ad88b5d633f876e3b778813021dab \
+    --hash=sha256:b03fd10a1709d0101c054883b550f7c4c5e974f751e2680318759af005964990 \
+    --hash=sha256:b0ca2c60d3966dfd6608f5f8c49b8a0fcf76de6654f2eda55fc6ef038d5a6f27 \
+    --hash=sha256:b2604c6450f9dd2c42e223b1f5dca9643a23cfecc9fde4a94bb38e0d2693b136 \
+    --hash=sha256:ca0e7a658fbafcddcaefaa07ba8dae9384be2343468a8e011061791588d839fa \
+    --hash=sha256:d0e9ac04065a814d4cf2c6791a2ad563f739ae3ae830d716d54245c2b96fead6 \
+    --hash=sha256:d50e8c1e571ee39b5dfbc295c11ad65988879f68009dd281a6e1edbc2ff6c18c \
+    --hash=sha256:d840adcad7354be6f2ec28d0706528b0026e4c3934cc6566b84eac18633eab1b \
+    --hash=sha256:e0bbee6c2a5bf2a0017a9b5e397babb88f230e6f07c3cdff4a4c4bc75ed7c617 \
+    --hash=sha256:e5afe0a7ea0e3a7a257907060bee6724a6002b7eec55d0db16fd32409795f3e1 \
+    --hash=sha256:e68be81cd8c22b029924b6d0ee814c337c0e706b8d88495a617319e5dd5441c3 \
+    --hash=sha256:ec9be0f4826cdb3a3a517509dcc5f87f370251b76362051ab59e42b6b765f8c4 \
+    --hash=sha256:f04f97797df35e442ed09f529ad1235d1f1c0f30878e2fe09a2676b71a8801e0 \
+    --hash=sha256:f41e57ad63d336fe50d3a67bb8eaa26c09f6dda6a59f76777a99b8ccd8e26aec
+    # via filterpy
+multidict==6.0.2 \
+    --hash=sha256:0327292e745a880459ef71be14e709aaea2f783f3537588fb4ed09b6c01bca60 \
+    --hash=sha256:041b81a5f6b38244b34dc18c7b6aba91f9cdaf854d9a39e5ff0b58e2b5773b9c \
+    --hash=sha256:0556a1d4ea2d949efe5fd76a09b4a82e3a4a30700553a6725535098d8d9fb672 \
+    --hash=sha256:05f6949d6169878a03e607a21e3b862eaf8e356590e8bdae4227eedadacf6e51 \
+    --hash=sha256:07a017cfa00c9890011628eab2503bee5872f27144936a52eaab449be5eaf032 \
+    --hash=sha256:0b9e95a740109c6047602f4db4da9949e6c5945cefbad34a1299775ddc9a62e2 \
+    --hash=sha256:19adcfc2a7197cdc3987044e3f415168fc5dc1f720c932eb1ef4f71a2067e08b \
+    --hash=sha256:19d9bad105dfb34eb539c97b132057a4e709919ec4dd883ece5838bcbf262b80 \
+    --hash=sha256:225383a6603c086e6cef0f2f05564acb4f4d5f019a4e3e983f572b8530f70c88 \
+    --hash=sha256:23b616fdc3c74c9fe01d76ce0d1ce872d2d396d8fa8e4899398ad64fb5aa214a \
+    --hash=sha256:2957489cba47c2539a8eb7ab32ff49101439ccf78eab724c828c1a54ff3ff98d \
+    --hash=sha256:2d36e929d7f6a16d4eb11b250719c39560dd70545356365b494249e2186bc389 \
+    --hash=sha256:2e4a0785b84fb59e43c18a015ffc575ba93f7d1dbd272b4cdad9f5134b8a006c \
+    --hash=sha256:3368bf2398b0e0fcbf46d85795adc4c259299fec50c1416d0f77c0a843a3eed9 \
+    --hash=sha256:373ba9d1d061c76462d74e7de1c0c8e267e9791ee8cfefcf6b0b2495762c370c \
+    --hash=sha256:4070613ea2227da2bfb2c35a6041e4371b0af6b0be57f424fe2318b42a748516 \
+    --hash=sha256:45183c96ddf61bf96d2684d9fbaf6f3564d86b34cb125761f9a0ef9e36c1d55b \
+    --hash=sha256:4571f1beddff25f3e925eea34268422622963cd8dc395bb8778eb28418248e43 \
+    --hash=sha256:47e6a7e923e9cada7c139531feac59448f1f47727a79076c0b1ee80274cd8eee \
+    --hash=sha256:47fbeedbf94bed6547d3aa632075d804867a352d86688c04e606971595460227 \
+    --hash=sha256:497988d6b6ec6ed6f87030ec03280b696ca47dbf0648045e4e1d28b80346560d \
+    --hash=sha256:4bae31803d708f6f15fd98be6a6ac0b6958fcf68fda3c77a048a4f9073704aae \
+    --hash=sha256:50bd442726e288e884f7be9071016c15a8742eb689a593a0cac49ea093eef0a7 \
+    --hash=sha256:514fe2b8d750d6cdb4712346a2c5084a80220821a3e91f3f71eec11cf8d28fd4 \
+    --hash=sha256:5774d9218d77befa7b70d836004a768fb9aa4fdb53c97498f4d8d3f67bb9cfa9 \
+    --hash=sha256:5fdda29a3c7e76a064f2477c9aab1ba96fd94e02e386f1e665bca1807fc5386f \
+    --hash=sha256:5ff3bd75f38e4c43f1f470f2df7a4d430b821c4ce22be384e1459cb57d6bb013 \
+    --hash=sha256:626fe10ac87851f4cffecee161fc6f8f9853f0f6f1035b59337a51d29ff3b4f9 \
+    --hash=sha256:6701bf8a5d03a43375909ac91b6980aea74b0f5402fbe9428fc3f6edf5d9677e \
+    --hash=sha256:684133b1e1fe91eda8fa7447f137c9490a064c6b7f392aa857bba83a28cfb693 \
+    --hash=sha256:6f3cdef8a247d1eafa649085812f8a310e728bdf3900ff6c434eafb2d443b23a \
+    --hash=sha256:75bdf08716edde767b09e76829db8c1e5ca9d8bb0a8d4bd94ae1eafe3dac5e15 \
+    --hash=sha256:7c40b7bbece294ae3a87c1bc2abff0ff9beef41d14188cda94ada7bcea99b0fb \
+    --hash=sha256:8004dca28e15b86d1b1372515f32eb6f814bdf6f00952699bdeb541691091f96 \
+    --hash=sha256:8064b7c6f0af936a741ea1efd18690bacfbae4078c0c385d7c3f611d11f0cf87 \
+    --hash=sha256:89171b2c769e03a953d5969b2f272efa931426355b6c0cb508022976a17fd376 \
+    --hash=sha256:8cbf0132f3de7cc6c6ce00147cc78e6439ea736cee6bca4f068bcf892b0fd658 \
+    --hash=sha256:9cc57c68cb9139c7cd6fc39f211b02198e69fb90ce4bc4a094cf5fe0d20fd8b0 \
+    --hash=sha256:a007b1638e148c3cfb6bf0bdc4f82776cef0ac487191d093cdc316905e504071 \
+    --hash=sha256:a2c34a93e1d2aa35fbf1485e5010337c72c6791407d03aa5f4eed920343dd360 \
+    --hash=sha256:a45e1135cb07086833ce969555df39149680e5471c04dfd6a915abd2fc3f6dbc \
+    --hash=sha256:ac0e27844758d7177989ce406acc6a83c16ed4524ebc363c1f748cba184d89d3 \
+    --hash=sha256:aef9cc3d9c7d63d924adac329c33835e0243b5052a6dfcbf7732a921c6e918ba \
+    --hash=sha256:b9d153e7f1f9ba0b23ad1568b3b9e17301e23b042c23870f9ee0522dc5cc79e8 \
+    --hash=sha256:bfba7c6d5d7c9099ba21f84662b037a0ffd4a5e6b26ac07d19e423e6fdf965a9 \
+    --hash=sha256:c207fff63adcdf5a485969131dc70e4b194327666b7e8a87a97fbc4fd80a53b2 \
+    --hash=sha256:d0509e469d48940147e1235d994cd849a8f8195e0bca65f8f5439c56e17872a3 \
+    --hash=sha256:d16cce709ebfadc91278a1c005e3c17dd5f71f5098bfae1035149785ea6e9c68 \
+    --hash=sha256:d48b8ee1d4068561ce8033d2c344cf5232cb29ee1a0206a7b828c79cbc5982b8 \
+    --hash=sha256:de989b195c3d636ba000ee4281cd03bb1234635b124bf4cd89eeee9ca8fcb09d \
+    --hash=sha256:e07c8e79d6e6fd37b42f3250dba122053fddb319e84b55dd3a8d6446e1a7ee49 \
+    --hash=sha256:e2c2e459f7050aeb7c1b1276763364884595d47000c1cddb51764c0d8976e608 \
+    --hash=sha256:e5b20e9599ba74391ca0cfbd7b328fcc20976823ba19bc573983a25b32e92b57 \
+    --hash=sha256:e875b6086e325bab7e680e4316d667fc0e5e174bb5611eb16b3ea121c8951b86 \
+    --hash=sha256:f4f052ee022928d34fe1f4d2bc743f32609fb79ed9c49a1710a5ad6b2198db20 \
+    --hash=sha256:fcb91630817aa8b9bc4a74023e4198480587269c272c58b3279875ed7235c293 \
+    --hash=sha256:fd9fc9c4849a07f3635ccffa895d57abce554b467d611a5009ba4f39b78a8849 \
+    --hash=sha256:feba80698173761cddd814fa22e88b0661e98cb810f9f986c54aa34d281e4937 \
+    --hash=sha256:feea820722e69451743a3d56ad74948b68bf456984d63c1a92e8347b7b88452d
+    # via
+    #   aiohttp
+    #   yarl
+networkx==2.8.8 \
+    --hash=sha256:230d388117af870fce5647a3c52401fcf753e94720e6ea6b4197a5355648885e \
+    --hash=sha256:e435dfa75b1d7195c7b8378c3859f0445cd88c6b0375c181ed66823a9ceb7524
+    # via scikit-image
+numba==0.56.4 \
+    --hash=sha256:0240f9026b015e336069329839208ebd70ec34ae5bfbf402e4fcc8e06197528e \
+    --hash=sha256:03634579d10a6129181129de293dd6b5eaabee86881369d24d63f8fe352dd6cb \
+    --hash=sha256:03fe94cd31e96185cce2fae005334a8cc712fc2ba7756e52dff8c9400718173f \
+    --hash=sha256:0611e6d3eebe4cb903f1a836ffdb2bda8d18482bcd0a0dcc56e79e2aa3fefef5 \
+    --hash=sha256:0da583c532cd72feefd8e551435747e0e0fbb3c0530357e6845fcc11e38d6aea \
+    --hash=sha256:14dbbabf6ffcd96ee2ac827389afa59a70ffa9f089576500434c34abf9b054a4 \
+    --hash=sha256:32d9fef412c81483d7efe0ceb6cf4d3310fde8b624a9cecca00f790573ac96ee \
+    --hash=sha256:3a993349b90569518739009d8f4b523dfedd7e0049e6838c0e17435c3e70dcc4 \
+    --hash=sha256:3cb1a07a082a61df80a468f232e452d818f5ae254b40c26390054e4e868556e0 \
+    --hash=sha256:42f9e1be942b215df7e6cc9948cf9c15bb8170acc8286c063a9e57994ef82fd1 \
+    --hash=sha256:4373da9757049db7c90591e9ec55a2e97b2b36ba7ae3bf9c956a513374077470 \
+    --hash=sha256:4e08e203b163ace08bad500b0c16f6092b1eb34fd1fce4feaf31a67a3a5ecf3b \
+    --hash=sha256:553da2ce74e8862e18a72a209ed3b6d2924403bdd0fb341fa891c6455545ba7c \
+    --hash=sha256:720886b852a2d62619ae3900fe71f1852c62db4f287d0c275a60219e1643fc04 \
+    --hash=sha256:85dbaed7a05ff96492b69a8900c5ba605551afb9b27774f7f10511095451137c \
+    --hash=sha256:8a95ca9cc77ea4571081f6594e08bd272b66060634b8324e99cd1843020364f9 \
+    --hash=sha256:91f021145a8081f881996818474ef737800bcc613ffb1e618a655725a0f9e246 \
+    --hash=sha256:9f62672145f8669ec08762895fe85f4cf0ead08ce3164667f2b94b2f62ab23c3 \
+    --hash=sha256:a12ef323c0f2101529d455cfde7f4135eaa147bad17afe10b48634f796d96abd \
+    --hash=sha256:c602d015478b7958408d788ba00a50272649c5186ea8baa6cf71d4a1c761bba1 \
+    --hash=sha256:c75e8a5f810ce80a0cfad6e74ee94f9fde9b40c81312949bf356b7304ef20740 \
+    --hash=sha256:d0ae9270a7a5cc0ede63cd234b4ff1ce166c7a749b91dbbf45e0000c56d3eade \
+    --hash=sha256:d69ad934e13c15684e7887100a8f5f0f61d7a8e57e0fd29d9993210089a5b531 \
+    --hash=sha256:dbcc847bac2d225265d054993a7f910fda66e73d6662fe7156452cac0325b073 \
+    --hash=sha256:e64d338b504c9394a4a34942df4627e1e6cb07396ee3b49fe7b8d6420aa5104f \
+    --hash=sha256:f4cfc3a19d1e26448032049c79fc60331b104f694cf570a9e94f4e2c9d0932bb \
+    --hash=sha256:fbfb45e7b297749029cb28694abf437a78695a100e7c2033983d69f0ba2698d4 \
+    --hash=sha256:fcdf84ba3ed8124eb7234adfbb8792f311991cbf8aed1cad4b1b1a7ee08380c1
+    # via facexlib
+numpy==1.23.4 \
+    --hash=sha256:0fe563fc8ed9dc4474cbf70742673fc4391d70f4363f917599a7fa99f042d5a8 \
+    --hash=sha256:12ac457b63ec8ded85d85c1e17d85efd3c2b0967ca39560b307a35a6703a4735 \
+    --hash=sha256:2341f4ab6dba0834b685cce16dad5f9b6606ea8a00e6da154f5dbded70fdc4dd \
+    --hash=sha256:296d17aed51161dbad3c67ed6d164e51fcd18dbcd5dd4f9d0a9c6055dce30810 \
+    --hash=sha256:488a66cb667359534bc70028d653ba1cf307bae88eab5929cd707c761ff037db \
+    --hash=sha256:4d52914c88b4930dafb6c48ba5115a96cbab40f45740239d9f4159c4ba779962 \
+    --hash=sha256:5e13030f8793e9ee42f9c7d5777465a560eb78fa7e11b1c053427f2ccab90c79 \
+    --hash=sha256:61be02e3bf810b60ab74e81d6d0d36246dbfb644a462458bb53b595791251911 \
+    --hash=sha256:7607b598217745cc40f751da38ffd03512d33ec06f3523fb0b5f82e09f6f676d \
+    --hash=sha256:7a70a7d3ce4c0e9284e92285cba91a4a3f5214d87ee0e95928f3614a256a1488 \
+    --hash=sha256:7ab46e4e7ec63c8a5e6dbf5c1b9e1c92ba23a7ebecc86c336cb7bf3bd2fb10e5 \
+    --hash=sha256:8981d9b5619569899666170c7c9748920f4a5005bf79c72c07d08c8a035757b0 \
+    --hash=sha256:8c053d7557a8f022ec823196d242464b6955a7e7e5015b719e76003f63f82d0f \
+    --hash=sha256:926db372bc4ac1edf81cfb6c59e2a881606b409ddc0d0920b988174b2e2a767f \
+    --hash=sha256:95d79ada05005f6f4f337d3bb9de8a7774f259341c70bc88047a1f7b96a4bcb2 \
+    --hash=sha256:95de7dc7dc47a312f6feddd3da2500826defdccbc41608d0031276a24181a2c0 \
+    --hash=sha256:a0882323e0ca4245eb0a3d0a74f88ce581cc33aedcfa396e415e5bba7bf05f68 \
+    --hash=sha256:a8365b942f9c1a7d0f0dc974747d99dd0a0cdfc5949a33119caf05cb314682d3 \
+    --hash=sha256:a8aae2fb3180940011b4862b2dd3756616841c53db9734b27bb93813cd79fce6 \
+    --hash=sha256:c237129f0e732885c9a6076a537e974160482eab8f10db6292e92154d4c67d71 \
+    --hash=sha256:c67b833dbccefe97cdd3f52798d430b9d3430396af7cdb2a0c32954c3ef73894 \
+    --hash=sha256:ce03305dd694c4873b9429274fd41fc7eb4e0e4dea07e0af97a933b079a5814f \
+    --hash=sha256:d331afac87c92373826af83d2b2b435f57b17a5c74e6268b79355b970626e329 \
+    --hash=sha256:dada341ebb79619fe00a291185bba370c9803b1e1d7051610e01ed809ef3a4ba \
+    --hash=sha256:ed2cc92af0efad20198638c69bb0fc2870a58dabfba6eb722c933b48556c686c \
+    --hash=sha256:f260da502d7441a45695199b4e7fd8ca87db659ba1c78f2bbf31f934fe76ae0e \
+    --hash=sha256:f2f390aa4da44454db40a1f0201401f9036e8d578a25f01a6e237cea238337ef \
+    --hash=sha256:f76025acc8e2114bb664294a07ede0727aa75d63a06d2fae96bf29a81747e4a7
+    # via
+    #   accelerate
+    #   albumentations
+    #   altair
+    #   basicsr
+    #   clean-fid
+    #   contourpy
+    #   diffusers
+    #   facexlib
+    #   filterpy
+    #   gfpgan
+    #   imageio
+    #   matplotlib
+    #   numba
+    #   opencv-python
+    #   opencv-python-headless
+    #   pandas
+    #   pyarrow
+    #   pydeck
+    #   pytorch-lightning
+    #   pywavelets
+    #   qudida
+    #   realesrgan
+    #   scikit-image
+    #   scikit-learn
+    #   scipy
+    #   streamlit
+    #   taming-transformers-rom1504
+    #   tb-nightly
+    #   tensorboard
+    #   test-tube
+    #   tifffile
+    #   torch-fidelity
+    #   torchmetrics
+    #   torchvision
+    #   transformers
+oauthlib==3.2.2 \
+    --hash=sha256:8139f29aac13e25d502680e9e19963e83f16838d48a0d71c287fe40e7067fbca \
+    --hash=sha256:9859c40929662bec5d64f34d01c99e093149682a3f38915dc0655d5a633dd918
+    # via requests-oauthlib
+omegaconf==2.2.3 \
+    --hash=sha256:59ff9fba864ffbb5fb710b64e8a9ba37c68fa339a2e2bb4f1b648d6901552523 \
+    --hash=sha256:d6f2cbf79a992899eb76c6cb1aedfcf0fe7456a8654382edd5ee0c1b199c0657
+    # via taming-transformers-rom1504
+opencv-python==4.6.0.66 \
+    --hash=sha256:0dc82a3d8630c099d2f3ac1b1aabee164e8188db54a786abb7a4e27eba309440 \
+    --hash=sha256:5af8ba35a4fcb8913ffb86e92403e9a656a4bff4a645d196987468f0f8947875 \
+    --hash=sha256:6e32af22e3202748bd233ed8f538741876191863882eba44e332d1a34993165b \
+    --hash=sha256:c5bfae41ad4031e66bb10ec4a0a2ffd3e514d092652781e8b1ac98d1b59f1158 \
+    --hash=sha256:dbdc84a9b4ea2cbae33861652d25093944b9959279200b7ae0badd32439f74de \
+    --hash=sha256:e6e448b62afc95c5b58f97e87ef84699e6607fe5c58730a03301c52496005cae \
+    --hash=sha256:f482e78de6e7b0b060ff994ffd859bddc3f7f382bb2019ef157b0ea8ca8712f5
+    # via
+    #   basicsr
+    #   facexlib
+    #   gfpgan
+    #   realesrgan
+opencv-python-headless==4.6.0.66 \
+    --hash=sha256:21e70f8b0c04098cdf466d27184fe6c3820aaef944a22548db95099959c95889 \
+    --hash=sha256:2c032c373e447c3fc2a670bca20e2918a1205a6e72854df60425fd3f82c78c32 \
+    --hash=sha256:3bacd806cce1f1988e58f3d6f761538e0215d6621d316de94c009dc0acbd6ad3 \
+    --hash=sha256:d5291d7e10aa2c19cab6fd86f0d61af8617290ecd2d7ffcb051e446868d04cc5 \
+    --hash=sha256:e6c069bc963d7e8fcec21b3e33e594d35948badd63eccb2e80f88b0a12102c03 \
+    --hash=sha256:eec6281054346103d6af93f173b7c6a206beb2663d3adc04aa3ddc66e85093df \
+    --hash=sha256:ffbf26fcd697af996408440a93bc69c49c05a36845771f984156dfbeaa95d497
+    # via
+    #   albumentations
+    #   qudida
+packaging==21.3 \
+    --hash=sha256:dd47c42927d89ab911e606518907cc2d3a1f38bbd026385970643f9c5b8ecfeb \
+    --hash=sha256:ef103e05f519cdc783ae24ea4e2e0f508a9c99b2d4969652eed6a2e1ea5bd522
+    # via
+    #   accelerate
+    #   huggingface-hub
+    #   kornia
+    #   matplotlib
+    #   pytorch-lightning
+    #   scikit-image
+    #   streamlit
+    #   torchmetrics
+    #   transformers
+pandas==1.5.1 \
+    --hash=sha256:04e51b01d5192499390c0015630975f57836cc95c7411415b499b599b05c0c96 \
+    --hash=sha256:05c527c64ee02a47a24031c880ee0ded05af0623163494173204c5b72ddce658 \
+    --hash=sha256:0a78e05ec09731c5b3bd7a9805927ea631fe6f6cb06f0e7c63191a9a778d52b4 \
+    --hash=sha256:17da7035d9e6f9ea9cdc3a513161f8739b8f8489d31dc932bc5a29a27243f93d \
+    --hash=sha256:249cec5f2a5b22096440bd85c33106b6102e0672204abd2d5c014106459804ee \
+    --hash=sha256:2c25e5c16ee5c0feb6cf9d982b869eec94a22ddfda9aa2fbed00842cbb697624 \
+    --hash=sha256:32e3d9f65606b3f6e76555bfd1d0b68d94aff0929d82010b791b6254bf5a4b96 \
+    --hash=sha256:36aa1f8f680d7584e9b572c3203b20d22d697c31b71189322f16811d4ecfecd3 \
+    --hash=sha256:5b0c970e2215572197b42f1cff58a908d734503ea54b326412c70d4692256391 \
+    --hash=sha256:5cee0c74e93ed4f9d39007e439debcaadc519d7ea5c0afc3d590a3a7b2edf060 \
+    --hash=sha256:669c8605dba6c798c1863157aefde959c1796671ffb342b80fcb80a4c0bc4c26 \
+    --hash=sha256:66a1ad667b56e679e06ba73bb88c7309b3f48a4c279bd3afea29f65a766e9036 \
+    --hash=sha256:683779e5728ac9138406c59a11e09cd98c7d2c12f0a5fc2b9c5eecdbb4a00075 \
+    --hash=sha256:6bb391659a747cf4f181a227c3e64b6d197100d53da98dcd766cc158bdd9ec68 \
+    --hash=sha256:81f0674fa50b38b6793cd84fae5d67f58f74c2d974d2cb4e476d26eee33343d0 \
+    --hash=sha256:927e59c694e039c75d7023465d311277a1fc29ed7236b5746e9dddf180393113 \
+    --hash=sha256:932d2d7d3cab44cfa275601c982f30c2d874722ef6396bb539e41e4dc4618ed4 \
+    --hash=sha256:a52419d9ba5906db516109660b114faf791136c94c1a636ed6b29cbfff9187ee \
+    --hash=sha256:b156a971bc451c68c9e1f97567c94fd44155f073e3bceb1b0d195fd98ed12048 \
+    --hash=sha256:bcf1a82b770b8f8c1e495b19a20d8296f875a796c4fe6e91da5ef107f18c5ecb \
+    --hash=sha256:cb2a9cf1150302d69bb99861c5cddc9c25aceacb0a4ef5299785d0f5389a3209 \
+    --hash=sha256:d8c709f4700573deb2036d240d140934df7e852520f4a584b2a8d5443b71f54d \
+    --hash=sha256:db45b94885000981522fb92349e6b76f5aee0924cc5315881239c7859883117d \
+    --hash=sha256:ddf46b940ef815af4e542697eaf071f0531449407a7607dd731bf23d156e20a7 \
+    --hash=sha256:e675f8fe9aa6c418dc8d3aac0087b5294c1a4527f1eacf9fe5ea671685285454 \
+    --hash=sha256:eb7e8cf2cf11a2580088009b43de84cabbf6f5dae94ceb489f28dba01a17cb77 \
+    --hash=sha256:f340331a3f411910adfb4bbe46c2ed5872d9e473a783d7f14ecf49bc0869c594
+    # via
+    #   altair
+    #   streamlit
+    #   test-tube
+pathtools==0.1.2 \
+    --hash=sha256:7c35c5421a39bb82e58018febd90e3b6e5db34c5443aaaf742b3f33d4655f1c0
+    # via wandb
+pillow==9.3.0 \
+    --hash=sha256:03150abd92771742d4a8cd6f2fa6246d847dcd2e332a18d0c15cc75bf6703040 \
+    --hash=sha256:073adb2ae23431d3b9bcbcff3fe698b62ed47211d0716b067385538a1b0f28b8 \
+    --hash=sha256:0b07fffc13f474264c336298d1b4ce01d9c5a011415b79d4ee5527bb69ae6f65 \
+    --hash=sha256:0b7257127d646ff8676ec8a15520013a698d1fdc48bc2a79ba4e53df792526f2 \
+    --hash=sha256:12ce4932caf2ddf3e41d17fc9c02d67126935a44b86df6a206cf0d7161548627 \
+    --hash=sha256:15c42fb9dea42465dfd902fb0ecf584b8848ceb28b41ee2b58f866411be33f07 \
+    --hash=sha256:18498994b29e1cf86d505edcb7edbe814d133d2232d256db8c7a8ceb34d18cef \
+    --hash=sha256:1c7c8ae3864846fc95f4611c78129301e203aaa2af813b703c55d10cc1628535 \
+    --hash=sha256:22b012ea2d065fd163ca096f4e37e47cd8b59cf4b0fd47bfca6abb93df70b34c \
+    --hash=sha256:276a5ca930c913f714e372b2591a22c4bd3b81a418c0f6635ba832daec1cbcfc \
+    --hash=sha256:2e0918e03aa0c72ea56edbb00d4d664294815aa11291a11504a377ea018330d3 \
+    --hash=sha256:3033fbe1feb1b59394615a1cafaee85e49d01b51d54de0cbf6aa8e64182518a1 \
+    --hash=sha256:3168434d303babf495d4ba58fc22d6604f6e2afb97adc6a423e917dab828939c \
+    --hash=sha256:32a44128c4bdca7f31de5be641187367fe2a450ad83b833ef78910397db491aa \
+    --hash=sha256:3dd6caf940756101205dffc5367babf288a30043d35f80936f9bfb37f8355b32 \
+    --hash=sha256:40e1ce476a7804b0fb74bcfa80b0a2206ea6a882938eaba917f7a0f004b42502 \
+    --hash=sha256:41e0051336807468be450d52b8edd12ac60bebaa97fe10c8b660f116e50b30e4 \
+    --hash=sha256:4390e9ce199fc1951fcfa65795f239a8a4944117b5935a9317fb320e7767b40f \
+    --hash=sha256:502526a2cbfa431d9fc2a079bdd9061a2397b842bb6bc4239bb176da00993812 \
+    --hash=sha256:51e0e543a33ed92db9f5ef69a0356e0b1a7a6b6a71b80df99f1d181ae5875636 \
+    --hash=sha256:57751894f6618fd4308ed8e0c36c333e2f5469744c34729a27532b3db106ee20 \
+    --hash=sha256:5d77adcd56a42d00cc1be30843d3426aa4e660cab4a61021dc84467123f7a00c \
+    --hash=sha256:655a83b0058ba47c7c52e4e2df5ecf484c1b0b0349805896dd350cbc416bdd91 \
+    --hash=sha256:68943d632f1f9e3dce98908e873b3a090f6cba1cbb1b892a9e8d97c938871fbe \
+    --hash=sha256:6c738585d7a9961d8c2821a1eb3dcb978d14e238be3d70f0a706f7fa9316946b \
+    --hash=sha256:73bd195e43f3fadecfc50c682f5055ec32ee2c933243cafbfdec69ab1aa87cad \
+    --hash=sha256:772a91fc0e03eaf922c63badeca75e91baa80fe2f5f87bdaed4280662aad25c9 \
+    --hash=sha256:77ec3e7be99629898c9a6d24a09de089fa5356ee408cdffffe62d67bb75fdd72 \
+    --hash=sha256:7db8b751ad307d7cf238f02101e8e36a128a6cb199326e867d1398067381bff4 \
+    --hash=sha256:801ec82e4188e935c7f5e22e006d01611d6b41661bba9fe45b60e7ac1a8f84de \
+    --hash=sha256:82409ffe29d70fd733ff3c1025a602abb3e67405d41b9403b00b01debc4c9a29 \
+    --hash=sha256:828989c45c245518065a110434246c44a56a8b2b2f6347d1409c787e6e4651ee \
+    --hash=sha256:829f97c8e258593b9daa80638aee3789b7df9da5cf1336035016d76f03b8860c \
+    --hash=sha256:871b72c3643e516db4ecf20efe735deb27fe30ca17800e661d769faab45a18d7 \
+    --hash=sha256:89dca0ce00a2b49024df6325925555d406b14aa3efc2f752dbb5940c52c56b11 \
+    --hash=sha256:90fb88843d3902fe7c9586d439d1e8c05258f41da473952aa8b328d8b907498c \
+    --hash=sha256:97aabc5c50312afa5e0a2b07c17d4ac5e865b250986f8afe2b02d772567a380c \
+    --hash=sha256:9aaa107275d8527e9d6e7670b64aabaaa36e5b6bd71a1015ddd21da0d4e06448 \
+    --hash=sha256:9f47eabcd2ded7698106b05c2c338672d16a6f2a485e74481f524e2a23c2794b \
+    --hash=sha256:a0a06a052c5f37b4ed81c613a455a81f9a3a69429b4fd7bb913c3fa98abefc20 \
+    --hash=sha256:ab388aaa3f6ce52ac1cb8e122c4bd46657c15905904b3120a6248b5b8b0bc228 \
+    --hash=sha256:ad58d27a5b0262c0c19b47d54c5802db9b34d38bbf886665b626aff83c74bacd \
+    --hash=sha256:ae5331c23ce118c53b172fa64a4c037eb83c9165aba3a7ba9ddd3ec9fa64a699 \
+    --hash=sha256:af0372acb5d3598f36ec0914deed2a63f6bcdb7b606da04dc19a88d31bf0c05b \
+    --hash=sha256:afa4107d1b306cdf8953edde0534562607fe8811b6c4d9a486298ad31de733b2 \
+    --hash=sha256:b03ae6f1a1878233ac620c98f3459f79fd77c7e3c2b20d460284e1fb370557d4 \
+    --hash=sha256:b0915e734b33a474d76c28e07292f196cdf2a590a0d25bcc06e64e545f2d146c \
+    --hash=sha256:b4012d06c846dc2b80651b120e2cdd787b013deb39c09f407727ba90015c684f \
+    --hash=sha256:b472b5ea442148d1c3e2209f20f1e0bb0eb556538690fa70b5e1f79fa0ba8dc2 \
+    --hash=sha256:b59430236b8e58840a0dfb4099a0e8717ffb779c952426a69ae435ca1f57210c \
+    --hash=sha256:b90f7616ea170e92820775ed47e136208e04c967271c9ef615b6fbd08d9af0e3 \
+    --hash=sha256:b9a65733d103311331875c1dca05cb4606997fd33d6acfed695b1232ba1df193 \
+    --hash=sha256:bac18ab8d2d1e6b4ce25e3424f709aceef668347db8637c2296bcf41acb7cf48 \
+    --hash=sha256:bca31dd6014cb8b0b2db1e46081b0ca7d936f856da3b39744aef499db5d84d02 \
+    --hash=sha256:be55f8457cd1eac957af0c3f5ece7bc3f033f89b114ef30f710882717670b2a8 \
+    --hash=sha256:c7025dce65566eb6e89f56c9509d4f628fddcedb131d9465cacd3d8bac337e7e \
+    --hash=sha256:c935a22a557a560108d780f9a0fc426dd7459940dc54faa49d83249c8d3e760f \
+    --hash=sha256:dbb8e7f2abee51cef77673be97760abff1674ed32847ce04b4af90f610144c7b \
+    --hash=sha256:e6ea6b856a74d560d9326c0f5895ef8050126acfdc7ca08ad703eb0081e82b74 \
+    --hash=sha256:ebf2029c1f464c59b8bdbe5143c79fa2045a581ac53679733d3a91d400ff9efb \
+    --hash=sha256:f1ff2ee69f10f13a9596480335f406dd1f70c3650349e2be67ca3139280cade0
+    # via
+    #   basicsr
+    #   clean-fid
+    #   diffusers
+    #   facexlib
+    #   imageio
+    #   k-diffusion
+    #   matplotlib
+    #   realesrgan
+    #   scikit-image
+    #   streamlit
+    #   torch-fidelity
+    #   torchvision
+promise==2.3 \
+    --hash=sha256:dfd18337c523ba4b6a58801c164c1904a9d4d1b1747c7d5dbf45b693a49d93d0
+    # via wandb
+protobuf==3.19.6 \
+    --hash=sha256:010be24d5a44be7b0613750ab40bc8b8cedc796db468eae6c779b395f50d1fa1 \
+    --hash=sha256:0469bc66160180165e4e29de7f445e57a34ab68f49357392c5b2f54c656ab25e \
+    --hash=sha256:0c0714b025ec057b5a7600cb66ce7c693815f897cfda6d6efb58201c472e3437 \
+    --hash=sha256:11478547958c2dfea921920617eb457bc26867b0d1aa065ab05f35080c5d9eb6 \
+    --hash=sha256:14082457dc02be946f60b15aad35e9f5c69e738f80ebbc0900a19bc83734a5a4 \
+    --hash=sha256:2b2d2913bcda0e0ec9a784d194bc490f5dc3d9d71d322d070b11a0ade32ff6ba \
+    --hash=sha256:30a15015d86b9c3b8d6bf78d5b8c7749f2512c29f168ca259c9d7727604d0e39 \
+    --hash=sha256:30f5370d50295b246eaa0296533403961f7e64b03ea12265d6dfce3a391d8992 \
+    --hash=sha256:347b393d4dd06fb93a77620781e11c058b3b0a5289262f094379ada2920a3730 \
+    --hash=sha256:4bc98de3cdccfb5cd769620d5785b92c662b6bfad03a202b83799b6ed3fa1fa7 \
+    --hash=sha256:5057c64052a1f1dd7d4450e9aac25af6bf36cfbfb3a1cd89d16393a036c49157 \
+    --hash=sha256:559670e006e3173308c9254d63facb2c03865818f22204037ab76f7a0ff70b5f \
+    --hash=sha256:5a0d7539a1b1fb7e76bf5faa0b44b30f812758e989e59c40f77a7dab320e79b9 \
+    --hash=sha256:5f5540d57a43042389e87661c6eaa50f47c19c6176e8cf1c4f287aeefeccb5c4 \
+    --hash=sha256:7a552af4dc34793803f4e735aabe97ffc45962dfd3a237bdde242bff5a3de684 \
+    --hash=sha256:84a04134866861b11556a82dd91ea6daf1f4925746b992f277b84013a7cc1229 \
+    --hash=sha256:878b4cd080a21ddda6ac6d1e163403ec6eea2e206cf225982ae04567d39be7b0 \
+    --hash=sha256:90b0d02163c4e67279ddb6dc25e063db0130fc299aefabb5d481053509fae5c8 \
+    --hash=sha256:91d5f1e139ff92c37e0ff07f391101df77e55ebb97f46bbc1535298d72019462 \
+    --hash=sha256:a8ce5ae0de28b51dff886fb922012dad885e66176663950cb2344c0439ecb473 \
+    --hash=sha256:aa3b82ca1f24ab5326dcf4ea00fcbda703e986b22f3d27541654f749564d778b \
+    --hash=sha256:bb6776bd18f01ffe9920e78e03a8676530a5d6c5911934c6a1ac6eb78973ecb6 \
+    --hash=sha256:bbf5cea5048272e1c60d235c7bd12ce1b14b8a16e76917f371c718bd3005f045 \
+    --hash=sha256:c0ccd3f940fe7f3b35a261b1dd1b4fc850c8fde9f74207015431f174be5976b3 \
+    --hash=sha256:d0b635cefebd7a8a0f92020562dead912f81f401af7e71f16bf9506ff3bdbb38
+    # via
+    #   streamlit
+    #   tb-nightly
+    #   tensorboard
+    #   wandb
+psutil==5.9.3 \
+    --hash=sha256:07d880053c6461c9b89cd5d4808f3b8336665fa3acdefd6777662c5ed73a851a \
+    --hash=sha256:12500d761ac091f2426567f19f95fd3f15a197d96befb44a5c1e3cbe6db5752c \
+    --hash=sha256:1b540599481c73408f6b392cdffef5b01e8ff7a2ac8caae0a91b8222e88e8f1e \
+    --hash=sha256:35feafe232d1aaf35d51bd42790cbccb882456f9f18cdc411532902370d660df \
+    --hash=sha256:3a7826e68b0cf4ce2c1ee385d64eab7d70e3133171376cac53d7c1790357ec8f \
+    --hash=sha256:46907fa62acaac364fff0b8a9da7b360265d217e4fdeaca0a2397a6883dffba2 \
+    --hash=sha256:4bd4854f0c83aa84a5a40d3b5d0eb1f3c128f4146371e03baed4589fe4f3c931 \
+    --hash=sha256:538fcf6ae856b5e12d13d7da25ad67f02113c96f5989e6ad44422cb5994ca7fc \
+    --hash=sha256:547ebb02031fdada635452250ff39942db8310b5c4a8102dfe9384ee5791e650 \
+    --hash=sha256:5e8b50241dd3c2ed498507f87a6602825073c07f3b7e9560c58411c14fe1e1c9 \
+    --hash=sha256:5fa88e3d5d0b480602553d362c4b33a63e0c40bfea7312a7bf78799e01e0810b \
+    --hash=sha256:68fa227c32240c52982cb931801c5707a7f96dd8927f9102d6c7771ea1ff5698 \
+    --hash=sha256:6ced1ad823ecfa7d3ce26fe8aa4996e2e53fb49b7fed8ad81c80958501ec0619 \
+    --hash=sha256:71b1206e7909792d16933a0d2c1c7f04ae196186c51ba8567abae1d041f06dcb \
+    --hash=sha256:767ef4fa33acda16703725c0473a91e1832d296c37c63896c7153ba81698f1ab \
+    --hash=sha256:7ccfcdfea4fc4b0a02ca2c31de7fcd186beb9cff8207800e14ab66f79c773af6 \
+    --hash=sha256:7e4939ff75149b67aef77980409f156f0082fa36accc475d45c705bb00c6c16a \
+    --hash=sha256:828c9dc9478b34ab96be75c81942d8df0c2bb49edbb481f597314d92b6441d89 \
+    --hash=sha256:8a4e07611997acf178ad13b842377e3d8e9d0a5bac43ece9bfc22a96735d9a4f \
+    --hash=sha256:941a6c2c591da455d760121b44097781bc970be40e0e43081b9139da485ad5b7 \
+    --hash=sha256:9a4af6ed1094f867834f5f07acd1250605a0874169a5fcadbcec864aec2496a6 \
+    --hash=sha256:9ec296f565191f89c48f33d9544d8d82b0d2af7dd7d2d4e6319f27a818f8d1cc \
+    --hash=sha256:9ec95df684583b5596c82bb380c53a603bb051cf019d5c849c47e117c5064395 \
+    --hash=sha256:a04a1836894c8279e5e0a0127c0db8e198ca133d28be8a2a72b4db16f6cf99c1 \
+    --hash=sha256:a3d81165b8474087bb90ec4f333a638ccfd1d69d34a9b4a1a7eaac06648f9fbe \
+    --hash=sha256:b4a247cd3feaae39bb6085fcebf35b3b8ecd9b022db796d89c8f05067ca28e71 \
+    --hash=sha256:ba38cf9984d5462b506e239cf4bc24e84ead4b1d71a3be35e66dad0d13ded7c1 \
+    --hash=sha256:beb57d8a1ca0ae0eb3d08ccaceb77e1a6d93606f0e1754f0d60a6ebd5c288837 \
+    --hash=sha256:d266cd05bd4a95ca1c2b9b5aac50d249cf7c94a542f47e0b22928ddf8b80d1ef \
+    --hash=sha256:d8c3cc6bb76492133474e130a12351a325336c01c96a24aae731abf5a47fe088 \
+    --hash=sha256:db8e62016add2235cc87fb7ea000ede9e4ca0aa1f221b40cef049d02d5d2593d \
+    --hash=sha256:e7507f6c7b0262d3e7b0eeda15045bf5881f4ada70473b87bc7b7c93b992a7d7 \
+    --hash=sha256:ed15edb14f52925869250b1375f0ff58ca5c4fa8adefe4883cfb0737d32f5c02 \
+    --hash=sha256:f57d63a2b5beaf797b87024d018772439f9d3103a395627b77d17a8d72009543 \
