Commit Graph

20 Commits

Author SHA1 Message Date
Lincoln Stein
9b157b6532 fix several issues with Windows installs
1. resize installer window to give more room for configure and download forms
2. replace '\' with '/' in directory names to allow user to drag-and-drop
   folders into the dialogue boxes that accept directories.
3. similar change in CLI for the !import_model and !convert_model commands
4. better error reporting when a model download fails due to network errors
5. put the launcher scripts into a loop so that menu reappears after
   invokeai, merge script, etc exits. User can quit with "Q".
6. do not try to download fp16 of sd-ft-mse-vae, since it doesn't exist.
7. cleaned up status reporting when installing models
2023-02-23 00:49:59 -05:00
Lincoln Stein
fff41a7349 merged with main 2023-02-21 12:20:59 -05:00
Lincoln Stein
9d8236c59d tested and working on Ubuntu
- You can now achieve several effects:

   `invokeai-configure`
   This will use console-based UI to initialize invokeai.init,
   download support models, and choose and download SD models

   `invokeai-configure --yes`
   Without activating the GUI, populate invokeai.init with default values,
   download support models and download the "recommended" SD models

   `invokeai-configure --default_only`
   As above, but only download the default SD model (currently SD-1.5)

   `invokeai-model-install`
   Select and install models. This can be used to download arbitrary
   models from the Internet, install HuggingFace models using their repo_id,
   or watch a directory for models to load at startup time

   `invokeai-model-install --yes`
   Import the recommended SD models without a GUI

   `invokeai-model-install --default_only`
   As above, but only import the default model
2023-02-19 16:08:58 -05:00
Lincoln Stein
7545e38655 frontend design done; functionality not hooked up yet 2023-02-14 00:02:19 -05:00
Lincoln Stein
714fff39ba add new console frontend to initial model selection, and other improvements
1. The invokeai-configure script has now been refactored. The work of
   selecting and downloading initial models at install time is now done
   by a script named invokeai-initial-models (module
   name is ldm.invoke.config.initial_model_select)

   The calling arguments for invokeai-configure have not changed, so
   nothing should break. After initializing the root directory, the
   script calls invokeai-initial-models to let the user select the
   starting models to install.

2. invokeai-initial-models puts up a console GUI with checkboxes to
   indicate which models to install. It respects the --default_only
   and --yes arguments so that CI will continue to work.

3. User can now edit the VAE assigned to diffusers models in the CLI.

4. Fixed a bug that caused a crash during model loading when the VAE
   is set to None, rather than being empty.
2023-02-12 23:52:44 -05:00
Lincoln Stein
24e32f6ae2 add 'update' action to launcher script
- Adds an update action to launcher script
- This action calls new python script `invokeai-update`, which prompts
  user to update to latest release version, main development version,
  or an arbitrary git tag or branch name.
- It then uses `pip` to update to whatever tag was specified.
2023-02-11 22:32:48 -05:00
Lincoln Stein
c6d13e679f make menu options in invoke.bat the same as options in invoke.sh
- This makes the launcher options menu on Windows look and act the same
  as the Linux/Mac launcher, which previously was lacking the command-line
  help option and didn't list item (6) as an option.
2023-02-08 11:04:00 -05:00
Lincoln Stein
61c3886843
Merge branch 'main' into bugfix/use-cu117-wheel 2023-02-04 09:43:52 -05:00
Lincoln Stein
c50b64ec1d correct default menu entry in install.bat file 2023-02-03 13:30:21 -05:00
Lincoln Stein
ca0f3ec0e4 fix launcher shell script to use correct names for ti and merge functions 2023-02-03 09:45:57 -05:00
Lincoln Stein
3996ee843c fix bugs in launcher script installation
- launcher scripts are installed *before* the configure script runs,
  so that if something goes wrong in the configure script, the user
  can run invoke.{sh,bat} and get the option to re-run configure
- fixed typo in invoke.sh which misspelled name of invokeai-configure
2023-02-01 19:14:07 -05:00
Eugene Brodsky
6cbdd88fe2 (installer) correctly call invokeai entrypoints in .bat launch script 2023-02-01 00:08:18 -05:00
Eugene Brodsky
34e0d7aaa8 (config) rename all mentions of scripts/configure_invokeai.py to the new invokeai-configure command 2023-01-28 17:39:33 -05:00
Eugene Brodsky
71733bcfa1 (installer) copy launch/update scripts to the root dir; improve launch experience on Linux/Mac
- install.sh is now a thin wrapper around the pythonized install script
- install.bat not done yet - to follow
- user messaging is tailored to the current platform (paste shortcuts, file paths, etc)
- emit invoke.sh/invoke.bat scripts to the runtime dir
- improve launch scripts (add help option, etc)
- only emit the platform-specific scripts
2023-01-28 17:39:33 -05:00
Lincoln Stein
2817f8a428 update launcher shell scripts for new script names & paths 2023-01-26 15:26:38 -05:00
Lincoln Stein
48deb3e49d add model merging documentation and launcher script menu entries 2023-01-23 00:20:28 -05:00
Kevin Turner
6fdbc1978d
use 🧨diffusers model (#1583)
* initial commit of DiffusionPipeline class

