installers download branch HEAD not tag

- 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.

.dockerignore

@@ -0,0 +1,3 @@
+*
+!environment*.yml
+!docker-build

.github/ISSUE_TEMPLATE/BUG_REPORT.yml

@@ -0,0 +1,102 @@
+name: 🐞 Bug Report
+
+description: File a bug report
+
+title: '[bug]: '
+
+labels: ['bug']
+
+# assignees:
+#   - moderator_bot
+#   - lstein
+
+body:
+  - type: markdown
+    attributes:
+      value: |
+        Thanks for taking the time to fill out this Bug Report!
+
+  - type: checkboxes
+    attributes:
+      label: Is there an existing issue for this?
+      description: |
+        Please use the [search function](https://github.com/invoke-ai/InvokeAI/issues?q=is%3Aissue+is%3Aopen+label%3Abug)
+        irst to see if an issue already exists for the bug you encountered.
+      options:
+        - label: I have searched the existing issues
+          required: true
+
+  - type: markdown
+    attributes:
+      value: __Describe your environment__
+
+  - type: dropdown
+    id: os_dropdown
+    attributes:
+      label: OS
+      description: Which operating System did you use when the bug occured
+      multiple: false
+      options:
+        - 'Linux'
+        - 'Windows'
+        - 'macOS'
+    validations:
+      required: true
+
+  - type: dropdown
+    id: gpu_dropdown
+    attributes:
+      label: GPU
+      description: Which kind of Graphic-Adapter is your System using
+      multiple: false
+      options:
+        - 'cuda'
+        - 'amd'
+        - 'mps'
+        - 'cpu'
+    validations:
+      required: true
+
+  - type: input
+    id: vram
+    attributes:
+      label: VRAM
+      description: Size of the VRAM if known
+      placeholder: 8GB
+    validations:
+      required: false
+
+  - type: textarea
+    id: what-happened
+    attributes:
+      label: What happened?
+      description: |
+        Briefly describe what happened, what you expected to happen and how to reproduce this bug.
+      placeholder: When using the webinterface and right-clicking on button X instead of the popup-menu there error Y appears
+    validations:
+      required: true
+
+  - type: textarea
+    attributes:
+      label: Screenshots
+      description: If applicable, add screenshots to help explain your problem
+      placeholder: this is what the result looked like <screenshot>
+    validations:
+      required: false
+
+  - type: textarea
+    attributes:
+      label: Additional context
+      description: Add any other context about the problem here
+      placeholder: Only happens when there is full moon and Friday the 13th on Christmas Eve 🎅🏻
+    validations:
+      required: false
+
+  - type: input
+    id: contact
+    attributes:
+      label: Contact Details
+      description: __OPTIONAL__ How can we get in touch with you if we need more info (besides this issue)?
+      placeholder: ex. email@example.com, discordname, twitter, ...
+    validations:
+      required: false

.github/ISSUE_TEMPLATE/FEATURE_REQUEST.yml

@@ -0,0 +1,56 @@
+name: Feature Request
+description: Commit a idea or Request a new feature
+title: '[enhancement]: '
+labels: ['enhancement']
+# assignees:
+#   - lstein
+#   - tildebyte
+body:
+  - type: markdown
+    attributes:
+      value: |
+        Thanks for taking the time to fill out this Feature request!
+
+  - type: checkboxes
+    attributes:
+      label: Is there an existing issue for this?
+      description: |
+        Please make use of the [search function](https://github.com/invoke-ai/InvokeAI/labels/enhancement)
+        to see if a simmilar issue already exists for the feature you want to request
+      options:
+        - label: I have searched the existing issues
+          required: true
+
+  - type: input
+    id: contact
+    attributes:
+      label: Contact Details
+      description: __OPTIONAL__ How could we get in touch with you if we need more info (besides this issue)?
+      placeholder: ex. email@example.com, discordname, twitter, ...
+    validations:
+      required: false
+
+  - type: textarea
+    id: whatisexpected
+    attributes:
+      label: What should this feature add?
+      description: Please try to explain the functionality this feature should add
+      placeholder: |
+        Instead of one huge textfield, it would be nice to have forms for bug-reports, feature-requests, ...
+        Great benefits with automatic labeling, assigning and other functionalitys not available in that form
+        via old-fashioned markdown-templates. I would also love to see the use of a moderator bot 🤖 like
+        https://github.com/marketplace/actions/issue-moderator-with-commands to auto close old issues and other things
+    validations:
+      required: true
+
+  - type: textarea
+    attributes:
+      label: Alternatives
+      description: Describe alternatives you've considered
+      placeholder: A clear and concise description of any alternative solutions or features you've considered.
+
+  - type: textarea
+    attributes:
+      label: Aditional Content
+      description: Add any other context or screenshots about the feature request here.
+      placeholder: This is a Mockup of the design how I imagine it <screenshot>

.github/ISSUE_TEMPLATE/bug_report.md

@@ -1,36 +0,0 @@
----
-name: Bug report
-about: Create a report to help us improve
-title: ''
-labels: ''
-assignees: ''
-
----
-
-**Describe your environment**
-- GPU: [cuda/amd/mps/cpu]
-- VRAM: [if known]
-- CPU arch: [x86/arm]
-- OS: [Linux/Windows/macOS]
-- Python: [Anaconda/miniconda/miniforge/pyenv/other (explain)]
-- Branch: [if `git status` says anything other than "On branch main" paste it here]
-- Commit: [run `git show` and paste the line that starts with "Merge" here]
-
-**Describe the bug**
-A clear and concise description of what the bug is.
-
-**To Reproduce**
-Steps to reproduce the behavior:
-1. Go to '...'
-2. Click on '....'
-3. Scroll down to '....'
-4. See error
-
-**Expected behavior**
-A clear and concise description of what you expected to happen.
-
-**Screenshots**
-If applicable, add screenshots to help explain your problem.
-
-**Additional context**
-Add any other context about the problem here.

.github/ISSUE_TEMPLATE/config.yml

@@ -0,0 +1,14 @@
+blank_issues_enabled: false
+contact_links:
+  - name: Project-Documentation
+    url: https://invoke-ai.github.io/InvokeAI/
+    about: Should be your first place to go when looking for manuals/FAQs regarding our InvokeAI Toolkit
+  - name: Discord
+    url: https://discord.gg/ZmtBAhwWhy
+    about: Our Discord Community could maybe help you out via live-chat
+  - name: GitHub Community Support
+    url: https://github.com/orgs/community/discussions
+    about: Please ask and answer questions regarding the GitHub Platform here.
+  - name: GitHub Security Bug Bounty
+    url: https://bounty.github.com/
+    about: Please report security vulnerabilities of the GitHub Platform here.

.github/ISSUE_TEMPLATE/feature_request.md

@@ -1,20 +0,0 @@
----
-name: Feature request
-about: Suggest an idea for this project
-title: ''
-labels: ''
-assignees: ''
-
----
-
-**Is your feature request related to a problem? Please describe.**
-A clear and concise description of what the problem is. Ex. I'm always frustrated when [...]
-
-**Describe the solution you'd like**
-A clear and concise description of what you want to happen.
-
-**Describe alternatives you've considered**
-A clear and concise description of any alternative solutions or features you've considered.
-
-**Additional context**
-Add any other context or screenshots about the feature request here.

.github/workflows/build-container.yml

@@ -0,0 +1,48 @@
+# Building the Image without pushing to confirm it is still buildable
+# confirum functionality would unfortunately need way more resources
+name: build container image
+on:
+  push:
+    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:
+          repository: ${{ github.repository }}
+        run: echo "dockertag=${repository,,}" >> $GITHUB_ENV
+      - name: Checkout
+        uses: actions/checkout@v3
+      - name: Set up QEMU
+        uses: docker/setup-qemu-action@v2
+      - name: Set up Docker Buildx
+        uses: docker/setup-buildx-action@v2
+      - name: Build container
+        uses: docker/build-push-action@v3
+        with:
+          context: .
+          file: docker-build/Dockerfile
+          platforms: Linux/${{ matrix.arch }}
+          push: false
+          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 }}

.github/workflows/create-caches.yml

@@ -1,97 +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 -o models/ldm/stable-diffusion-v1/model.ckpt ${{ secrets.SD_V1_4_URL }}
-
-      - name: Use cached Conda Environment
-        uses: actions/cache@v3
-        env:
-          cache-name: cache-conda-env-${{ env.CONDA_ENV_NAME }}
-          conda-env-file: ${{ matrix.environment-file }}
-        with:
-          path: ${{ env.CONDA_ROOT }}/envs/${{ env.CONDA_ENV_NAME }}
-          key: ${{ env.cache-name }}
-          restore-keys: ${{ env.cache-name }}-${{ runner.os }}-${{ hashFiles(env.conda-env-file) }}
-
-      - name: Use cached Conda Packages
-        uses: actions/cache@v3
-        env:
-          cache-name: cache-conda-env-${{ env.CONDA_ENV_NAME }}
-          conda-env-file: ${{ matrix.environment-file }}
-        with:
-          path: ${{ env.CONDA_PKGS_DIR }}
-          key: ${{ env.cache-name }}
-          restore-keys: ${{ env.cache-name }}-${{ runner.os }}-${{ hashFiles(env.conda-env-file) }}
-
-      - 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

.github/workflows/test-invoke-conda.yml

@@ -1,4 +1,4 @@
-name: Test Invoke with Conda
+name: Test invoke.py
 on:
   push:
     branches:
@@ -11,31 +11,60 @@ on:
       - 'development'
 
 jobs:
-  os_matrix:
+  matrix:
     strategy:
+      fail-fast: false
       matrix:
-        os: [ubuntu-latest, macos-latest]
+        stable-diffusion-model:
+          # - 'https://huggingface.co/CompVis/stable-diffusion-v-1-4-original/resolve/main/sd-v1-4.ckpt'
+          - 'https://huggingface.co/runwayml/stable-diffusion-v1-5/resolve/main/v1-5-pruned-emaonly.ckpt'
+        os:
+          - ubuntu-latest
+          - macOS-12
         include:
           - os: ubuntu-latest
-            environment-file: environment.yml
+            environment-file: environment-lin-cuda.yml
             default-shell: bash -l {0}
-          - os: macos-latest
+          - os: macOS-12
             environment-file: environment-mac.yml
             default-shell: bash -l {0}
-    name: Test invoke.py on ${{ matrix.os }} with conda
+          # - stable-diffusion-model: https://huggingface.co/CompVis/stable-diffusion-v-1-4-original/resolve/main/sd-v1-4.ckpt
+          #   stable-diffusion-model-dl-path: models/ldm/stable-diffusion-v1/sd-v1-4.ckpt
+          #   stable-diffusion-model-switch: stable-diffusion-1.4
+          - stable-diffusion-model: https://huggingface.co/runwayml/stable-diffusion-v1-5/resolve/main/v1-5-pruned-emaonly.ckpt
+            stable-diffusion-model-dl-path: models/ldm/stable-diffusion-v1/v1-5-pruned-emaonly.ckpt
+            stable-diffusion-model-switch: stable-diffusion-1.5
+    name: ${{ matrix.os }} with ${{ matrix.stable-diffusion-model-switch }}
     runs-on: ${{ matrix.os }}
+    env:
+      CONDA_ENV_NAME: invokeai
     defaults:
       run:
         shell: ${{ matrix.default-shell }}
     steps:
       - name: Checkout sources
+        id: checkout-sources
         uses: actions/checkout@v3
 
-      - name: setup miniconda
+      - name: create models.yaml from example
+        run: cp configs/models.yaml.example configs/models.yaml
+
+      - name: create environment.yml
+        run: cp environments-and-requirements/${{ matrix.environment-file }} environment.yml
+
+      - name: Use cached conda packages
+        id: use-cached-conda-packages
+        uses: actions/cache@v3
+        with:
+          path: ~/conda_pkgs_dir
+          key: conda-pkgs-${{ runner.os }}-${{ runner.arch }}-${{ hashFiles(matrix.environment-file) }}
+
+      - name: Activate Conda Env
+        id: activate-conda-env
         uses: conda-incubator/setup-miniconda@v2
         with:
-          auto-activate-base: false
-          auto-update-conda: false
+          activate-environment: ${{ env.CONDA_ENV_NAME }}
+          environment-file: environment.yml
           miniconda-version: latest
 
       - name: set test prompt to main branch validation
@@ -48,79 +77,50 @@ jobs:
 
       - name: set test prompt to Pull Request validation
         if: ${{ github.ref != 'refs/heads/main' && github.ref != 'refs/heads/development' }}
-        run: echo "TEST_PROMPTS=tests/pr_prompt.txt" >> $GITHUB_ENV
+        run: echo "TEST_PROMPTS=tests/validate_pr_prompt.txt" >> $GITHUB_ENV
 
-      - name: set conda environment name
-        run: echo "CONDA_ENV_NAME=invokeai" >> $GITHUB_ENV
-
-      - name: Use Cached Stable Diffusion v1.4 Model
-        id: cache-sd-v1-4
+      - name: Use Cached Stable Diffusion Model
+        id: cache-sd-model
         uses: actions/cache@v3
         env:
-          cache-name: cache-sd-v1-4
+          cache-name: cache-${{ matrix.stable-diffusion-model-switch }}
         with:
-          path: models/ldm/stable-diffusion-v1/model.ckpt
+          path: ${{ matrix.stable-diffusion-model-dl-path }}
           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' }}
+      - 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
-          [[ -r models/ldm/stable-diffusion-v1/model.ckpt ]] \
-            || curl -o models/ldm/stable-diffusion-v1/model.ckpt ${{ secrets.SD_V1_4_URL }}
-
-      - name: Use cached Conda Environment
-        uses: actions/cache@v3
-        env:
-          cache-name: cache-conda-env-${{ env.CONDA_ENV_NAME }}
-          conda-env-file: ${{ matrix.environment-file }}
-        with:
-          path: ${{ env.CONDA }}/envs/${{ env.CONDA_ENV_NAME }}
-          key: env-${{ env.cache-name }}-${{ runner.os }}-${{ hashFiles(env.conda-env-file) }}
-
-      - name: Use cached Conda Packages
-        uses: actions/cache@v3
-        env:
-          cache-name: cache-conda-pkgs-${{ env.CONDA_ENV_NAME }}
-          conda-env-file: ${{ matrix.environment-file }}
-        with:
-          path: ${{ env.CONDA_PKGS_DIR }}
-          key: pkgs-${{ env.cache-name }}-${{ runner.os }}-${{ hashFiles(env.conda-env-file) }}
-
-      - 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') }}
+          curl \
+            -H "Authorization: Bearer ${{ secrets.HUGGINGFACE_TOKEN }}" \
+            -o ${{ matrix.stable-diffusion-model-dl-path }} \
+            -L ${{ matrix.stable-diffusion-model }}
 
       - name: run preload_models.py
-        run: python scripts/preload_models.py
+        id: run-preload-models
+        run: |
+          python scripts/preload_models.py \
+            --no-interactive
 
       - name: Run the tests
+        id: run-tests
         run: |
           time python scripts/invoke.py \
+            --model ${{ matrix.stable-diffusion-model-switch }} \
             --from_file ${{ env.TEST_PROMPTS }}
 
       - name: export conda env
+        id: export-conda-env
         run: |
           mkdir -p outputs/img-samples
-          conda env export --name ${{ env.CONDA_ENV_NAME }} > outputs/img-samples/environment-${{ runner.os }}.yml
+          conda env export --name ${{ env.CONDA_ENV_NAME }} > outputs/img-samples/environment-${{ runner.os }}-${{ runner.arch }}.yml
 
       - name: Archive results
+        id: archive-results
         uses: actions/upload-artifact@v3
         with:
-          name: results_${{ matrix.os }}
+          name: results_${{ matrix.os }}_${{ matrix.stable-diffusion-model-switch }}
           path: outputs/img-samples

.gitignore

@@ -3,6 +3,10 @@ outputs/
 models/ldm/stable-diffusion-v1/model.ckpt
 **/restoration/codeformer/weights
 
+# ignore user models config
+configs/models.user.yaml
+config/models.user.yml
+
 # ignore the Anaconda/Miniconda installer used while building Docker image
 anaconda.sh
 
@@ -190,12 +194,40 @@ checkpoints
 
 # Let the frontend manage its own gitignore
 !frontend/*
+frontend/apt-get
+frontend/dist
+frontend/sudo
+frontend/update
 
 # Scratch folder
 .scratch/
 .vscode/
 gfpgan/
-models/ldm/stable-diffusion-v1/model.sha256
+models/ldm/stable-diffusion-v1/*.sha256
+
 
 # GFPGAN model files
 gfpgan/
+
+# config file (will be created by installer)
+configs/models.yaml
+
+# weights (will be created by installer)
+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
+
+# this may be present if the user created a venv
+invokeai

README.md

@@ -2,7 +2,7 @@
 
 # InvokeAI: A Stable Diffusion Toolkit
 
-_Formally known as lstein/stable-diffusion_
+_Formerly known as lstein/stable-diffusion_
 
 ![project logo](docs/assets/logo.png)
 
@@ -42,7 +42,7 @@ generation process. It runs on Windows, Mac and Linux machines, with
 GPU cards with as little as 4 GB of RAM. It provides both a polished
 Web interface (see below), and an easy-to-use command-line interface.
 