+    --hash=sha256:fa5e32c7d9b60b2528108ade2929b115167fe98d59f89555574715054f50fa31 \
+    --hash=sha256:fe79b4ad4836e3da6c4650cb85a663b3a51aef22e1a829c384e18fae87e5e727
+    # via
+    #   accelerate
+    #   wandb
+pyarrow==10.0.0 \
+    --hash=sha256:10e031794d019425d34406edffe7e32157359e9455f9edb97a1732f8dabf802f \
+    --hash=sha256:25f51dca780fc22cfd7ac30f6bdfe70eb99145aee9acfda987f2c49955d66ed9 \
+    --hash=sha256:2d326a9d47ac237d81b8c4337e9d30a0b361835b536fc7ea53991455ce761fbd \
+    --hash=sha256:3d2694f08c8d4482d14e3798ff036dbd81ae6b1c47948f52515e1aa90fbec3f0 \
+    --hash=sha256:4051664d354b14939b5da35cfa77821ade594bc1cf56dd2032b3068c96697d74 \
+    --hash=sha256:511735040b83f2993f78d7fb615e7b88253d75f41500e87e587c40156ff88120 \
+    --hash=sha256:65d4a312f3ced318423704355acaccc7f7bdfe242472e59bdd54aa0f8837adf8 \
+    --hash=sha256:68ccb82c04c0f7abf7a95541d5e9d9d94290fc66a2d36d3f6ea0777f40c15654 \
+    --hash=sha256:69b8a1fd99201178799b02f18498633847109b701856ec762f314352a431b7d0 \
+    --hash=sha256:758284e1ebd3f2a9abb30544bfec28d151a398bb7c0f2578cbca5ee5b000364a \
+    --hash=sha256:7be7f42f713068293308c989a4a3a2de03b70199bdbe753901c6595ff8640c64 \
+    --hash=sha256:7ce026274cd5d9934cd3694e89edecde4e036018bbc6cb735fd33b9e967e7d47 \
+    --hash=sha256:7e6b837cc44cd62a0e280c8fc4de94ebce503d6d1190e6e94157ab49a8bea67b \
+    --hash=sha256:b153b05765393557716e3729cf988442b3ae4f5567364ded40d58c07feed27c2 \
+    --hash=sha256:b3e3148468d3eed3779d68241f1d13ed4ee7cca4c6dbc7c07e5062b93ad4da33 \
+    --hash=sha256:b45f969ed924282e9d4ede38f3430630d809c36dbff65452cabce03141943d28 \
+    --hash=sha256:b9f63ceb8346aac0bcb487fafe9faca642ad448ca649fcf66a027c6e120cbc12 \
+    --hash=sha256:c79300e1a3e23f2bf4defcf0d70ff5ea25ef6ebf6f121d8670ee14bb662bb7ca \
+    --hash=sha256:d45a59e2f47826544c0ca70bc0f7ed8ffa5ad23f93b0458230c7e983bcad1acf \
+    --hash=sha256:e4c6da9f9e1ff96781ee1478f7cc0860e66c23584887b8e297c4b9905c3c9066 \
+    --hash=sha256:f329951d56b3b943c353f7b27c894e02367a7efbb9fef7979c6b24e02dbfcf55 \
+    --hash=sha256:f76157d9579571c865860e5fd004537c03e21139db76692d96fd8a186adab1f2
+    # via streamlit
+pyasn1==0.4.8 \
+    --hash=sha256:39c7e2ec30515947ff4e87fb6f456dfc6e84857d34be479c9d4a4ba4bf46aa5d \
+    --hash=sha256:aef77c9fb94a3ac588e87841208bdec464471d9871bd5050a287cc9a475cd0ba
+    # via
+    #   pyasn1-modules
+    #   rsa
+pyasn1-modules==0.2.8 \
+    --hash=sha256:905f84c712230b2c592c19470d3ca8d552de726050d1d1716282a1f6146be65e \
+    --hash=sha256:a50b808ffeb97cb3601dd25981f6b016cbb3d31fbf57a8b8a87428e6158d0c74
+    # via google-auth
+pydeck==0.8.0 \
+    --hash=sha256:07edde833f7cfcef6749124351195aa7dcd24663d4909fd7898dbd0b6fbc01ec \
+    --hash=sha256:a8fa7757c6f24bba033af39db3147cb020eef44012ba7e60d954de187f9ed4d5
+    # via streamlit
+pydeprecate==0.3.2 \
+    --hash=sha256:d481116cc5d7f6c473e7c4be820efdd9b90a16b594b350276e9e66a6cb5bdd29 \
+    --hash=sha256:ed86b68ed837e6465245904a3de2f59bf9eef78ac7a2502ee280533d04802457
+    # via pytorch-lightning
+pygments==2.13.0 \
+    --hash=sha256:56a8508ae95f98e2b9bdf93a6be5ae3f7d8af858b43e02c5a2ff083726be40c1 \
+    --hash=sha256:f643f331ab57ba3c9d89212ee4a2dabc6e94f117cf4eefde99a0574720d14c42
+    # via rich
+pympler==1.0.1 \
+    --hash=sha256:993f1a3599ca3f4fcd7160c7545ad06310c9e12f70174ae7ae8d4e25f6c5d3fa \
+    --hash=sha256:d260dda9ae781e1eab6ea15bacb84015849833ba5555f141d2d9b7b7473b307d
+    # via streamlit
+pyparsing==3.0.9 \
+    --hash=sha256:2b020ecf7d21b687f219b71ecad3631f644a47f01403fa1d1036b0c6416d70fb \
+    --hash=sha256:5026bae9a10eeaefb61dab2f09052b9f4307d44aee4eda64b309723d8d206bbc
+    # via
+    #   matplotlib
+    #   packaging
+pyreadline3==3.4.1 \
+    --hash=sha256:6f3d1f7b8a31ba32b73917cefc1f28cc660562f39aea8646d30bd6eff21f7bae \
+    --hash=sha256:b0efb6516fd4fb07b45949053826a62fa4cb353db5be2bbb4a7aa1fdd1e345fb
+    # via -r requirements.in
+pyrsistent==0.19.2 \
+    --hash=sha256:055ab45d5911d7cae397dc418808d8802fb95262751872c841c170b0dbf51eed \
+    --hash=sha256:111156137b2e71f3a9936baf27cb322e8024dac3dc54ec7fb9f0bcf3249e68bb \
+    --hash=sha256:187d5730b0507d9285a96fca9716310d572e5464cadd19f22b63a6976254d77a \
+    --hash=sha256:21455e2b16000440e896ab99e8304617151981ed40c29e9507ef1c2e4314ee95 \
+    --hash=sha256:2aede922a488861de0ad00c7630a6e2d57e8023e4be72d9d7147a9fcd2d30712 \
+    --hash=sha256:3ba4134a3ff0fc7ad225b6b457d1309f4698108fb6b35532d015dca8f5abed73 \
+    --hash=sha256:456cb30ca8bff00596519f2c53e42c245c09e1a4543945703acd4312949bfd41 \
+    --hash=sha256:71d332b0320642b3261e9fee47ab9e65872c2bd90260e5d225dabeed93cbd42b \
+    --hash=sha256:879b4c2f4d41585c42df4d7654ddffff1239dc4065bc88b745f0341828b83e78 \
+    --hash=sha256:9cd3e9978d12b5d99cbdc727a3022da0430ad007dacf33d0bf554b96427f33ab \
+    --hash=sha256:a178209e2df710e3f142cbd05313ba0c5ebed0a55d78d9945ac7a4e09d923308 \
+    --hash=sha256:b39725209e06759217d1ac5fcdb510e98670af9e37223985f330b611f62e7425 \
+    --hash=sha256:bfa0351be89c9fcbcb8c9879b826f4353be10f58f8a677efab0c017bf7137ec2 \
+    --hash=sha256:bfd880614c6237243ff53a0539f1cb26987a6dc8ac6e66e0c5a40617296a045e \
+    --hash=sha256:c43bec251bbd10e3cb58ced80609c5c1eb238da9ca78b964aea410fb820d00d6 \
+    --hash=sha256:d690b18ac4b3e3cab73b0b7aa7dbe65978a172ff94970ff98d82f2031f8971c2 \
+    --hash=sha256:d6982b5a0237e1b7d876b60265564648a69b14017f3b5f908c5be2de3f9abb7a \
+    --hash=sha256:dec3eac7549869365fe263831f576c8457f6c833937c68542d08fde73457d291 \
+    --hash=sha256:e371b844cec09d8dc424d940e54bba8f67a03ebea20ff7b7b0d56f526c71d584 \
+    --hash=sha256:e5d8f84d81e3729c3b506657dddfe46e8ba9c330bf1858ee33108f8bb2adb38a \
+    --hash=sha256:ea6b79a02a28550c98b6ca9c35b9f492beaa54d7c5c9e9949555893c8a9234d0 \
+    --hash=sha256:f1258f4e6c42ad0b20f9cfcc3ada5bd6b83374516cd01c0960e3cb75fdca6770
+    # via jsonschema
+python-dateutil==2.8.2 \
+    --hash=sha256:0123cacc1627ae19ddf3c27a5de5bd67ee4586fbdd6440d9748f8abb483d3e86 \
+    --hash=sha256:961d03dc3453ebbc59dbdea9e4e11c5651520a876d0f4db161e8674aae935da9
+    # via
+    #   matplotlib
+    #   pandas
+    #   streamlit
+python-engineio==4.3.4 \
+    --hash=sha256:7454314a529bba20e745928601ffeaf101c1b5aca9a6c4e48ad397803d10ea0c \
+    --hash=sha256:d8d8b072799c36cadcdcc2b40d2a560ce09797ab3d2d596b2ad519a5e4df19ae
+    # via python-socketio
+python-socketio==5.7.2 \
+    --hash=sha256:92395062d9db3c13d30e7cdedaa0e1330bba78505645db695415f9a3c628d097 \
+    --hash=sha256:d9a9f047e6fdd306c852fbac36516f4b495c2096f8ad9ceb8803b8e5ff5622e3
+    # via flask-socketio
+pytorch-lightning==1.7.7 \
+    --hash=sha256:27c2dd01a18db2415168e3fa3775ccb5a1fa1e2961a50439ad9365507fe9d4ae \
+    --hash=sha256:4438b8284d7f7fdb06cf3566a7b5b6f102ac8971cf7bb6d3f1b1de64628241f3
+    # via taming-transformers-rom1504
+pytz==2022.6 \
+    --hash=sha256:222439474e9c98fced559f1709d89e6c9cbf8d79c794ff3eb9f8800064291427 \
+    --hash=sha256:e89512406b793ca39f5971bc999cc538ce125c0e51c27941bef4568b460095e2
+    # via pandas
+pytz-deprecation-shim==0.1.0.post0 \
+    --hash=sha256:8314c9692a636c8eb3bda879b9f119e350e93223ae83e70e80c31675a0fdc1a6 \
+    --hash=sha256:af097bae1b616dde5c5744441e2ddc69e74dfdcb0c263129610d85b87445a59d
+    # via tzlocal
+pywavelets==1.4.1 \
+    --hash=sha256:030670a213ee8fefa56f6387b0c8e7d970c7f7ad6850dc048bd7c89364771b9b \
+    --hash=sha256:058b46434eac4c04dd89aeef6fa39e4b6496a951d78c500b6641fd5b2cc2f9f4 \
+    --hash=sha256:231b0e0b1cdc1112f4af3c24eea7bf181c418d37922a67670e9bf6cfa2d544d4 \
+    --hash=sha256:23bafd60350b2b868076d976bdd92f950b3944f119b4754b1d7ff22b7acbf6c6 \
+    --hash=sha256:3f19327f2129fb7977bc59b966b4974dfd72879c093e44a7287500a7032695de \
+    --hash=sha256:47cac4fa25bed76a45bc781a293c26ac63e8eaae9eb8f9be961758d22b58649c \
+    --hash=sha256:578af438a02a86b70f1975b546f68aaaf38f28fb082a61ceb799816049ed18aa \
+    --hash=sha256:6437af3ddf083118c26d8f97ab43b0724b956c9f958e9ea788659f6a2834ba93 \
+    --hash=sha256:64c6bac6204327321db30b775060fbe8e8642316e6bff17f06b9f34936f88875 \
+    --hash=sha256:67a0d28a08909f21400cb09ff62ba94c064882ffd9e3a6b27880a111211d59bd \
+    --hash=sha256:71ab30f51ee4470741bb55fc6b197b4a2b612232e30f6ac069106f0156342356 \
+    --hash=sha256:7231461d7a8eb3bdc7aa2d97d9f67ea5a9f8902522818e7e2ead9c2b3408eeb1 \
+    --hash=sha256:754fa5085768227c4f4a26c1e0c78bc509a266d9ebd0eb69a278be7e3ece943c \
+    --hash=sha256:7ab8d9db0fe549ab2ee0bea61f614e658dd2df419d5b75fba47baa761e95f8f2 \
+    --hash=sha256:875d4d620eee655346e3589a16a73790cf9f8917abba062234439b594e706784 \
+    --hash=sha256:88aa5449e109d8f5e7f0adef85f7f73b1ab086102865be64421a3a3d02d277f4 \
+    --hash=sha256:91d3d393cffa634f0e550d88c0e3f217c96cfb9e32781f2960876f1808d9b45b \
+    --hash=sha256:9cb5ca8d11d3f98e89e65796a2125be98424d22e5ada360a0dbabff659fca0fc \
+    --hash=sha256:ab7da0a17822cd2f6545626946d3b82d1a8e106afc4b50e3387719ba01c7b966 \
+    --hash=sha256:ad987748f60418d5f4138db89d82ba0cb49b086e0cbb8fd5c3ed4a814cfb705e \
+    --hash=sha256:d0e56cd7a53aed3cceca91a04d62feb3a0aca6725b1912d29546c26f6ea90426 \
+    --hash=sha256:d854411eb5ee9cb4bc5d0e66e3634aeb8f594210f6a1bed96dbed57ec70f181c \
+    --hash=sha256:da7b9c006171be1f9ddb12cc6e0d3d703b95f7f43cb5e2c6f5f15d3233fcf202 \
+    --hash=sha256:daf0aa79842b571308d7c31a9c43bc99a30b6328e6aea3f50388cd8f69ba7dbc \
+    --hash=sha256:de7cd61a88a982edfec01ea755b0740e94766e00a1ceceeafef3ed4c85c605cd
+    # via scikit-image
+pyyaml==6.0 \
+    --hash=sha256:01b45c0191e6d66c470b6cf1b9531a771a83c1c4208272ead47a3ae4f2f603bf \
+    --hash=sha256:0283c35a6a9fbf047493e3a0ce8d79ef5030852c51e9d911a27badfde0605293 \
+    --hash=sha256:055d937d65826939cb044fc8c9b08889e8c743fdc6a32b33e2390f66013e449b \
+    --hash=sha256:07751360502caac1c067a8132d150cf3d61339af5691fe9e87803040dbc5db57 \
+    --hash=sha256:0b4624f379dab24d3725ffde76559cff63d9ec94e1736b556dacdfebe5ab6d4b \
+    --hash=sha256:0ce82d761c532fe4ec3f87fc45688bdd3a4c1dc5e0b4a19814b9009a29baefd4 \
+    --hash=sha256:1e4747bc279b4f613a09eb64bba2ba602d8a6664c6ce6396a4d0cd413a50ce07 \
+    --hash=sha256:213c60cd50106436cc818accf5baa1aba61c0189ff610f64f4a3e8c6726218ba \
+    --hash=sha256:231710d57adfd809ef5d34183b8ed1eeae3f76459c18fb4a0b373ad56bedcdd9 \
+    --hash=sha256:277a0ef2981ca40581a47093e9e2d13b3f1fbbeffae064c1d21bfceba2030287 \
+    --hash=sha256:2cd5df3de48857ed0544b34e2d40e9fac445930039f3cfe4bcc592a1f836d513 \
+    --hash=sha256:40527857252b61eacd1d9af500c3337ba8deb8fc298940291486c465c8b46ec0 \
+    --hash=sha256:432557aa2c09802be39460360ddffd48156e30721f5e8d917f01d31694216782 \
+    --hash=sha256:473f9edb243cb1935ab5a084eb238d842fb8f404ed2193a915d1784b5a6b5fc0 \
+    --hash=sha256:48c346915c114f5fdb3ead70312bd042a953a8ce5c7106d5bfb1a5254e47da92 \
+    --hash=sha256:50602afada6d6cbfad699b0c7bb50d5ccffa7e46a3d738092afddc1f9758427f \
+    --hash=sha256:68fb519c14306fec9720a2a5b45bc9f0c8d1b9c72adf45c37baedfcd949c35a2 \
+    --hash=sha256:77f396e6ef4c73fdc33a9157446466f1cff553d979bd00ecb64385760c6babdc \
+    --hash=sha256:81957921f441d50af23654aa6c5e5eaf9b06aba7f0a19c18a538dc7ef291c5a1 \
+    --hash=sha256:819b3830a1543db06c4d4b865e70ded25be52a2e0631ccd2f6a47a2822f2fd7c \
+    --hash=sha256:897b80890765f037df3403d22bab41627ca8811ae55e9a722fd0392850ec4d86 \
+    --hash=sha256:98c4d36e99714e55cfbaaee6dd5badbc9a1ec339ebfc3b1f52e293aee6bb71a4 \
+    --hash=sha256:9df7ed3b3d2e0ecfe09e14741b857df43adb5a3ddadc919a2d94fbdf78fea53c \
+    --hash=sha256:9fa600030013c4de8165339db93d182b9431076eb98eb40ee068700c9c813e34 \
+    --hash=sha256:a80a78046a72361de73f8f395f1f1e49f956c6be882eed58505a15f3e430962b \
+    --hash=sha256:afa17f5bc4d1b10afd4466fd3a44dc0e245382deca5b3c353d8b757f9e3ecb8d \
+    --hash=sha256:b3d267842bf12586ba6c734f89d1f5b871df0273157918b0ccefa29deb05c21c \
+    --hash=sha256:b5b9eccad747aabaaffbc6064800670f0c297e52c12754eb1d976c57e4f74dcb \
+    --hash=sha256:bfaef573a63ba8923503d27530362590ff4f576c626d86a9fed95822a8255fd7 \
+    --hash=sha256:c5687b8d43cf58545ade1fe3e055f70eac7a5a1a0bf42824308d868289a95737 \
+    --hash=sha256:cba8c411ef271aa037d7357a2bc8f9ee8b58b9965831d9e51baf703280dc73d3 \
+    --hash=sha256:d15a181d1ecd0d4270dc32edb46f7cb7733c7c508857278d3d378d14d606db2d \
+    --hash=sha256:d4b0ba9512519522b118090257be113b9468d804b19d63c71dbcf4a48fa32358 \
+    --hash=sha256:d4db7c7aef085872ef65a8fd7d6d09a14ae91f691dec3e87ee5ee0539d516f53 \
+    --hash=sha256:d4eccecf9adf6fbcc6861a38015c2a64f38b9d94838ac1810a9023a0609e1b78 \
+    --hash=sha256:d67d839ede4ed1b28a4e8909735fc992a923cdb84e618544973d7dfc71540803 \
+    --hash=sha256:daf496c58a8c52083df09b80c860005194014c3698698d1a57cbcfa182142a3a \
+    --hash=sha256:dbad0e9d368bb989f4515da330b88a057617d16b6a8245084f1b05400f24609f \
+    --hash=sha256:e61ceaab6f49fb8bdfaa0f92c4b57bcfbea54c09277b1b4f7ac376bfb7a7c174 \
+    --hash=sha256:f84fbc98b019fef2ee9a1cb3ce93e3187a6df0b2538a651bfb890254ba9f90b5
+    # via
+    #   accelerate
+    #   albumentations
+    #   basicsr
+    #   gfpgan
+    #   huggingface-hub
+    #   omegaconf
+    #   pytorch-lightning
+    #   transformers
+    #   wandb
+qudida==0.0.4 \
+    --hash=sha256:4519714c40cd0f2e6c51e1735edae8f8b19f4efe1f33be13e9d644ca5f736dd6 \
+    --hash=sha256:db198e2887ab0c9aa0023e565afbff41dfb76b361f85fd5e13f780d75ba18cc8
+    # via albumentations
+realesrgan==0.3.0 \
+    --hash=sha256:0d36da96ab9f447071606e91f502ccdfb08f80cc82ee4f8caf720c7745ccec7e \
+    --hash=sha256:59336c16c30dd5130eff350dd27424acb9b7281d18a6810130e265606c9a6088
+    # via -r requirements.in
+regex==2022.10.31 \
+    --hash=sha256:052b670fafbe30966bbe5d025e90b2a491f85dfe5b2583a163b5e60a85a321ad \
+    --hash=sha256:0653d012b3bf45f194e5e6a41df9258811ac8fc395579fa82958a8b76286bea4 \
+    --hash=sha256:0a069c8483466806ab94ea9068c34b200b8bfc66b6762f45a831c4baaa9e8cdd \
+    --hash=sha256:0cf0da36a212978be2c2e2e2d04bdff46f850108fccc1851332bcae51c8907cc \
+    --hash=sha256:131d4be09bea7ce2577f9623e415cab287a3c8e0624f778c1d955ec7c281bd4d \
+    --hash=sha256:144486e029793a733e43b2e37df16a16df4ceb62102636ff3db6033994711066 \
+    --hash=sha256:1ddf14031a3882f684b8642cb74eea3af93a2be68893901b2b387c5fd92a03ec \
+    --hash=sha256:1eba476b1b242620c266edf6325b443a2e22b633217a9835a52d8da2b5c051f9 \
+    --hash=sha256:20f61c9944f0be2dc2b75689ba409938c14876c19d02f7585af4460b6a21403e \
+    --hash=sha256:22960019a842777a9fa5134c2364efaed5fbf9610ddc5c904bd3a400973b0eb8 \
+    --hash=sha256:22e7ebc231d28393dfdc19b185d97e14a0f178bedd78e85aad660e93b646604e \
+    --hash=sha256:23cbb932cc53a86ebde0fb72e7e645f9a5eec1a5af7aa9ce333e46286caef783 \
+    --hash=sha256:29c04741b9ae13d1e94cf93fca257730b97ce6ea64cfe1eba11cf9ac4e85afb6 \
+    --hash=sha256:2bde29cc44fa81c0a0c8686992c3080b37c488df167a371500b2a43ce9f026d1 \
+    --hash=sha256:2cdc55ca07b4e70dda898d2ab7150ecf17c990076d3acd7a5f3b25cb23a69f1c \
+    --hash=sha256:370f6e97d02bf2dd20d7468ce4f38e173a124e769762d00beadec3bc2f4b3bc4 \
+    --hash=sha256:395161bbdbd04a8333b9ff9763a05e9ceb4fe210e3c7690f5e68cedd3d65d8e1 \
+    --hash=sha256:44136355e2f5e06bf6b23d337a75386371ba742ffa771440b85bed367c1318d1 \
+    --hash=sha256:44a6c2f6374e0033873e9ed577a54a3602b4f609867794c1a3ebba65e4c93ee7 \
+    --hash=sha256:4919899577ba37f505aaebdf6e7dc812d55e8f097331312db7f1aab18767cce8 \
+    --hash=sha256:4b4b1fe58cd102d75ef0552cf17242705ce0759f9695334a56644ad2d83903fe \
+    --hash=sha256:4bdd56ee719a8f751cf5a593476a441c4e56c9b64dc1f0f30902858c4ef8771d \
+    --hash=sha256:4bf41b8b0a80708f7e0384519795e80dcb44d7199a35d52c15cc674d10b3081b \
+    --hash=sha256:4cac3405d8dda8bc6ed499557625585544dd5cbf32072dcc72b5a176cb1271c8 \
+    --hash=sha256:4fe7fda2fe7c8890d454f2cbc91d6c01baf206fbc96d89a80241a02985118c0c \
+    --hash=sha256:50921c140561d3db2ab9f5b11c5184846cde686bb5a9dc64cae442926e86f3af \
+    --hash=sha256:5217c25229b6a85049416a5c1e6451e9060a1edcf988641e309dbe3ab26d3e49 \
+    --hash=sha256:5352bea8a8f84b89d45ccc503f390a6be77917932b1c98c4cdc3565137acc714 \
+    --hash=sha256:542e3e306d1669b25936b64917285cdffcd4f5c6f0247636fec037187bd93542 \
+    --hash=sha256:543883e3496c8b6d58bd036c99486c3c8387c2fc01f7a342b760c1ea3158a318 \
+    --hash=sha256:586b36ebda81e6c1a9c5a5d0bfdc236399ba6595e1397842fd4a45648c30f35e \
+    --hash=sha256:597f899f4ed42a38df7b0e46714880fb4e19a25c2f66e5c908805466721760f5 \
+    --hash=sha256:5a260758454580f11dd8743fa98319bb046037dfab4f7828008909d0aa5292bc \
+    --hash=sha256:5aefb84a301327ad115e9d346c8e2760009131d9d4b4c6b213648d02e2abe144 \
+    --hash=sha256:5e6a5567078b3eaed93558842346c9d678e116ab0135e22eb72db8325e90b453 \
+    --hash=sha256:5ff525698de226c0ca743bfa71fc6b378cda2ddcf0d22d7c37b1cc925c9650a5 \
+    --hash=sha256:61edbca89aa3f5ef7ecac8c23d975fe7261c12665f1d90a6b1af527bba86ce61 \
+    --hash=sha256:659175b2144d199560d99a8d13b2228b85e6019b6e09e556209dfb8c37b78a11 \
+    --hash=sha256:6a9a19bea8495bb419dc5d38c4519567781cd8d571c72efc6aa959473d10221a \
+    --hash=sha256:6b30bddd61d2a3261f025ad0f9ee2586988c6a00c780a2fb0a92cea2aa702c54 \
+    --hash=sha256:6ffd55b5aedc6f25fd8d9f905c9376ca44fcf768673ffb9d160dd6f409bfda73 \
+    --hash=sha256:702d8fc6f25bbf412ee706bd73019da5e44a8400861dfff7ff31eb5b4a1276dc \
+    --hash=sha256:74bcab50a13960f2a610cdcd066e25f1fd59e23b69637c92ad470784a51b1347 \
+    --hash=sha256:75f591b2055523fc02a4bbe598aa867df9e953255f0b7f7715d2a36a9c30065c \
+    --hash=sha256:763b64853b0a8f4f9cfb41a76a4a85a9bcda7fdda5cb057016e7706fde928e66 \
+    --hash=sha256:76c598ca73ec73a2f568e2a72ba46c3b6c8690ad9a07092b18e48ceb936e9f0c \
+    --hash=sha256:78d680ef3e4d405f36f0d6d1ea54e740366f061645930072d39bca16a10d8c93 \
+    --hash=sha256:7b280948d00bd3973c1998f92e22aa3ecb76682e3a4255f33e1020bd32adf443 \
+    --hash=sha256:7db345956ecce0c99b97b042b4ca7326feeec6b75facd8390af73b18e2650ffc \
+    --hash=sha256:7dbdce0c534bbf52274b94768b3498abdf675a691fec5f751b6057b3030f34c1 \
+    --hash=sha256:7ef6b5942e6bfc5706301a18a62300c60db9af7f6368042227ccb7eeb22d0892 \
+    --hash=sha256:7f5a3ffc731494f1a57bd91c47dc483a1e10048131ffb52d901bfe2beb6102e8 \
+    --hash=sha256:8a45b6514861916c429e6059a55cf7db74670eaed2052a648e3e4d04f070e001 \
+    --hash=sha256:8ad241da7fac963d7573cc67a064c57c58766b62a9a20c452ca1f21050868dfa \
+    --hash=sha256:8b0886885f7323beea6f552c28bff62cbe0983b9fbb94126531693ea6c5ebb90 \
+    --hash=sha256:8ca88da1bd78990b536c4a7765f719803eb4f8f9971cc22d6ca965c10a7f2c4c \
+    --hash=sha256:8e0caeff18b96ea90fc0eb6e3bdb2b10ab5b01a95128dfeccb64a7238decf5f0 \
+    --hash=sha256:957403a978e10fb3ca42572a23e6f7badff39aa1ce2f4ade68ee452dc6807692 \
+    --hash=sha256:9af69f6746120998cd9c355e9c3c6aec7dff70d47247188feb4f829502be8ab4 \
+    --hash=sha256:9c94f7cc91ab16b36ba5ce476f1904c91d6c92441f01cd61a8e2729442d6fcf5 \
+    --hash=sha256:a37d51fa9a00d265cf73f3de3930fa9c41548177ba4f0faf76e61d512c774690 \
+    --hash=sha256:a3a98921da9a1bf8457aeee6a551948a83601689e5ecdd736894ea9bbec77e83 \
+    --hash=sha256:a3c1ebd4ed8e76e886507c9eddb1a891673686c813adf889b864a17fafcf6d66 \
+    --hash=sha256:a5f9505efd574d1e5b4a76ac9dd92a12acb2b309551e9aa874c13c11caefbe4f \
+    --hash=sha256:a8ff454ef0bb061e37df03557afda9d785c905dab15584860f982e88be73015f \
+    --hash=sha256:a9d0b68ac1743964755ae2d89772c7e6fb0118acd4d0b7464eaf3921c6b49dd4 \
+    --hash=sha256:aa62a07ac93b7cb6b7d0389d8ef57ffc321d78f60c037b19dfa78d6b17c928ee \
+    --hash=sha256:ac741bf78b9bb432e2d314439275235f41656e189856b11fb4e774d9f7246d81 \
+    --hash=sha256:ae1e96785696b543394a4e3f15f3f225d44f3c55dafe3f206493031419fedf95 \
+    --hash=sha256:b683e5fd7f74fb66e89a1ed16076dbab3f8e9f34c18b1979ded614fe10cdc4d9 \
+    --hash=sha256:b7a8b43ee64ca8f4befa2bea4083f7c52c92864d8518244bfa6e88c751fa8fff \
+    --hash=sha256:b8e38472739028e5f2c3a4aded0ab7eadc447f0d84f310c7a8bb697ec417229e \
+    --hash=sha256:bfff48c7bd23c6e2aec6454aaf6edc44444b229e94743b34bdcdda2e35126cf5 \
+    --hash=sha256:c14b63c9d7bab795d17392c7c1f9aaabbffd4cf4387725a0ac69109fb3b550c6 \
+    --hash=sha256:c27cc1e4b197092e50ddbf0118c788d9977f3f8f35bfbbd3e76c1846a3443df7 \
+    --hash=sha256:c28d3309ebd6d6b2cf82969b5179bed5fefe6142c70f354ece94324fa11bf6a1 \
+    --hash=sha256:c670f4773f2f6f1957ff8a3962c7dd12e4be54d05839b216cb7fd70b5a1df394 \
+    --hash=sha256:ce6910b56b700bea7be82c54ddf2e0ed792a577dfaa4a76b9af07d550af435c6 \
+    --hash=sha256:d0213671691e341f6849bf33cd9fad21f7b1cb88b89e024f33370733fec58742 \
+    --hash=sha256:d03fe67b2325cb3f09be029fd5da8df9e6974f0cde2c2ac6a79d2634e791dd57 \
+    --hash=sha256:d0e5af9a9effb88535a472e19169e09ce750c3d442fb222254a276d77808620b \
+    --hash=sha256:d243b36fbf3d73c25e48014961e83c19c9cc92530516ce3c43050ea6276a2ab7 \
+    --hash=sha256:d26166acf62f731f50bdd885b04b38828436d74e8e362bfcb8df221d868b5d9b \
+    --hash=sha256:d403d781b0e06d2922435ce3b8d2376579f0c217ae491e273bab8d092727d244 \
+    --hash=sha256:d8716f82502997b3d0895d1c64c3b834181b1eaca28f3f6336a71777e437c2af \
+    --hash=sha256:e4f781ffedd17b0b834c8731b75cce2639d5a8afe961c1e58ee7f1f20b3af185 \
+    --hash=sha256:e613a98ead2005c4ce037c7b061f2409a1a4e45099edb0ef3200ee26ed2a69a8 \
+    --hash=sha256:ef4163770525257876f10e8ece1cf25b71468316f61451ded1a6f44273eedeb5
+    # via
+    #   clip
+    #   diffusers
+    #   transformers
+requests==2.25.1 \
+    --hash=sha256:27973dd4a904a4f13b263a19c866c13b92a39ed1c964655f025f3f8d3d75b804 \
+    --hash=sha256:c210084e36a42ae6b9219e00e48287def368a26d03a048ddad7bfee44f75871e
+    # via
+    #   basicsr
+    #   clean-fid
+    #   diffusers
+    #   fsspec
+    #   huggingface-hub
+    #   requests-oauthlib
+    #   streamlit
+    #   tb-nightly
+    #   tensorboard
+    #   torchvision
+    #   transformers
+    #   wandb
+requests-oauthlib==1.3.1 \
+    --hash=sha256:2577c501a2fb8d05a304c09d090d6e47c306fef15809d102b327cf8364bddab5 \
+    --hash=sha256:75beac4a47881eeb94d5ea5d6ad31ef88856affe2332b9aafb52c6452ccf0d7a
+    # via google-auth-oauthlib
+resize-right==0.0.2 \
+    --hash=sha256:78351ca3eda0872208fcbc90861b45de559f90fb4027ce41825fdeb9b995005c \
+    --hash=sha256:7dc35b72ce4012b77f7cc9049835163793ab98a58ab8893610fb119fe59af520
+    # via k-diffusion
+rich==12.6.0 \
+    --hash=sha256:a4eb26484f2c82589bd9a17c73d32a010b1e29d89f1604cd9bf3a2097b81bb5e \
+    --hash=sha256:ba3a3775974105c221d31141f2c116f4fd65c5ceb0698657a11e9f295ec93fd0
+    # via streamlit
+rsa==4.9 \
+    --hash=sha256:90260d9058e514786967344d0ef75fa8727eed8a7d2e43ce9f4bcf1b536174f7 \
+    --hash=sha256:e38464a49c6c85d7f1351b0126661487a7e0a14a50f1675ec50eb34d4f20ef21
+    # via google-auth
+scikit-image==0.19.3 \
+    --hash=sha256:03779a7e1736fdf89d83c0ba67d44110496edd736a3bfce61a2b5177a1c8a099 \
+    --hash=sha256:0b0a199157ce8487c77de4fde0edc0b42d6d42818881c11f459262351d678b2d \
+    --hash=sha256:19a21a101a20c587a3b611a2cf6f86c35aae9f8d9563279b987e83ee1c9a9790 \
+    --hash=sha256:24b5367de1762da6ee126dd8f30cc4e7efda474e0d7d70685433f0e3aa2ec450 \
+    --hash=sha256:2a02d1bd0e2b53e36b952bd5fd6118d9ccc3ee51de35705d63d8eb1f2e86adef \
+    --hash=sha256:2f50b923f8099c1045fcde7418d86b206c87e333e43da980f41d8577b9605245 \
+    --hash=sha256:32fb88cc36203b99c9672fb972c9ef98635deaa5fc889fe969f3e11c44f22919 \
+    --hash=sha256:33dfd463ee6cc509defa279b963829f2230c9e0639ccd3931045be055878eea6 \
+    --hash=sha256:3a01372ae4bca223873304b0bff79b9d92446ac6d6177f73d89b45561e2d09d8 \
+    --hash=sha256:651de1c2ce1fbee834753b46b8e7d81cb12a5594898babba63ac82b30ddad49d \
+    --hash=sha256:6b6a8f98f2ac9bb73706461fd1dec875f6a5141759ed526850a5a49e90003d19 \
+    --hash=sha256:7f9f8a1387afc6c70f2bed007c3854a2d7489f9f7713c242f16f32ee05934bc2 \
+    --hash=sha256:84baa3179f3ae983c3a5d81c1e404bc92dcf7daeb41bfe9369badcda3fb22b92 \
+    --hash=sha256:8d8917fcf85b987b1f287f823f3a1a7dac38b70aaca759bc0200f3bc292d5ced \
+    --hash=sha256:9439e5294de3f18d6e82ec8eee2c46590231cf9c690da80545e83a0733b7a69e \
+    --hash=sha256:9fb0923a3bfa99457c5e17888f27b3b8a83a3600b4fef317992e7b7234764732 \
+    --hash=sha256:a7c3985c68bfe05f7571167ee021d14f5b8d1a4a250c91f0b13be7fb07e6af34 \
+    --hash=sha256:a8714348ddd671f819457a797c97d4c672166f093def66d66c3254cbd1d43f83 \
+    --hash=sha256:ad5d8000207a264d1a55681a9276e6a739d3f05cf4429004ad00d61d1892235f \
+    --hash=sha256:cc24177de3fdceca5d04807ad9c87d665f0bf01032ed94a9055cd1ed2b3f33e9 \
+    --hash=sha256:ce3d2207f253b8eb2c824e30d145a9f07a34a14212d57f3beca9f7e03c383cbe \
+    --hash=sha256:cfbb073f23deb48e0e60c47f8741d8089121d89cc78629ea8c5b51096efc5be7 \
+    --hash=sha256:e207c6ce5ce121d7d9b9d2b61b9adca57d1abed112c902d8ffbfdc20fb42c12b \
+    --hash=sha256:fd9dd3994bb6f9f7a35f228323f3c4dc44b3cf2ff15fd72d895216e9333550c6 \
+    --hash=sha256:fdf48d9b1f13af69e4e2c78e05067e322e9c8c97463c315cd0ecb47a94e259fc \
+    --hash=sha256:ff3b1025356508d41f4fe48528e509d95f9e4015e90cf158cd58c56dc63e0ac5
+    # via
+    #   albumentations
+    #   basicsr
+    #   k-diffusion
+scikit-learn==1.1.3 \
+    --hash=sha256:23fb9e74b813cc2528b5167d82ed08950b11106ccf50297161875e45152fb311 \
+    --hash=sha256:250da993701da88bf475e7c5746abf1285ea0ae47e4d0917cd13afd6600bb162 \
+    --hash=sha256:28b2bd6a1419acd522ff45d282c8ba23dbccb5338802ab0ee12baa4ade0aba4c \
+    --hash=sha256:2ee2c649f2231b68511aabb0dc827edd8936aad682acc6263c34aed11bc95dac \
+    --hash=sha256:30e27721adc308e8fd9f419f43068e43490005f911edf4476a9e585059fa8a83 \
+    --hash=sha256:38814f66285318f2e241305cca545eaa9b4126c65aa5dd78c69371f235f78e2b \
+    --hash=sha256:4d3a19166d4e1cdfcab975c68f471e046ce01e74c42a9a33fa89a14c2fcedf60 \
+    --hash=sha256:5699cded6c0685426433c7e5afe0fecad80ec831ec7fa264940e50c796775cc5 \
+    --hash=sha256:6785b8a3093329bf90ac01801be5525551728ae73edb11baa175df660820add4 \
+    --hash=sha256:6d1c1394e38a3319ace620381f6f23cc807d8780e9915c152449a86fc8f1db21 \
+    --hash=sha256:701181792a28c82fecae12adb5d15d0ecf57bffab7cf4bdbb52c7b3fd428d540 \
+    --hash=sha256:748f2bd632d6993e8918d43f1a26c380aeda4e122a88840d4c3a9af99d4239fe \
+    --hash=sha256:8e9dd76c7274055d1acf4526b8efb16a3531c26dcda714a0c16da99bf9d41900 \
+    --hash=sha256:bef51978a51ec19977700fe7b86aecea49c825884f3811756b74a3b152bb4e35 \
+    --hash=sha256:cd55c6fbef7608dbce1f22baf289dfcc6eb323247daa3c3542f73d389c724786 \
+    --hash=sha256:da5a2e95fef9805b1750e4abda4e834bf8835d26fc709a391543b53feee7bd0e \
+    --hash=sha256:ee47f68d973cee7009f06edb956f2f5588a0f230f24a2a70175fd0ecf36e2653 \
+    --hash=sha256:f4931f2a6c06e02c6c17a05f8ae397e2545965bc7a0a6cb38c8cd7d4fba8624d \
+    --hash=sha256:f5644663987ee221f5d1f47a593271b966c271c236fe05634e6bdc06041b5a2b \
+    --hash=sha256:f5d4231af7199531e77da1b78a4cc6b3d960a00b1ec672578ac818aae2b9c35d \
+    --hash=sha256:fd3ee69d36d42a7dcbb17e355a5653af5fd241a7dfd9133080b3dde8d9e2aafb
+    # via qudida
+scipy==1.9.3 \
+    --hash=sha256:06d2e1b4c491dc7d8eacea139a1b0b295f74e1a1a0f704c375028f8320d16e31 \
+    --hash=sha256:0d54222d7a3ba6022fdf5773931b5d7c56efe41ede7f7128c7b1637700409108 \
+    --hash=sha256:1884b66a54887e21addf9c16fb588720a8309a57b2e258ae1c7986d4444d3bc0 \
+    --hash=sha256:1a72d885fa44247f92743fc20732ae55564ff2a519e8302fb7e18717c5355a8b \
+    --hash=sha256:2318bef588acc7a574f5bfdff9c172d0b1bf2c8143d9582e05f878e580a3781e \
+    --hash=sha256:4db5b30849606a95dcf519763dd3ab6fe9bd91df49eba517359e450a7d80ce2e \
+    --hash=sha256:545c83ffb518094d8c9d83cce216c0c32f8c04aaf28b92cc8283eda0685162d5 \
+    --hash=sha256:5a04cd7d0d3eff6ea4719371cbc44df31411862b9646db617c99718ff68d4840 \
+    --hash=sha256:5b88e6d91ad9d59478fafe92a7c757d00c59e3bdc3331be8ada76a4f8d683f58 \
+    --hash=sha256:68239b6aa6f9c593da8be1509a05cb7f9efe98b80f43a5861cd24c7557e98523 \
+    --hash=sha256:83b89e9586c62e787f5012e8475fbb12185bafb996a03257e9675cd73d3736dd \
+    --hash=sha256:83c06e62a390a9167da60bedd4575a14c1f58ca9dfde59830fc42e5197283dab \