* spike: proof of concept using diffusers for txt2img

* doc: type hints for Generator

* refactor(model_cache): factor out load_ckpt

* model_cache: add ability to load a diffusers model pipeline

and update associated things in Generate & Generator to not instantly fail when that happens

* model_cache: fix model default image dimensions

* txt2img: support switching diffusers schedulers

* diffusers: let the scheduler do its scaling of the initial latents

Remove IPNDM scheduler; it is not behaving.

* web server: update image_progress callback for diffusers data

* diffusers: restore prompt weighting feature

* diffusers: fix set-sampler error following model switch

* diffusers: use InvokeAIDiffuserComponent for conditioning

* cross_attention_control: stub (no-op) implementations for diffusers

* model_cache: let offload_model work with DiffusionPipeline, sorta.

* models.yaml.example: add diffusers-format model, set as default

* test-invoke-conda: use diffusers-format model
test-invoke-conda: put huggingface-token where the library can use it

* environment-mac: upgrade to diffusers 0.7 (from 0.6)

this was already done for linux; mac must have been lost in the merge.

* preload_models: explicitly load diffusers models

In non-interactive mode too, as long as you're logged in.

* fix(model_cache): don't check `model.config` in diffusers format

clean-up from recent merge.

* diffusers integration: support img2img

* dev: upgrade to diffusers 0.8 (from 0.7.1)

We get to remove some code by using methods that were factored out in the base class.

* refactor: remove backported img2img.get_timesteps

now that we can use it directly from diffusers 0.8.1

* ci: use diffusers model

* dev: upgrade to diffusers 0.9 (from 0.8.1)

* lint: correct annotations for Python 3.9.

* lint: correct AttributeError.name reference for Python 3.9.

* CI: prefer diffusers-1.4 because it no longer requires a token

The RunwayML models still do.

* build: there's yet another place to update requirements?

* configure: try to download models even without token

Models in the CompVis and stabilityai repos no longer require them. (But runwayml still does.)

* configure: add troubleshooting info for config-not-found

* fix(configure): prepend root to config path

* fix(configure): remove second `default: true` from models example

* CI: simplify test-on-push logic now that we don't need secrets

The "test on push but only in forks" logic was only necessary when tests didn't work for PRs-from-forks.

* create an embedding_manager for diffusers

* internal: avoid importing diffusers DummyObject

see https://github.com/huggingface/diffusers/issues/1479

* fix "config attributes…not expected" diffusers warnings.

* fix deprecated scheduler construction

* work around an apparent MPS torch bug that causes conditioning to have no effect

* 🚧 post-rebase repair

* preliminary support for outpainting (no masking yet)

* monkey-patch diffusers.attention and use Invoke lowvram code

* add always_use_cpu arg to bypass MPS

* add cross-attention control support to diffusers (fails on MPS)

For unknown reasons MPS produces garbage output with .swap(). Use
--always_use_cpu arg to invoke.py for now to test this code on MPS.

* diffusers support for the inpainting model

* fix debug_image to not crash with non-RGB images.

* inpainting for the normal model [WIP]

This seems to be performing well until the LAST STEP, at which point it dissolves to confetti.

* fix off-by-one bug in cross-attention-control (#1774)

prompt token sequences begin with a "beginning-of-sequence" marker <bos> and end with a repeated "end-of-sequence" marker <eos> - to make a default prompt length of <bos> + 75 prompt tokens + <eos>. the .swap() code was failing to take the column for <bos> at index 0 into account. the changes here do that, and also add extra handling for a single <eos> (which may be redundant but which is included for completeness).

based on my understanding and some assumptions about how this all works, the reason .swap() nevertheless seemed to do the right thing, to some extent, is because over multiple steps the conditioning process in Stable Diffusion operates as a feedback loop. a change to token n-1 has flow-on effects to how the [1x4x64x64] latent tensor is modified by all the tokens after it, - and as the next step is processed, all the tokens before it as well. intuitively, a token's conditioning effects "echo" throughout the whole length of the prompt. so even though the token at n-1 was being edited when what the user actually wanted was to edit the token at n, it nevertheless still had some non-negligible effect, in roughly the right direction, often enough that it seemed like it was working properly.