-**Quick links**: [<a href="https://discord.gg/NwVCmKwY">Discord Server</a>] [<a href="https://invoke-ai.github.io/InvokeAI/">Documentation and Tutorials</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>]
+**Quick links**: [<a href="https://discord.gg/ZmtBAhwWhy">Discord Server</a>] [<a href="https://invoke-ai.github.io/InvokeAI/">Documentation and Tutorials</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>]
 
 <div align="center"><img src="docs/assets/invoke-web-server-1.png" width=640></div>
 

backend/server.py

@@ -1,822 +0,0 @@
-import mimetypes
-import transformers
-import json
-import os
-import traceback
-import eventlet
-import glob
-import shlex
-import math
-import shutil
-import sys
-
-sys.path.append(".")
-
-from argparse import ArgumentTypeError
-from modules.create_cmd_parser import create_cmd_parser
-
-parser = create_cmd_parser()
-opt = parser.parse_args()
-
-
-from flask_socketio import SocketIO
-from flask import Flask, send_from_directory, url_for, jsonify
-from pathlib import Path
-from PIL import Image
-from pytorch_lightning import logging
-from threading import Event
-from uuid import uuid4
-from send2trash import send2trash
-
-
-from ldm.generate import Generate
-from ldm.invoke.restoration import Restoration
-from ldm.invoke.pngwriter import PngWriter, retrieve_metadata
-from ldm.invoke.args import APP_ID, APP_VERSION, calculate_init_img_hash
-from ldm.invoke.conditioning import split_weighted_subprompts
-
-from modules.parameters import parameters_to_command
-
-
-"""
-USER CONFIG
-"""
-if opt.cors and "*" in opt.cors:
-    raise ArgumentTypeError('"*" is not an allowed CORS origin')
-
-
-output_dir = "outputs/"  # Base output directory for images
-host = opt.host  # Web & socket.io host
-port = opt.port  # Web & socket.io port
-verbose = opt.verbose  # enables copious socket.io logging
-precision = opt.precision
-free_gpu_mem = opt.free_gpu_mem
-embedding_path = opt.embedding_path
-additional_allowed_origins = (
-    opt.cors if opt.cors else []
-)  # additional CORS allowed origins
-model = "stable-diffusion-1.4"
-
-"""
-END USER CONFIG
-"""
-
-
-print("* Initializing, be patient...\n")
-
-
-"""
-SERVER SETUP
-"""
-
-
-# fix missing mimetypes on windows due to registry wonkiness
-mimetypes.add_type("application/javascript", ".js")
-mimetypes.add_type("text/css", ".css")
-
-app = Flask(__name__, static_url_path="", static_folder="../frontend/dist/")
-
-
-app.config["OUTPUTS_FOLDER"] = "../outputs"
-
-
-@app.route("/outputs/<path:filename>")
-def outputs(filename):
-    return send_from_directory(app.config["OUTPUTS_FOLDER"], filename)
-
-
-@app.route("/", defaults={"path": ""})
-def serve(path):
-    return send_from_directory(app.static_folder, "index.html")
-
-
-logger = True if verbose else False
-engineio_logger = True if verbose else False
-
-# default 1,000,000, needs to be higher for socketio to accept larger images
-max_http_buffer_size = 10000000
-
-cors_allowed_origins = [f"http://{host}:{port}"] + additional_allowed_origins
-
-socketio = SocketIO(
-    app,
-    logger=logger,
-    engineio_logger=engineio_logger,
-    max_http_buffer_size=max_http_buffer_size,
-    cors_allowed_origins=cors_allowed_origins,
-    ping_interval=(50, 50),
-    ping_timeout=60,
-)
-
-
-"""
-END SERVER SETUP
-"""
-
-
-"""
-APP SETUP
-"""
-
-
-class CanceledException(Exception):
-    pass
-
-
-try:
-    gfpgan, codeformer, esrgan = None, None, None
-    from ldm.invoke.restoration.base import Restoration
-
-    restoration = Restoration()
-    gfpgan, codeformer = restoration.load_face_restore_models()
-    esrgan = restoration.load_esrgan()
-
-    # coreformer.process(self, image, strength, device, seed=None, fidelity=0.75)
-
-except (ModuleNotFoundError, ImportError):
-    print(traceback.format_exc(), file=sys.stderr)
-    print(">> You may need to install the ESRGAN and/or GFPGAN modules")
-
-canceled = Event()
-
-# reduce logging outputs to error
-transformers.logging.set_verbosity_error()
-logging.getLogger("pytorch_lightning").setLevel(logging.ERROR)
-
-# Initialize and load model
-generate = Generate(
-    model,
-    precision=precision,
-    embedding_path=embedding_path,
-)
-generate.free_gpu_mem = free_gpu_mem
-generate.load_model()
-
-
-# location for "finished" images
-result_path = os.path.join(output_dir, "img-samples/")
-
-# temporary path for intermediates
-intermediate_path = os.path.join(result_path, "intermediates/")
-
-# path for user-uploaded init images and masks
-init_image_path = os.path.join(result_path, "init-images/")
-mask_image_path = os.path.join(result_path, "mask-images/")
-
-# txt log
-log_path = os.path.join(result_path, "invoke_log.txt")
-
-# make all output paths
-[
-    os.makedirs(path, exist_ok=True)
-    for path in [result_path, intermediate_path, init_image_path, mask_image_path]
-]
-
-
-"""
-END APP SETUP
-"""
-
-
-"""
-SOCKET.IO LISTENERS
-"""
-
-
-@socketio.on("requestSystemConfig")
-def handle_request_capabilities():
-    print(f">> System config requested")
-    config = get_system_config()
-    socketio.emit("systemConfig", config)
-
-
-@socketio.on("requestImages")
-def handle_request_images(page=1, offset=0, last_mtime=None):
-    chunk_size = 50
-
-    if last_mtime:
-        print(f">> Latest images requested")
-    else:
-        print(
-            f">> Page {page} of images requested (page size {chunk_size} offset {offset})"
-        )
-
-    paths = glob.glob(os.path.join(result_path, "*.png"))
-    sorted_paths = sorted(paths, key=lambda x: os.path.getmtime(x), reverse=True)
-
-    if last_mtime:
-        image_paths = filter(lambda x: os.path.getmtime(x) > last_mtime, sorted_paths)
-    else:
-
-        image_paths = sorted_paths[
-            slice(chunk_size * (page - 1) + offset, chunk_size * page + offset)
-        ]
-        page = page + 1
-
-    image_array = []
-
-    for path in image_paths:
-        metadata = retrieve_metadata(path)
-        image_array.append(
-            {
-                "url": path,
-                "mtime": os.path.getmtime(path),
-                "metadata": metadata["sd-metadata"],
-            }
-        )
-
-    socketio.emit(
-        "galleryImages",
-        {
-            "images": image_array,
-            "nextPage": page,
-            "offset": offset,
-            "onlyNewImages": True if last_mtime else False,
-        },
-    )
-
-
-@socketio.on("generateImage")
-def handle_generate_image_event(
-    generation_parameters, esrgan_parameters, gfpgan_parameters
-):
-    print(
-        f">> Image generation requested: {generation_parameters}\nESRGAN parameters: {esrgan_parameters}\nGFPGAN parameters: {gfpgan_parameters}"
-    )
-    generate_images(generation_parameters, esrgan_parameters, gfpgan_parameters)
-
-
-@socketio.on("runESRGAN")
-def handle_run_esrgan_event(original_image, esrgan_parameters):
-    print(
-        f'>> ESRGAN upscale requested for "{original_image["url"]}": {esrgan_parameters}'
-    )
-    progress = {
-        "currentStep": 1,
-        "totalSteps": 1,
-        "currentIteration": 1,
-        "totalIterations": 1,
-        "currentStatus": "Preparing",
-        "isProcessing": True,
-        "currentStatusHasSteps": False,
-    }
-
-    socketio.emit("progressUpdate", progress)
-    eventlet.sleep(0)
-
-    image = Image.open(original_image["url"])
-
-    seed = (
-        original_image["metadata"]["seed"]
-        if "seed" in original_image["metadata"]
-        else "unknown_seed"
-    )
-
-    progress["currentStatus"] = "Upscaling"
-    socketio.emit("progressUpdate", progress)
-    eventlet.sleep(0)
-
-    image = esrgan.process(
-        image=image,
-        upsampler_scale=esrgan_parameters["upscale"][0],
-        strength=esrgan_parameters["upscale"][1],
-        seed=seed,
-    )
-
-    progress["currentStatus"] = "Saving image"
-    socketio.emit("progressUpdate", progress)
-    eventlet.sleep(0)
-
-    esrgan_parameters["seed"] = seed
-    metadata = parameters_to_post_processed_image_metadata(
-        parameters=esrgan_parameters,
-        original_image_path=original_image["url"],
-        type="esrgan",
-    )
-    command = parameters_to_command(esrgan_parameters)
-
-    path = save_image(image, command, metadata, result_path, postprocessing="esrgan")
-
-    write_log_message(f'[Upscaled] "{original_image["url"]}" > "{path}": {command}')
-
-    progress["currentStatus"] = "Finished"
-    progress["currentStep"] = 0
-    progress["totalSteps"] = 0
-    progress["currentIteration"] = 0
-    progress["totalIterations"] = 0
-    progress["isProcessing"] = False
-    socketio.emit("progressUpdate", progress)
-    eventlet.sleep(0)
-
-    socketio.emit(
-        "esrganResult",
-        {
-            "url": os.path.relpath(path),
-            "mtime": os.path.getmtime(path),
-            "metadata": metadata,
-        },
-    )
-
-
-@socketio.on("runGFPGAN")
-def handle_run_gfpgan_event(original_image, gfpgan_parameters):
-    print(
-        f'>> GFPGAN face fix requested for "{original_image["url"]}": {gfpgan_parameters}'
-    )
-    progress = {
-        "currentStep": 1,
-        "totalSteps": 1,
-        "currentIteration": 1,
-        "totalIterations": 1,
-        "currentStatus": "Preparing",
-        "isProcessing": True,
-        "currentStatusHasSteps": False,
-    }
-
-    socketio.emit("progressUpdate", progress)
-    eventlet.sleep(0)
-
-    image = Image.open(original_image["url"])
-
-    seed = (
-        original_image["metadata"]["seed"]
-        if "seed" in original_image["metadata"]
-        else "unknown_seed"
-    )
-
-    progress["currentStatus"] = "Fixing faces"
-    socketio.emit("progressUpdate", progress)
-    eventlet.sleep(0)
-
-    image = gfpgan.process(
-        image=image, strength=gfpgan_parameters["facetool_strength"], seed=seed
-    )
-
-    progress["currentStatus"] = "Saving image"
-    socketio.emit("progressUpdate", progress)
-    eventlet.sleep(0)
-
-    gfpgan_parameters["seed"] = seed
-    metadata = parameters_to_post_processed_image_metadata(
-        parameters=gfpgan_parameters,
-        original_image_path=original_image["url"],
-        type="gfpgan",
-    )
-    command = parameters_to_command(gfpgan_parameters)
-
-    path = save_image(image, command, metadata, result_path, postprocessing="gfpgan")
-
-    write_log_message(f'[Fixed faces] "{original_image["url"]}" > "{path}": {command}')
-
-    progress["currentStatus"] = "Finished"
-    progress["currentStep"] = 0
-    progress["totalSteps"] = 0
-    progress["currentIteration"] = 0
-    progress["totalIterations"] = 0
-    progress["isProcessing"] = False
-    socketio.emit("progressUpdate", progress)
-    eventlet.sleep(0)
-
-    socketio.emit(
-        "gfpganResult",
-        {
-            "url": os.path.relpath(path),
-            "mtime": os.path.mtime(path),
-            "metadata": metadata,
-        },
-    )
-
-
-@socketio.on("cancel")
-def handle_cancel():
-    print(f">> Cancel processing requested")
-    canceled.set()
-    socketio.emit("processingCanceled")
-
-
-# TODO: I think this needs a safety mechanism.
-@socketio.on("deleteImage")
-def handle_delete_image(path, uuid):
-    print(f'>> Delete requested "{path}"')
-    send2trash(path)
-    socketio.emit("imageDeleted", {"url": path, "uuid": uuid})
-
-
-# TODO: I think this needs a safety mechanism.
-@socketio.on("uploadInitialImage")
-def handle_upload_initial_image(bytes, name):
-    print(f'>> Init image upload requested "{name}"')
-    uuid = uuid4().hex
-    split = os.path.splitext(name)
-    name = f"{split[0]}.{uuid}{split[1]}"
-    file_path = os.path.join(init_image_path, name)
-    os.makedirs(os.path.dirname(file_path), exist_ok=True)
-    newFile = open(file_path, "wb")
-    newFile.write(bytes)
-    socketio.emit("initialImageUploaded", {"url": file_path, "uuid": ""})
-
-
-# TODO: I think this needs a safety mechanism.
-@socketio.on("uploadMaskImage")
-def handle_upload_mask_image(bytes, name):
-    print(f'>> Mask image upload requested "{name}"')
-    uuid = uuid4().hex
-    split = os.path.splitext(name)
-    name = f"{split[0]}.{uuid}{split[1]}"
-    file_path = os.path.join(mask_image_path, name)
-    os.makedirs(os.path.dirname(file_path), exist_ok=True)
-    newFile = open(file_path, "wb")
-    newFile.write(bytes)
-    socketio.emit("maskImageUploaded", {"url": file_path, "uuid": ""})
-
-
-"""
-END SOCKET.IO LISTENERS
-"""
-
-
-"""
-ADDITIONAL FUNCTIONS
-"""
-
-
-def get_system_config():
-    return {
-        "model": "stable diffusion",
-        "model_id": model,
-        "model_hash": generate.model_hash,
-        "app_id": APP_ID,
-        "app_version": APP_VERSION,
-    }
-
-
-def parameters_to_post_processed_image_metadata(parameters, original_image_path, type):
-    # top-level metadata minus `image` or `images`
-    metadata = get_system_config()
-
-    orig_hash = calculate_init_img_hash(original_image_path)
-
-    image = {"orig_path": original_image_path, "orig_hash": orig_hash}
-
-    if type == "esrgan":
-        image["type"] = "esrgan"
-        image["scale"] = parameters["upscale"][0]
-        image["strength"] = parameters["upscale"][1]
-    elif type == "gfpgan":
-        image["type"] = "gfpgan"
-        image["strength"] = parameters["facetool_strength"]
-    else:
-        raise TypeError(f"Invalid type: {type}")
-
-    metadata["image"] = image
-    return metadata
-
-
-def parameters_to_generated_image_metadata(parameters):
-    # top-level metadata minus `image` or `images`
-
-    metadata = get_system_config()
-    # remove any image keys not mentioned in RFC #266
-    rfc266_img_fields = [
-        "type",
-        "postprocessing",
-        "sampler",
-        "prompt",
-        "seed",
-        "variations",
-        "steps",
-        "cfg_scale",
-        "threshold",
-        "perlin",
-        "step_number",
-        "width",
-        "height",
-        "extra",
-        "seamless",
-        "hires_fix",
-    ]
-
-    rfc_dict = {}
-
-    for item in parameters.items():
-        key, value = item
-        if key in rfc266_img_fields:
-            rfc_dict[key] = value
-
-    postprocessing = []
-
-    # 'postprocessing' is either null or an
-    if "facetool_strength" in parameters:
-
-        postprocessing.append(
-            {"type": "gfpgan", "strength": float(parameters["facetool_strength"])}
-        )
-
-    if "upscale" in parameters:
-        postprocessing.append(
-            {
-                "type": "esrgan",
-                "scale": int(parameters["upscale"][0]),
-                "strength": float(parameters["upscale"][1]),
-            }
-        )
-
-    rfc_dict["postprocessing"] = postprocessing if len(postprocessing) > 0 else None
-
-    # semantic drift
-    rfc_dict["sampler"] = parameters["sampler_name"]
-
-    # display weighted subprompts (liable to change)
-    subprompts = split_weighted_subprompts(parameters["prompt"])
-    subprompts = [{"prompt": x[0], "weight": x[1]} for x in subprompts]
-    rfc_dict["prompt"] = subprompts
-
-    # 'variations' should always exist and be an array, empty or consisting of {'seed': seed, 'weight': weight} pairs
-    variations = []
-
-    if "with_variations" in parameters:
-        variations = [
-            {"seed": x[0], "weight": x[1]} for x in parameters["with_variations"]
-        ]
-
-    rfc_dict["variations"] = variations
-
-    if "init_img" in parameters:
-        rfc_dict["type"] = "img2img"
-        rfc_dict["strength"] = parameters["strength"]
-        rfc_dict["fit"] = parameters["fit"]  # TODO: Noncompliant
-        rfc_dict["orig_hash"] = calculate_init_img_hash(parameters["init_img"])
-        rfc_dict["init_image_path"] = parameters["init_img"]  # TODO: Noncompliant
-        rfc_dict["sampler"] = "ddim"  # TODO: FIX ME WHEN IMG2IMG SUPPORTS ALL SAMPLERS
-        if "init_mask" in parameters:
-            rfc_dict["mask_hash"] = calculate_init_img_hash(
-                parameters["init_mask"]
-            )  # TODO: Noncompliant
-            rfc_dict["mask_image_path"] = parameters["init_mask"]  # TODO: Noncompliant
-    else:
-        rfc_dict["type"] = "txt2img"
-
-    metadata["image"] = rfc_dict
-
-    return metadata
-
-
-def make_unique_init_image_filename(name):
-    uuid = uuid4().hex
-    split = os.path.splitext(name)
-    name = f"{split[0]}.{uuid}{split[1]}"
-    return name
-
-
-def write_log_message(message, log_path=log_path):
-    """Logs the filename and parameters used to generate or process that image to log file"""
-    message = f"{message}\n"
-    with open(log_path, "a", encoding="utf-8") as file:
-        file.writelines(message)
-
-
-def save_image(
-    image, command, metadata, output_dir, step_index=None, postprocessing=False
-):
-    pngwriter = PngWriter(output_dir)
-    prefix = pngwriter.unique_prefix()
-
-    seed = "unknown_seed"
-
-    if "image" in metadata:
-        if "seed" in metadata["image"]:
-            seed = metadata["image"]["seed"]
-
-    filename = f"{prefix}.{seed}"
-
-    if step_index:
-        filename += f".{step_index}"
-    if postprocessing:
-        filename += f".postprocessed"
-
-    filename += ".png"
-
-    path = pngwriter.save_image_and_prompt_to_png(
-        image=image, dream_prompt=command, metadata=metadata, name=filename
-    )
-
-    return path
-
-
-def calculate_real_steps(steps, strength, has_init_image):
-    return math.floor(strength * steps) if has_init_image else steps
-
-
-def generate_images(generation_parameters, esrgan_parameters, gfpgan_parameters):
-    canceled.clear()
-
-    step_index = 1
-    prior_variations = (
-        generation_parameters["with_variations"]
-        if "with_variations" in generation_parameters
-        else []
-    )
-    """
-    If a result image is used as an init image, and then deleted, we will want to be
-    able to use it as an init image in the future. Need to copy it.
-
-    If the init/mask image doesn't exist in the init_image_path/mask_image_path,
-    make a unique filename for it and copy it there.
-    """
-    if "init_img" in generation_parameters:
-        filename = os.path.basename(generation_parameters["init_img"])
-        if not os.path.exists(os.path.join(init_image_path, filename)):
-            unique_filename = make_unique_init_image_filename(filename)
-            new_path = os.path.join(init_image_path, unique_filename)
-            shutil.copy(generation_parameters["init_img"], new_path)
-            generation_parameters["init_img"] = new_path
-        if "init_mask" in generation_parameters:
-            filename = os.path.basename(generation_parameters["init_mask"])
-            if not os.path.exists(os.path.join(mask_image_path, filename)):
-                unique_filename = make_unique_init_image_filename(filename)
-                new_path = os.path.join(init_image_path, unique_filename)
-                shutil.copy(generation_parameters["init_img"], new_path)
-                generation_parameters["init_mask"] = new_path
-
-    totalSteps = calculate_real_steps(
-        steps=generation_parameters["steps"],
-        strength=generation_parameters["strength"]
-        if "strength" in generation_parameters
-        else None,
-        has_init_image="init_img" in generation_parameters,
-    )
-
-    progress = {
-        "currentStep": 1,
-        "totalSteps": totalSteps,
-        "currentIteration": 1,
-        "totalIterations": generation_parameters["iterations"],
-        "currentStatus": "Preparing",
-        "isProcessing": True,
-        "currentStatusHasSteps": False,
-    }
-
-    socketio.emit("progressUpdate", progress)
-    eventlet.sleep(0)
-
-    def image_progress(sample, step):
-        if canceled.is_set():
-            raise CanceledException
-
-        nonlocal step_index
-        nonlocal generation_parameters
-        nonlocal progress
-
-        progress["currentStep"] = step + 1
-        progress["currentStatus"] = "Generating"
-        progress["currentStatusHasSteps"] = True
-
-        if (
-            generation_parameters["progress_images"]
-            and step % 5 == 0
-            and step < generation_parameters["steps"] - 1
-        ):
-            image = generate.sample_to_image(sample)
-
-            metadata = parameters_to_generated_image_metadata(generation_parameters)
-            command = parameters_to_command(generation_parameters)
-            path = save_image(image, command, metadata, intermediate_path, step_index=step_index, postprocessing=False)
-
-            step_index += 1
-            socketio.emit(
-                "intermediateResult",
-                {
-                    "url": os.path.relpath(path),
-                    "mtime": os.path.getmtime(path),
-                    "metadata": metadata,
-                },
-            )
-        socketio.emit("progressUpdate", progress)
-        eventlet.sleep(0)
-
-    def image_done(image, seed, first_seed):
-        nonlocal generation_parameters
-        nonlocal esrgan_parameters
-        nonlocal gfpgan_parameters
-        nonlocal progress
-
-        step_index = 1
-        nonlocal prior_variations
-
-        progress["currentStatus"] = "Generation complete"
-        socketio.emit("progressUpdate", progress)
-        eventlet.sleep(0)
-
-        all_parameters = generation_parameters
-        postprocessing = False
-
-        if (
-            "variation_amount" in all_parameters
-            and all_parameters["variation_amount"] > 0
-        ):
-            first_seed = first_seed or seed
-            this_variation = [[seed, all_parameters["variation_amount"]]]
-            all_parameters["with_variations"] = prior_variations + this_variation
-            all_parameters["seed"] = first_seed
-        elif ("with_variations" in all_parameters):
-            all_parameters["seed"] = first_seed
-        else:
-            all_parameters["seed"] = seed
-
-        if esrgan_parameters:
-            progress["currentStatus"] = "Upscaling"
-            progress["currentStatusHasSteps"] = False
-            socketio.emit("progressUpdate", progress)
-            eventlet.sleep(0)
-
-            image = esrgan.process(
-                image=image,
-                upsampler_scale=esrgan_parameters["level"],
-                strength=esrgan_parameters["strength"],
-                seed=seed,
-            )
-
-            postprocessing = True
-            all_parameters["upscale"] = [
-                esrgan_parameters["level"],
-                esrgan_parameters["strength"],
-            ]
-
-        if gfpgan_parameters:
-            progress["currentStatus"] = "Fixing faces"
-            progress["currentStatusHasSteps"] = False
-            socketio.emit("progressUpdate", progress)
-            eventlet.sleep(0)
-
-            image = gfpgan.process(
-                image=image, strength=gfpgan_parameters["strength"], seed=seed
-            )
-            postprocessing = True
-            all_parameters["facetool_strength"] = gfpgan_parameters["strength"]
-
-        progress["currentStatus"] = "Saving image"
-        socketio.emit("progressUpdate", progress)
-        eventlet.sleep(0)
-
-        metadata = parameters_to_generated_image_metadata(all_parameters)
-        command = parameters_to_command(all_parameters)
-
-        path = save_image(
-            image, command, metadata, result_path, postprocessing=postprocessing
-        )
-
-        print(f'>> Image generated: "{path}"')
-        write_log_message(f'[Generated] "{path}": {command}')
-
-        if progress["totalIterations"] > progress["currentIteration"]:
-            progress["currentStep"] = 1
-            progress["currentIteration"] += 1
-            progress["currentStatus"] = "Iteration finished"
-            progress["currentStatusHasSteps"] = False
-        else:
-            progress["currentStep"] = 0
-            progress["totalSteps"] = 0
-            progress["currentIteration"] = 0
-            progress["totalIterations"] = 0
-            progress["currentStatus"] = "Finished"
-            progress["isProcessing"] = False
-
-        socketio.emit("progressUpdate", progress)
-        eventlet.sleep(0)
-
-        socketio.emit(
-            "generationResult",
-            {
-                "url": os.path.relpath(path),
-                "mtime": os.path.getmtime(path),
-                "metadata": metadata,
-            },
-        )
-        eventlet.sleep(0)
-
-    try:
-        generate.prompt2image(
-            **generation_parameters,
-            step_callback=image_progress,
-            image_callback=image_done,
-        )
-
-    except KeyboardInterrupt:
-        raise
-    except CanceledException:
-        pass
-    except Exception as e:
-        socketio.emit("error", {"message": (str(e))})
-        print("\n")
-        traceback.print_exc()
-        print("\n")
-
-
-"""
-END ADDITIONAL FUNCTIONS
-"""
-
-
-if __name__ == "__main__":
-    print(f">> Starting server at http://{host}:{port}")
-    socketio.run(app, host=host, port=port)