+    --hash=sha256:90453d2b93ea82a9f434e4e1cba043e779ff67b92f7a0e85d05d286a3625df3c \
+    --hash=sha256:abaf921531b5aeaafced90157db505e10345e45038c39e5d9b6c7922d68085cb \
+    --hash=sha256:b41bc822679ad1c9a5f023bc93f6d0543129ca0f37c1ce294dd9d386f0a21096 \
+    --hash=sha256:c68db6b290cbd4049012990d7fe71a2abd9ffbe82c0056ebe0f01df8be5436b0 \
+    --hash=sha256:cff3a5295234037e39500d35316a4c5794739433528310e117b8a9a0c76d20fc \
+    --hash=sha256:d01e1dd7b15bd2449c8bfc6b7cc67d630700ed655654f0dfcf121600bad205c9 \
+    --hash=sha256:d644a64e174c16cb4b2e41dfea6af722053e83d066da7343f333a54dae9bc31c \
+    --hash=sha256:da8245491d73ed0a994ed9c2e380fd058ce2fa8a18da204681f2fe1f57f98f95 \
+    --hash=sha256:fbc5c05c85c1a02be77b1ff591087c83bc44579c6d2bd9fb798bb64ea5e1a027
+    # via
+    #   albumentations
+    #   basicsr
+    #   clean-fid
+    #   facexlib
+    #   filterpy
+    #   gfpgan
+    #   k-diffusion
+    #   scikit-image
+    #   scikit-learn
+    #   torch-fidelity
+    #   torchdiffeq
+semver==2.13.0 \
+    --hash=sha256:ced8b23dceb22134307c1b8abfa523da14198793d9787ac838e70e29e77458d4 \
+    --hash=sha256:fa0fe2722ee1c3f57eac478820c3a5ae2f624af8264cbdf9000c980ff7f75e3f
+    # via streamlit
+send2trash==1.8.0 \
+    --hash=sha256:d2c24762fd3759860a0aff155e45871447ea58d2be6bdd39b5c8f966a0c99c2d \
+    --hash=sha256:f20eaadfdb517eaca5ce077640cb261c7d2698385a6a0f072a4a5447fd49fa08
+    # via -r requirements.in
+sentry-sdk==1.10.1 \
+    --hash=sha256:06c0fa9ccfdc80d7e3b5d2021978d6eb9351fa49db9b5847cf4d1f2a473414ad \
+    --hash=sha256:105faf7bd7b7fa25653404619ee261527266b14103fe1389e0ce077bd23a9691
+    # via wandb
+setproctitle==1.3.2 \
+    --hash=sha256:1c5d5dad7c28bdd1ec4187d818e43796f58a845aa892bb4481587010dc4d362b \
+    --hash=sha256:1c8d9650154afaa86a44ff195b7b10d683c73509d085339d174e394a22cccbb9 \
+    --hash=sha256:1f0cde41857a644b7353a0060b5f94f7ba7cf593ebde5a1094da1be581ac9a31 \
+    --hash=sha256:1f29b75e86260b0ab59adb12661ef9f113d2f93a59951373eb6d68a852b13e83 \
+    --hash=sha256:1fa1a0fbee72b47dc339c87c890d3c03a72ea65c061ade3204f285582f2da30f \
+    --hash=sha256:1ff863a20d1ff6ba2c24e22436a3daa3cd80be1dfb26891aae73f61b54b04aca \
+    --hash=sha256:265ecbe2c6eafe82e104f994ddd7c811520acdd0647b73f65c24f51374cf9494 \
+    --hash=sha256:288943dec88e178bb2fd868adf491197cc0fc8b6810416b1c6775e686bab87fe \
+    --hash=sha256:2e3ac25bfc4a0f29d2409650c7532d5ddfdbf29f16f8a256fc31c47d0dc05172 \
+    --hash=sha256:2fbd8187948284293f43533c150cd69a0e4192c83c377da837dbcd29f6b83084 \
+    --hash=sha256:4058564195b975ddc3f0462375c533cce310ccdd41b80ac9aed641c296c3eff4 \
+    --hash=sha256:4749a2b0c9ac52f864d13cee94546606f92b981b50e46226f7f830a56a9dc8e1 \
+    --hash=sha256:4d8938249a7cea45ab7e1e48b77685d0f2bab1ebfa9dde23e94ab97968996a7c \
+    --hash=sha256:5194b4969f82ea842a4f6af2f82cd16ebdc3f1771fb2771796e6add9835c1973 \
+    --hash=sha256:55ce1e9925ce1765865442ede9dca0ba9bde10593fcd570b1f0fa25d3ec6b31c \
+    --hash=sha256:589be87172b238f839e19f146b9ea47c71e413e951ef0dc6db4218ddacf3c202 \
+    --hash=sha256:5b932c3041aa924163f4aab970c2f0e6b4d9d773f4d50326e0ea1cd69240e5c5 \
+    --hash=sha256:5fb4f769c02f63fac90989711a3fee83919f47ae9afd4758ced5d86596318c65 \
+    --hash=sha256:630f6fe5e24a619ccf970c78e084319ee8be5be253ecc9b5b216b0f474f5ef18 \
+    --hash=sha256:65d884e22037b23fa25b2baf1a3316602ed5c5971eb3e9d771a38c3a69ce6e13 \
+    --hash=sha256:6c877691b90026670e5a70adfbcc735460a9f4c274d35ec5e8a43ce3f8443005 \
+    --hash=sha256:710e16fa3bade3b026907e4a5e841124983620046166f355bbb84be364bf2a02 \
+    --hash=sha256:7a55fe05f15c10e8c705038777656fe45e3bd676d49ad9ac8370b75c66dd7cd7 \
+    --hash=sha256:7aa0aac1711fadffc1d51e9d00a3bea61f68443d6ac0241a224e4d622489d665 \
+    --hash=sha256:7f0bed90a216ef28b9d227d8d73e28a8c9b88c0f48a082d13ab3fa83c581488f \
+    --hash=sha256:7f2719a398e1a2c01c2a63bf30377a34d0b6ef61946ab9cf4d550733af8f1ef1 \
+    --hash=sha256:7fe9df7aeb8c64db6c34fc3b13271a363475d77bc157d3f00275a53910cb1989 \
+    --hash=sha256:8ff3c8cb26afaed25e8bca7b9dd0c1e36de71f35a3a0706b5c0d5172587a3827 \
+    --hash=sha256:9124bedd8006b0e04d4e8a71a0945da9b67e7a4ab88fdad7b1440dc5b6122c42 \
+    --hash=sha256:92c626edc66169a1b09e9541b9c0c9f10488447d8a2b1d87c8f0672e771bc927 \
+    --hash=sha256:a149a5f7f2c5a065d4e63cb0d7a4b6d3b66e6e80f12e3f8827c4f63974cbf122 \
+    --hash=sha256:a47d97a75fd2d10c37410b180f67a5835cb1d8fdea2648fd7f359d4277f180b9 \
+    --hash=sha256:a499fff50387c1520c085a07578a000123f519e5f3eee61dd68e1d301659651f \
+    --hash=sha256:ab45146c71ca6592c9cc8b354a2cc9cc4843c33efcbe1d245d7d37ce9696552d \
+    --hash=sha256:b2c9cb2705fc84cb8798f1ba74194f4c080aaef19d9dae843591c09b97678e98 \
+    --hash=sha256:b34baef93bfb20a8ecb930e395ccd2ae3268050d8cf4fe187de5e2bd806fd796 \
+    --hash=sha256:b617f12c9be61e8f4b2857be4a4319754756845dbbbd9c3718f468bbb1e17bcb \
+    --hash=sha256:b9fb97907c830d260fa0658ed58afd48a86b2b88aac521135c352ff7fd3477fd \
+    --hash=sha256:bae283e85fc084b18ffeb92e061ff7ac5af9e183c9d1345c93e178c3e5069cbe \
+    --hash=sha256:c2c46200656280a064073447ebd363937562debef329482fd7e570c8d498f806 \
+    --hash=sha256:c8a09d570b39517de10ee5b718730e171251ce63bbb890c430c725c8c53d4484 \
+    --hash=sha256:c91b9bc8985d00239f7dc08a49927a7ca1ca8a6af2c3890feec3ed9665b6f91e \
+    --hash=sha256:dad42e676c5261eb50fdb16bdf3e2771cf8f99a79ef69ba88729aeb3472d8575 \
+    --hash=sha256:de3a540cd1817ede31f530d20e6a4935bbc1b145fd8f8cf393903b1e02f1ae76 \
+    --hash=sha256:e00c9d5c541a2713ba0e657e0303bf96ddddc412ef4761676adc35df35d7c246 \
+    --hash=sha256:e1aafc91cbdacc9e5fe712c52077369168e6b6c346f3a9d51bf600b53eae56bb \
+    --hash=sha256:e425be62524dc0c593985da794ee73eb8a17abb10fe692ee43bb39e201d7a099 \
+    --hash=sha256:e43f315c68aa61cbdef522a2272c5a5b9b8fd03c301d3167b5e1343ef50c676c \
+    --hash=sha256:e49ae693306d7624015f31cb3e82708916759d592c2e5f72a35c8f4cc8aef258 \
+    --hash=sha256:e5c50e164cd2459bc5137c15288a9ef57160fd5cbf293265ea3c45efe7870865 \
+    --hash=sha256:e8579a43eafd246e285eb3a5b939e7158073d5087aacdd2308f23200eac2458b \
+    --hash=sha256:e85e50b9c67854f89635a86247412f3ad66b132a4d8534ac017547197c88f27d \
+    --hash=sha256:f0452282258dfcc01697026a8841258dd2057c4438b43914b611bccbcd048f10 \
+    --hash=sha256:f4bfc89bd33ebb8e4c0e9846a09b1f5a4a86f5cb7a317e75cc42fee1131b4f4f \
+    --hash=sha256:fa2f50678f04fda7a75d0fe5dd02bbdd3b13cbe6ed4cf626e4472a7ccf47ae94 \
+    --hash=sha256:faec934cfe5fd6ac1151c02e67156c3f526e82f96b24d550b5d51efa4a5527c6 \
+    --hash=sha256:fcd3cf4286a60fdc95451d8d14e0389a6b4f5cebe02c7f2609325eb016535963 \
+    --hash=sha256:fe8a988c7220c002c45347430993830666e55bc350179d91fcee0feafe64e1d4 \
+    --hash=sha256:fed18e44711c5af4b681c2b3b18f85e6f0f1b2370a28854c645d636d5305ccd8 \
+    --hash=sha256:ffc61a388a5834a97953d6444a2888c24a05f2e333f9ed49f977a87bb1ad4761
+    # via wandb
+shortuuid==1.0.9 \
+    --hash=sha256:459f12fa1acc34ff213b1371467c0325169645a31ed989e268872339af7563d5 \
+    --hash=sha256:b2bb9eb7773170e253bb7ba25971023acb473517a8b76803d9618668cb1dd46f
+    # via wandb
+six==1.16.0 \
+    --hash=sha256:1e61c37477a1626458e36f7b1d82aa5c9b094fa4802892072e49de9c60c4c926 \
+    --hash=sha256:8abb2f1d86890a2dfb989f9a77cfcfd3e47c2a354b01111771326f8aa26e0254
+    # via
+    #   docker-pycreds
+    #   eventlet
+    #   flask-cors
+    #   google-auth
+    #   grpcio
+    #   promise
+    #   python-dateutil
+    #   validators
+    #   wandb
+smmap==5.0.0 \
+    --hash=sha256:2aba19d6a040e78d8b09de5c57e96207b09ed71d8e55ce0959eeee6c8e190d94 \
+    --hash=sha256:c840e62059cd3be204b0c9c9f74be2c09d5648eddd4580d9314c3ecde0b30936
+    # via gitdb
+streamlit==1.14.0 \
+    --hash=sha256:62556d873567e1b3427bcd118a57ee6946619f363bd6bba38df2d1f8225ecba0 \
+    --hash=sha256:e078b8143d150ba721bdb9194218e311c5fe1d6d4156473a2dea6cc848a6c9fc
+    # via -r requirements.in
+taming-transformers-rom1504==0.0.6 \
+    --hash=sha256:051b5804c58caa247bcd51d17ddb525b4d5f892a29d42dc460f40e3e9e34e5d8 \
+    --hash=sha256:73fe5fc1108accee4236ee6976e0987ab236afad0af06cb9f037641a908d2c32
+    # via -r requirements.in
+tb-nightly==2.11.0a20221106 \
+    --hash=sha256:8940457ee42db92f01da8bcdbbea1a476735eda559dde5976f5728919960af4a
+    # via
+    #   basicsr
+    #   gfpgan
+tensorboard==2.10.1 \
+    --hash=sha256:fb9222c1750e2fa35ef170d998a1e229f626eeced3004494a8849c88c15d8c1c
+    # via
+    #   pytorch-lightning
+    #   test-tube
+tensorboard-data-server==0.6.1 \
+    --hash=sha256:809fe9887682d35c1f7d1f54f0f40f98bb1f771b14265b453ca051e2ce58fca7 \
+    --hash=sha256:d8237580755e58eff68d1f3abefb5b1e39ae5c8b127cc40920f9c4fb33f4b98a \
+    --hash=sha256:fa8cef9be4fcae2f2363c88176638baf2da19c5ec90addb49b1cde05c95c88ee
+    # via
+    #   tb-nightly
+    #   tensorboard
+tensorboard-plugin-wit==1.8.1 \
+    --hash=sha256:ff26bdd583d155aa951ee3b152b3d0cffae8005dc697f72b44a8e8c2a77a8cbe
+    # via
+    #   tb-nightly
+    #   tensorboard
+test-tube==0.7.5 \
+    --hash=sha256:1379c33eb8cde3e9b36610f87da0f16c2e06496b1cfebac473df4e7be2faa124
+    # via -r requirements.in
+threadpoolctl==3.1.0 \
+    --hash=sha256:8b99adda265feb6773280df41eece7b2e6561b772d21ffd52e372f999024907b \
+    --hash=sha256:a335baacfaa4400ae1f0d8e3a58d6674d2f8828e3716bb2802c44955ad391380
+    # via scikit-learn
+tifffile==2022.10.10 \
+    --hash=sha256:50b61ba943b866d191295bc38a00191c9fdab23ece063544c7f1a264e3f6aa8e \
+    --hash=sha256:87f3aee8a0d06b74655269a105de75c1958a24653e1930d523eb516100043503
+    # via scikit-image
+tokenizers==0.13.1 \
+    --hash=sha256:0a3412830ad66a643723d6b0fc3202e64e9e299bd9c9eda04b2914b5b1e0ddb0 \
+    --hash=sha256:126bcb18a77cf65961ece04f54bd10ef3713412195e543d9d3eda2f0e4fd677c \
+    --hash=sha256:16434c61c5eb72f6692b9bc52052b07ca92d3eba9dd72a1bc371890e1bdc3f07 \
+    --hash=sha256:1d4acfdb6e7ef974677bb8445462db7fed240185fdc0f5b061db357d4ef8d85d \
+    --hash=sha256:3333d1cee5c8f47c96362ea0abc1f81c77c9b92c6c3d11cbf1d01985f0d5cf1d \
+    --hash=sha256:3acf3cae4c4739fc9ec49fa0e6cce224c1aa0fabc8f8d1358fd7de5c7d49cdca \
+    --hash=sha256:3ba43b3f6f6b41c97c1d041785342cd72ba142999f6c4605d628e8e937398f20 \
+    --hash=sha256:3c69a8389fd88bc32115e99db70f63bef577ba5c54f40a632580038a49612856 \
+    --hash=sha256:3de653a551cc616a442a123da21706cb3a3163cf6919973f978f0921eee1bdf0 \
+    --hash=sha256:4b3be8af87b357340b9b049d28067287b5e5e296e3120b6e4875d3b8469b67e6 \
+    --hash=sha256:680bc0e357b7da6d0d01634bffbd002e866fdaccde303e1d1af58f32464cf308 \
+    --hash=sha256:70de69681a264a5808d39f4bb6331be9a4dec51fd48cd1b959a94da76c4939cc \
+    --hash=sha256:73198cda6e1d991c583ed798514863e16763aa600eb7aa6df7722373290575b2 \
+    --hash=sha256:80864f456f715829f901ad5bb85af97e9ae52fc902270944804f6476ab8c6699 \
+    --hash=sha256:80b9552295fdce0a2513dcb795a3f8591eca1a8dcf8afe0de3214209e6924ad1 \
+    --hash=sha256:84fa41b58a8d3b7363ecdf3397d4b38f345fcf7d4dd14565b4360e7bffc9cae0 \
+    --hash=sha256:890d2139100b5c8ac6d585438d5e145ede1d7b32b4090a6c078db6db0ef1daea \
+    --hash=sha256:8b3f97041f7716998e474d3c7ffd19ac6941f117616696aef2b5ba927bf091e3 \
+    --hash=sha256:910479e92d5fbdf91e8106b4c658fd43d418893d7cfd5fb11983c54a1ff53869 \
+    --hash=sha256:96a1beef1e64d44597627f4e29d794047a66ad4d7474d93daf5a0ee27928e012 \
+    --hash=sha256:98bef54cf51ac335fda1408112df7ff3e584107633bd9066616033e12b0bd519 \
+    --hash=sha256:afcb1bd6d9ed59d5c8e633765589cab12f98aae09804f156b5965b4463b8b8e3 \
+    --hash=sha256:b72dec85488af3e1e8d58fb4b86b5dbe5171c176002b5e098ea6d52a70004bb5 \
+    --hash=sha256:c3109ba62bea56c68c7c2a976250b040afee61b5f86fc791f17afaa2a09fce94 \
+    --hash=sha256:c73b9e6c107e980e65077b89c54311d8d645f6a9efdde82990777fa43c0a8cae \
+    --hash=sha256:d8fca8b492a4697b0182e0c40b164cb0c44a9669d9c98033fec2f88174605eb0 \
+    --hash=sha256:db6872294339bf35c158219fc65bad939ba87d14c936ae7a33a3ca2d1532c5b1 \
+    --hash=sha256:e1a90bc97f53600f52e902f3ae097424de712d8ae0e42d957efc7ed908573a20 \
+    --hash=sha256:f75f476fe183c03c515a0f0f5d195cb05d93fcdc76e31fe3c9753d01f3ee990b \
+    --hash=sha256:fd17b14f84bec0b171869abd17ca0d9bfc564aa1e7f3451f44da526949a911c1 \
+    --hash=sha256:fea71780b66f8c278ebae7221c8959404cf7343b8d2f4b7308aa668cf6f02364
+    # via transformers
+toml==0.10.2 \
+    --hash=sha256:806143ae5bfb6a3c6e736a764057db0e6a0e05e338b5630894a5f779cabb4f9b \
+    --hash=sha256:b3bda1d108d5dd99f4a20d24d9c348e91c4db7ab1b749200bded2f839ccbe68f
+    # via streamlit
+toolz==0.12.0 \
+    --hash=sha256:2059bd4148deb1884bb0eb770a3cde70e7f954cfbbdc2285f1f2de01fd21eb6f \
+    --hash=sha256:88c570861c440ee3f2f6037c4654613228ff40c93a6c25e0eba70d17282c6194
+    # via altair
+torch==1.12.1+cu116 \
+    --hash=sha256:7725420dabebfcaf44984edce3283eea91f98f0f7d5874bc68c7a164bd8126e3 \
+    --hash=sha256:832effad8b21109700323a5aa137a2e4bdea711dac3d8491ff542f798dab0101 \
+    --hash=sha256:84f031e4ee25d95368d7531aa58e79da9808d3fa53b4b363ea03a2450b6fd0af \
+    --hash=sha256:b6bc31244aa2818929fbb30c483c221df471e9d856e805c5a1ff72b131ae9e7b \
+    --hash=sha256:b8e8906e770bcad12e67c269e1bcdd7661a8abd96519a4ba643e86440bbcc1bf \
+    --hash=sha256:bca5a77071d7eb901beb775648b125e6d9279f231d1f23e56530b5a189df8975 \
+    --hash=sha256:dda312901220895087cc83d3665464a3dc171d04460c61c31af463efbfb54896 \
+    --hash=sha256:fc9b4786ec54be67eaa8b0c7c9999e2f4ae2b89a1c18e41de1515a190440c691
+    # via
+    #   accelerate
+    #   basicsr
+    #   clean-fid
+    #   clip
+    #   facexlib
+    #   gfpgan
+    #   k-diffusion
+    #   kornia
+    #   pytorch-lightning
+    #   realesrgan
+    #   taming-transformers-rom1504
+    #   test-tube
+    #   torch-fidelity
+    #   torchdiffeq
+    #   torchmetrics
+    #   torchvision
+torch-fidelity==0.3.0 \
+    --hash=sha256:3d3e33db98919759cc4f3f24cb27e1e74bdc7c905d90a780630e4e1c18492b66 \
+    --hash=sha256:d01284825595feb7dc3eae3dc9a0d8ced02be764813a3483f109bc142b52a1d3
+    # via -r requirements.in
+torchdiffeq==0.2.3 \
+    --hash=sha256:b5b01ec1294a2d8d5f77e567bf17c5de1237c0573cb94deefa88326f0e18c338 \
+    --hash=sha256:fe75f434b9090ac0c27702e02bed21472b0f87035be6581f51edc5d4013ea31a
+    # via k-diffusion
+torchmetrics==0.10.2 \
+    --hash=sha256:43757d82266969906fc74b6e80766fcb2a0d52d6c3d09e3b7c98cf3b733fd20c \
+    --hash=sha256:daa29d96bff5cff04d80eec5b9f5076993d6ac9c2d2163e88b6b31f8d38f7c25
+    # via pytorch-lightning
+torchvision==0.13.1+cu116 ; platform_system == "Linux" or platform_system == "Windows" \
+    --hash=sha256:0c9a2b605ac30fcf475d60f79ba378af0073a22de585453f8c3dd6c1452ab9bc \
+    --hash=sha256:75986abe572138258eb9795cb4cd73f40b2bdf8374fefa1af6ff6bb0dbc972c6 \
+    --hash=sha256:8a4c395bb72cf51eb4318c6861c9a5ea490d48ec36a3d767220ef182445449cb \
+    --hash=sha256:92e4685c6010b6b1c228ebb5fe93105d0a71e5b586483a942e04529a43e0bb42 \
+    --hash=sha256:9ec5654c56a22fe420dc0af0ff5cd31105f583fdb0240043ff26a7cfed7e05fb \
+    --hash=sha256:ba8b7d3c33f63feb29c7dd8c0db68b735d0c9d924ff4e84121b4b20b17cec7a5 \
+    --hash=sha256:c3ceb2b3f456f0c984af71ef55f8637f178a29dc3e13a66fbb010ceead2891e1 \
+    --hash=sha256:dcf32f6d998493e76ec21a38bbb856b7402295cf7a67fb09ce5bde7e7e725756
+    # via
+    #   -r requirements.in
+    #   basicsr
+    #   clean-fid
+    #   clip
+    #   facexlib
+    #   gfpgan
+    #   k-diffusion
+    #   realesrgan
+    #   taming-transformers-rom1504
+    #   torch-fidelity
+tornado==6.2 \
+    --hash=sha256:1d54d13ab8414ed44de07efecb97d4ef7c39f7438cf5e976ccd356bebb1b5fca \
+    --hash=sha256:20f638fd8cc85f3cbae3c732326e96addff0a15e22d80f049e00121651e82e72 \
+    --hash=sha256:5c87076709343557ef8032934ce5f637dbb552efa7b21d08e89ae7619ed0eb23 \
+    --hash=sha256:5f8c52d219d4995388119af7ccaa0bcec289535747620116a58d830e7c25d8a8 \
+    --hash=sha256:6fdfabffd8dfcb6cf887428849d30cf19a3ea34c2c248461e1f7d718ad30b66b \
+    --hash=sha256:87dcafae3e884462f90c90ecc200defe5e580a7fbbb4365eda7c7c1eb809ebc9 \
+    --hash=sha256:9b630419bde84ec666bfd7ea0a4cb2a8a651c2d5cccdbdd1972a0c859dfc3c13 \
+    --hash=sha256:b8150f721c101abdef99073bf66d3903e292d851bee51910839831caba341a75 \
+    --hash=sha256:ba09ef14ca9893954244fd872798b4ccb2367c165946ce2dd7376aebdde8e3ac \
+    --hash=sha256:d3a2f5999215a3a06a4fc218026cd84c61b8b2b40ac5296a6db1f1451ef04c1e \
+    --hash=sha256:e5f923aa6a47e133d1cf87d60700889d7eae68988704e20c75fb2d65677a8e4b
+    # via streamlit
+tqdm==4.64.1 \
+    --hash=sha256:5f4f682a004951c1b450bc753c710e9280c5746ce6ffedee253ddbcbf54cf1e4 \
+    --hash=sha256:6fee160d6ffcd1b1c68c65f14c829c22832bc401726335ce92c52d395944a6a1
+    # via
+    #   basicsr
+    #   clean-fid
+    #   clip
+    #   facexlib
+    #   gfpgan
+    #   huggingface-hub
+    #   k-diffusion
+    #   pytorch-lightning
+    #   realesrgan
+    #   taming-transformers-rom1504
+    #   torch-fidelity
+    #   transformers
+transformers==4.24.0 \
+    --hash=sha256:486f353a8e594002e48be0e2aba723d96eda839e63bfe274702a4b5eda85559b \
+    --hash=sha256:b7ab50039ef9bf817eff14ab974f306fd20a72350bdc9df3a858fd009419322e
+    # via -r requirements.in
+typing-extensions==4.4.0 \
+    --hash=sha256:1511434bb92bf8dd198c12b1cc812e800d4181cfcb867674e0f8279cc93087aa \
+    --hash=sha256:16fa4864408f655d35ec496218b85f79b3437c829e93320c7c9215ccfd92489e
+    # via
+    #   huggingface-hub
+    #   pytorch-lightning
+    #   qudida
+    #   streamlit
+    #   torch
+    #   torchvision
+tzdata==2022.6 \
+    --hash=sha256:04a680bdc5b15750c39c12a448885a51134a27ec9af83667663f0b3a1bf3f342 \
+    --hash=sha256:91f11db4503385928c15598c98573e3af07e7229181bee5375bd30f1695ddcae
+    # via
+    #   pytz-deprecation-shim
+    #   tzlocal
+tzlocal==4.2 \
+    --hash=sha256:89885494684c929d9191c57aa27502afc87a579be5cdd3225c77c463ea043745 \
+    --hash=sha256:ee5842fa3a795f023514ac2d801c4a81d1743bbe642e3940143326b3a00addd7
+    # via streamlit
+urllib3==1.26.12 \
+    --hash=sha256:3fa96cf423e6987997fc326ae8df396db2a8b7c667747d47ddd8ecba91f4a74e \
+    --hash=sha256:b930dd878d5a8afb066a637fbb35144fe7901e3b209d1cd4f524bd0e9deee997
+    # via
+    #   requests
+    #   sentry-sdk
+validators==0.18.2 \
+    --hash=sha256:0143dcca8a386498edaf5780cbd5960da1a4c85e0719f3ee5c9b41249c4fefbd \
+    --hash=sha256:37cd9a9213278538ad09b5b9f9134266e7c226ab1fede1d500e29e0a8fbb9ea6
+    # via streamlit
+wandb==0.13.5 \
+    --hash=sha256:11f30a22e30abaa9c187e8b6aa4c12d76160b40bbe98a6f14b0dde9297bbfbe2 \
+    --hash=sha256:60d5bcc524b8a314c8e072c03f7702dbd5406261b00a4ce75e7556b805fdc765
+    # via k-diffusion
+watchdog==2.1.9 \
+    --hash=sha256:083171652584e1b8829581f965b9b7723ca5f9a2cd7e20271edf264cfd7c1412 \
+    --hash=sha256:117ffc6ec261639a0209a3252546b12800670d4bf5f84fbd355957a0595fe654 \
+    --hash=sha256:186f6c55abc5e03872ae14c2f294a153ec7292f807af99f57611acc8caa75306 \
+    --hash=sha256:195fc70c6e41237362ba720e9aaf394f8178bfc7fa68207f112d108edef1af33 \
+    --hash=sha256:226b3c6c468ce72051a4c15a4cc2ef317c32590d82ba0b330403cafd98a62cfd \
+    --hash=sha256:247dcf1df956daa24828bfea5a138d0e7a7c98b1a47cf1fa5b0c3c16241fcbb7 \
+    --hash=sha256:255bb5758f7e89b1a13c05a5bceccec2219f8995a3a4c4d6968fe1de6a3b2892 \
+    --hash=sha256:43ce20ebb36a51f21fa376f76d1d4692452b2527ccd601950d69ed36b9e21609 \
+    --hash=sha256:4f4e1c4aa54fb86316a62a87b3378c025e228178d55481d30d857c6c438897d6 \
+    --hash=sha256:5952135968519e2447a01875a6f5fc8c03190b24d14ee52b0f4b1682259520b1 \
+    --hash=sha256:64a27aed691408a6abd83394b38503e8176f69031ca25d64131d8d640a307591 \
+    --hash=sha256:6b17d302850c8d412784d9246cfe8d7e3af6bcd45f958abb2d08a6f8bedf695d \
+    --hash=sha256:70af927aa1613ded6a68089a9262a009fbdf819f46d09c1a908d4b36e1ba2b2d \
+    --hash=sha256:7a833211f49143c3d336729b0020ffd1274078e94b0ae42e22f596999f50279c \
+    --hash=sha256:8250546a98388cbc00c3ee3cc5cf96799b5a595270dfcfa855491a64b86ef8c3 \
+    --hash=sha256:97f9752208f5154e9e7b76acc8c4f5a58801b338de2af14e7e181ee3b28a5d39 \
+    --hash=sha256:9f05a5f7c12452f6a27203f76779ae3f46fa30f1dd833037ea8cbc2887c60213 \
+    --hash=sha256:a735a990a1095f75ca4f36ea2ef2752c99e6ee997c46b0de507ba40a09bf7330 \
+    --hash=sha256:ad576a565260d8f99d97f2e64b0f97a48228317095908568a9d5c786c829d428 \
+    --hash=sha256:b530ae007a5f5d50b7fbba96634c7ee21abec70dc3e7f0233339c81943848dc1 \
+    --hash=sha256:bfc4d351e6348d6ec51df007432e6fe80adb53fd41183716017026af03427846 \
+    --hash=sha256:d3dda00aca282b26194bdd0adec21e4c21e916956d972369359ba63ade616153 \
+    --hash=sha256:d9820fe47c20c13e3c9dd544d3706a2a26c02b2b43c993b62fcd8011bcc0adb3 \
+    --hash=sha256:ed80a1628cee19f5cfc6bb74e173f1b4189eb532e705e2a13e3250312a62e0c9 \
+    --hash=sha256:ee3e38a6cc050a8830089f79cbec8a3878ec2fe5160cdb2dc8ccb6def8552658
+    # via streamlit
+wcwidth==0.2.5 \
+    --hash=sha256:beb4802a9cebb9144e99086eff703a642a13d6a0052920003a230f3294bbe784 \
+    --hash=sha256:c4d647b99872929fdb7bdcaa4fbe7f01413ed3d98077df798530e5b04f116c83
+    # via ftfy
+werkzeug==2.2.2 \
+    --hash=sha256:7ea2d48322cc7c0f8b3a215ed73eabd7b5d75d0b50e31ab006286ccff9e00b8f \
+    --hash=sha256:f979ab81f58d7318e064e99c4506445d60135ac5cd2e177a2de0089bfd4c9bd5
+    # via
+    #   flask
+    #   tb-nightly
+    #   tensorboard
+wheel==0.38.2 \
+    --hash=sha256:3d492ef22379a156ec923d2a77051cedfd4df4b667864e9e41ba83f0b70b7149 \
+    --hash=sha256:7a5a3095dceca97a3cac869b8fef4e89b83fafde21b6688f47b6fda7600eb441
+    # via
+    #   tb-nightly
+    #   tensorboard
+whichcraft==0.6.1 \
+    --hash=sha256:acdbb91b63d6a15efbd6430d1d7b2d36e44a71697e93e19b7ded477afd9fce87 \
+    --hash=sha256:deda9266fbb22b8c64fd3ee45c050d61139cd87419765f588e37c8d23e236dd9
+    # via flaskwebgui
+yapf==0.32.0 \
+    --hash=sha256:8fea849025584e486fd06d6ba2bed717f396080fd3cc236ba10cb97c4c51cf32 \
+    --hash=sha256:a3f5085d37ef7e3e004c4ba9f9b3e40c54ff1901cd111f05145ae313a7c67d1b
+    # via
+    #   basicsr
+    #   gfpgan
+yarl==1.8.1 \
+    --hash=sha256:076eede537ab978b605f41db79a56cad2e7efeea2aa6e0fa8f05a26c24a034fb \
+    --hash=sha256:07b21e274de4c637f3e3b7104694e53260b5fc10d51fb3ec5fed1da8e0f754e3 \
+    --hash=sha256:0ab5a138211c1c366404d912824bdcf5545ccba5b3ff52c42c4af4cbdc2c5035 \
+    --hash=sha256:0c03f456522d1ec815893d85fccb5def01ffaa74c1b16ff30f8aaa03eb21e453 \
+    --hash=sha256:12768232751689c1a89b0376a96a32bc7633c08da45ad985d0c49ede691f5c0d \
+    --hash=sha256:19cd801d6f983918a3f3a39f3a45b553c015c5aac92ccd1fac619bd74beece4a \
+    --hash=sha256:1ca7e596c55bd675432b11320b4eacc62310c2145d6801a1f8e9ad160685a231 \
+    --hash=sha256:1e4808f996ca39a6463f45182e2af2fae55e2560be586d447ce8016f389f626f \
+    --hash=sha256:205904cffd69ae972a1707a1bd3ea7cded594b1d773a0ce66714edf17833cdae \
+    --hash=sha256:20df6ff4089bc86e4a66e3b1380460f864df3dd9dccaf88d6b3385d24405893b \
+    --hash=sha256:21ac44b763e0eec15746a3d440f5e09ad2ecc8b5f6dcd3ea8cb4773d6d4703e3 \
+    --hash=sha256:29e256649f42771829974e742061c3501cc50cf16e63f91ed8d1bf98242e5507 \
+    --hash=sha256:2d800b9c2eaf0684c08be5f50e52bfa2aa920e7163c2ea43f4f431e829b4f0fd \
+    --hash=sha256:2d93a049d29df172f48bcb09acf9226318e712ce67374f893b460b42cc1380ae \
+    --hash=sha256:31a9a04ecccd6b03e2b0e12e82131f1488dea5555a13a4d32f064e22a6003cfe \
+    --hash=sha256:3d1a50e461615747dd93c099f297c1994d472b0f4d2db8a64e55b1edf704ec1c \
+    --hash=sha256:449c957ffc6bc2309e1fbe67ab7d2c1efca89d3f4912baeb8ead207bb3cc1cd4 \
+    --hash=sha256:4a88510731cd8d4befaba5fbd734a7dd914de5ab8132a5b3dde0bbd6c9476c64 \
+    --hash=sha256:4c322cbaa4ed78a8aac89b2174a6df398faf50e5fc12c4c191c40c59d5e28357 \
+    --hash=sha256:5395da939ffa959974577eff2cbfc24b004a2fb6c346918f39966a5786874e54 \
+    --hash=sha256:5587bba41399854703212b87071c6d8638fa6e61656385875f8c6dff92b2e461 \
+    --hash=sha256:56c11efb0a89700987d05597b08a1efcd78d74c52febe530126785e1b1a285f4 \
+    --hash=sha256:5999c4662631cb798496535afbd837a102859568adc67d75d2045e31ec3ac497 \
+    --hash=sha256:59ddd85a1214862ce7c7c66457f05543b6a275b70a65de366030d56159a979f0 \
+    --hash=sha256:6347f1a58e658b97b0a0d1ff7658a03cb79bdbda0331603bed24dd7054a6dea1 \
+    --hash=sha256:6628d750041550c5d9da50bb40b5cf28a2e63b9388bac10fedd4f19236ef4957 \
+    --hash=sha256:6afb336e23a793cd3b6476c30f030a0d4c7539cd81649683b5e0c1b0ab0bf350 \
+    --hash=sha256:6c8148e0b52bf9535c40c48faebb00cb294ee577ca069d21bd5c48d302a83780 \
+    --hash=sha256:76577f13333b4fe345c3704811ac7509b31499132ff0181f25ee26619de2c843 \
+    --hash=sha256:7c0da7e44d0c9108d8b98469338705e07f4bb7dab96dbd8fa4e91b337db42548 \
+    --hash=sha256:7de89c8456525650ffa2bb56a3eee6af891e98f498babd43ae307bd42dca98f6 \
+    --hash=sha256:7ec362167e2c9fd178f82f252b6d97669d7245695dc057ee182118042026da40 \
+    --hash=sha256:7fce6cbc6c170ede0221cc8c91b285f7f3c8b9fe28283b51885ff621bbe0f8ee \
+    --hash=sha256:85cba594433915d5c9a0d14b24cfba0339f57a2fff203a5d4fd070e593307d0b \
+    --hash=sha256:8b0af1cf36b93cee99a31a545fe91d08223e64390c5ecc5e94c39511832a4bb6 \
+    --hash=sha256:9130ddf1ae9978abe63808b6b60a897e41fccb834408cde79522feb37fb72fb0 \
+    --hash=sha256:99449cd5366fe4608e7226c6cae80873296dfa0cde45d9b498fefa1de315a09e \
+    --hash=sha256:9de955d98e02fab288c7718662afb33aab64212ecb368c5dc866d9a57bf48880 \
+    --hash=sha256:a0fb2cb4204ddb456a8e32381f9a90000429489a25f64e817e6ff94879d432fc \
+    --hash=sha256:a165442348c211b5dea67c0206fc61366212d7082ba8118c8c5c1c853ea4d82e \
+    --hash=sha256:ab2a60d57ca88e1d4ca34a10e9fb4ab2ac5ad315543351de3a612bbb0560bead \
+    --hash=sha256:abc06b97407868ef38f3d172762f4069323de52f2b70d133d096a48d72215d28 \
+    --hash=sha256:af887845b8c2e060eb5605ff72b6f2dd2aab7a761379373fd89d314f4752abbf \
+    --hash=sha256:b19255dde4b4f4c32e012038f2c169bb72e7f081552bea4641cab4d88bc409dd \
+    --hash=sha256:b3ded839a5c5608eec8b6f9ae9a62cb22cd037ea97c627f38ae0841a48f09eae \
+    --hash=sha256:c1445a0c562ed561d06d8cbc5c8916c6008a31c60bc3655cdd2de1d3bf5174a0 \
+    --hash=sha256:d0272228fabe78ce00a3365ffffd6f643f57a91043e119c289aaba202f4095b0 \
+    --hash=sha256:d0b51530877d3ad7a8d47b2fff0c8df3b8f3b8deddf057379ba50b13df2a5eae \
+    --hash=sha256:d0f77539733e0ec2475ddcd4e26777d08996f8cd55d2aef82ec4d3896687abda \
+    --hash=sha256:d2b8f245dad9e331540c350285910b20dd913dc86d4ee410c11d48523c4fd546 \
+    --hash=sha256:dd032e8422a52e5a4860e062eb84ac94ea08861d334a4bcaf142a63ce8ad4802 \
+    --hash=sha256:de49d77e968de6626ba7ef4472323f9d2e5a56c1d85b7c0e2a190b2173d3b9be \
+    --hash=sha256:de839c3a1826a909fdbfe05f6fe2167c4ab033f1133757b5936efe2f84904c07 \
+    --hash=sha256:e80ed5a9939ceb6fda42811542f31c8602be336b1fb977bccb012e83da7e4936 \
+    --hash=sha256:ea30a42dc94d42f2ba4d0f7c0ffb4f4f9baa1b23045910c0c32df9c9902cb272 \
+    --hash=sha256:ea513a25976d21733bff523e0ca836ef1679630ef4ad22d46987d04b372d57fc \
+    --hash=sha256:ed19b74e81b10b592084a5ad1e70f845f0aacb57577018d31de064e71ffa267a \
+    --hash=sha256:f5af52738e225fcc526ae64071b7e5342abe03f42e0e8918227b38c9aa711e28 \
+    --hash=sha256:fae37373155f5ef9b403ab48af5136ae9851151f7aacd9926251ab26b953118b
+    # via aiohttp
+zipp==3.10.0 \
+    --hash=sha256:4fcb6f278987a6605757302a6e40e896257570d11c51628968ccb2a47e80c6c1 \
+    --hash=sha256:7a7262fd930bd3e36c50b9a64897aec3fafff3dfdeec9623ae22b40e93f99bb8
+    # via importlib-metadata
+
+# The following packages are considered to be unsafe in a requirements file:
+setuptools==65.5.1 \
+    --hash=sha256:d0b9a8433464d5800cbe05094acf5c6d52a91bfac9b52bcfc4d41382be5d5d31 \
+    --hash=sha256:e197a19aa8ec9722928f2206f8de752def0e4c9fc6953527360d1c36d94ddb2f
+    # via
+    #   numba
+    #   tb-nightly
+    #   tensorboard
+    #   wandb
diff --git a/installer/readme.txt b/installer/readme.txt
index a5b8537a39..31456327e9 100644
--- a/installer/readme.txt
+++ b/installer/readme.txt
@@ -3,9 +3,15 @@ InvokeAI
 Project homepage: https://github.com/invoke-ai/InvokeAI
 