* refactor common CrossAttention stuff into a mixin so that the old ldm code can still work if necessary

* inpainting for the normal model. I think it works this time.

* diffusers: reset num_vectors_per_token

sync with 44a0055571

* diffusers: txt2img2img (hires_fix)

with so much slicing and dicing of pipeline methods to stitch them together

* refactor(diffusers): reduce some code duplication amongst the different tasks

* fixup! refactor(diffusers): reduce some code duplication amongst the different tasks

* diffusers: enable DPMSolver++ scheduler

* diffusers: upgrade to diffusers 0.10, add Heun scheduler

* diffusers(ModelCache): stopgap to make from_cpu compatible with diffusers

* CI: default to diffusers-1.5 now that runwayml token requirement is gone

* diffusers: update to 0.10 (and transformers to 4.25)

* diffusers: use xformers when available

diffusers no longer auto-enables this as of 0.10.2.

* diffusers: make masked img2img behave better with multi-step schedulers

re-randomizing the noise each step was confusing them.

* diffusers: work more better with more models.

fixed relative path problem with local models.

fixed models on hub not always having a `fp16` branch.

* diffusers: stopgap fix for attention_maps_callback crash after recent merge

* fixup import merge conflicts

correction for 061c5369a2

* test: add tests/inpainting inputs for masked img2img

* diffusers(AddsMaskedGuidance): partial fix for k-schedulers

Prevents them from crashing, but results are still hot garbage.

* fix --safety_checker arg parsing

and add note to diffusers loader about where safety checker gets called

* generate: fix import error

* CI: don't try to read the old init location

* diffusers: support loading an alternate VAE

* CI: remove sh-syntax if-statement so it doesn't crash powershell

* CI: fold strings in yaml because backslash is not line-continuation in powershell

* attention maps callback stuff for diffusers

* build: fix syntax error in environment-mac

* diffusers: add INITIAL_MODELS with diffusers-compatible repos

* re-enable the embedding manager; closes #1778

* Squashed commit of the following:

commit e4a956abc37fcb5cf188388b76b617bc5c8fda7d
Author: Damian Stewart <d@damianstewart.com>
Date:   Sun Dec 18 15:43:07 2022 +0100

    import new load handling from EmbeddingManager and cleanup

commit c4abe91a5ba0d415b45bf734068385668b7a66e6
Merge: 032e856e 1efc6397
Author: Damian Stewart <d@damianstewart.com>
Date:   Sun Dec 18 15:09:53 2022 +0100

    Merge branch 'feature_textual_inversion_mgr' into dev/diffusers_with_textual_inversion_manager

commit 032e856eefb3bbc39534f5daafd25764bcfcef8b
Merge: 8b4f0fe9 bc515e24
Author: Damian Stewart <d@damianstewart.com>
Date:   Sun Dec 18 15:08:01 2022 +0100

    Merge remote-tracking branch 'upstream/dev/diffusers' into dev/diffusers_with_textual_inversion_manager

commit 1efc6397fc6e61c1aff4b0258b93089d61de5955
Author: Damian Stewart <d@damianstewart.com>
Date:   Sun Dec 18 15:04:28 2022 +0100

    cleanup and add performance notes

commit e400f804ac471a0ca2ba432fd658778b20c7bdab
Author: Damian Stewart <d@damianstewart.com>
Date:   Sun Dec 18 14:45:07 2022 +0100

    fix bug and update unit tests

commit deb9ae0ae1016750e93ce8275734061f7285a231
Author: Damian Stewart <d@damianstewart.com>
Date:   Sun Dec 18 14:28:29 2022 +0100

    textual inversion manager seems to work

commit 162e02505dec777e91a983c4d0fb52e950d25ff0
Merge: cbad4583 12769b3d
Author: Damian Stewart <d@damianstewart.com>
Date:   Sun Dec 18 11:58:03 2022 +0100

    Merge branch 'main' into feature_textual_inversion_mgr

commit cbad45836c6aace6871a90f2621a953f49433131
Author: Damian Stewart <d@damianstewart.com>
Date:   Sun Dec 18 11:54:10 2022 +0100

    use position embeddings

commit 070344c69b0e0db340a183857d0a787b348681d3
Author: Damian Stewart <d@damianstewart.com>
Date:   Sun Dec 18 11:53:47 2022 +0100