configs/autoencoder/autoencoder_kl_16x16x16.yaml

@@ -1,54 +0,0 @@
-model:
-  base_learning_rate: 4.5e-6
-  target: ldm.models.autoencoder.AutoencoderKL
-  params:
-    monitor: "val/rec_loss"
-    embed_dim: 16
-    lossconfig:
-      target: ldm.modules.losses.LPIPSWithDiscriminator
-      params:
-        disc_start: 50001
-        kl_weight: 0.000001
-        disc_weight: 0.5
-
-    ddconfig:
-      double_z: True
-      z_channels: 16
-      resolution: 256
-      in_channels: 3
-      out_ch: 3
-      ch: 128
-      ch_mult: [ 1,1,2,2,4]  # num_down = len(ch_mult)-1
-      num_res_blocks: 2
-      attn_resolutions: [16]
-      dropout: 0.0
-
-
-data:
-  target: main.DataModuleFromConfig
-  params:
-    batch_size: 12
-    wrap: True
-    train:
-      target: ldm.data.imagenet.ImageNetSRTrain
-      params:
-        size: 256
-        degradation: pil_nearest
-    validation:
-      target: ldm.data.imagenet.ImageNetSRValidation
-      params:
-        size: 256
-        degradation: pil_nearest
-
-lightning:
-  callbacks:
-    image_logger:
-      target: main.ImageLogger
-      params:
-        batch_frequency: 1000
-        max_images: 8
-        increase_log_steps: True
-
-  trainer:
-    benchmark: True
-    accumulate_grad_batches: 2

configs/autoencoder/autoencoder_kl_32x32x4.yaml

@@ -1,53 +0,0 @@
-model:
-  base_learning_rate: 4.5e-6
-  target: ldm.models.autoencoder.AutoencoderKL
-  params:
-    monitor: "val/rec_loss"
-    embed_dim: 4
-    lossconfig:
-      target: ldm.modules.losses.LPIPSWithDiscriminator
-      params:
-        disc_start: 50001
-        kl_weight: 0.000001
-        disc_weight: 0.5
-
-    ddconfig:
-      double_z: True
-      z_channels: 4
-      resolution: 256
-      in_channels: 3
-      out_ch: 3
-      ch: 128
-      ch_mult: [ 1,2,4,4 ]  # num_down = len(ch_mult)-1
-      num_res_blocks: 2
-      attn_resolutions: [ ]
-      dropout: 0.0
-
-data:
-  target: main.DataModuleFromConfig
-  params:
-    batch_size: 12
-    wrap: True
-    train:
-      target: ldm.data.imagenet.ImageNetSRTrain
-      params:
-        size: 256
-        degradation: pil_nearest
-    validation:
-      target: ldm.data.imagenet.ImageNetSRValidation
-      params:
-        size: 256
-        degradation: pil_nearest
-
-lightning:
-  callbacks:
-    image_logger:
-      target: main.ImageLogger
-      params:
-        batch_frequency: 1000
-        max_images: 8
-        increase_log_steps: True
-
-  trainer:
-    benchmark: True
-    accumulate_grad_batches: 2

configs/autoencoder/autoencoder_kl_64x64x3.yaml

@@ -1,54 +0,0 @@
-model:
-  base_learning_rate: 4.5e-6
-  target: ldm.models.autoencoder.AutoencoderKL
-  params:
-    monitor: "val/rec_loss"
-    embed_dim: 3
-    lossconfig:
-      target: ldm.modules.losses.LPIPSWithDiscriminator
-      params:
-        disc_start: 50001
-        kl_weight: 0.000001
-        disc_weight: 0.5
-
-    ddconfig:
-      double_z: True
-      z_channels: 3
-      resolution: 256
-      in_channels: 3
-      out_ch: 3
-      ch: 128
-      ch_mult: [ 1,2,4 ]  # num_down = len(ch_mult)-1
-      num_res_blocks: 2
-      attn_resolutions: [ ]
-      dropout: 0.0
-
-
-data:
-  target: main.DataModuleFromConfig
-  params:
-    batch_size: 12
-    wrap: True
-    train:
-      target: ldm.data.imagenet.ImageNetSRTrain
-      params:
-        size: 256
-        degradation: pil_nearest
-    validation:
-      target: ldm.data.imagenet.ImageNetSRValidation
-      params:
-        size: 256
-        degradation: pil_nearest
-
-lightning:
-  callbacks:
-    image_logger:
-      target: main.ImageLogger
-      params:
-        batch_frequency: 1000
-        max_images: 8
-        increase_log_steps: True
-
-  trainer:
-    benchmark: True
-    accumulate_grad_batches: 2

configs/autoencoder/autoencoder_kl_8x8x64.yaml

@@ -1,53 +0,0 @@
-model:
-  base_learning_rate: 4.5e-6
-  target: ldm.models.autoencoder.AutoencoderKL
-  params:
-    monitor: "val/rec_loss"
-    embed_dim: 64
-    lossconfig:
-      target: ldm.modules.losses.LPIPSWithDiscriminator
-      params:
-        disc_start: 50001
-        kl_weight: 0.000001
-        disc_weight: 0.5
-
-    ddconfig:
-      double_z: True
-      z_channels: 64
-      resolution: 256
-      in_channels: 3
-      out_ch: 3
-      ch: 128
-      ch_mult: [ 1,1,2,2,4,4]  # num_down = len(ch_mult)-1
-      num_res_blocks: 2
-      attn_resolutions: [16,8]
-      dropout: 0.0
-
-data:
-  target: main.DataModuleFromConfig
-  params:
-    batch_size: 12
-    wrap: True
-    train:
-      target: ldm.data.imagenet.ImageNetSRTrain
-      params:
-        size: 256
-        degradation: pil_nearest
-    validation:
-      target: ldm.data.imagenet.ImageNetSRValidation
-      params:
-        size: 256
-        degradation: pil_nearest
-
-lightning:
-  callbacks:
-    image_logger:
-      target: main.ImageLogger
-      params:
-        batch_frequency: 1000
-        max_images: 8
-        increase_log_steps: True
-
-  trainer:
-    benchmark: True
-    accumulate_grad_batches: 2

configs/latent-diffusion/celebahq-ldm-vq-4.yaml

@@ -1,86 +0,0 @@
-model:
-  base_learning_rate: 2.0e-06
-  target: ldm.models.diffusion.ddpm.LatentDiffusion
-  params:
-    linear_start: 0.0015
-    linear_end: 0.0195
-    num_timesteps_cond: 1
-    log_every_t: 200
-    timesteps: 1000
-    first_stage_key: image
-    image_size: 64
-    channels: 3
-    monitor: val/loss_simple_ema
-
-    unet_config:
-      target: ldm.modules.diffusionmodules.openaimodel.UNetModel
-      params:
-        image_size: 64
-        in_channels: 3
-        out_channels: 3
-        model_channels: 224
-        attention_resolutions:
-        # note: this isn\t actually the resolution but
-        # the downsampling factor, i.e. this corresnponds to
-        # attention on spatial resolution 8,16,32, as the
-        # spatial reolution of the latents is 64 for f4
-        - 8
-        - 4
-        - 2
-        num_res_blocks: 2
-        channel_mult:
-        - 1
-        - 2
-        - 3
-        - 4
-        num_head_channels: 32
-    first_stage_config:
-      target: ldm.models.autoencoder.VQModelInterface
-      params:
-        embed_dim: 3
-        n_embed: 8192
-        ckpt_path: models/first_stage_models/vq-f4/model.ckpt
-        ddconfig:
-          double_z: false
-          z_channels: 3
-          resolution: 256
-          in_channels: 3
-          out_ch: 3
-          ch: 128
-          ch_mult:
-          - 1
-          - 2
-          - 4
-          num_res_blocks: 2
-          attn_resolutions: []
-          dropout: 0.0
-        lossconfig:
-          target: torch.nn.Identity
-    cond_stage_config: __is_unconditional__
-data:
-  target: main.DataModuleFromConfig
-  params:
-    batch_size: 48
-    num_workers: 5
-    wrap: false
-    train:
-      target: taming.data.faceshq.CelebAHQTrain
-      params:
-        size: 256
-    validation:
-      target: taming.data.faceshq.CelebAHQValidation
-      params:
-        size: 256
-
-
-lightning:
-  callbacks:
-    image_logger:
-      target: main.ImageLogger
-      params:
-        batch_frequency: 5000
-        max_images: 8
-        increase_log_steps: False
-
-  trainer:
-    benchmark: True

configs/latent-diffusion/cin-ldm-vq-f8.yaml

@@ -1,98 +0,0 @@
-model:
-  base_learning_rate: 1.0e-06
-  target: ldm.models.diffusion.ddpm.LatentDiffusion
-  params:
-    linear_start: 0.0015
-    linear_end: 0.0195
-    num_timesteps_cond: 1
-    log_every_t: 200
-    timesteps: 1000
-    first_stage_key: image
-    cond_stage_key: class_label
-    image_size: 32
-    channels: 4
-    cond_stage_trainable: true
-    conditioning_key: crossattn
-    monitor: val/loss_simple_ema
-    unet_config:
-      target: ldm.modules.diffusionmodules.openaimodel.UNetModel
-      params:
-        image_size: 32
-        in_channels: 4
-        out_channels: 4
-        model_channels: 256
-        attention_resolutions:
-        #note: this isn\t actually the resolution but
-        # the downsampling factor, i.e. this corresnponds to
-        # attention on spatial resolution 8,16,32, as the
-        # spatial reolution of the latents is 32 for f8
-        - 4
-        - 2
-        - 1
-        num_res_blocks: 2
-        channel_mult:
-        - 1
-        - 2
-        - 4
-        num_head_channels: 32
-        use_spatial_transformer: true
-        transformer_depth: 1
-        context_dim: 512
-    first_stage_config:
-      target: ldm.models.autoencoder.VQModelInterface
-      params:
-        embed_dim: 4
-        n_embed: 16384
-        ckpt_path: configs/first_stage_models/vq-f8/model.yaml
-        ddconfig:
-          double_z: false
-          z_channels: 4
-          resolution: 256
-          in_channels: 3
-          out_ch: 3
-          ch: 128
-          ch_mult:
-          - 1
-          - 2
-          - 2
-          - 4
-          num_res_blocks: 2
-          attn_resolutions:
-          - 32
-          dropout: 0.0
-        lossconfig:
-          target: torch.nn.Identity
-    cond_stage_config:
-      target: ldm.modules.encoders.modules.ClassEmbedder
-      params:
-        embed_dim: 512
-        key: class_label
-data:
-  target: main.DataModuleFromConfig
-  params:
-    batch_size: 64
-    num_workers: 12
-    wrap: false
-    train:
-      target: ldm.data.imagenet.ImageNetTrain
-      params:
-        config:
-          size: 256
-    validation:
-      target: ldm.data.imagenet.ImageNetValidation
-      params:
-        config:
-          size: 256
-
-
-lightning:
-  callbacks:
-    image_logger:
-      target: main.ImageLogger
-      params:
-        batch_frequency: 5000
-        max_images: 8
-        increase_log_steps: False
-
-  trainer:
-    benchmark: True