 Installation on Windows:
- Please double-click the 'install.bat' file (while keeping it inside the invokeAI folder).
+    NOTE: You might need to enable Windows Long Paths. If you're not sure,
+    then you almost certainly need to. Simply double-click the 'WinLongPathsEnabled.reg'
+    file. Note that you will need to have admin privileges in order to
+    do this.
+
+    Please double-click the 'install.bat' file (while keeping it inside the invokeAI folder).
 
 Installation on Linux and Mac:
- Please open the terminal, and run './install.sh' (while keeping it inside the invokeAI folder).
+    Please open the terminal, and run './install.sh' (while keeping it inside the invokeAI folder).
 
-After installation, please run the 'invoke.bat' file (on Windows) or 'invoke.sh' file (on Linux/Mac) to start InvokeAI.
\ No newline at end of file
+After installation, please run the 'invoke.bat' file (on Windows) or 'invoke.sh'
+file (on Linux/Mac) to start InvokeAI.
diff --git a/installer/requirements.in b/installer/requirements.in
new file mode 100644
index 0000000000..71967f1cf2
--- /dev/null
+++ b/installer/requirements.in
@@ -0,0 +1,24 @@
+--prefer-binary
+--extra-index-url https://download.pytorch.org/whl/cu116
+--trusted-host https://download.pytorch.org
+albumentations
+diffusers
+eventlet
+flask_cors
+flask_socketio
+flaskwebgui
+getpass_asterisk
+imageio-ffmpeg
+pyreadline3
+realesrgan
+send2trash
+streamlit
+taming-transformers-rom1504
+test-tube
+torch-fidelity
+torchvision==0.13.1 ; platform_system == 'Darwin'
+torchvision==0.13.1+cu116 ; platform_system == 'Linux' or platform_system == 'Windows'
+transformers
+https://github.com/openai/CLIP/archive/d50d76daa670286dd6cacf3bcd80b5e4823fc8e1.zip
+https://github.com/TencentARC/GFPGAN/archive/2eac2033893ca7f427f4035d80fe95b92649ac56.zip
+https://github.com/invoke-ai/k-diffusion/archive/7f16b2c33411f26b3eae78d10648d625cb0c1095.zip
diff --git a/ldm/generate.py b/ldm/generate.py
index a5df663077..9aebe314f3 100644
--- a/ldm/generate.py
+++ b/ldm/generate.py
@@ -142,6 +142,7 @@ class Generate:
             ddim_eta = 0.0,  # deterministic
             full_precision = False,
             precision = 'auto',
+            outdir = 'outputs/img-samples',
             gfpgan=None,
             codeformer=None,
             esrgan=None,
@@ -175,6 +176,7 @@ class Generate:
         self.generators     = {}
         self.base_generator = None
         self.seed           = None
+        self.outdir = outdir
         self.gfpgan = gfpgan
         self.codeformer = codeformer
         self.esrgan = esrgan
@@ -243,11 +245,11 @@ class Generate:
         return outputs
 
     def txt2img(self, prompt, **kwargs):
-        outdir = kwargs.pop('outdir', 'outputs/img-samples')
+        outdir = kwargs.pop('outdir', self.outdir)
         return self.prompt2png(prompt, outdir, **kwargs)
 
     def img2img(self, prompt, **kwargs):
-        outdir = kwargs.pop('outdir', 'outputs/img-samples')
+        outdir = kwargs.pop('outdir', self.outdir)
         assert (
             'init_img' in kwargs
         ), 'call to img2img() must include the init_img argument'
@@ -276,6 +278,7 @@ class Generate:
             threshold        = 0.0,
             perlin           = 0.0,
             karras_max       = None,
+            outdir         = None,
             # these are specific to img2img and inpaint
             init_img         = None,
             init_mask        = None,
@@ -288,6 +291,7 @@ class Generate:
             embiggen       =    None,
             embiggen_tiles =    None,
             # these are specific to GFPGAN/ESRGAN
+            gfpgan_strength=    0,
             facetool         = None,
             facetool_strength  = 0,
             codeformer_fidelity = None,
@@ -295,6 +299,9 @@ class Generate:
             upscale          = None,
             # this is specific to inpainting and causes more extreme inpainting
             inpaint_replace  = 0.0,
+            # This controls the size at which inpaint occurs (scaled up for inpaint, then back down for the result)
+            inpaint_width    = None,
+            inpaint_height   = None,
             # This will help match inpainted areas to the original image more smoothly
             mask_blur_radius: int = 8,
             # Set this True to handle KeyboardInterrupt internally
@@ -364,6 +371,7 @@ class Generate:
         ddim_eta = ddim_eta or self.ddim_eta
         iterations = iterations or self.iterations
         strength = strength or self.strength
+        outdir                = outdir     or self.outdir
         self.seed = seed
         self.log_tokenization = log_tokenization
         self.step_callback = step_callback
@@ -485,7 +493,9 @@ class Generate:
                 seam_strength = seam_strength,
                 seam_steps = seam_steps,
                 tile_size = tile_size,
-                force_outpaint = force_outpaint
+                force_outpaint = force_outpaint,
+                inpaint_width  = inpaint_width,
+                inpaint_height = inpaint_height
             )
 
             if init_color:
@@ -551,17 +561,15 @@ class Generate:
             ):
         # retrieve the seed from the image;
         seed   = None
-        image_metadata = None
         prompt = None
 
-        args   = metadata_from_png(image_path)
-        seed   = args.seed
-        prompt = args.prompt
-        print(f'>> retrieved seed {seed} and prompt "{prompt}" from {image_path}')
-
-        if not seed:
-            print('* Could not recover seed for image. Replacing with 42. This will not affect image quality')
-            seed = 42
+        args = metadata_from_png(image_path)
+        seed = opt.seed or args.seed
+        if seed is None or seed < 0:
+            seed = random.randrange(0, np.iinfo(np.uint32).max)
+        
+        prompt = opt.prompt or args.prompt or ''
+        print(f'>> using seed {seed} and prompt "{prompt}" for {image_path}')
 
         # try to reuse the same filename prefix as the original file.
         # we take everything up to the first period
@@ -608,7 +616,11 @@ class Generate:
                     extend_instructions[direction]=int(pixels)
                 except ValueError:
                     print(f'** invalid extension instruction. Use <directions> <pixels>..., as in "top 64 left 128 right 64 bottom 64"')
-            if len(extend_instructions)>0:
+
+            opt.seed = seed
+            opt.prompt = prompt
+            
+            if len(extend_instructions) > 0:
                 restorer = Outcrop(image,self,)
                 return restorer.process (
                     extend_instructions,
@@ -792,6 +804,10 @@ class Generate:
 
         # the model cache does the loading and offloading
         cache = self.model_cache
+        if not cache.valid_model(model_name):
+            print(f'** "{model_name}" is not a known model name. Please check your models.yaml file')
+            return self.model
+        
         cache.print_vram_usage()
 
         # have to get rid of all references to model in order
@@ -1019,7 +1035,9 @@ class Generate:
                 return True
         return False
 