    Don't crash CLI on exceptions

commit b035ac8c6772dfd9ba41b8eeb9103181cda028f8
Author: Damian Stewart <d@damianstewart.com>
Date:   Sun Dec 18 11:11:55 2022 +0100

    add missing position_embeddings

commit 12769b3d3562ef71e0f54946b532ad077e10043c
Author: Damian Stewart <d@damianstewart.com>
Date:   Fri Dec 16 13:33:25 2022 +0100

    debugging why it don't work

commit bafb7215eabe1515ca5e8388fd3bb2f3ac5362cf
Author: Damian Stewart <d@damianstewart.com>
Date:   Fri Dec 16 13:21:33 2022 +0100

    debugging why it don't work

commit 664a6e9e14
Author: Damian Stewart <d@damianstewart.com>
Date:   Fri Dec 16 12:48:38 2022 +0100

    use TextualInversionManager in place of embeddings (wip, doesn't work)

commit 8b4f0fe9d6e4e2643b36dfa27864294785d7ba4e
Author: Damian Stewart <d@damianstewart.com>
Date:   Fri Dec 16 12:48:38 2022 +0100

    use TextualInversionManager in place of embeddings (wip, doesn't work)

commit ffbe1ab11163ba712e353d89404e301d0e0c6cdf
Merge: 6e4dad60 023df37e
Author: Damian Stewart <d@damianstewart.com>
Date:   Fri Dec 16 02:37:31 2022 +0100

    Merge branch 'feature_textual_inversion_mgr' into dev/diffusers

commit 023df37eff
Author: Damian Stewart <d@damianstewart.com>
Date:   Fri Dec 16 02:36:54 2022 +0100

    cleanup

commit 05fac594ea
Author: Damian Stewart <d@damianstewart.com>
Date:   Fri Dec 16 02:07:49 2022 +0100

    tweak error checking

commit 009f32ed39
Author: damian <null@damianstewart.com>
Date:   Thu Dec 15 21:29:47 2022 +0100

    unit tests passing for embeddings with vector length >1

commit beb1b08d9a
Author: Damian Stewart <d@damianstewart.com>
Date:   Thu Dec 15 13:39:09 2022 +0100

    more explicit equality tests when overwriting

commit 44d8a5a7c8
Author: Damian Stewart <d@damianstewart.com>
Date:   Thu Dec 15 13:30:13 2022 +0100

    wip textual inversion manager (unit tests passing for 1v embedding overwriting)

commit 417c2b57d9
Author: Damian Stewart <d@damianstewart.com>
Date:   Thu Dec 15 12:30:55 2022 +0100

    wip textual inversion manager (unit tests passing for base stuff + padding)

commit 2e80872e3b
Author: Damian Stewart <d@damianstewart.com>
Date:   Thu Dec 15 10:57:57 2022 +0100

    wip new TextualInversionManager

* stop using WeightedFrozenCLIPEmbedder

* store diffusion models locally

- configure_invokeai.py reconfigured to store diffusion models rather than
  CompVis models
- hugging face caching model is used, but cache is set to ~/invokeai/models/repo_id
- models.yaml does **NOT** use path, just repo_id
- "repo_name" changed to "repo_id" to following hugging face conventions
- Models are loaded with full precision pending further work.

* allow non-local files during development

* path takes priority over repo_id

* MVP for model_cache and configure_invokeai

- Feature complete (almost)

- configure_invokeai.py downloads both .ckpt and diffuser models,
  along with their VAEs. Both types of download are controlled by
  a unified INITIAL_MODELS.yaml file.

- model_cache can load both type of model and switches back and forth
  in CPU. No memory leaks detected

TO DO:

  1. I have not yet turned on the LocalOnly flag for diffuser models, so
     the code will check the Hugging Face repo for updates before using the
     locally cached models. This will break firewalled systems. I am thinking
     of putting in a global check for internet connectivity at startup time
     and setting the LocalOnly flag based on this. It would be good to check
     updates if there is connectivity.

  2. I have not gone completely through INITIAL_MODELS.yaml to check which
     models are available as diffusers and which are not. So models like
     PaperCut and VoxelArt may not load properly. The runway and stability
     models are checked, as well as the Trinart models.