configs/latent-diffusion/cin256-v2.yaml

@@ -1,68 +0,0 @@
-model:
-  base_learning_rate: 0.0001
-  target: ldm.models.diffusion.ddpm.LatentDiffusion
-  params:
-    linear_start: 0.0015
-    linear_end: 0.0195
-    num_timesteps_cond: 1
-    log_every_t: 200
-    timesteps: 1000
-    first_stage_key: image
-    cond_stage_key: class_label
-    image_size: 64
-    channels: 3
-    cond_stage_trainable: true
-    conditioning_key: crossattn
-    monitor: val/loss
-    use_ema: False
-    
-    unet_config:
-      target: ldm.modules.diffusionmodules.openaimodel.UNetModel
-      params:
-        image_size: 64
-        in_channels: 3
-        out_channels: 3
-        model_channels: 192
-        attention_resolutions:
-        - 8
-        - 4
-        - 2
-        num_res_blocks: 2
-        channel_mult:
-        - 1
-        - 2
-        - 3
-        - 5
-        num_heads: 1
-        use_spatial_transformer: true
-        transformer_depth: 1
-        context_dim: 512
-    
-    first_stage_config:
-      target: ldm.models.autoencoder.VQModelInterface
-      params:
-        embed_dim: 3
-        n_embed: 8192
-        ddconfig:
-          double_z: false
-          z_channels: 3
-          resolution: 256
-          in_channels: 3
-          out_ch: 3
-          ch: 128
-          ch_mult:
-          - 1
-          - 2
-          - 4
-          num_res_blocks: 2
-          attn_resolutions: []
-          dropout: 0.0
-        lossconfig:
-          target: torch.nn.Identity
-    
-    cond_stage_config:
-      target: ldm.modules.encoders.modules.ClassEmbedder
-      params:
-        n_classes: 1001
-        embed_dim: 512
-        key: class_label

configs/latent-diffusion/ffhq-ldm-vq-4.yaml

@@ -1,85 +0,0 @@
-model:
-  base_learning_rate: 2.0e-06
-  target: ldm.models.diffusion.ddpm.LatentDiffusion
-  params:
-    linear_start: 0.0015
-    linear_end: 0.0195
-    num_timesteps_cond: 1
-    log_every_t: 200
-    timesteps: 1000
-    first_stage_key: image
-    image_size: 64
-    channels: 3
-    monitor: val/loss_simple_ema
-    unet_config:
-      target: ldm.modules.diffusionmodules.openaimodel.UNetModel
-      params:
-        image_size: 64
-        in_channels: 3
-        out_channels: 3
-        model_channels: 224
-        attention_resolutions:
-        # note: this isn\t actually the resolution but
-        # the downsampling factor, i.e. this corresnponds to
-        # attention on spatial resolution 8,16,32, as the
-        # spatial reolution of the latents is 64 for f4
-        - 8
-        - 4
-        - 2
-        num_res_blocks: 2
-        channel_mult:
-        - 1
-        - 2
-        - 3
-        - 4
-        num_head_channels: 32
-    first_stage_config:
-      target: ldm.models.autoencoder.VQModelInterface
-      params:
-        embed_dim: 3
-        n_embed: 8192
-        ckpt_path: configs/first_stage_models/vq-f4/model.yaml
-        ddconfig:
-          double_z: false
-          z_channels: 3
-          resolution: 256
-          in_channels: 3
-          out_ch: 3
-          ch: 128
-          ch_mult:
-          - 1
-          - 2
-          - 4
-          num_res_blocks: 2
-          attn_resolutions: []
-          dropout: 0.0
-        lossconfig:
-          target: torch.nn.Identity
-    cond_stage_config: __is_unconditional__
-data:
-  target: main.DataModuleFromConfig
-  params:
-    batch_size: 42
-    num_workers: 5
-    wrap: false
-    train:
-      target: taming.data.faceshq.FFHQTrain
-      params:
-        size: 256
-    validation:
-      target: taming.data.faceshq.FFHQValidation
-      params:
-        size: 256
-
-
-lightning:
-  callbacks:
-    image_logger:
-      target: main.ImageLogger
-      params:
-        batch_frequency: 5000
-        max_images: 8
-        increase_log_steps: False
-
-  trainer:
-    benchmark: True

configs/latent-diffusion/lsun_bedrooms-ldm-vq-4.yaml

@@ -1,85 +0,0 @@
-model:
-  base_learning_rate: 2.0e-06
-  target: ldm.models.diffusion.ddpm.LatentDiffusion
-  params:
-    linear_start: 0.0015
-    linear_end: 0.0195
-    num_timesteps_cond: 1
-    log_every_t: 200
-    timesteps: 1000
-    first_stage_key: image
-    image_size: 64
-    channels: 3
-    monitor: val/loss_simple_ema
-    unet_config:
-      target: ldm.modules.diffusionmodules.openaimodel.UNetModel
-      params:
-        image_size: 64
-        in_channels: 3
-        out_channels: 3
-        model_channels: 224
-        attention_resolutions:
-        # note: this isn\t actually the resolution but
-        # the downsampling factor, i.e. this corresnponds to
-        # attention on spatial resolution 8,16,32, as the
-        # spatial reolution of the latents is 64 for f4
-        - 8
-        - 4
-        - 2
-        num_res_blocks: 2
-        channel_mult:
-        - 1
-        - 2
-        - 3
-        - 4
-        num_head_channels: 32
-    first_stage_config:
-      target: ldm.models.autoencoder.VQModelInterface
-      params:
-        ckpt_path: configs/first_stage_models/vq-f4/model.yaml
-        embed_dim: 3
-        n_embed: 8192
-        ddconfig:
-          double_z: false
-          z_channels: 3
-          resolution: 256
-          in_channels: 3
-          out_ch: 3
-          ch: 128
-          ch_mult:
-          - 1
-          - 2
-          - 4
-          num_res_blocks: 2
-          attn_resolutions: []
-          dropout: 0.0
-        lossconfig:
-          target: torch.nn.Identity
-    cond_stage_config: __is_unconditional__
-data:
-  target: main.DataModuleFromConfig
-  params:
-    batch_size: 48
-    num_workers: 5
-    wrap: false
-    train:
-      target: ldm.data.lsun.LSUNBedroomsTrain
-      params:
-        size: 256
-    validation:
-      target: ldm.data.lsun.LSUNBedroomsValidation
-      params:
-        size: 256
-
-
-lightning:
-  callbacks:
-    image_logger:
-      target: main.ImageLogger
-      params:
-        batch_frequency: 5000
-        max_images: 8
-        increase_log_steps: False
-
-  trainer:
-    benchmark: True

configs/latent-diffusion/lsun_churches-ldm-kl-8.yaml

@@ -1,91 +0,0 @@
-model:
-  base_learning_rate: 5.0e-5   # set to target_lr by starting main.py with '--scale_lr False'
-  target: ldm.models.diffusion.ddpm.LatentDiffusion
-  params:
-    linear_start: 0.0015
-    linear_end: 0.0155
-    num_timesteps_cond: 1
-    log_every_t: 200
-    timesteps: 1000
-    loss_type: l1
-    first_stage_key: "image"
-    cond_stage_key: "image"
-    image_size: 32
-    channels: 4
-    cond_stage_trainable: False
-    concat_mode: False
-    scale_by_std: True
-    monitor: 'val/loss_simple_ema'
-
-    scheduler_config: # 10000 warmup steps
-      target: ldm.lr_scheduler.LambdaLinearScheduler
-      params:
-        warm_up_steps: [10000]
-        cycle_lengths: [10000000000000]
-        f_start: [1.e-6]
-        f_max: [1.]
-        f_min: [ 1.]
-
-    unet_config:
-      target: ldm.modules.diffusionmodules.openaimodel.UNetModel
-      params:
-        image_size: 32
-        in_channels: 4
-        out_channels: 4
-        model_channels: 192
-        attention_resolutions: [ 1, 2, 4, 8 ]   # 32, 16, 8, 4
-        num_res_blocks: 2
-        channel_mult: [ 1,2,2,4,4 ]  # 32, 16, 8, 4, 2
-        num_heads: 8
-        use_scale_shift_norm: True
-        resblock_updown: True
-
-    first_stage_config:
-      target: ldm.models.autoencoder.AutoencoderKL
-      params:
-        embed_dim: 4
-        monitor: "val/rec_loss"
-        ckpt_path: "models/first_stage_models/kl-f8/model.ckpt"
-        ddconfig:
-          double_z: True
-          z_channels: 4
-          resolution: 256
-          in_channels: 3
-          out_ch: 3
-          ch: 128
-          ch_mult: [ 1,2,4,4 ]  # num_down = len(ch_mult)-1
-          num_res_blocks: 2
-          attn_resolutions: [ ]
-          dropout: 0.0
-        lossconfig:
-          target: torch.nn.Identity
-
-    cond_stage_config: "__is_unconditional__"
-
-data:
-  target: main.DataModuleFromConfig
-  params:
-    batch_size: 96
-    num_workers: 5
-    wrap: False
-    train:
-      target: ldm.data.lsun.LSUNChurchesTrain
-      params:
-        size: 256
-    validation:
-      target: ldm.data.lsun.LSUNChurchesValidation
-      params:
-        size: 256
-
-lightning:
-  callbacks:
-    image_logger:
-      target: main.ImageLogger
-      params:
-        batch_frequency: 5000
-        max_images: 8
-        increase_log_steps: False
-
-
-  trainer:
-    benchmark: True

configs/latent-diffusion/txt2img-1p4B-eval.yaml

@@ -1,71 +0,0 @@
-model:
-  base_learning_rate: 5.0e-05
-  target: ldm.models.diffusion.ddpm.LatentDiffusion
-  params:
-    linear_start: 0.00085
-    linear_end: 0.012
-    num_timesteps_cond: 1
-    log_every_t: 200
-    timesteps: 1000
-    first_stage_key: image
-    cond_stage_key: caption
-    image_size: 32
-    channels: 4
-    cond_stage_trainable: true
-    conditioning_key: crossattn
-    monitor: val/loss_simple_ema
-    scale_factor: 0.18215
-    use_ema: False
-
-    unet_config:
-      target: ldm.modules.diffusionmodules.openaimodel.UNetModel
-      params:
-        image_size: 32
-        in_channels: 4
-        out_channels: 4
-        model_channels: 320
-        attention_resolutions:
-        - 4
-        - 2
-        - 1
-        num_res_blocks: 2
-        channel_mult:
-        - 1
-        - 2
-        - 4
-        - 4
-        num_heads: 8
-        use_spatial_transformer: true
-        transformer_depth: 1
-        context_dim: 1280
-        use_checkpoint: true
-        legacy: False
-
-    first_stage_config:
-      target: ldm.models.autoencoder.AutoencoderKL
-      params:
-        embed_dim: 4
-        monitor: val/rec_loss
-        ddconfig:
-          double_z: true
-          z_channels: 4
-          resolution: 256
-          in_channels: 3
-          out_ch: 3
-          ch: 128
-          ch_mult:
-          - 1
-          - 2
-          - 4
-          - 4
-          num_res_blocks: 2
-          attn_resolutions: []
-          dropout: 0.0
-        lossconfig:
-          target: torch.nn.Identity
-
-    cond_stage_config:
-      target: ldm.modules.encoders.modules.BERTEmbedder
-      params:
-        n_embed: 1280
-        n_layer: 32

configs/models.yaml

@@ -1,20 +0,0 @@
-# This file describes the alternative machine learning models
-#  available to the dream script.
-#
-# To add a new model, follow the examples below. Each
-# model requires a model config file, a weights file,
-# and the width and height of the images it
-# was trained on.
-
-laion400m:
-    config:  configs/latent-diffusion/txt2img-1p4B-eval.yaml
-    weights: models/ldm/text2img-large/model.ckpt
-    description: Latent Diffusion LAION400M model
-    width: 256
-    height: 256
-stable-diffusion-1.4:
-    config:  configs/stable-diffusion/v1-inference.yaml
-    weights: models/ldm/stable-diffusion-v1/model.ckpt
-    description: Stable Diffusion inference model version 1.4
-    width: 512
-    height: 512

configs/models.yaml.example

@@ -0,0 +1,27 @@
+# This file describes the alternative machine learning models
+# available to InvokeAI script.
+#
+# To add a new model, follow the examples below. Each
+# model requires a model config file, a weights file,
+# and the width and height of the images it
+# was trained on.
+stable-diffusion-1.5:
+  description: The newest Stable Diffusion version 1.5 weight file (4.27 GB)
+  weights: ./models/ldm/stable-diffusion-v1/v1-5-pruned-emaonly.ckpt
+  config: ./configs/stable-diffusion/v1-inference.yaml
+  width: 512
+  height: 512
+  vae: ./models/ldm/stable-diffusion-v1/vae-ft-mse-840000-ema-pruned.ckpt
+  default: true
+stable-diffusion-1.4:
+  description: Stable Diffusion inference model version 1.4
+  config: configs/stable-diffusion/v1-inference.yaml
+  weights: models/ldm/stable-diffusion-v1/sd-v1-4.ckpt
+  vae: models/ldm/stable-diffusion-v1/vae-ft-mse-840000-ema-pruned.ckpt
+  width: 512
+  height: 512
+inpainting-1.5:
+  weights: models/ldm/stable-diffusion-v1/sd-v1-5-inpainting.ckpt
+  config: configs/stable-diffusion/v1-inpainting-inference.yaml
+  vae: models/ldm/stable-diffusion-v1/vae-ft-mse-840000-ema-pruned.ckpt
+  description: RunwayML SD 1.5 model optimized for inpainting

configs/retrieval-augmented-diffusion/768x768.yaml

@@ -1,68 +0,0 @@
-model:
-  base_learning_rate: 0.0001
-  target: ldm.models.diffusion.ddpm.LatentDiffusion
-  params:
-    linear_start: 0.0015
-    linear_end: 0.015
-    num_timesteps_cond: 1
-    log_every_t: 200
-    timesteps: 1000
-    first_stage_key: jpg
-    cond_stage_key: nix
-    image_size: 48
-    channels: 16
-    cond_stage_trainable: false
-    conditioning_key: crossattn
-    monitor: val/loss_simple_ema
-    scale_by_std: false
-    scale_factor: 0.22765929
-    unet_config:
-      target: ldm.modules.diffusionmodules.openaimodel.UNetModel
-      params:
-        image_size: 48
-        in_channels: 16
-        out_channels: 16
-        model_channels: 448
-        attention_resolutions:
-        - 4
-        - 2
-        - 1
-        num_res_blocks: 2
-        channel_mult:
-        - 1
-        - 2
-        - 3
-        - 4
-        use_scale_shift_norm: false
-        resblock_updown: false
-        num_head_channels: 32
-        use_spatial_transformer: true
-        transformer_depth: 1
-        context_dim: 768
-        use_checkpoint: true
-    first_stage_config:
-      target: ldm.models.autoencoder.AutoencoderKL
-      params:
-        monitor: val/rec_loss
-        embed_dim: 16
-        ddconfig:
-          double_z: true
-          z_channels: 16
-          resolution: 256
-          in_channels: 3
-          out_ch: 3
-          ch: 128
-          ch_mult:
-          - 1
-          - 1
-          - 2
-          - 2
-          - 4
-          num_res_blocks: 2
-          attn_resolutions:
-          - 16
-          dropout: 0.0
-        lossconfig:
-          target: torch.nn.Identity
-    cond_stage_config:
-      target: torch.nn.Identity

configs/stable-diffusion/v1-inference.yaml

@@ -76,4 +76,4 @@ model:
           target: torch.nn.Identity
 
     cond_stage_config:
-      target: ldm.modules.encoders.modules.FrozenCLIPEmbedder
+      target: ldm.modules.encoders.modules.WeightedFrozenCLIPEmbedder

configs/stable-diffusion/v1-inpainting-inference.yaml

@@ -0,0 +1,79 @@
+model:
+  base_learning_rate: 7.5e-05
+  target: ldm.models.diffusion.ddpm.LatentInpaintDiffusion
+  params:
+    linear_start: 0.00085
+    linear_end: 0.0120
+    num_timesteps_cond: 1
+    log_every_t: 200
+    timesteps: 1000
+    first_stage_key: "jpg"
+    cond_stage_key: "txt"
+    image_size: 64
+    channels: 4
+    cond_stage_trainable: false   # Note: different from the one we trained before
+    conditioning_key: hybrid   # important
+    monitor: val/loss_simple_ema
+    scale_factor: 0.18215
+    finetune_keys: null
+
+    scheduler_config: # 10000 warmup steps
+      target: ldm.lr_scheduler.LambdaLinearScheduler
+      params:
+        warm_up_steps: [ 2500 ] # NOTE for resuming. use 10000 if starting from scratch
+        cycle_lengths: [ 10000000000000 ] # incredibly large number to prevent corner cases
+        f_start: [ 1.e-6 ]
+        f_max: [ 1. ]
+        f_min: [ 1. ]
+
+    personalization_config:
+      target: ldm.modules.embedding_manager.EmbeddingManager
+      params:
+        placeholder_strings: ["*"]
+        initializer_words: ['face', 'man', 'photo', 'africanmale']
+        per_image_tokens: false
+        num_vectors_per_token: 1
+        progressive_words: False
+
+    unet_config:
+      target: ldm.modules.diffusionmodules.openaimodel.UNetModel
+      params:
+        image_size: 32 # unused
+        in_channels: 9  # 4 data + 4 downscaled image + 1 mask
+        out_channels: 4
+        model_channels: 320
+        attention_resolutions: [ 4, 2, 1 ]
+        num_res_blocks: 2
+        channel_mult: [ 1, 2, 4, 4 ]
+        num_heads: 8
+        use_spatial_transformer: True
+        transformer_depth: 1
+        context_dim: 768
+        use_checkpoint: True
+        legacy: False
+
+    first_stage_config:
+      target: ldm.models.autoencoder.AutoencoderKL
+      params:
+        embed_dim: 4
+        monitor: val/rec_loss
+        ddconfig:
+          double_z: true
+          z_channels: 4
+          resolution: 256
+          in_channels: 3
+          out_ch: 3
+          ch: 128
+          ch_mult:
+          - 1
+          - 2
+          - 4
+          - 4
+          num_res_blocks: 2
+          attn_resolutions: []
+          dropout: 0.0
+        lossconfig:
+          target: torch.nn.Identity
+
+    cond_stage_config:
+      target: ldm.modules.encoders.modules.WeightedFrozenCLIPEmbedder

docker-build/Dockerfile

@@ -1,57 +1,84 @@
-FROM        debian 
+FROM ubuntu AS get_miniconda
 
-ARG         gsd
-ENV         GITHUB_STABLE_DIFFUSION $gsd
+SHELL ["/bin/bash", "-c"]
 