-    def _check_for_erasure(self, image):
+    def _check_for_erasure(self, image:Image.Image)->bool:
+        if image.mode not in ('RGBA','RGB'):
+            return False
         width, height = image.size
         pixdata = image.load()
         colored = 0
diff --git a/ldm/invoke/args.py b/ldm/invoke/args.py
index e0b6315e3b..a8a5dff4b5 100644
--- a/ldm/invoke/args.py
+++ b/ldm/invoke/args.py
@@ -87,6 +87,7 @@ import json
 import hashlib
 import os
 import re
+import sys
 import shlex
 import copy
 import base64
@@ -114,7 +115,8 @@ PRECISION_CHOICES = [
 
 # is there a way to pick this up during git commits?
 APP_ID      = 'invoke-ai/InvokeAI'
-APP_VERSION = 'v2.02'
+APP_VERSION = 'v2.1.2'
+INITFILE = os.path.expanduser('~/.invokeai')
 
 class ArgFormatter(argparse.RawTextHelpFormatter):
         # use defined argument order to display usage
@@ -141,11 +143,15 @@ class ArgFormatter(argparse.RawTextHelpFormatter):
 class PagingArgumentParser(argparse.ArgumentParser):
     '''
     A custom ArgumentParser that uses pydoc to page its output.
+    It also supports reading defaults from an init file.
     '''
     def print_help(self, file=None):
         text = self.format_help()
         pydoc.pager(text)
-    
+
+    def convert_arg_line_to_args(self, arg_line):
+        return shlex.split(arg_line,comments=True)
+
 class Args(object):
     def __init__(self,arg_parser=None,cmd_parser=None):
         '''
@@ -162,9 +168,16 @@ class Args(object):
     def parse_args(self):
         '''Parse the shell switches and store.'''
         try:
-            self._arg_switches = self._arg_parser.parse_args()
+            sysargs = sys.argv[1:]
+            if os.path.exists(INITFILE):
+                print(f'>> Initialization file {INITFILE} found. Loading...')
+                sysargs.insert(0,f'@{INITFILE}')
+            else:
+                print(f'>> Initialization file {INITFILE} not found. Applying default settings...')
+            self._arg_switches = self._arg_parser.parse_args(sysargs)
             return self._arg_switches
-        except:
+        except Exception as e:
+            print(f'An exception has occurred: {e}')
             return None
 
     def parse_cmd(self,cmd_string):
@@ -355,7 +368,7 @@ class Args(object):
         This defines all the arguments used on the command line when you launch
         the CLI or web backend.
         '''
-        parser = argparse.ArgumentParser(
+        parser = PagingArgumentParser(
             description=
             """
             Generate images using Stable Diffusion.
@@ -365,6 +378,7 @@ class Args(object):
             Other command-line arguments are defaults that can usually be overridden
             prompt the command prompt.
             """,
+            fromfile_prefix_chars='@',
         )
         model_group      = parser.add_argument_group('Model selection')
         file_group       = parser.add_argument_group('Input/output')
@@ -395,17 +409,6 @@ class Args(object):
             dest='png_compression',
             help='level of PNG compression, from 0 (none) to 9 (maximum). Default is 6.'
         )
-        model_group.add_argument(
-            '--sampler',
-            '-A',
-            '-m',
-            dest='sampler_name',
-            type=str,
-            choices=SAMPLER_CHOICES,
-            metavar='SAMPLER_NAME',
-            help=f'Switch to a different sampler. Supported samplers: {", ".join(SAMPLER_CHOICES)}',
-            default='k_lms',
-        )
         model_group.add_argument(
             '-F',
             '--full_precision',
@@ -465,10 +468,61 @@ class Args(object):
             type=str,
             help='Overwrite the filename format. You can use any argument as wildcard enclosed in curly braces. Default is {prefix}.{seed}.png',
         )
+        render_group.add_argument(
+            '-s',
+            '--steps',
+            type=int,
+            default=50,
+            help='Number of steps'
+        )
+        render_group.add_argument(
+            '-W',
+            '--width',
+            type=int,
+            help='Image width, multiple of 64',
+        )
+        render_group.add_argument(
+            '-H',
+            '--height',
+            type=int,
+            help='Image height, multiple of 64',
+        )
+        render_group.add_argument(
+            '-C',
+            '--cfg_scale',
+            default=7.5,
+            type=float,
+            help='Classifier free guidance (CFG) scale - higher numbers cause generator to "try" harder.',
+        )
+        render_group.add_argument(
+            '--sampler',
+            '-A',
+            '-m',
+            dest='sampler_name',
+            type=str,
+            choices=SAMPLER_CHOICES,
+            metavar='SAMPLER_NAME',
+            help=f'Set the default sampler. Supported samplers: {", ".join(SAMPLER_CHOICES)}',
+            default='k_lms',
+        )
+        render_group.add_argument(
+            '-f',
+            '--strength',
+            type=float,
+            help='img2img strength for noising/unnoising. 0.0 preserves image exactly, 1.0 replaces it completely',
+        )
+        render_group.add_argument(
+            '-T',
+            '-fit',
+            '--fit',
+            action=argparse.BooleanOptionalAction,
+            help='If specified, will resize the input image to fit within the dimensions of width x height (512x512 default)',
+        )
+
         render_group.add_argument(
             '--grid',
             '-g',
-            action='store_true',
+            action=argparse.BooleanOptionalAction,
             help='generate a grid'
         )
         render_group.add_argument(
@@ -498,13 +552,13 @@ class Args(object):
         postprocessing_group.add_argument(
             '--gfpgan_model_path',
             type=str,
-            default='experiments/pretrained_models/GFPGANv1.4.pth',
+            default='./GFPGANv1.4.pth',
             help='Indicates the path to the GFPGAN model, relative to --gfpgan_dir.',
         )
         postprocessing_group.add_argument(
             '--gfpgan_dir',
             type=str,
-            default='./src/gfpgan',
+            default='./models/gfpgan',
             help='Indicates the directory containing the GFPGAN code.',
         )
         web_server_group.add_argument(
@@ -597,7 +651,6 @@ class Args(object):
             '-s',
             '--steps',
             type=int,
-            default=50,
             help='Number of steps'
         )
         render_group.add_argument(
@@ -629,7 +682,6 @@ class Args(object):
         render_group.add_argument(
             '-C',
             '--cfg_scale',
-            default=7.5,
             type=float,
             help='Classifier free guidance (CFG) scale - higher numbers cause generator to "try" harder.',
         )
@@ -654,7 +706,7 @@ class Args(object):
         render_group.add_argument(
             '--grid',
             '-g',
-            action='store_true',
+            action=argparse.BooleanOptionalAction,
             help='generate a grid'
         )
         render_group.add_argument(
@@ -748,8 +800,7 @@ class Args(object):
             '-f',
             '--strength',
             type=float,
-            help='Strength for noising/unnoising. 0.0 preserves image exactly, 1.0 replaces it completely',
-            default=0.75,
+            help='img2img strength for noising/unnoising. 0.0 preserves image exactly, 1.0 replaces it completely',
         )
         inpainting_group.add_argument(
             '-M',
@@ -813,6 +864,11 @@ class Args(object):
             default=32,
             help='When outpainting, the tile size to use for filling outpaint areas',
         )
+        postprocessing_group.add_argument(
+            '--new_prompt',
+            type=str,
+            help='Change the text prompt applied during postprocessing (default, use original generation prompt)',
+        )
         postprocessing_group.add_argument(
             '-ft',
             '--facetool',
diff --git a/ldm/invoke/conditioning.py b/ldm/invoke/conditioning.py
index 04fbd7c10a..ba0dd84275 100644
--- a/ldm/invoke/conditioning.py
+++ b/ldm/invoke/conditioning.py
@@ -47,7 +47,6 @@ def get_uc_and_c_and_ec(prompt_string_uncleaned, model, log_tokens=False, skip_n
         parsed_prompt = pp.parse_conjunction(prompt_string_cleaned).prompts[0]
 
     parsed_negative_prompt: FlattenedPrompt = pp.parse_conjunction(unconditioned_words).prompts[0]
-    print(f">> Parsed prompt to {parsed_prompt}")
 
     conditioning = None
     cac_args:CrossAttentionControl.Arguments = None
diff --git a/ldm/invoke/generator/base.py b/ldm/invoke/generator/base.py
index 534fe2547c..003ac1b466 100644
--- a/ldm/invoke/generator/base.py
+++ b/ldm/invoke/generator/base.py
@@ -63,7 +63,7 @@ class Generator():
             **kwargs
         )
         results             = []
-        seed                = seed if seed is not None else self.new_seed()
+        seed                = seed if seed is not None and seed >= 0 else self.new_seed()
         first_seed          = seed
         seed, initial_noise = self.generate_initial_noise(seed, width, height)
 
@@ -119,19 +119,19 @@ class Generator():
         # write an approximate RGB image from latent samples for a single step to PNG
 
     def sample_to_lowres_estimated_image(self,samples):
-        # adapted from code by @erucipe and @keturn here:
+        # origingally adapted from code by @erucipe and @keturn here:
         # https://discuss.huggingface.co/t/decoding-latents-to-rgb-without-upscaling/23204/7
 
-        # these numbers were determined empirically by @keturn
-        v1_4_latent_rgb_factors = torch.tensor([
-                    # R        G        B
-                    [ 0.298, 0.207, 0.208],  # L1
-                    [ 0.187, 0.286, 0.173],  # L2
-                    [-0.158, 0.189, 0.264],  # L3
-                    [-0.184, -0.271, -0.473],  # L4
+        # these updated numbers for v1.5 are from @torridgristle
+        v1_5_latent_rgb_factors = torch.tensor([
+            #    R        G        B
+            [ 0.3444,  0.1385,  0.0670], # L1
+            [ 0.1247,  0.4027,  0.1494], # L2
+            [-0.3192,  0.2513,  0.2103], # L3
+            [-0.1307, -0.1874, -0.7445]  # L4
         ], dtype=samples.dtype, device=samples.device)
 
-        latent_image = samples[0].permute(1, 2, 0) @ v1_4_latent_rgb_factors
+        latent_image = samples[0].permute(1, 2, 0) @ v1_5_latent_rgb_factors
         latents_ubyte = (((latent_image + 1) / 2)
                          .clamp(0, 1)  # change scale from -1..1 to 0..1
                          .mul(0xFF)  # to 0..255
diff --git a/ldm/invoke/generator/inpaint.py b/ldm/invoke/generator/inpaint.py
index 2ba45ed804..792a01b3b1 100644
--- a/ldm/invoke/generator/inpaint.py
+++ b/ldm/invoke/generator/inpaint.py
@@ -150,29 +150,44 @@ class Inpaint(Img2Img):
                        seam_steps: int = 10,
                        tile_size: int = 32,
                        step_callback=None,
-                       inpaint_replace=False, **kwargs):
+                       inpaint_replace=False,
+                       inpaint_width=None,
+                       inpaint_height=None,
+                       **kwargs):
         """
         Returns a function returning an image derived from the prompt and
         the initial image + mask.  Return value depends on the seed at
         the time you call it.  kwargs are 'init_latent' and 'strength'
         """
 
+        self.inpaint_width = inpaint_width
+        self.inpaint_height = inpaint_height
+
         if isinstance(init_image, PIL.Image.Image):
             self.pil_image = init_image
 
             # Fill missing areas of original image
             init_filled = self.tile_fill_missing(
                 self.pil_image.copy(),
-                seed = self.seed,
+                seed = self.seed if self.seed >= 0 else self.new_seed(),
                 tile_size = tile_size
             )
             init_filled.paste(init_image, (0,0), init_image.split()[-1])
 
+            # Resize if requested for inpainting
+            if inpaint_width and inpaint_height:
+                init_filled = init_filled.resize((inpaint_width, inpaint_height))
+
             # Create init tensor
             init_image = self._image_to_tensor(init_filled.convert('RGB'))
 
         if isinstance(mask_image, PIL.Image.Image):
             self.pil_mask = mask_image
+
+            # Resize if requested for inpainting
+            if inpaint_width and inpaint_height:
+                mask_image = mask_image.resize((inpaint_width, inpaint_height))
+
             mask_image = mask_image.resize(
                 (
                     mask_image.width // downsampling,
@@ -307,6 +322,10 @@ class Inpaint(Img2Img):
     def sample_to_image(self, samples)->Image.Image:
         gen_result = super().sample_to_image(samples).convert('RGB')
 
+        # Resize if necessary
+        if self.inpaint_width and self.inpaint_height:
+            gen_result = gen_result.resize(self.pil_image.size)
+
         if self.pil_image is None or self.pil_mask is None:
             return gen_result
         
diff --git a/ldm/invoke/model_cache.py b/ldm/invoke/model_cache.py
index 7b434941df..d4007c46de 100644
--- a/ldm/invoke/model_cache.py
+++ b/ldm/invoke/model_cache.py
@@ -41,15 +41,22 @@ class ModelCache(object):
         self.stack = []  # this is an LRU FIFO
         self.current_model = None
 
+    def valid_model(self, model_name:str)->bool:
+        '''
+        Given a model name, returns True if it is a valid
+        identifier.
+        '''
+        return model_name in self.config
+    
     def get_model(self, model_name:str):
         '''
         Given a model named identified in models.yaml, return
         the model object. If in RAM will load into GPU VRAM.
         If on disk, will load from there.
         '''
-        if model_name not in self.config:
+        if not self.valid_model(model_name):
             print(f'** "{model_name}" is not a known model name. Please check your models.yaml file')
-            return None
+            return self.current_model
 
         if self.current_model != model_name:
             if model_name not in self.models: # make room for a new one
@@ -102,10 +109,13 @@ class ModelCache(object):
         Set the default model. The change will not take
         effect until you call model_cache.commit()
         '''
+        print(f'DEBUG: before set_default_model()\n{OmegaConf.to_yaml(self.config)}')
         assert model_name in self.models,f"unknown model '{model_name}'"
-        for model in self.models:
-            self.models[model].pop('default',None)
-        self.models[model_name]['default'] = True
+        config = self.config
+        for model in config:
+            config[model].pop('default',None)
+        config[model_name]['default'] = True
+        print(f'DEBUG: after set_default_model():\n{OmegaConf.to_yaml(self.config)}')
 
     def list_models(self) -> dict:
         '''
diff --git a/ldm/invoke/prompt_parser.py b/ldm/invoke/prompt_parser.py
index 0b3890597f..42c83188aa 100644
--- a/ldm/invoke/prompt_parser.py
+++ b/ldm/invoke/prompt_parser.py
@@ -28,7 +28,7 @@ class Prompt():
     def __init__(self, parts: list):
         for c in parts:
             if type(c) is not Attention and not issubclass(type(c), BaseFragment) and type(c) is not pp.ParseResults:
-                raise PromptParser.ParsingException(f"Prompt cannot contain {type(c).__name__} {c}, only {BaseFragment.__subclasses__()} are allowed")
+                raise PromptParser.ParsingException(f"Prompt cannot contain {type(c).__name__} ({c}), only {[c.__name__ for c in BaseFragment.__subclasses__()]} are allowed")
         self.children = parts
     def __repr__(self):
         return f"Prompt:{self.children}"
@@ -102,12 +102,18 @@ class Attention():
     Do not traverse directly; instead obtain a FlattenedPrompt by calling Flatten() on a top-level Conjunction object.
     """
     def __init__(self, weight: float, children: list):
+        if type(weight) is not float:
+            raise PromptParser.ParsingException(
+                f"Attention weight must be float (got {type(weight).__name__} {weight})")
         self.weight = weight
+        if type(children) is not list:
+            raise PromptParser.ParsingException(f"cannot make Attention with non-list of children (got {type(children)})")
+        assert(type(children) is list)
         self.children = children
         #print(f"A: requested attention '{children}' to {weight}")
 
     def __repr__(self):
-        return f"Attention:'{self.children}' @ {self.weight}"
+        return f"Attention:{self.children} * {self.weight}"
     def __eq__(self, other):
         return type(other) is Attention and other.weight == self.weight and other.fragment == self.fragment
 
@@ -136,9 +142,9 @@ class CrossAttentionControlSubstitute(CrossAttentionControlledFragment):
             Fragment('sitting on a car')
         ])
     """
-    def __init__(self, original: Union[Fragment, list], edited: Union[Fragment, list], options: dict=None):
-        self.original = original
-        self.edited = edited
+    def __init__(self, original: list, edited: list, options: dict=None):
+        self.original = original if len(original)>0 else [Fragment('')]
+        self.edited = edited if len(edited)>0 else [Fragment('')]
 
         default_options = {
             's_start': 0.0,
@@ -190,12 +196,12 @@ class Conjunction():
     """
     def __init__(self, prompts: list, weights: list = None):
         # force everything to be a Prompt
-        #print("making conjunction with", parts)
+        #print("making conjunction with", prompts, "types", [type(p).__name__ for p in prompts])
         self.prompts = [x if (type(x) is Prompt
                           or type(x) is Blend
                           or type(x) is FlattenedPrompt)
                       else Prompt(x) for x in prompts]
-        self.weights = [1.0]*len(self.prompts) if weights is None else list(weights)
+        self.weights = [1.0]*len(self.prompts) if (weights is None or len(weights)==0) else list(weights)
         if len(self.weights) != len(self.prompts):
             raise PromptParser.ParsingException(f"while parsing Conjunction: mismatched parts/weights counts {prompts}, {weights}")
         self.type = 'AND'
@@ -216,6 +222,7 @@ class Blend():
     """
     def __init__(self, prompts: list, weights: list[float], normalize_weights: bool=True):
         #print("making Blend with prompts", prompts, "and weights", weights)
+        weights = [1.0]*len(prompts) if (weights is None or len(weights)==0) else list(weights)
         if len(prompts) != len(weights):
             raise PromptParser.ParsingException(f"while parsing Blend: mismatched prompts/weights counts {prompts}, {weights}")
         for p in prompts:
@@ -244,6 +251,10 @@ class PromptParser():
     class ParsingException(Exception):
         pass
 
+    class UnrecognizedOperatorException(ParsingException):
+        def __init__(self, operator:str):
+            super().__init__("Unrecognized operator: " + operator)
+
     def __init__(self, attention_plus_base=1.1, attention_minus_base=0.9):
 
         self.conjunction, self.prompt = build_parser_syntax(attention_plus_base, attention_minus_base)
@@ -279,7 +290,7 @@ class PromptParser():
         return Blend(prompts=flattened_prompts, weights=weights, normalize_weights=True)
 
 
-    def flatten(self, root: Conjunction) -> Conjunction:
+    def flatten(self, root: Conjunction, verbose = False) -> Conjunction:
         """
         Flattening a Conjunction traverses all of the nested tree-like structures in each of its Prompts or Blends,
         producing from each of these walks a linear sequence of Fragment or CrossAttentionControlSubstitute objects
@@ -289,8 +300,6 @@ class PromptParser():
         :return: A Conjunction containing the result of flattening each of the prompts in the passed-in root.
         """
 
-        #print("flattening", root)
-
         def fuse_fragments(items):
             # print("fusing fragments in ", items)
             result = []
@@ -313,8 +322,8 @@ class PromptParser():
             return result
 
         def flatten_internal(node, weight_scale, results, prefix):
-            #print(prefix + "flattening", node, "...")
-            if type(node) is pp.ParseResults:
+            verbose and print(prefix + "flattening", node, "...")
+            if type(node) is pp.ParseResults or type(node) is list:
                 for x in node:
                     results = flatten_internal(x, weight_scale, results, prefix+' pr ')
                 #print(prefix, " ParseResults expanded, results is now", results)
@@ -345,67 +354,59 @@ class PromptParser():
                 #print(prefix + "after flattening Prompt, results is", results)
             else:
                 raise PromptParser.ParsingException(f"unhandled node type {type(node)} when flattening {node}")
-            #print(prefix + "-> after flattening", type(node).__name__, "results is", results)
+            verbose and print(prefix + "-> after flattening", type(node).__name__, "results is", results)
             return results
 
+        verbose and print("flattening", root)
 
         flattened_parts = []
         for part in root.prompts:
             flattened_parts += flatten_internal(part, 1.0, [], ' C| ')
 
-        #print("flattened to", flattened_parts)
+        verbose and print("flattened to", flattened_parts)
 
         weights = root.weights
         return Conjunction(flattened_parts, weights)
 
 
 
+
 def build_parser_syntax(attention_plus_base: float, attention_minus_base: float):
+    def make_operator_object(x):
+        #print('making operator for', x)
+        target = x[0]
+        operator = x[1]
+        arguments = x[2]
+        if operator == '.attend':
+            weight_raw = arguments[0]
+            weight = 1.0
+            if type(weight_raw) is float or type(weight_raw) is int:
+                weight = weight_raw
+            elif type(weight_raw) is str:
+                base = attention_plus_base if weight_raw[0] == '+' else attention_minus_base
+                weight = pow(base, len(weight_raw))
+            return Attention(weight=weight, children=[x for x in x[0]])
+        elif operator == '.swap':
+            return CrossAttentionControlSubstitute(target, arguments, x.as_dict())
+        elif operator == '.blend':
+            prompts = [Prompt(p) for p in x[0]]
+            weights_raw = x[2]
+            normalize_weights = True
+            if len(weights_raw) > 0 and weights_raw[-1][0] == 'no_normalize':
+                normalize_weights = False
+                weights_raw = weights_raw[:-1]
+            weights = [float(w[0]) for w in weights_raw]
+            return Blend(prompts=prompts, weights=weights, normalize_weights=normalize_weights)
+        elif operator == '.and' or operator == '.add':
+            prompts = [Prompt(p) for p in x[0]]
+            weights = [float(w[0]) for w in x[2]]
+            return Conjunction(prompts=prompts, weights=weights)
 
-    lparen = pp.Literal("(").suppress()
-    rparen = pp.Literal(")").suppress()
-    quotes = pp.Literal('"').suppress()
-    comma = pp.Literal(",").suppress()
+        raise PromptParser.UnrecognizedOperatorException(operator)
 
-    # accepts int or float notation, always maps to float
-    number = pp.pyparsing_common.real | \
-             pp.Combine(pp.Optional("-")+pp.Word(pp.nums)).set_parse_action(pp.token_map(float))
-
-    attention = pp.Forward()
-    quoted_fragment = pp.Forward()
-    parenthesized_fragment = pp.Forward()
-    cross_attention_substitute = pp.Forward()
-
-    def make_text_fragment(x):
-        #print("### making fragment for", x)
-        if type(x[0]) is Fragment:
-            assert(False)
-        if type(x) is str:
-            return Fragment(x)
-        elif type(x) is pp.ParseResults or type(x) is list:
-            #print(f'converting {type(x).__name__} to Fragment')
-            return Fragment(' '.join([s for s in x]))
-        else:
-            raise PromptParser.ParsingException("Cannot make fragment from " + str(x))
-
-    def build_escaped_word_parser_charbychar(escaped_chars_to_ignore: str):
-        escapes = []
-        for c in escaped_chars_to_ignore:
-            escapes.append(pp.Literal('\\'+c))
-        return pp.Combine(pp.OneOrMore(
-            pp.MatchFirst(escapes + [pp.CharsNotIn(
-                string.whitespace + escaped_chars_to_ignore,
-                exact=1
-            )])
-        ))
-
-
-
-    def parse_fragment_str(x, in_quotes: bool=False, in_parens: bool=False):
+    def parse_fragment_str(x, expression: pp.ParseExpression, in_quotes: bool = False, in_parens: bool = False):
         #print(f"parsing fragment string for {x}")
         fragment_string = x[0]
-        #print(f"ppparsing fragment string \"{fragment_string}\"")
-
         if len(fragment_string.strip()) == 0:
             return Fragment('')
 
@@ -413,234 +414,198 @@ def build_parser_syntax(attention_plus_base: float, attention_minus_base: float)
             # escape unescaped quotes
             fragment_string = fragment_string.replace('"', '\\"')
 
-        #fragment_parser = pp.Group(pp.OneOrMore(attention | cross_attention_substitute | (greedy_word.set_parse_action(make_text_fragment))))
         try:
-            result = pp.Group(pp.MatchFirst([
-                    pp.OneOrMore(quoted_fragment | attention | unquoted_word).set_name('pf_str_qfuq'),
-                    pp.Empty().set_parse_action(make_text_fragment) + pp.StringEnd()
-            ])).set_name('blend-result').set_debug(False).parse_string(fragment_string)
+            result = (expression + pp.StringEnd()).parse_string(fragment_string)
             #print("parsed to", result)
             return result
         except pp.ParseException as e:
             #print("parse_fragment_str couldn't parse prompt string:", e)
             raise
 
+    # meaningful symbols
+    lparen = pp.Literal("(").suppress()
+    rparen = pp.Literal(")").suppress()
+    quote = pp.Literal('"').suppress()
+    comma = pp.Literal(",").suppress()
+    dot = pp.Literal(".").suppress()
+    equals = pp.Literal("=").suppress()
+
+    escaped_lparen = pp.Literal('\\(')
+    escaped_rparen = pp.Literal('\\)')
+    escaped_quote = pp.Literal('\\"')
+    escaped_comma = pp.Literal('\\,')
+    escaped_dot = pp.Literal('\\.')
+    escaped_plus = pp.Literal('\\+')
+    escaped_minus = pp.Literal('\\-')
+    escaped_equals = pp.Literal('\\=')
+
+    syntactic_symbols = {
+        '(': escaped_lparen,
+        ')': escaped_rparen,
+        '"': escaped_quote,
+        ',': escaped_comma,
+        '.': escaped_dot,
+        '+': escaped_plus,
+        '-': escaped_minus,
+        '=': escaped_equals,
+    }
+    syntactic_chars = "".join(syntactic_symbols.keys())
+
+    # accepts int or float notation, always maps to float
+    number = pp.pyparsing_common.real | \
+             pp.Combine(pp.Optional("-")+pp.Word(pp.nums)).set_parse_action(pp.token_map(float))
+
+    # for options
+    keyword = pp.Word(pp.alphanums + '_')
+
+    # a word that absolutely does not contain any meaningful syntax
+    non_syntax_word = pp.Combine(pp.OneOrMore(pp.MatchFirst([
+            pp.Or(syntactic_symbols.values()),
+            pp.one_of(['-', '+']) + pp.NotAny(pp.White() | pp.Char(syntactic_chars) | pp.StringEnd()),
+            # build character-by-character
+            pp.CharsNotIn(string.whitespace + syntactic_chars, exact=1)
+        ])))
+    non_syntax_word.set_parse_action(lambda x: [Fragment(t) for t in x])
+    non_syntax_word.set_name('non_syntax_word')
+    non_syntax_word.set_debug(False)
+
+    # a word that can contain any character at all - greedily consumes syntax, so use with care
+    free_word = pp.CharsNotIn(string.whitespace).set_parse_action(lambda x: Fragment(x[0]))
+    free_word.set_name('free_word')
+    free_word.set_debug(False)
+
+
+    # ok here we go. forward declare some things..
+    attention = pp.Forward()
+    cross_attention_substitute = pp.Forward()
+    parenthesized_fragment = pp.Forward()
+    quoted_fragment = pp.Forward()
+
+    # the types of things that can go into a fragment, consisting of syntax-full and/or strictly syntax-free components
+    fragment_part_expressions = [
+        attention,
+        cross_attention_substitute,
+        parenthesized_fragment,
+        quoted_fragment,
+        non_syntax_word
+    ]
+    # a fragment that is permitted to contain commas
+    fragment_including_commas = pp.ZeroOrMore(pp.MatchFirst(
+        fragment_part_expressions + [
+            pp.Literal(',').set_parse_action(lambda x: Fragment(x[0]))
+        ]
+    ))
+    # a fragment that is not permitted to contain commas
+    fragment_excluding_commas = pp.ZeroOrMore(pp.MatchFirst(
+        fragment_part_expressions
+    ))
+
+    # a fragment in double quotes (may be nested)
     quoted_fragment << pp.QuotedString(quote_char='"', esc_char=None, esc_quote='\\"')
-    quoted_fragment.set_parse_action(lambda x: parse_fragment_str(x, in_quotes=True)).set_name('quoted_fragment')
+    quoted_fragment.set_parse_action(lambda x: parse_fragment_str(x, fragment_including_commas, in_quotes=True))
 
-    escaped_quote = pp.Literal('\\"')#.set_parse_action(lambda x: '"')
-    escaped_lparen = pp.Literal('\\(')#.set_parse_action(lambda x: '(')
-    escaped_rparen = pp.Literal('\\)')#.set_parse_action(lambda x: ')')
-    escaped_backslash = pp.Literal('\\\\')#.set_parse_action(lambda x: '"')
+    # a fragment inside parentheses (may be nested)
+    parenthesized_fragment << (lparen + fragment_including_commas + rparen)
+    parenthesized_fragment.set_name('parenthesized_fragment')
+    parenthesized_fragment.set_debug(False)
 
-    empty = (
-            (lparen + pp.ZeroOrMore(pp.Word(string.whitespace)) + rparen) |
-            (quotes + pp.ZeroOrMore(pp.Word(string.whitespace)) + quotes)).set_debug(False).set_name('empty')
-
-
-    def not_ends_with_swap(x):
-        #print("trying to match:", x)
-        return not x[0].endswith('.swap')
-
-    unquoted_word = (pp.Combine(pp.OneOrMore(
-            escaped_rparen | escaped_lparen | escaped_quote | escaped_backslash |
-            (pp.CharsNotIn(string.whitespace + '\\"()', exact=1)
-    )))
-            # don't whitespace when the next word starts with +, eg "badly +formed"
-         + (pp.White().suppress() |
-            # don't eat +/-
-            pp.NotAny(pp.Word('+') | pp.Word('-'))
-            )
-                     )
-
-    unquoted_word.set_parse_action(make_text_fragment).set_name('unquoted_word').set_debug(False)
-    #print(unquoted_fragment.parse_string("cat.swap(dog)"))
-
-    parenthesized_fragment << (lparen +
-       pp.Or([
-        (parenthesized_fragment),
-        (quoted_fragment.copy().set_parse_action(lambda x: parse_fragment_str(x, in_quotes=True)).set_debug(False)).set_name('-quoted_paren_internal').set_debug(False),
-        (pp.Combine(pp.OneOrMore(
-            escaped_quote | escaped_lparen | escaped_rparen | escaped_backslash |
-            pp.CharsNotIn(string.whitespace + '\\"()', exact=1) |
-            pp.White()
-        )).set_name('--combined').set_parse_action(lambda x: parse_fragment_str(x, in_parens=True)).set_debug(False)),
-        pp.Empty()
-       ]) + rparen)
-    parenthesized_fragment.set_name('parenthesized_fragment').set_debug(False)
-
-    debug_attention = False
-    # attention control of the form (phrase)+ / (phrase)+ / (phrase)<weight>
-    # phrase can be multiple words, can have multiple +/- signs to increase the effect or type a floating point or integer weight
-    attention_with_parens = pp.Forward()
-    attention_without_parens = pp.Forward()
-
-    attention_with_parens_foot = (number | pp.Word('+') | pp.Word('-'))\
-        .set_name("attention_foot")\
-        .set_debug(False)
-    attention_with_parens <<= pp.Group(
-        lparen +
-        pp.ZeroOrMore(quoted_fragment | attention_with_parens | parenthesized_fragment | cross_attention_substitute | attention_without_parens |
-                      (pp.Empty() + build_escaped_word_parser_charbychar('()')).set_name('undecorated_word').set_debug(debug_attention)#.set_parse_action(lambda t: t[0])
-                  )
-        + rparen + attention_with_parens_foot)
-    attention_with_parens.set_name('attention_with_parens').set_debug(debug_attention)
-
-    attention_without_parens_foot = (pp.NotAny(pp.White()) + pp.Or([pp.Word('+'), pp.Word('-')]) + pp.FollowedBy(pp.StringEnd() | pp.White() | pp.Literal('(') | pp.Literal(')') | pp.Literal(',') | pp.Literal('"')) ).set_name('attention_without_parens_foots')
-    attention_without_parens <<= pp.Group(pp.MatchFirst([
-        quoted_fragment.copy().set_name('attention_quoted_fragment_without_parens').set_debug(debug_attention) + attention_without_parens_foot,
-        pp.Combine(build_escaped_word_parser_charbychar('()+-')).set_name('attention_word_without_parens').set_debug(debug_attention)#.set_parse_action(lambda x: print('escapéd', x))
-                                 + attention_without_parens_foot#.leave_whitespace()
+    # a string of the form (<keyword>=<float|keyword> | <float> | <keyword>) where keyword is alphanumeric + '_'
+    option = pp.Group(pp.MatchFirst([
+        keyword + equals + (number | keyword),  # option=value
+        number.copy().set_parse_action(pp.token_map(str)), # weight
+        keyword  # flag
     ]))
-    attention_without_parens.set_name('attention_without_parens').set_debug(debug_attention)
+    # options for an operator, eg "s_start=0.1, 0.3, no_normalize"
+    options = pp.Dict(pp.Optional(pp.delimited_list(option)))
+    options.set_name('options')
+    options.set_debug(False)
 
+    # a fragment which can be used as the target for an operator - either quoted or in parentheses, or a bare vanilla word
+    potential_operator_target = (quoted_fragment | parenthesized_fragment | non_syntax_word)
 