  3. Add stanzas for SD 2.0 and 2.1 in INITIAL_MODELS.yaml

REMAINING PROBLEMS NOT DIRECTLY RELATED TO MODEL_CACHE:

  1. When loading a .ckpt file there are lots of messages like this:

     Warning! ldm.modules.attention.CrossAttention is no longer being
     maintained. Please use InvokeAICrossAttention instead.

     I'm not sure how to address this.

  2. The ckpt models ***don't actually run*** due to the lack of special-case
     support for them in the generator objects. For example, here's the hard
     crash you get when you run txt2img against the legacy waifu-diffusion-1.3
     model:
```
     >> An error occurred:
     Traceback (most recent call last):
       File "/data/lstein/InvokeAI/ldm/invoke/CLI.py", line 140, in main
           main_loop(gen, opt)
      File "/data/lstein/InvokeAI/ldm/invoke/CLI.py", line 371, in main_loop
         gen.prompt2image(
      File "/data/lstein/InvokeAI/ldm/generate.py", line 496, in prompt2image
	 results = generator.generate(
      File "/data/lstein/InvokeAI/ldm/invoke/generator/base.py", line 108, in generate
         image = make_image(x_T)
      File "/data/lstein/InvokeAI/ldm/invoke/generator/txt2img.py", line 33, in make_image
         pipeline_output = pipeline.image_from_embeddings(
      File "/home/lstein/invokeai/.venv/lib/python3.9/site-packages/torch/nn/modules/module.py", line 1265, in __getattr__
         raise AttributeError("'{}' object has no attribute '{}'".format(
     AttributeError: 'LatentDiffusion' object has no attribute 'image_from_embeddings'
```

  3. The inpainting diffusion model isn't working. Here's the output of "banana
     sushi" when inpainting-1.5 is loaded:

```
    Traceback (most recent call last):
      File "/data/lstein/InvokeAI/ldm/generate.py", line 496, in prompt2image
        results = generator.generate(
      File "/data/lstein/InvokeAI/ldm/invoke/generator/base.py", line 108, in generate
        image = make_image(x_T)
      File "/data/lstein/InvokeAI/ldm/invoke/generator/txt2img.py", line 33, in make_image
        pipeline_output = pipeline.image_from_embeddings(
      File "/data/lstein/InvokeAI/ldm/invoke/generator/diffusers_pipeline.py", line 301, in image_from_embeddings
        result_latents, result_attention_map_saver = self.latents_from_embeddings(
      File "/data/lstein/InvokeAI/ldm/invoke/generator/diffusers_pipeline.py", line 330, in latents_from_embeddings
        result: PipelineIntermediateState = infer_latents_from_embeddings(
      File "/data/lstein/InvokeAI/ldm/invoke/generator/diffusers_pipeline.py", line 185, in __call__
        for result in self.generator_method(*args, **kwargs):
      File "/data/lstein/InvokeAI/ldm/invoke/generator/diffusers_pipeline.py", line 367, in generate_latents_from_embeddings
        step_output = self.step(batched_t, latents, guidance_scale,
      File "/home/lstein/invokeai/.venv/lib/python3.9/site-packages/torch/autograd/grad_mode.py", line 27, in decorate_context
        return func(*args, **kwargs)
      File "/data/lstein/InvokeAI/ldm/invoke/generator/diffusers_pipeline.py", line 409, in step
        step_output = self.scheduler.step(noise_pred, timestep, latents, **extra_step_kwargs)
      File "/home/lstein/invokeai/.venv/lib/python3.9/site-packages/diffusers/schedulers/scheduling_lms_discrete.py", line 223, in step
        pred_original_sample = sample - sigma * model_output
    RuntimeError: The size of tensor a (9) must match the size of tensor b (4) at non-singleton dimension 1
```

* proper support for float32/float16

- configure script now correctly detects user's preference for
  fp16/32 and downloads the correct diffuser version. If fp16
  version not available, falls back to fp32 version.