-ARG         rsd
-ENV         REQS $rsd
+# install wget
+RUN apt-get update \
+  && apt-get install -y \
+    wget \
+  && apt-get clean \
+  && rm -rf /var/lib/apt/lists/*
 
-ARG         cs
-ENV         CONDA_SUBDIR $cs
+# download and install miniconda
+ARG conda_version=py39_4.12.0-Linux-x86_64
+ARG conda_prefix=/opt/conda
+RUN wget --progress=dot:giga -O /miniconda.sh \
+  https://repo.anaconda.com/miniconda/Miniconda3-${conda_version}.sh \
+  && bash /miniconda.sh -b -p ${conda_prefix} \
+  && rm -f /miniconda.sh
 
-ENV         PIP_EXISTS_ACTION="w"
+FROM ubuntu AS invokeai
 
-# TODO: Optimize image size
+# use bash
+SHELL [ "/bin/bash", "-c" ]
 
-SHELL       ["/bin/bash", "-c"]
+# clean bashrc
+RUN echo "" > ~/.bashrc
 
-WORKDIR     /
-RUN         apt update && apt upgrade -y \
-            && apt install -y \
-            git \
-            libgl1-mesa-glx \
-            libglib2.0-0 \
-            pip \
-            python3 \
-            && git clone $GITHUB_STABLE_DIFFUSION
+# Install necesarry packages
+RUN apt-get update \
+  && apt-get install -y \
+    --no-install-recommends \
+    gcc \
+    git \
+    libgl1-mesa-glx \
+    libglib2.0-0 \
+    pip \
+    python3 \
+    python3-dev \
+  && apt-get clean \
+  && rm -rf /var/lib/apt/lists/*
 
-# Install Anaconda or Miniconda
-COPY        anaconda.sh .
-RUN         bash anaconda.sh -b -u -p /anaconda && /anaconda/bin/conda init bash
+# clone repository, create models.yaml and create symlinks
+ARG invokeai_git=invoke-ai/InvokeAI
+ARG invokeai_branch=main
+ARG project_name=invokeai
+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"
 
-# SD 
-WORKDIR     /stable-diffusion 
-RUN         source ~/.bashrc \
-            && conda create -y --name ldm && conda activate ldm \
-            && conda config --env --set subdir $CONDA_SUBDIR \
-            && pip3 install -r $REQS \   
-            && pip3 install basicsr facexlib realesrgan \          
-            && mkdir models/ldm/stable-diffusion-v1 \
-            && ln -s "/data/sd-v1-4.ckpt" models/ldm/stable-diffusion-v1/model.ckpt 
+# set workdir
+WORKDIR "/${project_name}"
 
-# Face restoreation
-# by default expected in a sibling directory to stable-diffusion
-WORKDIR     /       
-RUN         git clone https://github.com/TencentARC/GFPGAN.git 
+# install conda env and preload models
+ARG conda_prefix=/opt/conda
+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}" \
+  && rm -Rf ~/.cache \
+  && conda clean -afy \
+  && echo "conda activate ${project_name}" >> ~/.bashrc
 
-WORKDIR     /GFPGAN 
-RUN         pip3 install -r requirements.txt \
-            && python3 setup.py develop \
-            && ln -s "/data/GFPGANv1.4.pth" experiments/pretrained_models/GFPGANv1.4.pth
+RUN source ~/.bashrc \
+  && python scripts/preload_models.py \
+    --no-interactive
 
-WORKDIR     /stable-diffusion 
-RUN         python3 scripts/preload_models.py         
-
-WORKDIR     /
-COPY        entrypoint.sh .
-ENTRYPOINT ["/entrypoint.sh"]
+# Copy entrypoint and set env
+ENV CONDA_PREFIX="${conda_prefix}"
+ENV PROJECT_NAME="${project_name}"
+COPY docker-build/entrypoint.sh /
+ENTRYPOINT [ "/entrypoint.sh" ]

docker-build/build.sh

@@ -0,0 +1,84 @@
+#!/usr/bin/env bash
+set -e
+# IMPORTANT: You need to have a token on huggingface.co to be able to download the checkpoint!!!
+# configure values by using env when executing build.sh
+# f.e. env ARCH=aarch64 GITHUB_INVOKE_AI=https://github.com/yourname/yourfork.git ./build.sh
+
+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-lin-cuda.yml}
+invokeai_git=${INVOKEAI_GIT:-invoke-ai/InvokeAI}
+invokeai_branch=${INVOKEAI_BRANCH:-main}
+huggingface_token=${HUGGINGFACE_TOKEN?}
+
+# print the settings
+echo "You are using these values:"
+echo -e "project_name:\t\t ${project_name}"
+echo -e "volumename:\t\t ${volumename}"
+echo -e "arch:\t\t\t ${arch}"
+echo -e "platform:\t\t ${platform}"
+echo -e "invokeai_conda_version:\t ${invokeai_conda_version}"
+echo -e "invokeai_conda_prefix:\t ${invokeai_conda_prefix}"
+echo -e "invokeai_conda_env_file: ${invokeai_conda_env_file}"
+echo -e "invokeai_git:\t\t ${invokeai_git}"
+echo -e "invokeai_tag:\t\t ${invokeai_tag}\n"
+
+_runAlpine() {
+  docker run \
+    --rm \
+    --interactive \
+    --tty \
+    --mount source="$volumename",target=/data \
+    --workdir /data \
+    alpine "$@"
+}
+
+_copyCheckpoints() {
+  echo "creating subfolders for models and outputs"
+  _runAlpine mkdir models
+  _runAlpine mkdir outputs
+  echo "downloading v1-5-pruned-emaonly.ckpt"
+  _runAlpine wget \
+    --header="Authorization: Bearer ${huggingface_token}" \
+    -O models/v1-5-pruned-emaonly.ckpt \
+    https://huggingface.co/runwayml/stable-diffusion-v1-5/resolve/main/v1-5-pruned-emaonly.ckpt
+  echo "done"
+}
+
+_checkVolumeContent() {
+  _runAlpine ls -lhA /data/models
+}
+
+_getModelMd5s() {
+  _runAlpine  \
+    alpine sh -c "md5sum /data/models/*.ckpt"
+}
+
+if [[ -n "$(docker volume ls -f name="${volumename}" -q)" ]]; then
+  echo "Volume already exists"
+  if [[ -z "$(_checkVolumeContent)" ]]; then
+    echo "looks empty, copying checkpoint"
+    _copyCheckpoints
+  fi
+  echo "Models in ${volumename}:"
+  _checkVolumeContent
+else
+  echo -n "createing docker volume "
+  docker volume create "${volumename}"
+  _copyCheckpoints
+fi
+
+# Build Container
+docker build \
+  --platform="${platform}" \
+  --tag "${invokeai_tag}" \
+  --build-arg project_name="${project_name}" \
+  --build-arg conda_version="${invokeai_conda_version}" \
+  --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 \
+  .

docker-build/entrypoint.sh

@@ -1,10 +1,8 @@
 #!/bin/bash
+set -e
 
-cd /stable-diffusion
+source "${CONDA_PREFIX}/etc/profile.d/conda.sh"
+conda activate "${PROJECT_NAME}"
 
-if [ $# -eq 0 ]; then
-    python3 scripts/dream.py --full_precision -o /data
-    # bash
-else
-    python3 scripts/dream.py --full_precision -o /data "$@"
-fi
+python scripts/invoke.py \
+  ${@:---web --host=0.0.0.0}

docker-build/env.sh

@@ -0,0 +1,13 @@
+#!/usr/bin/env bash
+
+project_name=${PROJECT_NAME:-invokeai}
+volumename=${VOLUMENAME:-${project_name}_data}
+arch=${ARCH:-x86_64}
+platform=${PLATFORM:-Linux/${arch}}
+invokeai_tag=${INVOKEAI_TAG:-${project_name}-${arch}}
+
+export project_name
+export volumename
+export arch
+export platform
+export invokeai_tag

docker-build/run.sh

@@ -0,0 +1,15 @@
+#!/usr/bin/env bash
+set -e
+
+source ./docker-build/env.sh || echo "please run from repository root" || exit 1
+
+docker run \
+  --interactive \
+  --tty \
+  --rm \
+  --platform "$platform" \
+  --name "$project_name" \
+  --hostname "$project_name" \
+  --mount source="$volumename",target=/data \
+  --publish 9090:9090 \
+  "$invokeai_tag" ${1:+$@}

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
 
 ---
 

docs/features/CLI.md

@@ -1,16 +1,14 @@
 ---
 title: CLI
-hide:
-  - toc
 ---
 
 # :material-bash: CLI
 
 ## **Interactive Command Line Interface**
 
-The `invoke.py` script, located in `scripts/dream.py`, provides an interactive
-interface to image generation similar to the "invoke mothership" bot that Stable
-AI provided on its Discord server.
+The `invoke.py` script, located in `scripts/`, provides an interactive interface
+to image generation similar to the "invoke mothership" bot that Stable AI
+provided on its Discord server.
 
 Unlike the `txt2img.py` and `img2img.py` scripts provided in the original
 [CompVis/stable-diffusion](https://github.com/CompVis/stable-diffusion) source
@@ -60,9 +58,9 @@ invoke> q
 ![invoke-py-demo](../assets/dream-py-demo.png)
 
 The `invoke>` prompt's arguments are pretty much identical to those used in the
-Discord bot, except you don't need to type `!invoke` (it doesn't hurt if you do).
-A significant change is that creation of individual images is now the default
-unless `--grid` (`-g`) is given. A full list is given in
+Discord bot, except you don't need to type `!invoke` (it doesn't hurt if you
+do). A significant change is that creation of individual images is now the
+default unless `--grid` (`-g`) is given. A full list is given in
 [List of prompt arguments](#list-of-prompt-arguments).
 
 ## Arguments
@@ -75,7 +73,8 @@ the location of the model weight files.
 
 These command-line arguments can be passed to `invoke.py` when you first run it
 from the Windows, Mac or Linux command line. Some set defaults that can be
-overridden on a per-prompt basis (see [List of prompt arguments](#list-of-prompt-arguments). Others
+overridden on a per-prompt basis (see
+[List of prompt arguments](#list-of-prompt-arguments). Others
 
 | Argument <img width="240" align="right"/> | Shortcut <img width="100" align="right"/> | Default <img width="320" align="right"/>       | Description                                                                                          |
 | ----------------------------------------- | ----------------------------------------- | ---------------------------------------------- | ---------------------------------------------------------------------------------------------------- |
@@ -85,19 +84,23 @@ overridden on a per-prompt basis (see [List of prompt arguments](#list-of-prompt
 | `--from_file <path>`                      |                                           | `None`                                         | Read list of prompts from a file. Use `-` to read from standard input                                |
 | `--model <modelname>`                     |                                           | `stable-diffusion-1.4`                         | Loads model specified in configs/models.yaml. Currently one of "stable-diffusion-1.4" or "laion400m" |
 | `--full_precision`                        | `-F`                                      | `False`                                        | Run in slower full-precision mode. Needed for Macintosh M1/M2 hardware and some older video cards.   |
-| `--png_compression <0-9>`                 | `-z<0-9>`                                 |  6                                             | Select level of compression for output files, from 0 (no compression) to 9 (max compression)         |
+| `--png_compression <0-9>`                 | `-z<0-9>`                                 | `6`                                            | Select level of compression for output files, from 0 (no compression) to 9 (max compression)         |
+| `--safety-checker`                        |                                           | `False`                                        | Activate safety checker for NSFW and other potentially disturbing imagery                            |
 | `--web`                                   |                                           | `False`                                        | Start in web server mode                                                                             |
 | `--host <ip addr>`                        |                                           | `localhost`                                    | Which network interface web server should listen on. Set to 0.0.0.0 to listen on any.                |
 | `--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.                                          |
 | `--embedding_path <path>`                 |                                           | `None`                                         | Path to pre-trained embedding manager checkpoints, for custom models                                 |
 | `--gfpgan_dir`                            |                                           | `src/gfpgan`                                   | Path to where GFPGAN is installed.                                                                   |
 | `--gfpgan_model_path`                     |                                           | `experiments/pretrained_models/GFPGANv1.4.pth` | Path to GFPGAN model file, relative to `--gfpgan_dir`.                                               |
-| `--device <device>`                       | `-d<device>`                              | `torch.cuda.current_device()`                  | Device to run SD on, e.g. "cuda:0"                                                                   |
 | `--free_gpu_mem`                          |                                           | `False`                                        | Free GPU memory after sampling, to allow image decoding and saving in low VRAM conditions            |
 | `--precision`                             |                                           | `auto`                                         | Set model precision, default is selected by device. Options: auto, float32, float16, autocast        |
 
@@ -107,7 +110,7 @@ overridden on a per-prompt basis (see [List of prompt arguments](#list-of-prompt
 
     | 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>
@@ -120,11 +123,34 @@ overridden on a per-prompt basis (see [List of prompt arguments](#list-of-prompt
       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>` prompt. Here you can enter information to generate images
-from text ([txt2img](#txt2img)), to embellish an existing image or sketch
+After the invoke.py script initializes, it will present you with a `invoke>`
+prompt. Here you can enter information to generate images from text
+([txt2img](#txt2img)), to embellish an existing image or sketch
 ([img2img](#img2img)), or to selectively alter chosen regions of the image
 ([inpainting](#inpainting)).
 