-    attention << pp.MatchFirst([attention_with_parens,
-                  attention_without_parens
-                  ])
+    # a fragment whose weight has been increased or decreased by a given amount
+    attention_weight_operator = pp.Word('+') | pp.Word('-') | number
+    attention_explicit = (
+        pp.Group(potential_operator_target)
+        + pp.Literal('.attend')
+        + lparen
+        + pp.Group(attention_weight_operator)
+        + rparen
+    )
+    attention_explicit.set_parse_action(make_operator_object)
+    attention_implicit = (
+        pp.Group(potential_operator_target)
+        + pp.NotAny(pp.White()) # do not permit whitespace between term and operator
+        + pp.Group(attention_weight_operator)
+    )
+    attention_implicit.set_parse_action(lambda x: make_operator_object([x[0], '.attend', x[1]]))
+    attention << (attention_explicit | attention_implicit)
     attention.set_name('attention')
+    attention.set_debug(False)
 
-    def make_attention(x):
-        #print("entered make_attention with", x)
-        children = x[0][:-1]
-        weight_raw = x[0][-1]
-        weight = 1.0
-        if type(weight_raw) is float or type(weight_raw) is int:
-            weight = weight_raw
-        elif type(weight_raw) is str:
-            base = attention_plus_base if weight_raw[0] == '+' else attention_minus_base
-            weight = pow(base, len(weight_raw))
-
-        #print("making Attention from", children, "with weight", weight)
-
-        return Attention(weight=weight, children=[(Fragment(x) if type(x) is str else x) for x in children])
-
-    attention_with_parens.set_parse_action(make_attention)
-    attention_without_parens.set_parse_action(make_attention)
-
-    #print("parsing test:", attention_with_parens.parse_string("mountain (man)1.1"))
-
-    # cross-attention control
-    empty_string = ((lparen + rparen) |
-                    pp.Literal('""').suppress() |
-                    (lparen + pp.Literal('""').suppress() + rparen)
-                    ).set_parse_action(lambda x: Fragment(""))
-    empty_string.set_name('empty_string')
-
-    # cross attention control
-    debug_cross_attention_control = False
-    original_fragment = pp.MatchFirst([
-                        quoted_fragment.set_debug(debug_cross_attention_control),
-                        parenthesized_fragment.set_debug(debug_cross_attention_control),
-                        pp.Combine(pp.OneOrMore(pp.CharsNotIn(string.whitespace + '.', exact=1))).set_parse_action(make_text_fragment) + pp.FollowedBy(".swap"),
-                        empty_string.set_debug(debug_cross_attention_control),
-               ])
-    # support keyword=number arguments
-    cross_attention_option_keyword = pp.Or([pp.Keyword("s_start"), pp.Keyword("s_end"), pp.Keyword("t_start"), pp.Keyword("t_end"), pp.Keyword("shape_freedom")])
-    cross_attention_option = pp.Group(cross_attention_option_keyword + pp.Literal("=").suppress() + number)
-    edited_fragment = pp.MatchFirst([
-        (lparen + rparen).set_parse_action(lambda x: Fragment('')),
-        lparen +
-            (quoted_fragment | attention |
-                pp.Group(pp.ZeroOrMore(build_escaped_word_parser_charbychar(',)').set_parse_action(make_text_fragment)))
-            ) +
-            pp.Dict(pp.ZeroOrMore(comma + cross_attention_option)) +
-        rparen,
-        parenthesized_fragment
-    ])
-    cross_attention_substitute << original_fragment + pp.Literal(".swap").set_debug(False).suppress() + edited_fragment
-
-    original_fragment.set_name('original_fragment').set_debug(debug_cross_attention_control)
-    edited_fragment.set_name('edited_fragment').set_debug(debug_cross_attention_control)
-    cross_attention_substitute.set_name('cross_attention_substitute').set_debug(debug_cross_attention_control)
-
-    def make_cross_attention_substitute(x):
-        #print("making cacs for", x[0], "->", x[1], "with options", x.as_dict())
-        #if len(x>2):
-        cacs = CrossAttentionControlSubstitute(x[0], x[1], options=x.as_dict())
-        #print("made", cacs)
-        return cacs
-    cross_attention_substitute.set_parse_action(make_cross_attention_substitute)
+    # cross-attention control by swapping one fragment for another
+    cross_attention_substitute << (
+        pp.Group(potential_operator_target).set_name('ca-target').set_debug(False)
+        + pp.Literal(".swap").set_name('ca-operator').set_debug(False)
+        + lparen
+        + pp.Group(fragment_excluding_commas).set_name('ca-replacement').set_debug(False)
+        + pp.Optional(comma + options).set_name('ca-options').set_debug(False)
+        + rparen
+    )
+    cross_attention_substitute.set_name('cross_attention_substitute')
+    cross_attention_substitute.set_debug(False)
+    cross_attention_substitute.set_parse_action(make_operator_object)
 
 
-    # root prompt definition
-    debug_root_prompt = False
-    prompt = (pp.OneOrMore(pp.MatchFirst([cross_attention_substitute.set_debug(debug_root_prompt),
-                                  attention.set_debug(debug_root_prompt),
-                                  quoted_fragment.set_debug(debug_root_prompt),
-                                  parenthesized_fragment.set_debug(debug_root_prompt),
-                                  unquoted_word.set_debug(debug_root_prompt),
-                                  empty.set_parse_action(make_text_fragment).set_debug(debug_root_prompt)])
-                           ) + pp.StringEnd()) \
-        .set_name('prompt') \
-        .set_parse_action(lambda x: Prompt(x)) \
-        .set_debug(debug_root_prompt)
+    # an entire self-contained prompt, which can be used in a Blend or Conjunction
+    prompt = pp.ZeroOrMore(pp.MatchFirst([
+        cross_attention_substitute,
+        attention,
+        quoted_fragment,
+        parenthesized_fragment,
+        free_word,
+        pp.White().suppress()
+    ]))
+    quoted_prompt = quoted_fragment.copy().set_parse_action(lambda x: parse_fragment_str(x, prompt, in_quotes=True))
 
-    #print("parsing test:", prompt.parse_string("spaced eyes--"))
-    #print("parsing test:", prompt.parse_string("eyes--"))
 
-    # weighted blend of prompts
-    # ("promptA", "promptB").blend(a, b) where "promptA" and "promptB" are valid prompts and a and b are float or
-    # int weights.
-    # can specify more terms eg ("promptA", "promptB", "promptC").blend(a,b,c)
+    # a blend/lerp between the feature vectors for two or more prompts
+    blend = (
+        lparen
+        + pp.Group(pp.delimited_list(pp.Group(potential_operator_target | quoted_prompt), min=1)).set_name('bl-target').set_debug(False)
+        + rparen
+        + pp.Literal(".blend").set_name('bl-operator').set_debug(False)
+        + lparen
+        + pp.Group(options).set_name('bl-options').set_debug(False)
+        + rparen
+    )
+    blend.set_name('blend')
+    blend.set_debug(False)
+    blend.set_parse_action(make_operator_object)
 
-    def make_prompt_from_quoted_string(x):
-        #print(' got quoted prompt', x)
+    # an operator to direct stable diffusion to step multiple times, once for each target, and then add the results together with different weights
+    explicit_conjunction = (
+        lparen
+        + pp.Group(pp.delimited_list(pp.Group(potential_operator_target | quoted_prompt), min=1)).set_name('cj-target').set_debug(False)
+        + rparen
+        + pp.one_of([".and", ".add"]).set_name('cj-operator').set_debug(False)
+        + lparen
+        + pp.Group(options).set_name('cj-options').set_debug(False)
+        + rparen
+    )
+    explicit_conjunction.set_name('explicit_conjunction')
+    explicit_conjunction.set_debug(False)
+    explicit_conjunction.set_parse_action(make_operator_object)
 
-        x_unquoted = x[0][1:-1]
-        if len(x_unquoted.strip()) == 0:
-            # print(' b : just an empty string')
-            return Prompt([Fragment('')])
-        #print(f' b parsing \'{x_unquoted}\'')
-        x_parsed = prompt.parse_string(x_unquoted)
-        #print(" quoted prompt was parsed to", type(x_parsed),":", x_parsed)
-        return x_parsed[0]
-
-    quoted_prompt = pp.dbl_quoted_string.set_parse_action(make_prompt_from_quoted_string)
-    quoted_prompt.set_name('quoted_prompt')
-
-    debug_blend=False
-    blend_terms = pp.delimited_list(quoted_prompt).set_name('blend_terms').set_debug(debug_blend)
-    blend_weights = (pp.delimited_list(number) + pp.Optional(pp.Char(",").suppress() + "no_normalize")).set_name('blend_weights').set_debug(debug_blend)
-    blend = pp.Group(lparen + pp.Group(blend_terms) + rparen
-                     + pp.Literal(".blend").suppress()
-                     + lparen + pp.Group(blend_weights) + rparen).set_name('blend')
-    blend.set_debug(debug_blend)
-
-    def make_blend(x):
-        prompts = x[0][0]
-        weights = x[0][1]
-        normalize = True
-        if weights[-1] == 'no_normalize':
-            normalize = False
-            weights = weights[:-1]
-        return Blend(prompts=prompts, weights=weights, normalize_weights=normalize)
-
-    blend.set_parse_action(make_blend)
-
-    conjunction_terms = blend_terms.copy().set_name('conjunction_terms')
-    conjunction_weights = blend_weights.copy().set_name('conjunction_weights')
-    conjunction_with_parens_and_quotes = pp.Group(lparen + pp.Group(conjunction_terms) + rparen
-                     + pp.Literal(".and").suppress()
-                     + lparen + pp.Optional(pp.Group(conjunction_weights)) + rparen).set_name('conjunction')
-    def make_conjunction(x):
-        parts_raw = x[0][0]
-        weights = x[0][1] if len(x[0])>1 else [1.0]*len(parts_raw)
-        parts = [part for part in parts_raw]
-        return Conjunction(parts, weights)
-    conjunction_with_parens_and_quotes.set_parse_action(make_conjunction)
-
-    implicit_conjunction = pp.OneOrMore(blend | prompt).set_name('implicit_conjunction')
+    # by default a prompt consists of a Conjunction with a single term
+    implicit_conjunction = (blend | pp.Group(prompt)) + pp.StringEnd()
     implicit_conjunction.set_parse_action(lambda x: Conjunction(x))
 
-    conjunction = conjunction_with_parens_and_quotes | implicit_conjunction
-    conjunction.set_debug(False)
+    conjunction = (explicit_conjunction | implicit_conjunction)
 
-    # top-level is a conjunction of one or more blends or prompts
     return conjunction, prompt
 
 
-
 def split_weighted_subprompts(text, skip_normalize=False)->list:
     """
     Legacy blend parsing.
@@ -671,7 +636,7 @@ def split_weighted_subprompts(text, skip_normalize=False)->list:
     weight_sum = sum(map(lambda x: x[1], parsed_prompts))
     if weight_sum == 0:
         print(
-            "Warning: Subprompt weights add up to zero. Discarding and using even weights instead.")
+            "* Warning: Subprompt weights add up to zero. Discarding and using even weights instead.")
         equal_weight = 1 / max(len(parsed_prompts), 1)
         return [(x[0], equal_weight) for x in parsed_prompts]
     return [(x[0], x[1] / weight_sum) for x in parsed_prompts]
diff --git a/ldm/invoke/readline.py b/ldm/invoke/readline.py
index 7d87ede755..4e95e9b063 100644
--- a/ldm/invoke/readline.py
+++ b/ldm/invoke/readline.py
@@ -284,6 +284,7 @@ class Completer(object):
             switch,partial_path  = match.groups()
         partial_path = partial_path.lstrip()
 
+
         matches = list()
         path = os.path.expanduser(partial_path)
 
@@ -321,6 +322,7 @@ class Completer(object):
                 matches.append(
                     switch+os.path.join(os.path.dirname(full_path), node)
                 )
+
         return matches
 
 class DummyCompleter(Completer):
diff --git a/ldm/invoke/restoration/gfpgan.py b/ldm/invoke/restoration/gfpgan.py
index 9042de5d6c..fa0058d09f 100644
--- a/ldm/invoke/restoration/gfpgan.py
+++ b/ldm/invoke/restoration/gfpgan.py
@@ -10,8 +10,9 @@ from PIL import Image
 class GFPGAN():
     def __init__(
             self,
-            gfpgan_dir='src/gfpgan',
-            gfpgan_model_path='experiments/pretrained_models/GFPGANv1.4.pth') -> None:
+            gfpgan_dir='models/gfpgan',
+            gfpgan_model_path='GFPGANv1.4.pth'
+    ) -> None:
 
         self.model_path = os.path.join(gfpgan_dir, gfpgan_model_path)
         self.gfpgan_model_exists = os.path.isfile(self.model_path)
@@ -74,6 +75,7 @@ class GFPGAN():
                 image = image.resize(res.size)
             res = Image.blend(image, res, strength)
 
+
         if torch.cuda.is_available():
             torch.cuda.empty_cache()
         self.gfpgan = None
diff --git a/ldm/invoke/restoration/outcrop.py b/ldm/invoke/restoration/outcrop.py
index b5d42250c5..2587bb5776 100644
--- a/ldm/invoke/restoration/outcrop.py
+++ b/ldm/invoke/restoration/outcrop.py
@@ -28,11 +28,12 @@ class Outcrop(object):
         self.generate._set_sampler()
 
         def wrapped_callback(img,seed,**kwargs):
-            image_callback(img,orig_opt.seed,use_prefix=prefix,**kwargs)
+            preferred_seed = orig_opt.seed if orig_opt.seed >= 0 else seed
+            image_callback(img,preferred_seed,use_prefix=prefix,**kwargs)
 
         result= self.generate.prompt2image(
-            orig_opt.prompt,
-            seed        = orig_opt.seed,    # uncomment to make it deterministic
+            opt.prompt,
+            seed        = opt.seed or orig_opt.seed,
             sampler     = self.generate.sampler,
             steps       = opt.steps,
             cfg_scale   = opt.cfg_scale,
diff --git a/ldm/invoke/txt2mask.py b/ldm/invoke/txt2mask.py
index 2103f2f48c..6bdd1814d1 100644
--- a/ldm/invoke/txt2mask.py
+++ b/ldm/invoke/txt2mask.py
@@ -29,14 +29,14 @@ work fine.
 
 import torch
 import numpy as  np
-from clipseg_models.clipseg import CLIPDensePredT
+from clipseg.clipseg import CLIPDensePredT
 from einops import rearrange, repeat
 from PIL import Image, ImageOps
 from torchvision import transforms
 
 CLIP_VERSION = 'ViT-B/16'
-CLIPSEG_WEIGHTS = 'src/clipseg/weights/rd64-uni.pth'
-CLIPSEG_WEIGHTS_REFINED = 'src/clipseg/weights/rd64-uni-refined.pth'
+CLIPSEG_WEIGHTS = 'models/clipseg/clipseg_weights/rd64-uni.pth'
+CLIPSEG_WEIGHTS_REFINED = 'models/clipseg/clipseg_weights/rd64-uni-refined.pth'
 CLIPSEG_SIZE = 352
 
 class SegmentedGrayscale(object):
diff --git a/ldm/models/diffusion/cross_attention_control.py b/ldm/models/diffusion/cross_attention_control.py
index 9c8c597869..ff90a24856 100644
--- a/ldm/models/diffusion/cross_attention_control.py
+++ b/ldm/models/diffusion/cross_attention_control.py
@@ -1,10 +1,13 @@
-from enum import Enum
+import enum
+from typing import Optional
 
 import torch
 
 # adapted from bloc97's CrossAttentionControl colab
 # https://github.com/bloc97/CrossAttentionControl
 
+
+
 class CrossAttentionControl:
 
     class Arguments:
@@ -27,7 +30,14 @@ class CrossAttentionControl:
                     print('warning: cross-attention control options are not working properly for >1 edit')
                 self.edit_options = non_none_edit_options[0]
 
+
     class Context:
+
+        class Action(enum.Enum):
+            NONE = 0
+            SAVE = 1,
+            APPLY = 2
+
         def __init__(self, arguments: 'CrossAttentionControl.Arguments', step_count: int):
             """
             :param arguments: Arguments for the cross-attention control process
@@ -36,14 +46,124 @@ class CrossAttentionControl:
             self.arguments = arguments
             self.step_count = step_count
 
+            self.self_cross_attention_module_identifiers = []
+            self.tokens_cross_attention_module_identifiers = []
+
+            self.saved_cross_attention_maps = {}
+
+            self.clear_requests(cleanup=True)
+
+        def register_cross_attention_modules(self, model):
+            for name,module in CrossAttentionControl.get_attention_modules(model,
+                                                                                 CrossAttentionControl.CrossAttentionType.SELF):
+                self.self_cross_attention_module_identifiers.append(name)
+            for name,module in CrossAttentionControl.get_attention_modules(model,
+                                                                                   CrossAttentionControl.CrossAttentionType.TOKENS):
+                self.tokens_cross_attention_module_identifiers.append(name)
+
+        def request_save_attention_maps(self, cross_attention_type: 'CrossAttentionControl.CrossAttentionType'):
+            if cross_attention_type == CrossAttentionControl.CrossAttentionType.SELF:
+                self.self_cross_attention_action = CrossAttentionControl.Context.Action.SAVE
+            else:
+                self.tokens_cross_attention_action = CrossAttentionControl.Context.Action.SAVE
+
+        def request_apply_saved_attention_maps(self, cross_attention_type: 'CrossAttentionControl.CrossAttentionType'):
+            if cross_attention_type == CrossAttentionControl.CrossAttentionType.SELF:
+                self.self_cross_attention_action = CrossAttentionControl.Context.Action.APPLY
+            else:
+                self.tokens_cross_attention_action = CrossAttentionControl.Context.Action.APPLY
+
+        def is_tokens_cross_attention(self, module_identifier) -> bool:
+            return module_identifier in self.tokens_cross_attention_module_identifiers
+
+        def get_should_save_maps(self, module_identifier: str) -> bool:
+            if module_identifier in self.self_cross_attention_module_identifiers:
+                return self.self_cross_attention_action == CrossAttentionControl.Context.Action.SAVE
+            elif module_identifier in self.tokens_cross_attention_module_identifiers:
+                return self.tokens_cross_attention_action == CrossAttentionControl.Context.Action.SAVE
+            return False
+
+        def get_should_apply_saved_maps(self, module_identifier: str) -> bool:
+            if module_identifier in self.self_cross_attention_module_identifiers:
+                return self.self_cross_attention_action == CrossAttentionControl.Context.Action.APPLY
+            elif module_identifier in self.tokens_cross_attention_module_identifiers:
+                return self.tokens_cross_attention_action == CrossAttentionControl.Context.Action.APPLY
+            return False
+
+        def get_active_cross_attention_control_types_for_step(self, percent_through:float=None)\
+                -> list['CrossAttentionControl.CrossAttentionType']:
+            """
+            Should cross-attention control be applied on the given step?
+            :param percent_through: How far through the step sequence are we (0.0=pure noise, 1.0=completely denoised image). Expected range 0.0..<1.0.
+            :return: A list of attention types that cross-attention control should be performed for on the given step. May be [].
+            """
+            if percent_through is None:
+                return [CrossAttentionControl.CrossAttentionType.SELF, CrossAttentionControl.CrossAttentionType.TOKENS]
+
+            opts = self.arguments.edit_options
+            to_control = []
+            if opts['s_start'] <= percent_through and percent_through < opts['s_end']:
+                to_control.append(CrossAttentionControl.CrossAttentionType.SELF)
+            if opts['t_start'] <= percent_through and percent_through < opts['t_end']:
+                to_control.append(CrossAttentionControl.CrossAttentionType.TOKENS)
+            return to_control
+
+        def save_slice(self, identifier: str, slice: torch.Tensor, dim: Optional[int], offset: int,
+                       slice_size: Optional[int]):
+            if identifier not in self.saved_cross_attention_maps:
+                self.saved_cross_attention_maps[identifier] = {
+                    'dim': dim,
+                    'slice_size': slice_size,
+                    'slices': {offset or 0: slice}
+                }
+            else:
+                self.saved_cross_attention_maps[identifier]['slices'][offset or 0] = slice
+
+        def get_slice(self, identifier: str, requested_dim: Optional[int], requested_offset: int, slice_size: int):
+            saved_attention_dict = self.saved_cross_attention_maps[identifier]
+            if requested_dim is None:
+                if saved_attention_dict['dim'] is not None:
+                    raise RuntimeError(f"dim mismatch: expected dim=None, have {saved_attention_dict['dim']}")
+                return saved_attention_dict['slices'][0]
+
+            if saved_attention_dict['dim'] == requested_dim:
+                if slice_size != saved_attention_dict['slice_size']:
+                    raise RuntimeError(
+                        f"slice_size mismatch: expected slice_size={slice_size}, have {saved_attention_dict['slice_size']}")
+                return saved_attention_dict['slices'][requested_offset]
+
+            if saved_attention_dict['dim'] == None:
+                whole_saved_attention = saved_attention_dict['slices'][0]
+                if requested_dim == 0:
+                    return whole_saved_attention[requested_offset:requested_offset + slice_size]
+                elif requested_dim == 1:
+                    return whole_saved_attention[:, requested_offset:requested_offset + slice_size]
+
+            raise RuntimeError(f"Cannot convert dim {saved_attention_dict['dim']} to requested dim {requested_dim}")
+
+        def get_slicing_strategy(self, identifier: str) -> Optional[tuple[int, int]]:
+            saved_attention = self.saved_cross_attention_maps.get(identifier, None)
+            if saved_attention is None:
+                return None, None
+            return saved_attention['dim'], saved_attention['slice_size']
+
+        def clear_requests(self, cleanup=True):
+            self.tokens_cross_attention_action = CrossAttentionControl.Context.Action.NONE
+            self.self_cross_attention_action = CrossAttentionControl.Context.Action.NONE
+            if cleanup:
+                self.saved_cross_attention_maps = {}
+
+        def offload_saved_attention_slices_to_cpu(self):
+            for key, map_dict in self.saved_cross_attention_maps.items():
+                for offset, slice in map_dict['slices'].items():
+                    map_dict[offset] = slice.to('cpu')
+
     @classmethod
     def remove_cross_attention_control(cls, model):
         cls.remove_attention_function(model)
 
     @classmethod
-    def setup_cross_attention_control(cls, model,
-                                      cross_attention_control_args: Arguments
-                                      ):
+    def setup_cross_attention_control(cls, model, context: Context):
         """
         Inject attention parameters and functions into the passed in model to enable cross attention editing.
 
@@ -53,7 +173,7 @@ class CrossAttentionControl:
         """
 
         # adapted from init_attention_edit
-        device = cross_attention_control_args.edited_conditioning.device
+        device = context.arguments.edited_conditioning.device
 
         # urgh. should this be hardcoded?
         max_length = 77
@@ -61,141 +181,82 @@ class CrossAttentionControl:
         mask = torch.zeros(max_length)
         indices_target = torch.arange(max_length, dtype=torch.long)
         indices = torch.zeros(max_length, dtype=torch.long)
-        for name, a0, a1, b0, b1 in cross_attention_control_args.edit_opcodes:
+        for name, a0, a1, b0, b1 in context.arguments.edit_opcodes:
             if b0 < max_length:
                 if name == "equal":# or (name == "replace" and a1 - a0 == b1 - b0):
                     # these tokens have not been edited
                     indices[b0:b1] = indices_target[a0:a1]
                     mask[b0:b1] = 1
 
-        cls.inject_attention_function(model)
-
-        for m in cls.get_attention_modules(model, cls.CrossAttentionType.SELF):
-            m.last_attn_slice_mask = None
-            m.last_attn_slice_indices = None
-
-        for m in cls.get_attention_modules(model, cls.CrossAttentionType.TOKENS):
-            m.last_attn_slice_mask = mask.to(device)
-            m.last_attn_slice_indices = indices.to(device)
+        context.register_cross_attention_modules(model)
+        context.cross_attention_mask = mask.to(device)
+        context.cross_attention_index_map = indices.to(device)
+        cls.inject_attention_function(model, context)
 
 
-    class CrossAttentionType(Enum):
+    class CrossAttentionType(enum.Enum):
         SELF = 1
         TOKENS = 2
 
-    @classmethod
-    def get_active_cross_attention_control_types_for_step(cls, context: 'CrossAttentionControl.Context', percent_through:float=None)\
-            -> list['CrossAttentionControl.CrossAttentionType']:
-        """
-        Should cross-attention control be applied on the given step?
-        :param percent_through: How far through the step sequence are we (0.0=pure noise, 1.0=completely denoised image). Expected range 0.0..<1.0.
-        :return: A list of attention types that cross-attention control should be performed for on the given step. May be [].
-        """
-        if percent_through is None:
-            return [cls.CrossAttentionType.SELF, cls.CrossAttentionType.TOKENS]
-
-        opts = context.arguments.edit_options
-        to_control = []
-        if opts['s_start'] <= percent_through and percent_through < opts['s_end']:
-            to_control.append(cls.CrossAttentionType.SELF)
-        if opts['t_start'] <= percent_through and percent_through < opts['t_end']:
-            to_control.append(cls.CrossAttentionType.TOKENS)
-        return to_control
-
-
     @classmethod
     def get_attention_modules(cls, model, which: CrossAttentionType):
         which_attn = "attn1" if which is cls.CrossAttentionType.SELF else "attn2"
-        return [module for name, module in model.named_modules() if
+        return [(name,module) for name, module in model.named_modules() if
                       type(module).__name__ == "CrossAttention" and which_attn in name]
 
-    @classmethod
-    def clear_requests(cls, model, clear_attn_slice=True):
-        self_attention_modules = cls.get_attention_modules(model, cls.CrossAttentionType.SELF)
-        tokens_attention_modules = cls.get_attention_modules(model, cls.CrossAttentionType.TOKENS)
-        for m in self_attention_modules+tokens_attention_modules:
-            m.save_last_attn_slice = False
-            m.use_last_attn_slice = False
-            if clear_attn_slice:
-                m.last_attn_slice = None
 
     @classmethod
-    def request_save_attention_maps(cls, model, cross_attention_type: CrossAttentionType):
-        modules = cls.get_attention_modules(model, cross_attention_type)
-        for m in modules:
-            # clear out the saved slice in case the outermost dim changes
-            m.last_attn_slice = None
-            m.save_last_attn_slice = True
-
-    @classmethod
-    def request_apply_saved_attention_maps(cls, model, cross_attention_type: CrossAttentionType):
-        modules = cls.get_attention_modules(model, cross_attention_type)
-        for m in modules:
-            m.use_last_attn_slice = True
-
-
-
-    @classmethod
-    def inject_attention_function(cls, unet):
+    def inject_attention_function(cls, unet, context: 'CrossAttentionControl.Context'):
         # ORIGINAL SOURCE CODE: https://github.com/huggingface/diffusers/blob/91ddd2a25b848df0fa1262d4f1cd98c7ccb87750/src/diffusers/models/attention.py#L276
 
-        def attention_slice_wrangler(self, attention_scores, suggested_attention_slice, dim, offset, slice_size):
+        def attention_slice_wrangler(module, suggested_attention_slice:torch.Tensor, dim, offset, slice_size):
 
-            #print("in wrangler with suggested_attention_slice shape", suggested_attention_slice.shape, "dim", dim)
+            #memory_usage = suggested_attention_slice.element_size() * suggested_attention_slice.nelement()
 
-            attn_slice = suggested_attention_slice
-            if dim is not None:
-                start = offset
-                end = start+slice_size
-                #print(f"in wrangler, sliced dim {dim} {start}-{end}, use_last_attn_slice is {self.use_last_attn_slice}, save_last_attn_slice is {self.save_last_attn_slice}")
-            #else:
-            #    print(f"in wrangler, whole, use_last_attn_slice is {self.use_last_attn_slice}, save_last_attn_slice is {self.save_last_attn_slice}")
+            attention_slice = suggested_attention_slice
 
-            if self.use_last_attn_slice:
-                if dim is None:
-                    last_attn_slice = self.last_attn_slice
-                    # print("took whole slice of shape", attn_slice.shape, "from complete shape", self.last_attn_slice.shape)
+            if context.get_should_save_maps(module.identifier):
+                #print(module.identifier, "saving suggested_attention_slice of shape",
+                #      suggested_attention_slice.shape, "dim", dim, "offset", offset)
+                slice_to_save = attention_slice.to('cpu') if dim is not None else attention_slice
+                context.save_slice(module.identifier, slice_to_save, dim=dim, offset=offset, slice_size=slice_size)
+            elif context.get_should_apply_saved_maps(module.identifier):
+                #print(module.identifier, "applying saved attention slice for dim", dim, "offset", offset)
+                saved_attention_slice = context.get_slice(module.identifier, dim, offset, slice_size)
+
+                # slice may have been offloaded to CPU
+                saved_attention_slice = saved_attention_slice.to(suggested_attention_slice.device)
+
+                if context.is_tokens_cross_attention(module.identifier):
+                    index_map = context.cross_attention_index_map
+                    remapped_saved_attention_slice = torch.index_select(saved_attention_slice, -1, index_map)
+                    this_attention_slice = suggested_attention_slice
+
+                    mask = context.cross_attention_mask
+                    saved_mask = mask
+                    this_mask = 1 - mask
+                    attention_slice = remapped_saved_attention_slice * saved_mask + \
+                                      this_attention_slice * this_mask
                 else:
-                    last_attn_slice = self.last_attn_slice[offset]
-
-                if self.last_attn_slice_mask is None:
                     # just use everything
-                    attn_slice = last_attn_slice
-                else:
-                    last_attn_slice_mask = self.last_attn_slice_mask
-                    remapped_last_attn_slice = torch.index_select(last_attn_slice, -1, self.last_attn_slice_indices)
+                    attention_slice = saved_attention_slice
 
-                    this_attn_slice = attn_slice
-                    this_attn_slice_mask = 1 - last_attn_slice_mask
-                    attn_slice = this_attn_slice * this_attn_slice_mask + \
-                                 remapped_last_attn_slice * last_attn_slice_mask
-
-            if self.save_last_attn_slice:
-                if dim is None:
-                    self.last_attn_slice = attn_slice
-                else:
-                    if self.last_attn_slice is None:
-                        self.last_attn_slice = { offset: attn_slice }
-                    else:
-                        self.last_attn_slice[offset] = attn_slice
-
-            return attn_slice
+            return attention_slice
 
         for name, module in unet.named_modules():
             module_name = type(module).__name__
             if module_name == "CrossAttention":
-                module.last_attn_slice = None
-                module.last_attn_slice_indices = None
-                module.last_attn_slice_mask = None
-                module.use_last_attn_weights = False
-                module.use_last_attn_slice = False
-                module.save_last_attn_slice = False
+                module.identifier = name
                 module.set_attention_slice_wrangler(attention_slice_wrangler)
+                module.set_slicing_strategy_getter(lambda module, module_identifier=name: \
+                                                       context.get_slicing_strategy(module_identifier))
 
     @classmethod
     def remove_attention_function(cls, unet):
+        # clear wrangler callback
         for name, module in unet.named_modules():
             module_name = type(module).__name__
             if module_name == "CrossAttention":
                 module.set_attention_slice_wrangler(None)
+                module.set_slicing_strategy_getter(None)
 
diff --git a/ldm/models/diffusion/shared_invokeai_diffusion.py b/ldm/models/diffusion/shared_invokeai_diffusion.py
index 5a9cc3eb74..0a18eb25c8 100644
--- a/ldm/models/diffusion/shared_invokeai_diffusion.py
+++ b/ldm/models/diffusion/shared_invokeai_diffusion.py
@@ -1,9 +1,11 @@
+import traceback
 from math import ceil
 from typing import Callable, Optional, Union
 
 import torch
 
 from ldm.models.diffusion.cross_attention_control import CrossAttentionControl
+from ldm.modules.attention import get_mem_free_total
 
 
 class InvokeAIDiffuserComponent:
@@ -34,7 +36,7 @@ class InvokeAIDiffuserComponent:
         """
         self.model = model
         self.model_forward_callback = model_forward_callback
-
+        self.cross_attention_control_context = None
 
     def setup_cross_attention_control(self, conditioning: ExtraConditioningInfo, step_count: int):
         self.conditioning = conditioning
@@ -42,11 +44,7 @@ class InvokeAIDiffuserComponent:
             arguments=self.conditioning.cross_attention_control_args,
             step_count=step_count
         )
-        CrossAttentionControl.setup_cross_attention_control(self.model,
-                                                            cross_attention_control_args=self.conditioning.cross_attention_control_args
-                                                            )
-        #todo: refactor  edited_conditioning, edit_opcodes, edit_options into a struct
-        #todo: apply edit_options using step_count
+        CrossAttentionControl.setup_cross_attention_control(self.model, self.cross_attention_control_context)
 
     def remove_cross_attention_control(self):
         self.conditioning = None
@@ -54,6 +52,7 @@ class InvokeAIDiffuserComponent:
         CrossAttentionControl.remove_cross_attention_control(self.model)
 
 
+
     def do_diffusion_step(self, x: torch.Tensor, sigma: torch.Tensor,
                                 unconditioning: Union[torch.Tensor,dict],
                                 conditioning: Union[torch.Tensor,dict],
@@ -70,12 +69,12 @@ class InvokeAIDiffuserComponent:
         :return: the new latents after applying the model to x using unscaled unconditioning and CFG-scaled conditioning.
         """
 
-        CrossAttentionControl.clear_requests(self.model)
 
         cross_attention_control_types_to_do = []
+        context: CrossAttentionControl.Context = self.cross_attention_control_context
         if self.cross_attention_control_context is not None:
             percent_through = self.estimate_percent_through(step_index, sigma)
-            cross_attention_control_types_to_do = CrossAttentionControl.get_active_cross_attention_control_types_for_step(self.cross_attention_control_context, percent_through)
+            cross_attention_control_types_to_do = context.get_active_cross_attention_control_types_for_step(percent_through)
 
         wants_cross_attention_control = (len(cross_attention_control_types_to_do) > 0)
         wants_hybrid_conditioning = isinstance(conditioning, dict)
@@ -124,7 +123,7 @@ class InvokeAIDiffuserComponent:
         return unconditioned_next_x, conditioned_next_x
 
 
-    def apply_cross_attention_controlled_conditioning(self, x, sigma, unconditioning, conditioning, cross_attention_control_types_to_do):
+    def apply_cross_attention_controlled_conditioning(self, x:torch.Tensor, sigma, unconditioning, conditioning, cross_attention_control_types_to_do):
         # print('pct', percent_through, ': doing cross attention control on', cross_attention_control_types_to_do)
         # slower non-batched path (20% slower on mac MPS)
         # We are only interested in using attention maps for conditioned_next_x, but batching them with generation of
@@ -134,32 +133,32 @@ class InvokeAIDiffuserComponent:
         # representing batched uncond + cond, but then when it comes to applying the saved attention, the
         # wrangler gets an attention tensor which only has shape[0]=8, representing just self.edited_conditionings.)
         # todo: give CrossAttentionControl's `wrangler` function more info so it can work with a batched call as well.
+        context:CrossAttentionControl.Context = self.cross_attention_control_context
 
         try:
             unconditioned_next_x = self.model_forward_callback(x, sigma, unconditioning)
 
             # process x using the original prompt, saving the attention maps
-            for type in cross_attention_control_types_to_do:
-                CrossAttentionControl.request_save_attention_maps(self.model, type)
+            #print("saving attention maps for", cross_attention_control_types_to_do)
+            for ca_type in cross_attention_control_types_to_do:
+                context.request_save_attention_maps(ca_type)
             _ = self.model_forward_callback(x, sigma, conditioning)
-            CrossAttentionControl.clear_requests(self.model, clear_attn_slice=False)
+            context.clear_requests(cleanup=False)
 
             # process x again, using the saved attention maps to control where self.edited_conditioning will be applied
-            for type in cross_attention_control_types_to_do:
-                CrossAttentionControl.request_apply_saved_attention_maps(self.model, type)
+            #print("applying saved attention maps for", cross_attention_control_types_to_do)
+            for ca_type in cross_attention_control_types_to_do:
+                context.request_apply_saved_attention_maps(ca_type)
             edited_conditioning = self.conditioning.cross_attention_control_args.edited_conditioning
             conditioned_next_x = self.model_forward_callback(x, sigma, edited_conditioning)
+            context.clear_requests(cleanup=True)
 
-            CrossAttentionControl.clear_requests(self.model)
-
-            return unconditioned_next_x, conditioned_next_x
-
-        except RuntimeError:
-            # make sure we clean out the attention slices we're storing on the model
-            # TODO don't store things on the model
-            CrossAttentionControl.clear_requests(self.model)
+        except:
+            context.clear_requests(cleanup=True)
             raise
 
+        return unconditioned_next_x, conditioned_next_x
+
     def estimate_percent_through(self, step_index, sigma):
         if step_index is not None and self.cross_attention_control_context is not None:
             # percent_through will never reach 1.0 (but this is intended)
diff --git a/ldm/modules/attention.py b/ldm/modules/attention.py
index 4c36fa8a6c..94bb8a2916 100644
--- a/ldm/modules/attention.py
+++ b/ldm/modules/attention.py
@@ -1,6 +1,6 @@
 from inspect import isfunction
 import math
-from typing import Callable
+from typing import Callable, Optional
 
 import torch
 import torch.nn.functional as F
@@ -151,6 +151,17 @@ class SpatialSelfAttention(nn.Module):
 
         return x+h_
 
+def get_mem_free_total(device):
+    #only on cuda
+    if not torch.cuda.is_available():
+        return None
+    stats = torch.cuda.memory_stats(device)
+    mem_active = stats['active_bytes.all.current']
+    mem_reserved = stats['reserved_bytes.all.current']
+    mem_free_cuda, _ = torch.cuda.mem_get_info(device)
+    mem_free_torch = mem_reserved - mem_active
+    mem_free_total = mem_free_cuda + mem_free_torch
+    return mem_free_total
 
 
 class CrossAttention(nn.Module):
@@ -173,31 +184,43 @@ class CrossAttention(nn.Module):
 
         self.mem_total_gb = psutil.virtual_memory().total // (1 << 30)
 
+        self.cached_mem_free_total = None
         self.attention_slice_wrangler = None
+        self.slicing_strategy_getter = None
 
-    def set_attention_slice_wrangler(self, wrangler:Callable[[nn.Module, torch.Tensor, torch.Tensor, int, int, int], torch.Tensor]):
+    def set_attention_slice_wrangler(self, wrangler: Optional[Callable[[nn.Module, torch.Tensor, int, int, int], torch.Tensor]]):
         '''
         Set custom attention calculator to be called when attention is calculated
-        :param wrangler: Callback, with args (self, attention_scores, suggested_attention_slice, dim, offset, slice_size),
+        :param wrangler: Callback, with args (module, suggested_attention_slice, dim, offset, slice_size),
         which returns either the suggested_attention_slice or an adjusted equivalent.
-            self is the current CrossAttention module for which the callback is being invoked.
-            attention_scores are the scores for attention
-            suggested_attention_slice is a softmax(dim=-1) over attention_scores
-            dim is -1 if the call is non-sliced, or 0 or 1 for dimension-0 or dimension-1 slicing.
-                If dim is >= 0, offset and slice_size specify the slice start and length.
+            `module` is the current CrossAttention module for which the callback is being invoked.
+            `suggested_attention_slice` is the default-calculated attention slice
+            `dim` is -1 if the attenion map has not been sliced, or 0 or 1 for dimension-0 or dimension-1 slicing.
+                If `dim` is >= 0, `offset` and `slice_size` specify the slice start and length.
 
         Pass None to use the default attention calculation.
         :return:
         '''
         self.attention_slice_wrangler = wrangler
 
+    def set_slicing_strategy_getter(self, getter: Optional[Callable[[nn.Module], tuple[int,int]]]):
+        self.slicing_strategy_getter = getter
+
+    def cache_free_memory_count(self, device):
+        self.cached_mem_free_total = get_mem_free_total(device)
+        print("free cuda memory: ", self.cached_mem_free_total)
+
+    def clear_cached_free_memory_count(self):
+        self.cached_mem_free_total = None
+
     def einsum_lowest_level(self, q, k, v, dim, offset, slice_size):
         # calculate attention scores
         attention_scores = einsum('b i d, b j d -> b i j', q, k)
-        # calculate attenion slice by taking the best scores for each latent pixel
+        # calculate attention slice by taking the best scores for each latent pixel
         default_attention_slice = attention_scores.softmax(dim=-1, dtype=attention_scores.dtype)
-        if self.attention_slice_wrangler is not None:
-            attention_slice = self.attention_slice_wrangler(self, attention_scores, default_attention_slice, dim, offset, slice_size)
+        attention_slice_wrangler = self.attention_slice_wrangler
+        if attention_slice_wrangler is not None:
+            attention_slice = attention_slice_wrangler(self, default_attention_slice, dim, offset, slice_size)
         else:
             attention_slice = default_attention_slice
 
@@ -240,17 +263,26 @@ class CrossAttention(nn.Module):
         return self.einsum_op_slice_dim1(q, k, v, max(q.shape[1] // div, 1))
 
     def einsum_op_cuda(self, q, k, v):
-        stats = torch.cuda.memory_stats(q.device)
-        mem_active = stats['active_bytes.all.current']
-        mem_reserved = stats['reserved_bytes.all.current']
-        mem_free_cuda, _ = torch.cuda.mem_get_info(q.device)
-        mem_free_torch = mem_reserved - mem_active
-        mem_free_total = mem_free_cuda + mem_free_torch
+        # check if we already have a slicing strategy (this should only happen during cross-attention controlled generation)
+        slicing_strategy_getter = self.slicing_strategy_getter
+        if slicing_strategy_getter is not None:
+            (dim, slice_size) = slicing_strategy_getter(self)
+            if dim is not None:
+                # print("using saved slicing strategy with dim", dim, "slice size", slice_size)
+                if dim == 0:
+                    return self.einsum_op_slice_dim0(q, k, v, slice_size)
+                elif dim == 1:
+                    return self.einsum_op_slice_dim1(q, k, v, slice_size)
+
+        # fallback for when there is no saved strategy, or saved strategy does not slice
+        mem_free_total = self.cached_mem_free_total or get_mem_free_total(q.device)
         # Divide factor of safety as there's copying and fragmentation
         return self.einsum_op_tensor_mem(q, k, v, mem_free_total / 3.3 / (1 << 20))
 
+
     def get_attention_mem_efficient(self, q, k, v):
         if q.device.type == 'cuda':
+            #print("in get_attention_mem_efficient with q shape", q.shape, ", k shape", k.shape, ", free memory is", get_mem_free_total(q.device))
             return self.einsum_op_cuda(q, k, v)
 
         if q.device.type == 'mps':
diff --git a/mkdocs.yml b/mkdocs.yml
index 36b42da75f..2cb956055a 100644
--- a/mkdocs.yml
+++ b/mkdocs.yml
@@ -1,3 +1,5 @@
+# yaml-language-server: $schema=https://squidfunk.github.io/mkdocs-material/schema.json
+
 # General
 site_name: Stable Diffusion Toolkit Docs
 site_url: https://invoke-ai.github.io/InvokeAI
@@ -30,6 +32,14 @@ theme:
       toggle:
         icon: material/lightbulb-outline
         name: Switch to light mode
+  features:
+    - navigation.instant
+    - navigation.tabs
+    - navigation.top
+    - navigation.tracking
+    - search.highlight
+    - search.suggest
+    - toc.integrate
 
 # Extensions
 markdown_extensions:
diff --git a/pyproject.toml.hide b/pyproject.toml.hide
deleted file mode 100644
index 5df66dd42b..0000000000
--- a/pyproject.toml.hide
+++ /dev/null
@@ -1,3 +0,0 @@
-[tool.blue]
-line-length = 90
-target-version = ['py310']
diff --git a/requirements-linux-arm64.txt b/requirements-linux-arm64.txt
deleted file mode 100644
index a0be77057b..0000000000
--- a/requirements-linux-arm64.txt
+++ /dev/null
@@ -1,27 +0,0 @@
-albumentations==0.4.3
-einops==0.3.0
-diffusers==0.6.0
-huggingface-hub==0.8.1
-imageio==2.9.0
-imageio-ffmpeg==0.4.2
-kornia==0.6.0
-numpy==1.23.1
---pre torch torchvision torchaudio --extra-index-url https://download.pytorch.org/whl/nightly/cpu
-omegaconf==2.1.1
-opencv-python==4.6.0.66
-pillow==9.2.0
-pudb==2019.2
-torch==1.12.1
-torchvision==0.13.0
-pytorch-lightning==1.7.7
-streamlit==1.12.0
-test-tube>=0.7.5
-torch-fidelity==0.3.0
-torchmetrics==0.6.0
-transformers==4.21.3
--e git+https://github.com/openai/CLIP.git@main#egg=clip
--e git+https://github.com/CompVis/taming-transformers.git@master#egg=taming-transformers
--e git+https://github.com/lstein/k-diffusion.git@master#egg=k-diffusion
--e git+https://github.com/TencentARC/GFPGAN.git#egg=gfpgan
--e git+https://github.com/invoke-ai/clipseg.git@models-rename#egg=clipseg
--e .
diff --git a/scripts/invoke.py b/scripts/invoke.py
old mode 100644
new mode 100755
index 1e9a84295e..b393ea9d20
--- a/scripts/invoke.py
+++ b/scripts/invoke.py
@@ -29,6 +29,7 @@ infile = None
 def main():
     """Initialize command-line parsers and the diffusion model"""
     global infile
+    print('* Initializing, be patient...')
     
     opt  = Args()
     args = opt.parse_args()
@@ -46,7 +47,6 @@ def main():
             print('--max_loaded_models must be >= 1; using 1')
             args.max_loaded_models = 1
 
-    print('* Initializing, be patient...')
     from ldm.generate import Generate
 
     # these two lines prevent a horrible warning message from appearing
@@ -90,7 +90,12 @@ def main():
             safety_checker=opt.safety_checker,
             max_loaded_models=opt.max_loaded_models,
             )
-    except (FileNotFoundError, IOError, KeyError) as e:
+    except FileNotFoundError:
+        print('** You appear to be missing configs/models.yaml')
+        print('** You can either exit this script and run scripts/preload_models.py, or fix the problem now.')
+        emergency_model_create(opt)
+        sys.exit(-1)
+    except (IOError, KeyError) as e:
         print(f'{e}. Aborting.')
         sys.exit(-1)
 
@@ -208,7 +213,10 @@ def main_loop(gen, opt):
                 setattr(opt,attr,path)
 
         # retrieve previous value of seed if requested
-        if opt.seed is not None and opt.seed < 0:   
+        # Exception: for postprocess operations negative seed values
+        # mean "discard the original seed and generate a new one"
+        # (this is a non-obvious hack and needs to be reworked)
+        if opt.seed is not None and opt.seed < 0 and operation != 'postprocess':
             try:
                 opt.seed = last_results[opt.seed][1]
                 print(f'>> Reusing previous seed {opt.seed}')
@@ -277,7 +285,7 @@ def main_loop(gen, opt):
                     filename = f'{prefix}.{use_prefix}.{seed}.png'
                     tm = opt.text_mask[0]
                     th = opt.text_mask[1] if len(opt.text_mask)>1 else 0.5
-                    formatted_dream_prompt = f'!mask {opt.prompt} -tm {tm} {th}'
+                    formatted_dream_prompt = f'!mask {opt.input_file_path} -tm {tm} {th}'
                     path = file_writer.save_image_and_prompt_to_png(
                         image           = image,
                         dream_prompt    = formatted_dream_prompt,
@@ -317,7 +325,7 @@ def main_loop(gen, opt):
                         tool = re.match('postprocess:(\w+)',opt.last_operation).groups()[0]
                         add_postprocessing_to_metadata(
                             opt,
-                            opt.prompt,
+                            opt.input_file_path,
                             filename,
                             tool,
                             formatted_dream_prompt,
@@ -482,6 +490,7 @@ def do_command(command:str, gen, opt:Args, completer) -> tuple:
         command = '-h'
     return command, operation
 
+
 def add_weights_to_config(model_path:str, gen, opt, completer):
     print(f'>> Model import in process. Please enter the values needed to configure this model:')
     print()
@@ -578,7 +587,7 @@ def write_config_file(conf_path, gen, model_name, new_config, clobber=False, mak
 
     try:
         print('>> Verifying that new model loads...')
-        yaml_str = gen.model_cache.add_model(model_name, new_config, clobber)
+        gen.model_cache.add_model(model_name, new_config, clobber)
         assert gen.set_model(model_name) is not None, 'model failed to load'
     except AssertionError as e:
         print(f'** aborting **')
@@ -614,10 +623,16 @@ def do_textmask(gen, opt, callback):
     )
 
 def do_postprocess (gen, opt, callback):
-    file_path = opt.prompt     # treat the prompt as the file pathname
+    file_path = opt.prompt      # treat the prompt as the file pathname
+    if opt.new_prompt is not None:
+        opt.prompt = opt.new_prompt
+    else:
+        opt.prompt = None
+        
     if os.path.dirname(file_path) == '': #basename given
         file_path = os.path.join(opt.outdir,file_path)
 
+    opt.input_file_path = file_path
     tool=None
     if opt.facetool_strength > 0:
         tool = opt.facetool
@@ -656,7 +671,10 @@ def do_postprocess (gen, opt, callback):
 def add_postprocessing_to_metadata(opt,original_file,new_file,tool,command):
     original_file = original_file if os.path.exists(original_file) else os.path.join(opt.outdir,original_file)
     new_file       = new_file     if os.path.exists(new_file)      else os.path.join(opt.outdir,new_file)
-    meta = retrieve_metadata(original_file)['sd-metadata']
+    try:
+        meta = retrieve_metadata(original_file)['sd-metadata']
+    except AttributeError:
+        meta = retrieve_metadata(new_file)['sd-metadata']
     if 'image' not in meta:
         meta = metadata_dumps(opt,seeds=[opt.seed])['image']
         meta['image'] = {}
@@ -704,7 +722,7 @@ def prepare_image_metadata(
     elif len(prior_variations) > 0:
         formatted_dream_prompt = opt.dream_prompt_str(seed=first_seed)
     elif operation == 'postprocess':
-        formatted_dream_prompt = '!fix '+opt.dream_prompt_str(seed=seed)
+        formatted_dream_prompt = '!fix '+opt.dream_prompt_str(seed=seed,prompt=opt.input_file_path)
     else:
         formatted_dream_prompt = opt.dream_prompt_str(seed=seed)
     return filename,formatted_dream_prompt
@@ -878,6 +896,36 @@ def write_commands(opt, file_path:str, outfilepath:str):
             f.write('\n'.join(commands))
         print(f'>> File {outfilepath} with commands created')
 
+def emergency_model_create(opt:Args):
+    completer   = get_completer(opt)
+    completer.complete_extensions(('.yaml','.yml','.ckpt','.vae.pt'))
+    completer.set_default_dir('.')
+    valid_path = False
+    while not valid_path:
+        weights_file = input('Enter the path to a downloaded models file, or ^C to exit: ')
+        valid_path = os.path.exists(weights_file)
+    dir,basename = os.path.split(weights_file)
+
+    valid_name = False
+    while not valid_name:
+        name = input('Enter a short name for this model (no spaces): ')
+        name = 'unnamed model' if len(name)==0 else name
+        valid_name = ' ' not in name
+
+    description = input('Enter a description for this model: ')
+    description = 'no description' if len(description)==0 else description
+
+    with open(opt.conf, 'w', encoding='utf-8') as f:
+        f.write(f'{name}:\n')
+        f.write(f'  description: {description}\n')
+        f.write(f'  weights: {weights_file}\n')
+        f.write(f'  config: ./configs/stable-diffusion/v1-inference.yaml\n')
+        f.write(f'  width: 512\n')
+        f.write(f'  height: 512\n')
+        f.write(f'  default: true\n')
+    print(f'Config file {opt.conf} is created. This script will now exit.')
+    print(f'After restarting you may examine the entry with !models and edit it with !edit.')
+    
 ######################################
 
 if __name__ == '__main__':
diff --git a/scripts/preload_models.py b/scripts/preload_models.py
index e0e46e74ba..831682bcb3 100644
--- a/scripts/preload_models.py
+++ b/scripts/preload_models.py
@@ -17,6 +17,7 @@ from omegaconf import OmegaConf
 from huggingface_hub import HfFolder, hf_hub_url
 from pathlib import Path
 from getpass_asterisk import getpass_asterisk
+from transformers import CLIPTokenizer, CLIPTextModel
 import traceback
 import requests
 import clip
@@ -30,10 +31,6 @@ warnings.filterwarnings('ignore')
 #warnings.filterwarnings('ignore',category=DeprecationWarning)
 #warnings.filterwarnings('ignore',category=UserWarning)
 
-# deferred loading so that help message can be printed quickly
-def load_libs():
-    pass
-
 #--------------------------globals--
 Model_dir = './models/ldm/stable-diffusion-v1/'
 Default_config_file = './configs/models.yaml'
@@ -107,13 +104,15 @@ def postscript():
     print(
         '''\n** Model Installation Successful **\nYou're all set! You may now launch InvokeAI using one of these two commands:
 Web version: 
-
     python scripts/invoke.py --web  (connect to http://localhost:9090)
-
 Command-line version:
-
    python scripts/invoke.py
 
+Remember to activate that 'invokeai' environment before running invoke.py.
+
+Or, if you used one of the automated installers, execute "invoke.sh" (Linux/Mac) 
+or "invoke.bat" (Windows) to start the script.
+
 Have fun!
 '''
 )
@@ -347,7 +346,7 @@ def update_config_file(successfully_downloaded:dict,opt:dict):
 
     try:
         if os.path.exists(Config_file):
-            print(f'* {Config_file} exists. Renaming to {Config_file}.orig')
+            print(f'** {Config_file} exists. Renaming to {Config_file}.orig')
             os.rename(Config_file,f'{Config_file}.orig')
         tmpfile = os.path.join(os.path.dirname(Config_file),'new_config.tmp')
         with open(tmpfile, 'w') as outfile:
@@ -418,10 +417,7 @@ def download_kornia():
 
 #---------------------------------------------
 def download_clip():
-    print('Loading CLIP model...',end='')
-    with warnings.catch_warnings():
-        warnings.filterwarnings('ignore', category=DeprecationWarning)
-        from transformers import CLIPTokenizer, CLIPTextModel
+    print('Loading CLIP model (ignore deprecation errors)...',end='')
     sys.stdout.flush()
     version = 'openai/clip-vit-large-patch14'
     tokenizer = CLIPTokenizer.from_pretrained(version)
@@ -430,7 +426,7 @@ def download_clip():
 
 #---------------------------------------------
 def download_gfpgan():
-    print('Installing models from RealESRGAN and facexlib...',end='')
+    print('Installing models from RealESRGAN and facexlib  (ignore deprecation errors)...',end='')
     try:
         from realesrgan import RealESRGANer
         from realesrgan.archs.srvgg_arch import SRVGGNetCompact
@@ -448,19 +444,19 @@ def download_gfpgan():
         print('Error loading ESRGAN:')
         print(traceback.format_exc())
 
-    print('Loading models from GFPGAN')
+    print('Loading models from GFPGAN...',end='')
     for model in (
             [
                 'https://github.com/TencentARC/GFPGAN/releases/download/v1.3.0/GFPGANv1.4.pth',
-                'src/gfpgan/experiments/pretrained_models/GFPGANv1.4.pth'
+                'models/gfpgan/GFPGANv1.4.pth'
             ],
             [
                 'https://github.com/xinntao/facexlib/releases/download/v0.1.0/detection_Resnet50_Final.pth',
-                './gfpgan/weights/detection_Resnet50_Final.pth'
+                'models/gfpgan/weights/detection_Resnet50_Final.pth'
             ],
             [
                 'https://github.com/xinntao/facexlib/releases/download/v0.2.2/parsing_parsenet.pth',
-                './gfpgan/weights/parsing_parsenet.pth'
+                'models/gfpgan/weights/parsing_parsenet.pth'
             ],
     ):
         model_url,model_dest  = model
@@ -495,22 +491,23 @@ def download_clipseg():
     import zipfile
     try:
         model_url  = 'https://owncloud.gwdg.de/index.php/s/ioHbRzFx6th32hn/download'
-        model_dest = 'src/clipseg/clipseg_weights.zip'
-        weights_dir = 'src/clipseg/weights'
-        if not os.path.exists(weights_dir):
+        model_dest = 'models/clipseg/clipseg_weights'
+        weights_zip = 'models/clipseg/weights.zip'
+        
+        if not os.path.exists(model_dest):
             os.makedirs(os.path.dirname(model_dest), exist_ok=True)
-        if not os.path.exists('src/clipseg/weights/rd64-uni-refined.pth'):
-            request.urlretrieve(model_url,model_dest)
-            with zipfile.ZipFile(model_dest,'r') as zip:
-                zip.extractall('src/clipseg')
-                os.rename('src/clipseg/clipseg_weights','src/clipseg/weights')
-            os.remove(model_dest)
-            from clipseg_models.clipseg import CLIPDensePredT
+        if not os.path.exists(f'{model_dest}/rd64-uni-refined.pth'):
+            request.urlretrieve(model_url,weights_zip)
+            with zipfile.ZipFile(weights_zip,'r') as zip:
+                zip.extractall('models/clipseg')
+            os.remove(weights_zip)
+
+            from clipseg.clipseg import CLIPDensePredT
             model = CLIPDensePredT(version='ViT-B/16', reduce_dim=64, )
             model.eval()
             model.load_state_dict(
                 torch.load(
-                    'src/clipseg/weights/rd64-uni-refined.pth',
+                    'models/clipseg/clipseg_weights/rd64-uni-refined.pth',
                     map_location=torch.device('cpu')
                     ),
                 strict=False,
@@ -550,7 +547,6 @@ if __name__ == '__main__':
                         default='./configs/models.yaml',
                         help='path to configuration file to create')
     opt = parser.parse_args()
-    load_libs()
     
     try:
         if opt.interactive:
@@ -562,16 +558,11 @@ if __name__ == '__main__':
                 if models is None:
                     if yes_or_no('Quit?',default_yes=False):
                         sys.exit(0)
-
-                done = False
-                while not done:
-                    print('** LICENSE AGREEMENT FOR WEIGHT FILES **')
-                    access_token = authenticate()
-                    print('\n** DOWNLOADING WEIGHTS **')
-                    successfully_downloaded = download_weight_datasets(models, access_token)
-                    done = successfully_downloaded is not None
+                print('** LICENSE AGREEMENT FOR WEIGHT FILES **')
+                access_token = authenticate()
+                print('\n** DOWNLOADING WEIGHTS **')
+                successfully_downloaded = download_weight_datasets(models, access_token)
                 update_config_file(successfully_downloaded,opt)
-
         print('\n** DOWNLOADING SUPPORT MODELS **')
         download_bert()
         download_kornia()
diff --git a/setup.py b/setup.py
index a544aa150a..bc6c7cdc31 100644
--- a/setup.py
+++ b/setup.py
@@ -2,8 +2,8 @@ from setuptools import setup, find_packages
 