@@ -141,58 +167,59 @@ from text ([txt2img](#txt2img)), to embellish an existing image or sketch
 
 Here are the invoke> command that apply to txt2img:
 
-| Argument <img width="680" align="right"/> | Shortcut <img width="420" align="right"/> | Default <img width="480" align="right"/> | Description |
-|--------------------|------------|---------------------|--------------|
-| "my prompt"        |            |                    | Text prompt to use. The quotation marks are optional. |
-| --width <int>      | -W<int>   | 512                 | Width of generated image |
-| --height <int>     | -H<int>   | 512                 | Height of generated image |
-| --iterations <int> | -n<int>   | 1                   | How many images to generate from this prompt |
-| --steps <int>      | -s<int>   | 50                  | How many steps of refinement to apply |
-| --cfg_scale <float>| -C<float> | 7.5                 | How hard to try to match the prompt to the generated image; any number greater than 1.0 works, but the useful range is roughly 5.0 to 20.0 |
-| --seed <int>       | -S<int>   | None                | Set the random seed for the next series of images. This can be used to recreate an image generated previously.|
-| --sampler <sampler>| -A<sampler>| k_lms              | Sampler to use. Use -h to get list of available samplers. |
-| --hires_fix        |           |                     | Larger images often have duplication artefacts. This option suppresses duplicates by generating the image at low res, and then using img2img to increase the resolution |
-| `--png_compression <0-9>` | `-z<0-9>` |  6           | Select level of compression for output files, from 0 (no compression) to 9 (max compression)         |
-| --grid             | -g        | False               | Turn on grid mode to return a single image combining all the images generated by this prompt |
-| --individual       | -i        | True                | Turn off grid mode (deprecated; leave off --grid instead) |
-| --outdir <path>    |  -o<path> | outputs/img_samples  | Temporarily change the location of these images |
-| --seamless         |           | False               | Activate seamless tiling for interesting effects |
-| --log_tokenization | -t        | False               | Display a color-coded list of the parsed tokens derived from the prompt |
-| --skip_normalization| -x       | False               | Weighted subprompts will not be normalized. See [Weighted Prompts](./OTHER.md#weighted-prompts) |
-| --upscale <int> <float> | -U <int> <float> | -U 1 0.75| Upscale image by magnification factor (2, 4), and set strength of upscaling (0.0-1.0). If strength not set, will default to 0.75. |
-| --facetool_strength <float>  | -G <float> | -G0        | Fix faces (defaults to using the GFPGAN algorithm); argument indicates how hard the algorithm should try (0.0-1.0) |
-| --facetool <name> | -ft <name> | -ft gfpgan | Select face restoration algorithm to use: gfpgan, codeformer |
-| --codeformer_fidelity | -cf <float> | 0.75 | Used along with CodeFormer. Takes values between 0 and 1. 0 produces high quality but low accuracy. 1 produces high accuracy but low quality |
-| --save_original    | -save_orig| False               | When upscaling or fixing faces, this will cause the original image to be saved rather than replaced. |
-| --variation <float>  |-v<float>| 0.0                 | Add a bit of noise (0.0=none, 1.0=high) to the image in order to generate a series of variations. Usually used in combination with -S<seed> and -n<int> to generate a series a riffs on a starting image. See [Variations](./VARIATIONS.md). |
-| --with_variations <pattern> |    | None              | Combine two or more variations. See [Variations](./VARIATIONS.md) for now to use this. |
-| --save_intermediates <n>    |    | None              | Save the image from every nth step into an "intermediates" folder inside the output directory |
+| Argument <img width="680" align="right"/> | Shortcut <img width="420" align="right"/> | Default <img width="480" align="right"/> | Description                                                                                                                                                                                                                                      |
+| ----------------------------------------- | ----------------------------------------- | ---------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
+| "my prompt"                               |                                           |                                          | Text prompt to use. The quotation marks are optional.                                                                                                                                                                                            |
+| `--width <int>`                           | `-W<int>`                                 | `512`                                    | Width of generated image                                                                                                                                                                                                                         |
+| `--height <int>`                          | `-H<int>`                                 | `512`                                    | Height of generated image                                                                                                                                                                                                                        |
+| `--iterations <int>`                      | `-n<int>`                                 | `1`                                      | How many images to generate from this prompt                                                                                                                                                                                                     |
+| `--steps <int>`                           | `-s<int>`                                 | `50`                                     | How many steps of refinement to apply                                                                                                                                                                                                            |
+| `--cfg_scale <float>`                     | `-C<float>`                               | `7.5`                                    | How hard to try to match the prompt to the generated image; any number greater than 1.0 works, but the useful range is roughly 5.0 to 20.0                                                                                                       |
+| `--seed <int>`                            | `-S<int>`                                 | `None`                                   | Set the random seed for the next series of images. This can be used to recreate an image generated previously.                                                                                                                                   |
+| `--sampler <sampler>`                     | `-A<sampler>`                             | `k_lms`                                  | Sampler to use. Use -h to get list of available samplers.                                                                                                                                                                                        |
+| `--karras_max <int>`                      |                                           | `29`                                     | When using k\_\* samplers, set the maximum number of steps before shifting from using the Karras noise schedule (good for low step counts) to the LatentDiffusion noise schedule (good for high step counts) This value is sticky. [29]          |
+| `--hires_fix`                             |                                           |                                          | Larger images often have duplication artefacts. This option suppresses duplicates by generating the image at low res, and then using img2img to increase the resolution                                                                          |
+| `--png_compression <0-9>`                 | `-z<0-9>`                                 | `6`                                      | Select level of compression for output files, from 0 (no compression) to 9 (max compression)                                                                                                                                                     |
+| `--grid`                                  | `-g`                                      | `False`                                  | Turn on grid mode to return a single image combining all the images generated by this prompt                                                                                                                                                     |
+| `--individual`                            | `-i`                                      | `True`                                   | Turn off grid mode (deprecated; leave off --grid instead)                                                                                                                                                                                        |
+| `--outdir <path>`                         | `-o<path>`                                | `outputs/img_samples`                    | Temporarily change the location of these images                                                                                                                                                                                                  |
+| `--seamless`                              |                                           | `False`                                  | Activate seamless tiling for interesting effects                                                                                                                                                                                                 |
+| `--seamless_axes`                         |                                           | `x,y`                                    | Specify which axes to use circular convolution on.                                                                                                                                                                                               |
+| `--log_tokenization`                      | `-t`                                      | `False`                                  | Display a color-coded list of the parsed tokens derived from the prompt                                                                                                                                                                          |
+| `--skip_normalization`                    | `-x`                                      | `False`                                  | Weighted subprompts will not be normalized. See [Weighted Prompts](./OTHER.md#weighted-prompts)                                                                                                                                                  |
+| `--upscale <int> <float>`                 | `-U <int> <float>`                        | `-U 1 0.75`                              | Upscale image by magnification factor (2, 4), and set strength of upscaling (0.0-1.0). If strength not set, will default to 0.75.                                                                                                                |
+| `--facetool_strength <float>`             | `-G <float> `                             | `-G0`                                    | Fix faces (defaults to using the GFPGAN algorithm); argument indicates how hard the algorithm should try (0.0-1.0)                                                                                                                               |
+| `--facetool <name>`                       | `-ft <name>`                              | `-ft gfpgan`                             | Select face restoration algorithm to use: gfpgan, codeformer                                                                                                                                                                                     |
+| `--codeformer_fidelity`                   | `-cf <float>`                             | `0.75`                                   | Used along with CodeFormer. Takes values between 0 and 1. 0 produces high quality but low accuracy. 1 produces high accuracy but low quality                                                                                                     |
+| `--save_original`                         | `-save_orig`                              | `False`                                  | When upscaling or fixing faces, this will cause the original image to be saved rather than replaced.                                                                                                                                             |
+| `--variation <float>`                     | `-v<float>`                               | `0.0`                                    | Add a bit of noise (0.0=none, 1.0=high) to the image in order to generate a series of variations. Usually used in combination with `-S<seed>` and `-n<int>` to generate a series a riffs on a starting image. See [Variations](./VARIATIONS.md). |
+| `--with_variations <pattern>`             |                                           | `None`                                   | Combine two or more variations. See [Variations](./VARIATIONS.md) for now to use this.                                                                                                                                                           |
+| `--save_intermediates <n>`                |                                           | `None`                                   | Save the image from every nth step into an "intermediates" folder inside the output directory                                                                                                                                                    |
 
-Note that the width and height of the image must be multiples of
-64. You can provide different values, but they will be rounded down to
-the nearest multiple of 64.
+Note that the width and height of the image must be multiples of 64. You can
+provide different values, but they will be rounded down to the nearest multiple
+of 64.
 
+### This is an example of img2img:
 
-### This is an example of img2img:	
-
-~~~~
+```
 invoke> waterfall and rainbow -I./vacation-photo.png -W640 -H480 --fit
-~~~~
+```
 
-This will modify the indicated vacation photograph by making it more
-like the prompt. Results will vary greatly depending on what is in the
-image. We also ask to --fit the image into a box no bigger than
-640x480. Otherwise the image size will be identical to the provided
-photo and you may run out of memory if it is large.
+This will modify the indicated vacation photograph by making it more like the
+prompt. Results will vary greatly depending on what is in the image. We also ask
+to --fit the image into a box no bigger than 640x480. Otherwise the image size
+will be identical to the provided photo and you may run out of memory if it is
+large.
 
-In addition to the command-line options recognized by txt2img, img2img
-accepts additional options:
+In addition to the command-line options recognized by txt2img, img2img accepts
+additional options:
 
-| Argument <img width="160" align="right"/> |  Shortcut   |  Default        |  Description |
-|----------------------|-------------|-----------------|--------------|
-| `--init_img <path>`  | `-I<path>`  | `None`          | Path to the initialization image |
-| `--fit`              | `-F`        | `False`         | Scale the image to fit into the specified -H and -W dimensions |
-| `--strength <float>` | `-s<float>` | `0.75`          | 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.|
+| Argument <img width="160" align="right"/> | Shortcut    | Default | Description                                                                                                                                |
+| ----------------------------------------- | ----------- | ------- | ------------------------------------------------------------------------------------------------------------------------------------------ |
+| `--init_img <path>`                       | `-I<path>`  | `None`  | Path to the initialization image                                                                                                           |
+| `--fit`                                   | `-F`        | `False` | Scale the image to fit into the specified -H and -W dimensions                                                                             |
+| `--strength <float>`                      | `-s<float>` | `0.75`  | 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. |
 
 ### inpainting
 
@@ -209,12 +236,39 @@ accepts additional options:
     the pixels underneath when you create the transparent areas. See
     [Inpainting](./INPAINTING.md) for details.
 
-inpainting accepts all the arguments used for txt2img and img2img, as
-well as the --mask (-M) argument:
+inpainting accepts all the arguments used for txt2img and img2img, as well as
+the --mask (-M) and --text_mask (-tm) arguments:
 
-| Argument <img width="100" align="right"/> |  Shortcut  |  Default            |  Description |
-|--------------------|------------|---------------------|--------------|
-| `--init_mask <path>` | `-M<path>`   | `None`                |Path to an image the same size as the initial_image, with areas for inpainting made transparent.|
+| Argument <img width="100" align="right"/> | Shortcut                 | Default | Description                                                                                      |
+| ----------------------------------------- | ------------------------ | ------- | ------------------------------------------------------------------------------------------------ |
+| `--init_mask <path>`                      | `-M<path>`               | `None`  | Path to an image the same size as the initial_image, with areas for inpainting made transparent. |
+| `--invert_mask `                          |                          | False   | If true, invert the mask so that transparent areas are opaque and vice versa.                    |
+| `--text_mask <prompt> [<float>]`          | `-tm <prompt> [<float>]` | <none>  | Create a mask from a text prompt describing part of the image                                    |
+
+The mask may either be an image with transparent areas, in which case the
+inpainting will occur in the transparent areas only, or a black and white image,
+in which case all black areas will be painted into.
+
+`--text_mask` (short form `-tm`) is a way to generate a mask using a text
+description of the part of the image to replace. For example, if you have an
+image of a breakfast plate with a bagel, toast and scrambled eggs, you can
+selectively mask the bagel and replace it with a piece of cake this way:
+
+```
+invoke> a piece of cake -I /path/to/breakfast.png -tm bagel
+```
+
+The algorithm uses <a
+href="https://github.com/timojl/clipseg">clipseg</a> to classify different
+regions of the image. The classifier puts out a confidence score for each region
+it identifies. Generally regions that score above 0.5 are reliable, but if you
+are getting too much or too little masking you can adjust the threshold down (to
+get more mask), or up (to get less). In this example, by passing `-tm` a higher
+value, we are insisting on a more stringent classification.
+
+```
+invoke> a piece of cake -I /path/to/breakfast.png -tm bagel 0.6
+```
 
 # Other Commands
 
@@ -222,31 +276,26 @@ The CLI offers a number of commands that begin with "!".
 
 ## Postprocessing images
 
-To postprocess a file using face restoration or upscaling, use the
-`!fix` command.
+To postprocess a file using face restoration or upscaling, use the `!fix`
+command.
 
 ### `!fix`
 
-This command runs a post-processor on a previously-generated image. It
-takes a PNG filename or path and applies your choice of the `-U`, `-G`, or
-`--embiggen` switches in order to fix faces or upscale. If you provide a
-filename, the script will look for it in the current output
-directory. Otherwise you can provide a full or partial path to the
-desired file.
+This command runs a post-processor on a previously-generated image. It takes a
+PNG filename or path and applies your choice of the `-U`, `-G`, or `--embiggen`
+switches in order to fix faces or upscale. If you provide a filename, the script
+will look for it in the current output directory. Otherwise you can provide a
+full or partial path to the desired file.
 
 Some examples:
 
-!!! example ""
-
-    Upscale to 4X its original size and fix faces using codeformer:
+!!! example "Upscale to 4X its original size and fix faces using codeformer"
 
     ```bash
     invoke> !fix 0000045.4829112.png -G1 -U4 -ft codeformer
     ```
 
-!!! example ""
-
-    Use the GFPGAN algorithm to fix faces, then upscale to 3X using --embiggen:
+!!! example "Use the GFPGAN algorithm to fix faces, then upscale to 3X using --embiggen"
 
     ```bash
     invoke> !fix 0000045.4829112.png -G0.8 -ft gfpgan
@@ -255,33 +304,41 @@ Some examples:
     >> GFPGAN - Restoring Faces for image seed:4829112
     Outputs:
     [1] outputs/img-samples/000017.4829112.gfpgan-00.png: !fix "outputs/img-samples/0000045.4829112.png" -s 50 -S  -W 512 -H 512 -C 7.5 -A k_lms -G 0.8
+    ```
 
-# Model selection and importation
+### !mask
 
-The CLI allows you to add new models on the fly, as well as to switch
-among them rapidly without leaving the script.
+This command takes an image, a text prompt, and uses the `clipseg` algorithm to
+automatically generate a mask of the area that matches the text prompt. It is
+useful for debugging the text masking process prior to inpainting with the
+`--text_mask` argument. See [INPAINTING.md] for details.
 
-## !models
+## Model selection and importation
 
-This prints out a list of the models defined in `config/models.yaml'.
-The active model is bold-faced
+The CLI allows you to add new models on the fly, as well as to switch among them
+rapidly without leaving the script.
+
+### !models
+
+This prints out a list of the models defined in `config/models.yaml'. The active
+model is bold-faced
 
 Example:
+
 <pre>
 laion400m                 not loaded  <no description>
 <b>stable-diffusion-1.4          active  Stable Diffusion v1.4</b>
 waifu-diffusion           not loaded  Waifu Diffusion v1.3
 </pre>
 
-## !switch <model>
+### !switch <model>
 
-This quickly switches from one model to another without leaving the 
-CLI script. `invoke.py` uses a memory caching system; once a model
-has been loaded, switching back and forth is quick. The following
-example shows this in action. Note how the second column of the 
-`!models` table changes to `cached` after a model is first loaded,
-and that the long initialization step is not needed when loading
-a cached model.
+This quickly switches from one model to another without leaving the CLI script.
+`invoke.py` uses a memory caching system; once a model has been loaded,
+switching back and forth is quick. The following example shows this in action.
+Note how the second column of the `!models` table changes to `cached` after a
+model is first loaded, and that the long initialization step is not needed when
+loading a cached model.
 
 <pre>
 invoke> !models
@@ -319,32 +376,30 @@ laion400m                 not loaded  <no description>
 waifu-diffusion               cached  Waifu Diffusion v1.3
 </pre>
 
-## !import_model <path/to/model/weights>
+### !import_model <path/to/model/weights>
 
-This command imports a new model weights file into InvokeAI, makes it
-available for image generation within the script, and writes out the
-configuration for the model into `config/models.yaml` for use in 
-subsequent sessions.
+This command imports a new model weights file into InvokeAI, makes it available
+for image generation within the script, and writes out the configuration for the
+model into `config/models.yaml` for use in subsequent sessions.
 
-Provide `!import_model` with the path to a weights file ending in
-`.ckpt`.  If you type a partial path and press tab, the CLI will
-autocomplete. Although it will also autocomplete to `.vae` files,
-these are not currenty supported (but will be soon).
+Provide `!import_model` with the path to a weights file ending in `.ckpt`. If
+you type a partial path and press tab, the CLI will autocomplete. Although it
+will also autocomplete to `.vae` files, these are not currenty supported (but
+will be soon).
 
-When you hit return, the CLI will prompt you to fill in additional
-information about the model, including the short name you wish to use
-for it with the `!switch` command, a brief description of the model,
-the default image width and height to use with this model, and the
-model's configuration file. The latter three fields are automatically
-filled with reasonable defaults. In the example below, the bold-faced
-text shows what the user typed in with the exception of the width,
-height and configuration file paths, which were filled in
+When you hit return, the CLI will prompt you to fill in additional information
+about the model, including the short name you wish to use for it with the
+`!switch` command, a brief description of the model, the default image width and
+height to use with this model, and the model's configuration file. The latter
+three fields are automatically filled with reasonable defaults. In the example
+below, the bold-faced text shows what the user typed in with the exception of
+the width, height and configuration file paths, which were filled in
 automatically.
 
 Example:
 
 <pre>
-invoke> <b>!import_model models/ldm/stable-diffusion-v1/	model-epoch08-float16.ckpt</b>
+invoke> <b>!import_model models/ldm/stable-diffusion-v1/model-epoch08-float16.ckpt</b>
 >> Model import in process. Please enter the values needed to configure this model:
 
 Name for this model: <b>waifu-diffusion</b>
@@ -371,14 +426,15 @@ OK to import [n]? <b>y</b>
 invoke> 
 </pre>
 
-##!edit_model <name_of_model>
+###!edit_model <name_of_model>
 
-The `!edit_model` command can be used to modify a model that is
-already defined in `config/models.yaml`. Call it with the short
-name of the model you wish to modify, and it will allow you to
-modify the model's `description`, `weights` and other fields.
+The `!edit_model` command can be used to modify a model that is already defined
+in `config/models.yaml`. Call it with the short name of the model you wish to
+modify, and it will allow you to modify the model's `description`, `weights` and
+other fields.
 
 Example:
+
 <pre>
 invoke> <b>!edit_model waifu-diffusion</b>
 >> Editing model waifu-diffusion from configuration file ./configs/models.yaml
@@ -401,36 +457,28 @@ OK to import [n]? y
 >> Loading waifu-diffusion from models/ldm/stable-diffusion-v1/model-epoch10-float16.ckpt
 ...
 </pre>
-=======
-    invoke> !fix 000017.4829112.gfpgan-00.png --embiggen 3
-    ...lots of text...
-    Outputs:
-    [2] outputs/img-samples/000018.2273800735.embiggen-00.png: !fix "outputs/img-samples/000017.243781548.gfpgan-00.png" -s 50 -S 2273800735 -W 512 -H 512 -C 7.5 -A k_lms --embiggen 3.0 0.75 0.25
-    ```
-# History processing
 
-The CLI provides a series of convenient commands for reviewing previous
-actions, retrieving them, modifying them, and re-running them.
-```bash
-invoke> !fetch 0000015.8929913.png
-# the script returns the next line, ready for editing and running:
-invoke> a fantastic alien landscape -W 576 -H 512 -s 60 -A plms -C 7.5
-```
+======= invoke> !fix 000017.4829112.gfpgan-00.png --embiggen 3 ...lots of
+text... Outputs: [2] outputs/img-samples/000018.2273800735.embiggen-00.png: !fix
+"outputs/img-samples/000017.243781548.gfpgan-00.png" -s 50 -S 2273800735 -W 512
+-H 512 -C 7.5 -A k_lms --embiggen 3.0 0.75 0.25 ```
 
-Note that this command may behave unexpectedly if given a PNG file that
-was not generated by InvokeAI.
+## History processing
 
-### `!history`
+The CLI provides a series of convenient commands for reviewing previous actions,
+retrieving them, modifying them, and re-running them.
 