 setup(
     name='invoke-ai',
-    version='2.0.2',
-    description='',
+    version='2.1.3',
+    description='InvokeAI text to image generation toolkit',
     packages=find_packages(),
     install_requires=[
         'torch',
@@ -11,3 +11,4 @@ setup(
         'tqdm',
     ],
 )
+
diff --git a/source_installer/create_installers.sh b/source_installer/create_installers.sh
new file mode 100755
index 0000000000..46f75d25da
--- /dev/null
+++ b/source_installer/create_installers.sh
@@ -0,0 +1,23 @@
+#!/bin/bash
+
+cd "$(dirname "${BASH_SOURCE[0]}")"
+
+# make the installer zip for linux and mac
+rm -rf invokeAI
+mkdir -p invokeAI
+cp install.sh invokeAI
+cp readme.txt invokeAI
+
+zip -r invokeAI-src-installer-linux.zip invokeAI
+zip -r invokeAI-src-installer-mac.zip invokeAI
+
+# make the installer zip for windows
+rm -rf invokeAI
+mkdir -p invokeAI
+cp install.bat invokeAI
+cp readme.txt invokeAI
+cp WinLongPathsEnabled.reg invokeAI
+
+zip -r invokeAI-src-installer-windows.zip invokeAI
+
+echo "The installer zips are ready to be distributed.."
diff --git a/source_installer/install.bat b/source_installer/install.bat
new file mode 100644
index 0000000000..eaace2c084
--- /dev/null
+++ b/source_installer/install.bat
@@ -0,0 +1,118 @@
+@echo off
+
+@rem This script will install git and conda (if not found on the PATH variable)
+@rem  using micromamba (an 8mb static-linked single-file binary, conda replacement).
+@rem For users who already have git and conda, this step will be skipped.
+
+@rem Next, it'll checkout the project's git repo, if necessary.
+@rem Finally, it'll create the conda environment and preload the models.
+
+@rem This enables a user to install this project without manually installing conda and git.
+
+echo "Installing InvokeAI.."
+echo.
+
+@rem config
+set MAMBA_ROOT_PREFIX=%cd%\installer_files\mamba
+set INSTALL_ENV_DIR=%cd%\installer_files\env
+set MICROMAMBA_DOWNLOAD_URL=https://github.com/cmdr2/stable-diffusion-ui/releases/download/v1.1/micromamba.exe
+set REPO_URL=https://github.com/invoke-ai/InvokeAI.git
+set umamba_exists=F
+@rem Change the download URL to an InvokeAI repo's release URL
+
+@rem figure out whether git and conda needs to be installed
+if exist "%INSTALL_ENV_DIR%" set PATH=%INSTALL_ENV_DIR%;%INSTALL_ENV_DIR%\Library\bin;%INSTALL_ENV_DIR%\Scripts;%INSTALL_ENV_DIR%\Library\usr\bin;%PATH%
+
+set PACKAGES_TO_INSTALL=
+
+call conda --version >.tmp1 2>.tmp2
+if "%ERRORLEVEL%" NEQ "0" set PACKAGES_TO_INSTALL=%PACKAGES_TO_INSTALL% conda
+
+call git --version >.tmp1 2>.tmp2
+if "%ERRORLEVEL%" NEQ "0" set PACKAGES_TO_INSTALL=%PACKAGES_TO_INSTALL% git
+
+call "%MAMBA_ROOT_PREFIX%\micromamba.exe" --version >.tmp1 2>.tmp2
+if "%ERRORLEVEL%" EQU "0" set umamba_exists=T
+
+@rem (if necessary) install git and conda into a contained environment
+if "%PACKAGES_TO_INSTALL%" NEQ "" (
+    @rem download micromamba
+    if "%umamba_exists%" == "F" (
+        echo "Downloading micromamba from %MICROMAMBA_DOWNLOAD_URL% to %MAMBA_ROOT_PREFIX%\micromamba.exe"
+
+        mkdir "%MAMBA_ROOT_PREFIX%"
+        call curl -L "%MICROMAMBA_DOWNLOAD_URL%" > "%MAMBA_ROOT_PREFIX%\micromamba.exe"
+
+        @rem test the mamba binary
+        echo Micromamba version:
+        call "%MAMBA_ROOT_PREFIX%\micromamba.exe" --version
+    )
+
+    @rem create the installer env
+    if not exist "%INSTALL_ENV_DIR%" (
+        call "%MAMBA_ROOT_PREFIX%\micromamba.exe" create -y --prefix "%INSTALL_ENV_DIR%"
+    )
+
+    echo "Packages to install:%PACKAGES_TO_INSTALL%"
+
+    call "%MAMBA_ROOT_PREFIX%\micromamba.exe" install -y --prefix "%INSTALL_ENV_DIR%" -c conda-forge %PACKAGES_TO_INSTALL%
+
+    if not exist "%INSTALL_ENV_DIR%" (
+        echo "There was a problem while installing%PACKAGES_TO_INSTALL% using micromamba. Cannot continue."
+        pause
+        exit /b
+    )
+)
+
+set PATH=%INSTALL_ENV_DIR%;%INSTALL_ENV_DIR%\Library\bin;%INSTALL_ENV_DIR%\Scripts;%INSTALL_ENV_DIR%\Library\usr\bin;%PATH%
+
+@rem get the repo (and load into the current directory)
+if not exist ".git" (
+    call git init
+    call git config --local init.defaultBranch main
+    call git remote add origin %REPO_URL%
+    call git fetch
+    call git checkout origin/main -ft
+)
+
+@rem activate the base env
+call conda activate
+
+@rem create the environment
+call conda env remove -n invokeai
+copy environments-and-requirements\environment-win-cuda.yml environment.yml 
+call conda env create
+if "%ERRORLEVEL%" NEQ "0" (
+   echo ""
+   echo "Something went wrong while installing Python libraries and cannot continue.
+   echo "Please visit https://invoke-ai.github.io/InvokeAI/#installation for alternative"
+   echo "installation methods."
+   echo "Press any key to continue"
+   pause
+   exit /b
+)
+
+copy source_installer\invoke.bat invoke.bat
+copy source_installer\update.bat update.bat
+
+call conda activate invokeai
+@rem preload the models
+call python scripts\preload_models.py
+if "%ERRORLEVEL%" NEQ "0" (
+   echo ""
+   echo "The preload_models.py script crashed or was cancelled."
+   echo "InvokeAI is not ready to run. To run preload_models.py again,"
+   echo "run the command 'update.bat' in this directory."
+   echo "Press any key to continue"
+   pause
+   exit /b
+)
+
+@rem tell the user their next steps
+echo ""
+echo "* InvokeAI installed successfully *"
+echo "You can now start generating images by double-clicking the 'invoke.bat' file (inside this folder)
+echo "Press any key to continue"
+pause
+exit 0
+
diff --git a/source_installer/install.sh b/source_installer/install.sh
new file mode 100755
index 0000000000..747d701611
--- /dev/null
+++ b/source_installer/install.sh
@@ -0,0 +1,138 @@
+#!/bin/bash
+
+# This script will install git and conda (if not found on the PATH variable)
+#  using micromamba (an 8mb static-linked single-file binary, conda replacement).
+# For users who already have git and conda, this step will be skipped.
+
+# Next, it'll checkout the project's git repo, if necessary.
+# Finally, it'll create the conda environment and preload the models.
+
+# This enables a user to install this project without manually installing conda and git.
+
+cd "$(dirname "${BASH_SOURCE[0]}")"
+
+echo "Installing InvokeAI.."
+echo ""
+
+OS_NAME=$(uname -s)
+case "${OS_NAME}" in
+    Linux*)     OS_NAME="linux";;
+    Darwin*)    OS_NAME="mac";;
+    *)          echo "Unknown OS: $OS_NAME! This script runs only on Linux or Mac" && exit
+esac
+
+OS_ARCH=$(uname -m)
+case "${OS_ARCH}" in
+    x86_64*)    OS_ARCH="64";;
+    arm64*)     OS_ARCH="arm64";;
+    *)          echo "Unknown system architecture: $OS_ARCH! This script runs only on x86_64 or arm64" && exit
+esac
+
+# https://mamba.readthedocs.io/en/latest/installation.html
+if [ "$OS_NAME" == "linux" ] && [ "$OS_ARCH" == "arm64" ]; then OS_ARCH="aarch64"; fi
+
+# config
+export MAMBA_ROOT_PREFIX="$(pwd)/installer_files/mamba"
+INSTALL_ENV_DIR="$(pwd)/installer_files/env"
+MICROMAMBA_DOWNLOAD_URL="https://micro.mamba.pm/api/micromamba/${OS_NAME}-${OS_ARCH}/latest"
+REPO_URL="https://github.com/invoke-ai/InvokeAI.git"
+umamba_exists="F"
+
+# figure out whether git and conda needs to be installed
+if [ -e "$INSTALL_ENV_DIR" ]; then export PATH="$INSTALL_ENV_DIR/bin:$PATH"; fi
+
+PACKAGES_TO_INSTALL=""
+if ! $(which conda) -V &>/dev/null; then PACKAGES_TO_INSTALL="$PACKAGES_TO_INSTALL conda"; fi
+if ! which git &>/dev/null; then PACKAGES_TO_INSTALL="$PACKAGES_TO_INSTALL git"; fi
+
+if "$MAMBA_ROOT_PREFIX/micromamba" --version &>/dev/null; then umamba_exists="T"; fi
+
+# (if necessary) install git and conda into a contained environment
+if [ "$PACKAGES_TO_INSTALL" != "" ]; then
+    # download micromamba
+    if [ "$umamba_exists" == "F" ]; then
+        echo "Downloading micromamba from $MICROMAMBA_DOWNLOAD_URL to $MAMBA_ROOT_PREFIX/micromamba"
+
+        mkdir -p "$MAMBA_ROOT_PREFIX"
+        curl -L "$MICROMAMBA_DOWNLOAD_URL" | tar -xvj bin/micromamba -O > "$MAMBA_ROOT_PREFIX/micromamba"
+
+        chmod u+x "$MAMBA_ROOT_PREFIX/micromamba"
+
+        # test the mamba binary
+        echo "Micromamba version:"
+        "$MAMBA_ROOT_PREFIX/micromamba" --version
+    fi
+
+    # create the installer env
+    if [ ! -e "$INSTALL_ENV_DIR" ]; then
+        "$MAMBA_ROOT_PREFIX/micromamba" create -y --prefix "$INSTALL_ENV_DIR"
+    fi
+
+    echo "Packages to install:$PACKAGES_TO_INSTALL"
+
+    "$MAMBA_ROOT_PREFIX/micromamba" install -y --prefix "$INSTALL_ENV_DIR" -c conda-forge $PACKAGES_TO_INSTALL
+
+    if [ ! -e "$INSTALL_ENV_DIR" ]; then
+        echo "There was a problem while initializing micromamba. Cannot continue."
+        exit
+    fi
+fi
+
+if [ -e "$INSTALL_ENV_DIR" ]; then export PATH="$INSTALL_ENV_DIR/bin:$PATH"; fi
+
+# get the repo (and load into the current directory)
+if [ ! -e ".git" ]; then
+    git init
+    git config --local init.defaultBranch main
+    git remote add origin "$REPO_URL"
+    git fetch
+    git checkout origin/main -ft
+fi
+
+# create the environment
+CONDA_BASEPATH=$(conda info --base)
+source "$CONDA_BASEPATH/etc/profile.d/conda.sh" # otherwise conda complains about 'shell not initialized' (needed when running in a script)
+
+conda activate
+if [ "$OS_NAME" == "mac" ]; then
+    echo "Macintosh system detected. Installing MPS and CPU support."
+    ln -sf environments-and-requirements/environment-mac.yml environment.yml
+else
+    if (lsmod | grep amdgpu) &>/dev/null ; then
+	echo "Linux system with AMD GPU driver detected. Installing ROCm and CPU support"
+	ln -sf environments-and-requirements/environment-lin-amd.yml environment.yml
+    else
+	echo "Linux system detected. Installing CUDA and CPU support."
+	ln -sf environments-and-requirements/environment-lin-cuda.yml environment.yml
+    fi
+fi
+conda env update
+
+status=$?
+
+if test $status -ne 0
+then
+   echo "Something went wrong while installing Python libraries and cannot continue."
+   echo "Please visit https://invoke-ai.github.io/InvokeAI/#installation for alternative"
+   echo "installation methods"
+else
+    ln -sf ./source_installer/invoke.sh .
+    ln -sf ./source_installer/update.sh .
+
+    conda activate invokeai
+    # preload the models
+    echo "Calling the preload_models.py script"
+    python scripts/preload_models.py
+    status=$?
+    if test $status -ne 0
+       then
+	   echo "The preload_models.py script crashed or was cancelled."
+           echo "InvokeAI is not ready to run. Try again by running"
+           echo "update.sh in this directory."
+       else
+           # tell the user their next steps
+	   echo "You can now start generating images by running invoke.sh (inside this folder), using ./invoke.sh"
+       fi
+fi
+
+conda activate invokeai
diff --git a/invoke.bat b/source_installer/invoke.bat
similarity index 100%
rename from invoke.bat
rename to source_installer/invoke.bat
diff --git a/invoke.sh b/source_installer/invoke.sh
similarity index 93%
rename from invoke.sh
rename to source_installer/invoke.sh
index 55a0292e6e..9bc1e879f4 100755
--- a/invoke.sh
+++ b/source_installer/invoke.sh
@@ -23,6 +23,7 @@ if [ "$0" != "bash" ]; then
         * ) echo "Invalid selection"; exit;;
     esac
 else # in developer console
-    which python
     python --version
+    echo "Press ^D to exit"
+    export PS1="(InvokeAI) \u@\h \w> "
 fi
diff --git a/source_installer/readme.txt b/source_installer/readme.txt
new file mode 100644
index 0000000000..8bfc0c9ce5
--- /dev/null
+++ b/source_installer/readme.txt
@@ -0,0 +1,16 @@
+InvokeAI
+
+Project homepage: https://github.com/invoke-ai/InvokeAI
+
+Installation on Windows:
+ You may need to enable Windows Long Paths to install InvokeAI. If you're not
+ sure what this is, you almost certainly need to do this. Simply double-click the
+ "WinLongPathsEnabled.reg" file located in this directory, and approve the Windows
+ warnings. Note that you will need to have admin privileges in order to do this.
+ 
+ Then double-click the 'install.bat' file (while keeping it inside the invokeAI folder).
+
+Installation on Linux and Mac:
+ Please open the terminal, and run './install.sh' (while keeping it inside the invokeAI folder).
+
+After installation, please run the 'invoke.bat' file (on Windows) or 'invoke.sh' file (on Linux/Mac) to start InvokeAI.
diff --git a/update.bat b/source_installer/update.bat
similarity index 100%
rename from update.bat
rename to source_installer/update.bat
diff --git a/update.sh b/source_installer/update.sh
similarity index 99%
rename from update.sh
rename to source_installer/update.sh
index e969826359..d634324760 100755
--- a/update.sh
+++ b/source_installer/update.sh
@@ -22,3 +22,5 @@ case "${OS_NAME}" in
 esac
 
 python scripts/preload_models.py
+
+
diff --git a/tests/test_prompt_parser.py b/tests/test_prompt_parser.py
index 03db9274d4..0c9bbf91f9 100644
--- a/tests/test_prompt_parser.py
+++ b/tests/test_prompt_parser.py
@@ -28,8 +28,8 @@ class PromptParserTestCase(unittest.TestCase):
         self.assertEqual(make_weighted_conjunction([('', 1)]), parse_prompt(''))
 
     def test_basic(self):
-        self.assertEqual(make_weighted_conjunction([('fire flames', 1)]), parse_prompt("fire (flames)"))
         self.assertEqual(make_weighted_conjunction([("fire flames", 1)]), parse_prompt("fire flames"))
+        self.assertEqual(make_weighted_conjunction([('fire flames', 1)]), parse_prompt("fire (flames)"))
         self.assertEqual(make_weighted_conjunction([("fire, flames", 1)]), parse_prompt("fire, flames"))
         self.assertEqual(make_weighted_conjunction([("fire, flames , fire", 1)]), parse_prompt("fire, flames , fire"))
         self.assertEqual(make_weighted_conjunction([("cat hot-dog eating", 1)]), parse_prompt("cat hot-dog eating"))
@@ -37,14 +37,25 @@ class PromptParserTestCase(unittest.TestCase):
 
     def test_attention(self):
         self.assertEqual(make_weighted_conjunction([('flames', 0.5)]), parse_prompt("(flames)0.5"))
+        self.assertEqual(make_weighted_conjunction([('flames', 0.5)]), parse_prompt("(flames).attend(0.5)"))
+        self.assertEqual(make_weighted_conjunction([('flames', 0.5)]), parse_prompt("flames.attend(0.5)"))
+        self.assertEqual(make_weighted_conjunction([('flames', 0.5)]), parse_prompt("\"flames\".attend(0.5)"))
         self.assertEqual(make_weighted_conjunction([('fire flames', 0.5)]), parse_prompt("(fire flames)0.5"))
+        self.assertEqual(make_weighted_conjunction([('fire flames', 0.5)]), parse_prompt("(fire flames).attend(0.5)"))
+
         self.assertEqual(make_weighted_conjunction([('flames', 1.1)]), parse_prompt("(flames)+"))
         self.assertEqual(make_weighted_conjunction([('flames', 1.1)]), parse_prompt("flames+"))
         self.assertEqual(make_weighted_conjunction([('flames', 1.1)]), parse_prompt("\"flames\"+"))
+        self.assertEqual(make_weighted_conjunction([('flames', 1.1)]), parse_prompt("flames.attend(+)"))
+        self.assertEqual(make_weighted_conjunction([('flames', 1.1)]), parse_prompt("(flames).attend(+)"))
+        self.assertEqual(make_weighted_conjunction([('flames', 1.1)]), parse_prompt("\"flames\".attend(+)"))
         self.assertEqual(make_weighted_conjunction([('flames', 0.9)]), parse_prompt("(flames)-"))
         self.assertEqual(make_weighted_conjunction([('flames', 0.9)]), parse_prompt("flames-"))
         self.assertEqual(make_weighted_conjunction([('flames', 0.9)]), parse_prompt("\"flames\"-"))
         self.assertEqual(make_weighted_conjunction([('fire', 1), ('flames', 0.5)]), parse_prompt("fire (flames)0.5"))
+        self.assertEqual(make_weighted_conjunction([('fire', 1), ('flames', 0.5)]), parse_prompt("fire flames.attend(0.5)"))
+        self.assertEqual(make_weighted_conjunction([('fire', 1), ('flames', 0.5)]), parse_prompt("fire (flames).attend(0.5)"))
+        self.assertEqual(make_weighted_conjunction([('fire', 1), ('flames', 0.5)]), parse_prompt("fire \"flames\".attend(0.5)"))
         self.assertEqual(make_weighted_conjunction([('flames', pow(1.1, 2))]), parse_prompt("(flames)++"))
         self.assertEqual(make_weighted_conjunction([('flames', pow(0.9, 2))]), parse_prompt("(flames)--"))
         self.assertEqual(make_weighted_conjunction([('flowers', pow(0.9, 3)), ('flames', pow(1.1, 3))]), parse_prompt("(flowers)--- flames+++"))
@@ -102,20 +113,17 @@ class PromptParserTestCase(unittest.TestCase):
 
         assert_if_prompt_string_not_untouched('a test prompt')
         assert_if_prompt_string_not_untouched('a badly formed +test prompt')
-        with self.assertRaises(pyparsing.ParseException):
-            parse_prompt('a badly (formed test prompt')
+        assert_if_prompt_string_not_untouched('a badly (formed test prompt')
+
         #with self.assertRaises(pyparsing.ParseException):
-        with self.assertRaises(pyparsing.ParseException):
-            parse_prompt('a badly (formed +test prompt')
+        assert_if_prompt_string_not_untouched('a badly (formed +test prompt')
         self.assertEqual(Conjunction([FlattenedPrompt([Fragment('a badly formed +test prompt',1)])]) , parse_prompt('a badly (formed +test )prompt'))
-        with self.assertRaises(pyparsing.ParseException):
-            parse_prompt('(((a badly (formed +test )prompt')
-        with self.assertRaises(pyparsing.ParseException):
-            parse_prompt('(a (ba)dly (f)ormed +test prompt')
-        with self.assertRaises(pyparsing.ParseException):
-            parse_prompt('(a (ba)dly (f)ormed +test +prompt')
-        with self.assertRaises(pyparsing.ParseException):
-            parse_prompt('("((a badly (formed +test ").blend(1.0)')
+        self.assertEqual(Conjunction([FlattenedPrompt([Fragment('(((a badly formed +test prompt',1)])]) , parse_prompt('(((a badly (formed +test )prompt'))
+
+        self.assertEqual(Conjunction([FlattenedPrompt([Fragment('(a ba dly f ormed +test prompt',1)])]) , parse_prompt('(a (ba)dly (f)ormed +test prompt'))
+        self.assertEqual(Conjunction([FlattenedPrompt([Fragment('(a ba dly f ormed +test +prompt',1)])]) , parse_prompt('(a (ba)dly (f)ormed +test +prompt'))
+        self.assertEqual(Conjunction([Blend([FlattenedPrompt([Fragment('((a badly (formed +test', 1)])], [1.0])]),
+                         parse_prompt('("((a badly (formed +test ").blend(1.0)'))
 
         self.assertEqual(Conjunction([FlattenedPrompt([Fragment('hamburger bun', 1)])]),
             parse_prompt("hamburger ((bun))"))
@@ -128,6 +136,26 @@ class PromptParserTestCase(unittest.TestCase):
 
 
     def test_blend(self):
+        self.assertEqual(Conjunction(
+            [Blend([FlattenedPrompt([('mountain', 1.0)]), FlattenedPrompt([('man', 1.0)])], [1.0, 1.0])]),
+                         parse_prompt("(\"mountain\", \"man\").blend()")
+                         )
+        self.assertEqual(Conjunction(
+            [Blend([FlattenedPrompt([('mountain', 1.0)]), FlattenedPrompt([('man', 1.0)])], [1.0, 1.0])]),
+                         parse_prompt("(mountain, man).blend()")
+                         )
+        self.assertEqual(Conjunction(
+            [Blend([FlattenedPrompt([('mountain', 1.0)]), FlattenedPrompt([('man', 1.0)])], [1.0, 1.0])]),
+                         parse_prompt("((mountain), (man)).blend()")
+                         )
+        self.assertEqual(Conjunction(
+            [Blend([FlattenedPrompt([('mountain', 1.0)]), FlattenedPrompt([('tall man', 1.0)])], [1.0, 1.0])]),
+                         parse_prompt("((mountain), (tall man)).blend()")
+                         )
+
+        with self.assertRaises(PromptParser.ParsingException):
+             print(parse_prompt("((mountain), \"cat.swap(dog)\").blend()"))
+
         self.assertEqual(Conjunction(
             [Blend([FlattenedPrompt([('fire', 1.0)]), FlattenedPrompt([('fire flames', 1.0)])], [0.7, 0.3])]),
                          parse_prompt("(\"fire\", \"fire flames\").blend(0.7, 0.3)")
@@ -166,10 +194,20 @@ class PromptParserTestCase(unittest.TestCase):
             )
 
         self.assertEqual(
-            Conjunction([Blend([FlattenedPrompt([('mountain, man, hairy', 1)]),
-                                FlattenedPrompt([('face, teeth,', 1), ('eyes', 0.9*0.9)])], weights=[1.0,-1.0])]),
+            Conjunction([Blend([FlattenedPrompt([('mountain , man , hairy', 1)]),
+                                FlattenedPrompt([('face , teeth ,', 1), ('eyes', 0.9*0.9)])], weights=[1.0,-1.0], normalize_weights=True)]),
             parse_prompt('("mountain, man, hairy", "face, teeth, eyes--").blend(1,-1)')
         )
+        self.assertEqual(
+            Conjunction([Blend([FlattenedPrompt([('mountain , man , hairy', 1)]),
+                                FlattenedPrompt([('face , teeth ,', 1), ('eyes', 0.9 * 0.9)])], weights=[1.0, -1.0], normalize_weights=False)]),
+            parse_prompt('("mountain, man, hairy", "face, teeth, eyes--").blend(1,-1,no_normalize)')
+        )
+
+        with self.assertRaises(PromptParser.ParsingException):
+             parse_prompt("(\"fire\", \"fire flames\").blend(0.7, 0.3, 0.1)")
+        with self.assertRaises(PromptParser.ParsingException):
+             parse_prompt("(\"fire\", \"fire flames\").blend(0.7)")
 
 
     def test_nested(self):
@@ -182,6 +220,9 @@ class PromptParserTestCase(unittest.TestCase):
 
     def test_cross_attention_control(self):
 
+        self.assertEqual(Conjunction([FlattenedPrompt([CrossAttentionControlSubstitute([Fragment('sun')], [Fragment('moon')])])]),
+                         parse_prompt("sun.swap(moon)"))
+
         self.assertEqual(Conjunction([
             FlattenedPrompt([Fragment('a', 1),
                              CrossAttentionControlSubstitute([Fragment('cat', 1)], [Fragment('dog', 1)]),
@@ -231,6 +272,9 @@ class PromptParserTestCase(unittest.TestCase):
         self.assertEqual(Conjunction([FlattenedPrompt([Fragment('a forest landscape', 1),
                                                                    CrossAttentionControlSubstitute([Fragment('',1)], [Fragment('in winter',1)])])]),
                          parse_prompt('a forest landscape "".swap("in winter")'))
+        self.assertEqual(Conjunction([FlattenedPrompt([Fragment('a forest landscape', 1),
+                                                                   CrossAttentionControlSubstitute([Fragment('',1)], [Fragment('in winter',1)])])]),
+                         parse_prompt('a forest landscape ().swap(in winter)'))
         self.assertEqual(Conjunction([FlattenedPrompt([Fragment('a forest landscape', 1),
                                                                    CrossAttentionControlSubstitute([Fragment('',1)], [Fragment('in winter',1)])])]),
                          parse_prompt('a forest landscape " ".swap("in winter")'))
@@ -259,6 +303,12 @@ class PromptParserTestCase(unittest.TestCase):
                                                         Fragment(',', 1), Fragment('fire', 2.0)])])
         self.assertEqual(flames_to_trees_fire, parse_prompt('"(fire (flames)0.5)0.5".swap("(trees)0.7 houses"), (fire)2.0'))
 
+        self.assertEqual(Conjunction([FlattenedPrompt([Fragment('a', 1),
+                                                       CrossAttentionControlSubstitute([Fragment('cat',1)], [Fragment('dog',1)]),
+                                                       Fragment('eating a', 1),
+                                                       CrossAttentionControlSubstitute([Fragment('hotdog',1)], [Fragment('hotdog', pow(1.1,4))])
+                                                       ])]),
+            parse_prompt("a cat.swap(dog) eating a hotdog.swap(hotdog++++)"))
         self.assertEqual(Conjunction([FlattenedPrompt([Fragment('a', 1),
                                                        CrossAttentionControlSubstitute([Fragment('cat',1)], [Fragment('dog',1)]),
                                                        Fragment('eating a', 1),
@@ -343,31 +393,31 @@ class PromptParserTestCase(unittest.TestCase):
         self.assertEqual(make_weighted_conjunction([('hairy', 1), ('mountain', 1.1), ('\(man\)', 1.1*1.1)]),parse_prompt('hairy (mountain (\(man\))+)+'))
         self.assertEqual(make_weighted_conjunction([('hairy', 1), ('\(man\)', 1.1*1.1), ('mountain', 1.1)]),parse_prompt('hairy ((\(man\))1.1 "mountain")+'))
         self.assertEqual(make_weighted_conjunction([('hairy', 1), ('mountain', 1.1), ('\(man\)', 1.1*1.1)]),parse_prompt('hairy ("mountain" (\(man\))1.1 )+'))
-        self.assertEqual(make_weighted_conjunction([('hairy', 1), ('mountain, man', 1.1)]),parse_prompt('hairy ("mountain, man")+'))
-        self.assertEqual(make_weighted_conjunction([('hairy', 1), ('mountain, man with a', 1.1), ('beard', 1.1*1.1)]), parse_prompt('hairy ("mountain, man" with a beard+)+'))
-        self.assertEqual(make_weighted_conjunction([('hairy', 1), ('mountain, man with a', 1.1), ('beard', 1.1*2.0)]), parse_prompt('hairy ("mountain, man" with a (beard)2.0)+'))
-        self.assertEqual(make_weighted_conjunction([('hairy', 1), ('mountain, \"man\" with a', 1.1), ('beard', 1.1*2.0)]), parse_prompt('hairy ("mountain, \\"man\\"" with a (beard)2.0)+'))
-        self.assertEqual(make_weighted_conjunction([('hairy', 1), ('mountain, m\"an\" with a', 1.1), ('beard', 1.1*2.0)]), parse_prompt('hairy ("mountain, m\\"an\\"" with a (beard)2.0)+'))
+        self.assertEqual(make_weighted_conjunction([('hairy', 1), ('mountain , man', 1.1)]),parse_prompt('hairy ("mountain, man")+'))
+        self.assertEqual(make_weighted_conjunction([('hairy', 1), ('mountain , man with a', 1.1), ('beard', 1.1*1.1)]), parse_prompt('hairy ("mountain, man" with a beard+)+'))
+        self.assertEqual(make_weighted_conjunction([('hairy', 1), ('mountain , man with a', 1.1), ('beard', 1.1*2.0)]), parse_prompt('hairy ("mountain, man" with a (beard)2.0)+'))
+        self.assertEqual(make_weighted_conjunction([('hairy', 1), ('mountain , \"man\" with a', 1.1), ('beard', 1.1*2.0)]), parse_prompt('hairy ("mountain, \\"man\\"" with a (beard)2.0)+'))
+        self.assertEqual(make_weighted_conjunction([('hairy', 1), ('mountain , m\"an\" with a', 1.1), ('beard', 1.1*2.0)]), parse_prompt('hairy ("mountain, m\\"an\\"" with a (beard)2.0)+'))
 
-        self.assertEqual(make_weighted_conjunction([('hairy', 1), ('mountain, \"man (with a', 1.1), ('beard', 1.1*2.0)]), parse_prompt('hairy ("mountain, \\\"man\" \(with a (beard)2.0)+'))
-        self.assertEqual(make_weighted_conjunction([('hairy', 1), ('mountain, \"man w(ith a', 1.1), ('beard', 1.1*2.0)]), parse_prompt('hairy ("mountain, \\\"man\" w\(ith a (beard)2.0)+'))
-        self.assertEqual(make_weighted_conjunction([('hairy', 1), ('mountain, \"man with( a', 1.1), ('beard', 1.1*2.0)]), parse_prompt('hairy ("mountain, \\\"man\" with\( a (beard)2.0)+'))
-        self.assertEqual(make_weighted_conjunction([('hairy', 1), ('mountain, \"man )with a', 1.1), ('beard', 1.1*2.0)]), parse_prompt('hairy ("mountain, \\\"man\" \)with a (beard)2.0)+'))
-        self.assertEqual(make_weighted_conjunction([('hairy', 1), ('mountain, \"man w)ith a', 1.1), ('beard', 1.1*2.0)]), parse_prompt('hairy ("mountain, \\\"man\" w\)ith a (beard)2.0)+'))
-        self.assertEqual(make_weighted_conjunction([('hairy', 1), ('mountain, \"man with) a', 1.1), ('beard', 1.1*2.0)]), parse_prompt('hairy ("mountain, \\\"man\" with\) a (beard)2.0)+'))
-        self.assertEqual(make_weighted_conjunction([('hairy', 1), ('mou)ntain, \"man (wit(h a', 1.1), ('beard', 1.1*2.0)]), parse_prompt('hairy ("mou\)ntain, \\\"man\" \(wit\(h a (beard)2.0)+'))
-        self.assertEqual(make_weighted_conjunction([('hai(ry', 1), ('mountain, \"man w)ith a', 1.1), ('beard', 1.1*2.0)]), parse_prompt('hai\(ry ("mountain, \\\"man\" w\)ith a (beard)2.0)+'))
-        self.assertEqual(make_weighted_conjunction([('hairy((', 1), ('mountain, \"man with a', 1.1), ('beard', 1.1*2.0)]), parse_prompt('hairy\(\( ("mountain, \\\"man\" with a (beard)2.0)+'))
+        self.assertEqual(make_weighted_conjunction([('hairy', 1), ('mountain , \"man (with a', 1.1), ('beard', 1.1*2.0)]), parse_prompt('hairy ("mountain, \\\"man\" \(with a (beard)2.0)+'))
+        self.assertEqual(make_weighted_conjunction([('hairy', 1), ('mountain , \"man w(ith a', 1.1), ('beard', 1.1*2.0)]), parse_prompt('hairy ("mountain, \\\"man\" w\(ith a (beard)2.0)+'))
+        self.assertEqual(make_weighted_conjunction([('hairy', 1), ('mountain , \"man with( a', 1.1), ('beard', 1.1*2.0)]), parse_prompt('hairy ("mountain, \\\"man\" with\( a (beard)2.0)+'))
+        self.assertEqual(make_weighted_conjunction([('hairy', 1), ('mountain , \"man )with a', 1.1), ('beard', 1.1*2.0)]), parse_prompt('hairy ("mountain, \\\"man\" \)with a (beard)2.0)+'))
+        self.assertEqual(make_weighted_conjunction([('hairy', 1), ('mountain , \"man w)ith a', 1.1), ('beard', 1.1*2.0)]), parse_prompt('hairy ("mountain, \\\"man\" w\)ith a (beard)2.0)+'))
+        self.assertEqual(make_weighted_conjunction([('hairy', 1), ('mountain , \"man with) a', 1.1), ('beard', 1.1*2.0)]), parse_prompt('hairy ("mountain, \\\"man\" with\) a (beard)2.0)+'))
+        self.assertEqual(make_weighted_conjunction([('hairy', 1), ('mou)ntain , \"man (wit(h a', 1.1), ('beard', 1.1*2.0)]), parse_prompt('hairy ("mou\)ntain, \\\"man\" \(wit\(h a (beard)2.0)+'))
+        self.assertEqual(make_weighted_conjunction([('hai(ry', 1), ('mountain , \"man w)ith a', 1.1), ('beard', 1.1*2.0)]), parse_prompt('hai\(ry ("mountain, \\\"man\" w\)ith a (beard)2.0)+'))
+        self.assertEqual(make_weighted_conjunction([('hairy((', 1), ('mountain , \"man with a', 1.1), ('beard', 1.1*2.0)]), parse_prompt('hairy\(\( ("mountain, \\\"man\" with a (beard)2.0)+'))
 
-        self.assertEqual(make_weighted_conjunction([('mountain, \"man (with a', 1.1), ('beard', 1.1*2.0), ('hairy', 1)]), parse_prompt('("mountain, \\\"man\" \(with a (beard)2.0)+ hairy'))
-        self.assertEqual(make_weighted_conjunction([('mountain, \"man w(ith a', 1.1), ('beard', 1.1*2.0), ('hairy', 1)]), parse_prompt('("mountain, \\\"man\" w\(ith a (beard)2.0)+hairy'))
-        self.assertEqual(make_weighted_conjunction([('mountain, \"man with( a', 1.1), ('beard', 1.1*2.0), ('hairy', 1)]), parse_prompt('("mountain, \\\"man\" with\( a (beard)2.0)+ hairy'))
-        self.assertEqual(make_weighted_conjunction([('mountain, \"man )with a', 1.1), ('beard', 1.1*2.0), ('hairy', 1)]), parse_prompt('("mountain, \\\"man\" \)with a (beard)2.0)+ hairy'))
-        self.assertEqual(make_weighted_conjunction([('mountain, \"man w)ith a', 1.1), ('beard', 1.1*2.0), ('hairy', 1)]), parse_prompt('("mountain, \\\"man\" w\)ith a (beard)2.0)+ hairy'))
-        self.assertEqual(make_weighted_conjunction([('mountain, \"man with) a', 1.1), ('beard', 1.1*2.0), ('hairy', 1)]), parse_prompt(' ("mountain, \\\"man\" with\) a (beard)2.0)+ hairy'))
-        self.assertEqual(make_weighted_conjunction([('mou)ntain, \"man (wit(h a', 1.1), ('beard', 1.1*2.0), ('hairy', 1)]), parse_prompt('("mou\)ntain, \\\"man\" \(wit\(h a (beard)2.0)+ hairy'))
-        self.assertEqual(make_weighted_conjunction([('mountain, \"man w)ith a', 1.1), ('beard', 1.1*2.0), ('hai(ry', 1)]), parse_prompt('("mountain, \\\"man\" w\)ith a (beard)2.0)+ hai\(ry '))
-        self.assertEqual(make_weighted_conjunction([('mountain, \"man with a', 1.1), ('beard', 1.1*2.0), ('hairy((', 1)]), parse_prompt('("mountain, \\\"man\" with a (beard)2.0)+ hairy\(\( '))
+        self.assertEqual(make_weighted_conjunction([('mountain , \"man (with a', 1.1), ('beard', 1.1*2.0), ('hairy', 1)]), parse_prompt('("mountain, \\\"man\" \(with a (beard)2.0)+ hairy'))
+        self.assertEqual(make_weighted_conjunction([('mountain , \"man w(ith a', 1.1), ('beard', 1.1*2.0), ('hairy', 1)]), parse_prompt('("mountain, \\\"man\" w\(ith a (beard)2.0)+hairy'))
+        self.assertEqual(make_weighted_conjunction([('mountain , \"man with( a', 1.1), ('beard', 1.1*2.0), ('hairy', 1)]), parse_prompt('("mountain, \\\"man\" with\( a (beard)2.0)+ hairy'))
+        self.assertEqual(make_weighted_conjunction([('mountain , \"man )with a', 1.1), ('beard', 1.1*2.0), ('hairy', 1)]), parse_prompt('("mountain, \\\"man\" \)with a (beard)2.0)+ hairy'))
+        self.assertEqual(make_weighted_conjunction([('mountain , \"man w)ith a', 1.1), ('beard', 1.1*2.0), ('hairy', 1)]), parse_prompt('("mountain, \\\"man\" w\)ith a (beard)2.0)+ hairy'))
+        self.assertEqual(make_weighted_conjunction([('mountain , \"man with) a', 1.1), ('beard', 1.1*2.0), ('hairy', 1)]), parse_prompt(' ("mountain, \\\"man\" with\) a (beard)2.0)+ hairy'))
+        self.assertEqual(make_weighted_conjunction([('mou)ntain , \"man (wit(h a', 1.1), ('beard', 1.1*2.0), ('hairy', 1)]), parse_prompt('("mou\)ntain, \\\"man\" \(wit\(h a (beard)2.0)+ hairy'))
+        self.assertEqual(make_weighted_conjunction([('mountain , \"man w)ith a', 1.1), ('beard', 1.1*2.0), ('hai(ry', 1)]), parse_prompt('("mountain, \\\"man\" w\)ith a (beard)2.0)+ hai\(ry '))
+        self.assertEqual(make_weighted_conjunction([('mountain , \"man with a', 1.1), ('beard', 1.1*2.0), ('hairy((', 1)]), parse_prompt('("mountain, \\\"man\" with a (beard)2.0)+ hairy\(\( '))
 
     def test_cross_attention_escaping(self):
 
@@ -433,6 +483,15 @@ class PromptParserTestCase(unittest.TestCase):
 
 
     def test_single(self):
+        self.assertEqual(Conjunction([FlattenedPrompt([("mountain man", 1.0)]),
+                                      FlattenedPrompt([("a person with a hat", 1.0),
+                                                       ("riding a", 1.1*1.1),
+                                                       CrossAttentionControlSubstitute(
+                                                           [Fragment("bicycle", pow(1.1,2))],
+                                                           [Fragment("skateboard", pow(1.1,2))])
+                                                       ])
+                                      ], weights=[0.5, 0.5]),
+                         parse_prompt("(\"mountain man\", \"a person with a hat (riding a bicycle.swap(skateboard))++\").and(0.5, 0.5)"))
         pass