-The invoke script keeps track of all the commands you issue during a
-session, allowing you to re-run them. On Mac and Linux systems, it
-also writes the command-line history out to disk, giving you access to
-the most recent 1000 commands issued.
+### !history
 
-The `!history` command will return a numbered list of all the commands
-issued during the session (Windows), or the most recent 1000 commands
-(Mac|Linux). You can then repeat a command by using the command `!NNN`,
-where "NNN" is the history line number. For example:
+The invoke script keeps track of all the commands you issue during a session,
+allowing you to re-run them. On Mac and Linux systems, it also writes the
+command-line history out to disk, giving you access to the most recent 1000
+commands issued.
+
+The `!history` command will return a numbered list of all the commands issued
+during the session (Windows), or the most recent 1000 commands (Mac|Linux). You
+can then repeat a command by using the command `!NNN`, where "NNN" is the
+history line number. For example:
 
 ```bash
 invoke> !history
@@ -445,21 +493,41 @@ invoke> !20
 invoke> watercolor of beautiful woman sitting under tree wearing broad hat and flowing garment -v0.2 -n6 -S2878767194
 ```
 
-## !fetch
+### !fetch
 
-This command retrieves the generation parameters from a previously
-generated image and either loads them into the command line.  You may
-provide either the name of a file in the current output directory, or
-a full file path.
+This command retrieves the generation parameters from a previously generated
+image and either loads them into the command line (Linux|Mac), or prints them
+out in a comment for copy-and-paste (Windows). You may provide either the name
+of a file in the current output directory, or a full file path. Specify path to
+a folder with image png files, and wildcard \*.png to retrieve the dream command
+used to generate the images, and save them to a file commands.txt for further
+processing.
 
-~~~
+This example loads the generation command for a single png file:
+
+```bash
 invoke> !fetch 0000015.8929913.png
 # the script returns the next line, ready for editing and running:
 invoke> a fantastic alien landscape -W 576 -H 512 -s 60 -A plms -C 7.5
-~~~
+```
 
-Note that this command may behave unexpectedly if given a PNG file that
-was not generated by InvokeAI.
+This one fetches the generation commands from a batch of files and stores them
+into `selected.txt`:
+
+```bash
+invoke> !fetch outputs\selected-imgs\*.png selected.txt
+```
+
+### !replay
+
+This command replays a text file generated by !fetch or created manually
+
+```
+invoke> !replay outputs\selected-imgs\selected.txt
+```
+
+Note that these commands may behave unexpectedly if given a PNG file that was
+not generated by InvokeAI.
 
 ### !search <search string>
 
@@ -473,42 +541,47 @@ invoke> !search surreal
 
 ### `!clear`
 
-This clears the search history from memory and disk. Be advised that
-this operation is irreversible and does not issue any warnings!
+This clears the search history from memory and disk. Be advised that this
+operation is irreversible and does not issue any warnings!
 
 ## Command-line editing and completion
 
-The command-line offers convenient history tracking, editing, and
-command completion.
+The command-line offers convenient history tracking, editing, and command
+completion.
 
-- To scroll through previous commands and potentially edit/reuse them, use the ++up++ and ++down++ keys.
-- To edit the current command, use the ++left++ and ++right++ keys to position the cursor, and then ++backspace++, ++delete++ or insert characters.
-- To move to the very beginning of the command, type ++ctrl+a++ (or ++command+a++ on the Mac)
+- To scroll through previous commands and potentially edit/reuse them, use the
+  ++up++ and ++down++ keys.
+- To edit the current command, use the ++left++ and ++right++ keys to position
+  the cursor, and then ++backspace++, ++delete++ or insert characters.
+- To move to the very beginning of the command, type ++ctrl+a++ (or
+  ++command+a++ on the Mac)
 - To move to the end of the command, type ++ctrl+e++.
-- To cut a section of the command, position the cursor where you want to start cutting and type ++ctrl+k++
-- To paste a cut section back in, position the cursor where you want to paste, and type ++ctrl+y++
+- To cut a section of the command, position the cursor where you want to start
+  cutting and type ++ctrl+k++
+- To paste a cut section back in, position the cursor where you want to paste,
+  and type ++ctrl+y++
 
-Windows users can get similar, but more limited, functionality if they
-launch `invoke.py` with the `winpty` program and have the `pyreadline3`
-library installed:
+Windows users can get similar, but more limited, functionality if they launch
+`invoke.py` with the `winpty` program and have the `pyreadline3` library
+installed:
 
 ```batch
 > winpty python scripts\invoke.py
 ```
 
-On the Mac and Linux platforms, when you exit invoke.py, the last 1000
-lines of your command-line history will be saved. When you restart
-`invoke.py`, you can access the saved history using the ++up++ key.
+On the Mac and Linux platforms, when you exit invoke.py, the last 1000 lines of
+your command-line history will be saved. When you restart `invoke.py`, you can
+access the saved history using the ++up++ key.
 
-In addition, limited command-line completion is installed. In various
-contexts, you can start typing your command and press ++tab++. A list of
-potential completions will be presented to you. You can then type a
-little more, hit ++tab++ again, and eventually autocomplete what you want.
+In addition, limited command-line completion is installed. In various contexts,
+you can start typing your command and press ++tab++. A list of potential
+completions will be presented to you. You can then type a little more, hit
+++tab++ again, and eventually autocomplete what you want.
 
-When specifying file paths using the one-letter shortcuts, the CLI
-will attempt to complete pathnames for you. This is most handy for the
-`-I` (init image) and `-M` (init mask) paths. To initiate completion, start
-the path with a slash (`/`) or `./`. For example:
+When specifying file paths using the one-letter shortcuts, the CLI will attempt
+to complete pathnames for you. This is most handy for the `-I` (init image) and
+`-M` (init mask) paths. To initiate completion, start the path with a slash
+(`/`) or `./`. For example:
 
 ```bash
 invoke> zebra with a mustache -I./test-pictures<TAB>

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
@@ -18,31 +19,33 @@ 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>
+![](https://user-images.githubusercontent.com/50542132/193946000-c42a96d8-5a74-4f8a-b4c3-5213e6cadcce.png)
 </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>
+![](https://user-images.githubusercontent.com/111189/194135515-53d4c060-e994-4016-8121-7c685e281ac9.png)
 </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)
 <caption markdown>photograph of a tree on a hill with a river</caption>
 </figure>
 
@@ -52,27 +55,37 @@ a very different image:
     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
 
-**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:
-~~~
+**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
-~~~
+```
 
 ## 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
 invoke> "fire" -s10 -W384 -H384 -S1592514025
@@ -82,9 +95,16 @@ 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 +114,9 @@ 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 +128,47 @@ 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  | ![](../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) |
 
-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`:
+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
 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
 invoke> "fire" -s50 -W384 -H384 -S1592514025 -I /tmp/fire-drawing.png -f 0.4
@@ -142,7 +178,8 @@ 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
@@ -152,7 +189,11 @@ 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)
@@ -164,15 +205,19 @@ 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)
@@ -182,4 +227,9 @@ 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.

docs/features/INPAINTING.md

@@ -6,55 +6,233 @@ title: Inpainting
 
 ## **Creating Transparent Regions for Inpainting**
 
-Inpainting is really cool. To do it, you start with an initial image
-and use a photoeditor to make one or more regions transparent
-(i.e. they have a "hole" in them). You then provide the path to this
-image at the dream> command line using the `-I` switch. Stable
-Diffusion will only paint within the transparent region.
+Inpainting is really cool. To do it, you start with an initial image and use a
+photoeditor to make one or more regions transparent (i.e. they have a "hole" in
+them). You then provide the path to this image at the dream> command line using
+the `-I` switch. Stable Diffusion will only paint within the transparent region.
 
-There's a catch. In the current implementation, you have to prepare
-the initial image correctly so that the underlying colors are
-preserved under the transparent area. Many imaging editing
-applications will by default erase the color information under the
-transparent pixels and replace them with white or black, which will
-lead to suboptimal inpainting. It often helps to apply incomplete
-transparency, such as any value between 1 and 99%
+There's a catch. In the current implementation, you have to prepare the initial
+image correctly so that the underlying colors are preserved under the
+transparent area. Many imaging editing applications will by default erase the
+color information under the transparent pixels and replace them with white or
+black, which will lead to suboptimal inpainting. It often helps to apply
+incomplete transparency, such as any value between 1 and 99%
 
-You also must take care to export the PNG file in such a way that the
-color information is preserved. There is often an option in the export
-dialog that lets you specify this.
+You also must take care to export the PNG file in such a way that the color
+information is preserved. There is often an option in the export dialog that
+lets you specify this.
 
-If your photoeditor is erasing the underlying color information,
-`dream.py` will give you a big fat warning. If you can't find a way to
-coax your photoeditor to retain color values under transparent areas,
-then you can combine the `-I` and `-M` switches to provide both the
-original unedited image and the masked (partially transparent) image:
+If your photoeditor is erasing the underlying color information, `dream.py` will
+give you a big fat warning. If you can't find a way to coax your photoeditor to
+retain color values under transparent areas, then you can combine the `-I` and
+`-M` switches to provide both the original unedited image and the masked
+(partially transparent) image:
 
 ```bash
 invoke> "man with cat on shoulder" -I./images/man.png -M./images/man-transparent.png
 ```
 
-We are hoping to get rid of the need for this workaround in an upcoming release.
+## **Masking using Text**
 
-### Inpainting is not changing the masked region enough!
+You can also create a mask using a text prompt to select the part of the image
+you want to alter, using the [clipseg](https://github.com/timojl/clipseg)
+algorithm. This works on any image, not just ones generated by InvokeAI.
 
-One of the things to understand about how inpainting works is that it
-is equivalent to running img2img on just the masked (transparent)
-area. img2img builds on top of the existing image data, and therefore
-will attempt to preserve colors, shapes and textures to the best of
-its ability. Unfortunately this means that if you want to make a
-dramatic change in the inpainted region, for example replacing a red
-wall with a blue one, the algorithm will fight you.
+The `--text_mask` (short form `-tm`) option takes two arguments. The first
+argument is a text description of the part of the image you wish to mask (paint
+over). If the text description contains a space, you must surround it with
+quotation marks. The optional second argument is the minimum threshold for the
+mask classifier's confidence score, described in more detail below.
 
-You have a couple of options. The first is to increase the values of
-the requested steps (`-sXXX`), strength (`-f0.XX`), and/or
-condition-free guidance (`-CXX.X`). If this is not working for you, a
-more extreme step is to provide the `--inpaint_replace 0.X` (`-r0.X`)
-option. This value ranges from 0.0 to 1.0. The higher it is the less
-attention the algorithm will pay to the data underneath the masked
-region. At high values this will enable you to replace colored regions
-entirely, but beware that the masked region mayl not blend in with the
-surrounding unmasked regions as well.
+To see how this works in practice, here's an image of a still life painting that
+I got off the web.
+
+<figure markdown>
+![still life scaled](../assets/still-life-scaled.jpg)
+</figure>
+
+You can selectively mask out the orange and replace it with a baseball in this
+way:
+
+```bash
+invoke> a baseball -I /path/to/still_life.png -tm orange
+```
+
+<figure markdown>
+![](../assets/still-life-inpainted.png)
+</figure>
+
+The clipseg classifier produces a confidence score for each region it
+identifies. Generally regions that score above 0.5 are reliable, but if you are
+getting too much or too little masking you can adjust the threshold down (to get
+more mask), or up (to get less). In this example, by passing `-tm` a higher
+value, we are insisting on a tigher mask. However, if you make it too high, the
+orange may not be picked up at all!
+
+```bash
+invoke> a baseball -I /path/to/breakfast.png -tm orange 0.6
+```
+
+The `!mask` command may be useful for debugging problems with the text2mask
+feature. The syntax is `!mask /path/to/image.png -tm <text> <threshold>`
+
+It will generate three files:
+
+- The image with the selected area highlighted.
+  - it will be named XXXXX.<imagename>.<prompt>.selected.png
+- The image with the un-selected area highlighted.
+  - it will be named XXXXX.<imagename>.<prompt>.deselected.png
+- The image with the selected area converted into a black and white image
+  according to the threshold level
+  - it will be named XXXXX.<imagename>.<prompt>.masked.png
+
+The `.masked.png` file can then be directly passed to the `invoke>` prompt in
+the CLI via the `-M` argument. Do not attempt this with the `selected.png` or
+`deselected.png` files, as they contain some transparency throughout the image
+and will not produce the desired results.
+
+Here is an example of how `!mask` works:
+
+```bash
+invoke> !mask ./test-pictures/curly.png -tm hair 0.5
+>> generating masks from ./test-pictures/curly.png
+>> Initializing clipseg model for text to mask inference
+Outputs:
+[941.1] outputs/img-samples/000019.curly.hair.deselected.png: !mask ./test-pictures/curly.png -tm hair 0.5
+[941.2] outputs/img-samples/000019.curly.hair.selected.png: !mask ./test-pictures/curly.png -tm hair 0.5
+[941.3] outputs/img-samples/000019.curly.hair.masked.png: !mask ./test-pictures/curly.png -tm hair 0.5
+```
+
+<figure markdown>
+![curly](../assets/outpainting/curly.png)
+<figcaption>Original image "curly.png"</figcaption>
+</figure>
+
+<figure markdown>
+![curly hair selected](../assets/inpainting/000019.curly.hair.selected.png)
+<figcaption>000019.curly.hair.selected.png</figcaption>
+</figure>
+
+<figure markdown>
+![curly hair deselected](../assets/inpainting/000019.curly.hair.deselected.png)
+<figcaption>000019.curly.hair.deselected.png</figcaption>
+</figure>
+
+<figure markdown>
+![curly hair masked](../assets/inpainting/000019.curly.hair.masked.png)
+<figcaption>000019.curly.hair.masked.png</figcaption>
+</figure>
+
+It looks like we selected the hair pretty well at the 0.5 threshold (which is
+the default, so we didn't actually have to specify it), so let's have some fun:
+
+```bash
+invoke> medusa with cobras -I ./test-pictures/curly.png -M 000019.curly.hair.masked.png -C20
+>> loaded input image of size 512x512 from ./test-pictures/curly.png
+...
+Outputs:
+[946] outputs/img-samples/000024.801380492.png: "medusa with cobras" -s 50 -S 801380492 -W 512 -H 512 -C 20.0 -I ./test-pictures/curly.png -A k_lms -f 0.75
+```
+
+<figure markdown>
+![](../assets/inpainting/000024.801380492.png)
+</figure>
+
+You can also skip the `!mask` creation step and just select the masked
+
+region directly:
+
+```bash
+invoke> medusa with cobras -I ./test-pictures/curly.png -tm hair -C20
+```
+
+## Using the RunwayML inpainting model
+
+The
+[RunwayML Inpainting Model v1.5](https://huggingface.co/runwayml/stable-diffusion-inpainting)
+is a specialized version of
+[Stable Diffusion v1.5](https://huggingface.co/spaces/runwayml/stable-diffusion-v1-5)
+that contains extra channels specifically designed to enhance inpainting and
+outpainting. While it can do regular `txt2img` and `img2img`, it really shines
+when filling in missing regions. It has an almost uncanny ability to blend the
+new regions with existing ones in a semantically coherent way.
+
+To install the inpainting model, follow the
+[instructions](../installation/INSTALLING_MODELS.md) for installing a new model.
+You may use either the CLI (`invoke.py` script) or directly edit the
+`configs/models.yaml` configuration file to do this. The main thing to watch out
+for is that the the model `config` option must be set up to use
+`v1-inpainting-inference.yaml` rather than the `v1-inference.yaml` file that is
+used by Stable Diffusion 1.4 and 1.5.
+
+After installation, your `models.yaml` should contain an entry that looks like
+this one:
+
+inpainting-1.5: weights: models/ldm/stable-diffusion-v1/sd-v1-5-inpainting.ckpt
+description: SD inpainting v1.5 config:
+configs/stable-diffusion/v1-inpainting-inference.yaml vae:
+models/ldm/stable-diffusion-v1/vae-ft-mse-840000-ema-pruned.ckpt width: 512
+height: 512
+
+As shown in the example, you may include a VAE fine-tuning weights file as well.
+This is strongly recommended.
+
+To use the custom inpainting model, launch `invoke.py` with the argument
+`--model inpainting-1.5` or alternatively from within the script use the
+`!switch inpainting-1.5` command to load and switch to the inpainting model.
+
+You can now do inpainting and outpainting exactly as described above, but there
+will (likely) be a noticeable improvement in coherence. Txt2img and Img2img will
+work as well.
+
+There are a few caveats to be aware of:
+
+1. The inpainting model is larger than the standard model, and will use nearly 4
+   GB of GPU VRAM. This makes it unlikely to run on a 4 GB graphics card.
+
+2. When operating in Img2img mode, the inpainting model is much less steerable
+   than the standard model. It is great for making small changes, such as
+   changing the pattern of a fabric, or slightly changing a subject's expression
+   or hair, but the model will resist making the dramatic alterations that the
+   standard model lets you do.
+
+3. While the `--hires` option works fine with the inpainting model, some special
+   features, such as `--embiggen` are disabled.
+
+4. Prompt weighting (`banana++ sushi`) and merging work well with the inpainting
+   model, but prompt swapping
+   (`a ("fluffy cat").swap("smiling dog") eating a hotdog`) will not have any
+   effect due to the way the model is set up. You may use text masking (with
+   `-tm thing-to-mask`) as an effective replacement.
+
+5. The model tends to oversharpen image if you use high step or CFG values. If
+   you need to do large steps, use the standard model.
+
+6. The `--strength` (`-f`) option has no effect on the inpainting model due to
+   its fundamental differences with the standard model. It will always take the
+   full number of steps you specify.
+
+## Troubleshooting
+
+Here are some troubleshooting tips for inpainting and outpainting.
+
+## Inpainting is not changing the masked region enough!
+
+One of the things to understand about how inpainting works is that it is
+equivalent to running img2img on just the masked (transparent) area. img2img
+builds on top of the existing image data, and therefore will attempt to preserve
+colors, shapes and textures to the best of its ability. Unfortunately this means
+that if you want to make a dramatic change in the inpainted region, for example
+replacing a red wall with a blue one, the algorithm will fight you.
+
+You have a couple of options. The first is to increase the values of the
+requested steps (`-sXXX`), strength (`-f0.XX`), and/or condition-free guidance
+(`-CXX.X`). If this is not working for you, a more extreme step is to provide
+the `--inpaint_replace 0.X` (`-r0.X`) option. This value ranges from 0.0 to 1.0.
+The higher it is the less attention the algorithm will pay to the data
+underneath the masked region. At high values this will enable you to replace
+colored regions entirely, but beware that the masked region mayl not blend in
+with the surrounding unmasked regions as well.
 
 ---
 
@@ -69,8 +247,8 @@ surrounding unmasked regions as well.
 5. Open the Layers toolbar (^L) and select "Floating Selection"
 6. Set opacity to a value between 0% and 99%
 7. Export as PNG
-8. In the export dialogue, Make sure the "Save colour values from
-   transparent pixels" checkbox is selected.
+8. In the export dialogue, Make sure the "Save colour values from transparent
+   pixels" checkbox is selected.
 
 ---
 
@@ -82,36 +260,47 @@ surrounding unmasked regions as well.
     ![step1](../assets/step1.png)
     </figure>
 
-2. Use any of the selection tools (Marquee, Lasso, or Wand) to select the area you desire to inpaint.
+2. Use any of the selection tools (Marquee, Lasso, or Wand) to select the area
+   you desire to inpaint.
 
     <figure markdown>
     ![step2](../assets/step2.png)
     </figure>
 
-3. Because we'll be applying a mask over the area we want to preserve, you should now select the inverse by using the ++shift+ctrl+i++ shortcut, or right clicking and using the "Select Inverse" option.
+3. Because we'll be applying a mask over the area we want to preserve, you
+   should now select the inverse by using the ++shift+ctrl+i++ shortcut, or
+   right clicking and using the "Select Inverse" option.
 
-4. You'll now create a mask by selecting the image layer, and Masking the selection. Make sure that you don't delete any of the underlying image, or your inpainting results will be dramatically impacted.
+4. You'll now create a mask by selecting the image layer, and Masking the
+   selection. Make sure that you don't delete any of the underlying image, or
+   your inpainting results will be dramatically impacted.
 
     <figure markdown>
     ![step4](../assets/step4.png)
     </figure>
 
-5. Make sure to hide any background layers that are present. You should see the mask applied to your image layer, and the image on your canvas should display the checkered background.
+5. Make sure to hide any background layers that are present. You should see the
+   mask applied to your image layer, and the image on your canvas should display
+   the checkered background.
 
     <figure markdown>
     ![step5](../assets/step5.png)
     </figure>
 
-6. Save the image as a transparent PNG by using `File`-->`Save a Copy` from the menu bar, or by using the keyboard shortcut ++alt+ctrl+s++
+6. Save the image as a transparent PNG by using `File`-->`Save a Copy` from the
+   menu bar, or by using the keyboard shortcut ++alt+ctrl+s++
 
     <figure markdown>
     ![step6](../assets/step6.png)
     </figure>
 
-7. After following the inpainting instructions above (either through the CLI or the Web UI), marvel at your newfound ability to selectively invoke. Lookin' good!
+7. After following the inpainting instructions above (either through the CLI or
+   the Web UI), marvel at your newfound ability to selectively invoke. Lookin'
+   good!
 
     <figure markdown>
    ![step7](../assets/step7.png)
     </figure>
 
-8. In the export dialogue, Make sure the "Save colour values from transparent pixels" checkbox is selected.  
+8. In the export dialogue, Make sure the "Save colour values from transparent
+   pixels" checkbox is selected.

docs/features/OTHER.md

@@ -26,6 +26,12 @@ for each `invoke>` prompt as shown here:
 invoke> "pond garden with lotus by claude monet" --seamless -s100 -n4
 ```
 
+By default this will tile on both the X and Y axes. However, you can also specify specific axes to tile on with `--seamless_axes`.
+Possible values are `x`, `y`, and `x,y`:
+```python
+invoke> "pond garden with lotus by claude monet" --seamless --seamless_axes=x -s100 -n4
+```
+
 ---
 
 ## **Shortcuts: Reusing Seeds**
@@ -69,6 +75,23 @@ combination of integers and floating point numbers, and they do not need to add
 
 ---
 
+## **Filename Format**
+
+The argument `--fnformat` allows to specify the filename of the
+ image. Supported wildcards are all arguments what can be set such as
+ `perlin`, `seed`, `threshold`, `height`, `width`, `gfpgan_strength`,
+ `sampler_name`, `steps`, `model`, `upscale`, `prompt`, `cfg_scale`,
+ `prefix`.
+
+The following prompt
+```bash
+dream> a red car --steps 25 -C 9.8 --perlin 0.1 --fnformat {prompt}_steps.{steps}_cfg.{cfg_scale}_perlin.{perlin}.png
+```
+
+generates a file with the name: `outputs/img-samples/a red car_steps.25_cfg.9.8_perlin.0.1.png`
+
+---
+
 ## **Thresholding and Perlin Noise Initialization Options**
 
 Two new options are the thresholding (`--threshold`) and the perlin noise initialization (`--perlin`) options. Thresholding limits the range of the latent values during optimization, which helps combat oversaturation with higher CFG scale values. Perlin noise initialization starts with a percentage (a value ranging from 0 to 1) of perlin noise mixed into the initial noise. Both features allow for more variations and options in the course of generating images.

docs/features/OUTPAINTING.md

@@ -6,22 +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.
+
+Pass the image as the argument to the `-I` switch as you would for regular
+inpainting:
+
+```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.
+
+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:
+
+| 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 allows you to extend the image in 64 pixel
-increments in any dimension. You can apply the module to any image
-previously-generated by InvokeAI. Note that it will **not** work with
-arbitrary photographs or Stable Diffusion images created by other
-implementations.
+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:
 
@@ -29,46 +70,71 @@ 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 64 right 64
+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 64 pixels, and the right of the image by the same
-amount. 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:
 
 <figure markdown>
-![curly_woman_outcrop](../assets/outpainting/curly-outcrop.png)
+![curly_woman_outcrop](../assets/outpainting/curly-outcrop-2.png)
 </figure>
 
-The new image is actually slightly larger than the original (576x576,
-because 64 pixels were added to the top and right sides.)
+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 seed of "0" 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.
+
+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
@@ -87,15 +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
 

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

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 -
@@ -32,26 +34,29 @@ 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.
 
 original prompt:
 
-`#!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" -s 20 -W 512 -H 768 -C 7.5 -A k_euler_a -S 1654590180`
+`#!bash "A fantastical translucent pony 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" -s 20 -W 512 -H 768 -C 7.5 -A k_euler_a -S 1654590180`
 
 <figure markdown>
 ![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`
 
@@ -84,88 +91,230 @@ Getting close - but there's no sense in having a saddle when our horse doesn't h
 
 ---
 
+## **Prompt Syntax Features**
+
+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).
+
+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`:
+
+![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`                                                                                                    |
+| ------------------------------------------------------------------------------------------------------------------------------ | --------------------------------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------ |
+| ![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`                                                                                                                                                                           |
+| ------------------------------------------------------------------------------------------------------------------------------------------ | --------------------------------------------------------------------------------------------------------------------------------------------------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
+| ![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`:
+
+![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`                              |
+| ---------------------------------------------- | ---------------------------------------------- | ---------------------------------------------- |
+| ![](../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++++`                             |
+| ---------------------------------------------- | ---------------------------------------------- | ---------------------------------------------- | ---------------------------------------------- |
+| ![](../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.
+
+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.
+
+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`.
+
+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
+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.
+
+---
+
 ## **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.

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
 

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.

docs/features/WEBUIHOTKEYS.md

@@ -0,0 +1,62 @@
+---
+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          |
+| ++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 |
+
+## Gallery
+
+| 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         |

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.

docs/index.md

@@ -14,45 +14,63 @@ 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/NwVCmKwY">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>]
+**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>]
 
 <div align="center"><img src="assets/invoke-web-server-1.png" width=640></div>
 
@@ -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).

docs/installation/INSTALL.md

@@ -0,0 +1,56 @@
+---
+title: Installation Overview
+---
+
+## Installation
+
+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.
+
+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.

docs/installation/INSTALLING_MODELS.md

@@ -0,0 +1,246 @@
+---
+title: Installing Models
+---
+
+# :octicons-paintbrush-16: 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.
+
+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.
+
+## Base Models
+
+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/     |
+
+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.
+
+## 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
+`--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.
+
+## Installation
+
+There are three ways to install weights files:
+
+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.
+
+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.
+
+To start, run `python scripts/preload_models.py` from within the InvokeAI:
+directory
+
+!!! example ""
+
+    ```text
+    Loading Python libraries...
+
+    ** 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.
+
+    ** WEIGHT SELECTION **
+    Would you like to download the Stable Diffusion model weights now? [y]
+
+    Choose the weight file(s) you wish to download. Before downloading you
+    will be given the option to view and change your selections.
+
+    [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 **
+
+    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:
+
+      https://huggingface.co
+
+      You will need to verify your email address as part of the HuggingFace
+      registration process.
+
+    2. After creating the account, login under your account and accept
+      the license terms located here:
+
+      https://huggingface.co/CompVis/stable-diffusion-v-1-4-original
+
+    Press <enter> when you are ready to continue:
+    ...
+    ```
+
+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.
+
+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`.
+
+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`
+
+    !!! tip "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.
+
+4.  Follow the wizard's instructions to complete installation as shown in the
+    example here:
+
+    !!! 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:
+
+        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
+        ```
+
+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 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.
+
+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`
+
+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
+  config: ./configs/stable-diffusion/v1-inference.yaml
+  width: 512
+  height: 512
+  vae: ./models/ldm/stable-diffusion-v1/vae-ft-mse-840000-ema-pruned.ckpt
+  default: false
+```
+
+| 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.

docs/installation/INSTALL_DOCKER.md

@@ -6,24 +6,23 @@ title: Docker
 
 ## Before you begin
 
-- For end users: Install Stable Diffusion locally using the instructions for
-  your OS.
+- For end users: Install InvokeAI 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.
 
 ## 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)
@@ -36,20 +35,6 @@ another environment with NVIDIA GPUs on-premises or in the cloud.
 
 ### Prerequisites
 
-#### Get the data files
-
-Go to
-[Hugging Face](https://huggingface.co/CompVis/stable-diffusion-v-1-4-original),
-and click "Access repository" to Download the model file `sd-v1-4.ckpt` (~4 GB)
-to `~/Downloads`. You'll need to create an account but it's quick and free.
-
-Also download the face restoration model.
-
-```Shell
-cd ~/Downloads
-wget https://github.com/TencentARC/GFPGAN/releases/download/v1.3.0/GFPGANv1.4.pth
-```
-
 #### Install [Docker](https://github.com/santisbon/guides#docker)
 
 On the Docker Desktop app, go to Preferences, Resources, Advanced. Increase the
@@ -57,86 +42,78 @@ 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)
+
 ### Setup
 
 Set the fork you want to use and other variables.
 
-```Shell
-TAG_STABLE_DIFFUSION="santisbon/stable-diffusion"
-PLATFORM="linux/arm64"
-GITHUB_STABLE_DIFFUSION="-b orig-gfpgan https://github.com/santisbon/stable-diffusion.git"
-REQS_STABLE_DIFFUSION="requirements-linux-arm64.txt"
-CONDA_SUBDIR="osx-arm64"
+!!! tip
 
-echo $TAG_STABLE_DIFFUSION
-echo $PLATFORM
-echo $GITHUB_STABLE_DIFFUSION
-echo $REQS_STABLE_DIFFUSION
-echo $CONDA_SUBDIR
+    I preffer to save my env vars
+    in the repository root in a `.env` (or `.envrc`) file to automatically re-apply
+    them when I come back.
+
+The build- and run- scripts contain default values for almost everything,
+besides the [Hugging Face Token](https://huggingface.co/settings/tokens) you
+created in the last step.
+
+Some Suggestions of variables you may want to change besides the Token:
+
+| 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
+
+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
 ```
 
-Create a Docker volume for the downloaded model files.
+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.
 
-```Shell
-docker volume create my-vol
+#### 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
 ```
 
-Copy the data files to the Docker volume using a lightweight Linux container.
-We'll need the models at run time. You just need to create the container with
-the mountpoint; no need to run this dummy container.
+When used without arguments, the container will start the website and provide
+you the link to open it. But if you want to use some other parameters you can
+also do so.
 
-```Shell
-cd ~/Downloads # or wherever you saved the files
+!!! example
 
-docker create --platform $PLATFORM --name dummy --mount source=my-vol,target=/data alpine
+    ```bash
+    docker-build/run.sh --from_file tests/validate_pr_prompt.txt
+    ```
 
-docker cp sd-v1-4.ckpt dummy:/data
-docker cp GFPGANv1.4.pth dummy:/data
-```
+    The output folder is located on the volume which is also used to store the model.
 
-Get the repo and download the Miniconda installer (we'll need it at build time).
-Replace the URL with the version matching your container OS and the architecture
-it will run on.
+    Find out more about available CLI-Parameter at [features/CLI.md](../features/CLI.md)
 
-```Shell
-cd ~
-git clone $GITHUB_STABLE_DIFFUSION
+---
 
-cd stable-diffusion/docker-build
-chmod +x entrypoint.sh
-wget https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-aarch64.sh -O anaconda.sh && chmod +x anaconda.sh
-```
+!!! warning "Deprecated"
 
-Build the Docker image. Give it any tag `-t` that you want.  
-Choose the Linux container's host platform: x86-64/Intel is `amd64`. Apple
-silicon is `arm64`. If deploying the container to the cloud to leverage powerful
-GPU instances you'll be on amd64 hardware but if you're just trying this out
-locally on Apple silicon choose arm64.  
-The application uses libraries that need to match the host environment so use
-the appropriate requirements file.  
-Tip: Check that your shell session has the env variables set above.
-
-```Shell
-docker build -t $TAG_STABLE_DIFFUSION \
---platform $PLATFORM \
---build-arg gsd=$GITHUB_STABLE_DIFFUSION \
---build-arg rsd=$REQS_STABLE_DIFFUSION \
---build-arg cs=$CONDA_SUBDIR \
-.
-```
-
-Run a container using your built image.  
-Tip: Make sure you've created and populated the Docker volume (above).
-
-```Shell
-docker run -it \
---rm \
---platform $PLATFORM \
---name stable-diffusion \
---hostname stable-diffusion \
---mount source=my-vol,target=/data \
-$TAG_STABLE_DIFFUSION
-```
+    From here on you will find the rest of the previous Docker-Docs, which will still
+    provide some usefull informations.
 
 ## Usage (time to have fun)
 
@@ -240,7 +217,8 @@ server with:
 python3 scripts/invoke.py --full_precision --web
 ```
 
-If it's running on your Mac point your Mac web browser to http://127.0.0.1:9090
+If it's running on your Mac point your Mac web browser to
+<http://127.0.0.1:9090>
 
 Press Control-C at the command line to stop the web server.
 

docs/installation/INSTALL_INVOKE.md

@@ -0,0 +1,52 @@
+---
+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.
+
+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.
+
+## 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.

docs/installation/INSTALL_JUPYTER.md

@@ -0,0 +1,28 @@
+---
+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 that 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