- misc code cleanup and simplification in model_cache

* add on-the-fly conversion of .ckpt to diffusers models

1. On-the-fly conversion code can be found in the file ldm/invoke/ckpt_to_diffusers.py.

2. A new !optimize command has been added to the CLI. Should be ported to Web GUI.

User experience on the CLI is this:

```
invoke> !optimize /home/lstein/invokeai/models/ldm/stable-diffusion-v1/sd-v1-4.ckpt
INFO: Converting legacy weights file /home/lstein/invokeai/models/ldm/stable-diffusion-v1/sd-v1-4.ckpt to optimized diffuser model.
      This operation will take 30-60s to complete.
Success. Optimized model is now located at /home/lstein/tmp/invokeai/models/optimized-ckpts/sd-v1-4
Writing new config file entry for sd-v1-4...

>> New configuration:
sd-v1-4:
  description: Optimized version of sd-v1-4
  format: diffusers
  path: /home/lstein/tmp/invokeai/models/optimized-ckpts/sd-v1-4

OK to import [n]? y
>> Verifying that new model loads...
>> Current VRAM usage:  2.60G
>> Offloading stable-diffusion-2.1 to CPU
>> Loading diffusers model from /home/lstein/tmp/invokeai/models/optimized-ckpts/sd-v1-4
  | Using faster float16 precision
You have disabled the safety checker for <class 'ldm.invoke.generator.diffusers_pipeline.StableDiffusionGeneratorPipeline'> by passing `safety_checker=None`. Ensure that you abide to the conditions of the Stable Diffusion \
license and do not expose unfiltered results in services or applications open to the public. Both the diffusers team and Hugging Face strongly recommend to keep the safety filter enabled in all public facing circumstances,\
 disabling it only for use-cases that involve analyzing network behavior or auditing its results. For more information, please have a look at https://github.com/huggingface/diffusers/pull/254 .
  | training width x height = (512 x 512)
>> Model loaded in 3.48s
>> Max VRAM used to load the model: 2.17G
>> Current VRAM usage:2.17G
>> Textual inversions available:
>> Setting Sampler to k_lms (LMSDiscreteScheduler)
Keep model loaded? [y]
```

* add parallel set of generator files for ckpt legacy generation

* generation using legacy ckpt models now working

* diffusers: fix missing attention_maps_callback

fix for 23eb80b404

* associate legacy CrossAttention with .ckpt models

* enable autoconvert

New --autoconvert CLI option will scan a designated directory for
new .ckpt files, convert them into diffuser models, and import
them into models.yaml.

Works like this:

   invoke.py --autoconvert /path/to/weights/directory

In ModelCache added two new methods:

  autoconvert_weights(config_path, weights_directory_path, models_directory_path)
  convert_and_import(ckpt_path, diffuser_path)

* diffusers: update to diffusers 0.11 (from 0.10.2)

* fix vae loading & width/height calculation

* refactor: encapsulate these conditioning data into one container

* diffusers: fix some noise-scaling issues by pushing the noise-mixing down to the common function

* add support for safetensors and accelerate

* set local_files_only when internet unreachable

* diffusers: fix error-handling path when model repo has no fp16 branch

* fix generatorinpaint error

Fixes :
  "ModuleNotFoundError: No module named 'ldm.invoke.generatorinpaint'
   https://github.com/invoke-ai/InvokeAI/pull/1583#issuecomment-1363634318

* quench diffuser safety-checker warning

* diffusers: support stochastic DDIM eta parameter

* fix conda env creation on macos

* fix cross-attention with diffusers 0.11

* diffusers: the VAE needs to be tiling as well as the U-Net

* diffusers: comment on subfolders

* diffusers: embiggen!

* diffusers: make model_cache.list_models serializable

* diffusers(inpaint): restore scaling functionality

* fix requirements clash between numba and numpy 1.24

* diffusers: allow inpainting model to do non-inpainting tasks

* start expanding model_cache functionality

* add import_ckpt_model() and import_diffuser_model() methods to model_manager

- in addition, model_cache.py is now renamed to model_manager.py

* allow "recommended" flag to be optional in INITIAL_MODELS.yaml

* configure_invokeai now downloads VAE diffusers in advance

* rename ModelCache to ModelManager

* remove support for `repo_name` in models.yaml

* check for and refuse to load embeddings trained on incompatible models

* models.yaml.example: s/repo_name/repo_id

and remove extra INITIAL_MODELS now that the main one has diffusers models in it.

* add MVP textual inversion script

* refactor(InvokeAIDiffuserComponent): factor out _combine()

* InvokeAIDiffuserComponent: implement threshold

* InvokeAIDiffuserComponent: diagnostic logs for threshold

...this does not look right

* add a curses-based frontend to textual inversion

- not quite working yet
- requires npyscreen installed
- on windows will also have the windows-curses requirement, but not added
  to requirements yet

* add curses-based interface for textual inversion

* fix crash in convert_and_import()

- This corrects a "local variable referenced before assignment" error
  in model_manager.convert_and_import()

* potential workaround for no 'state_dict' key error

- As reported in https://github.com/huggingface/diffusers/issues/1876

* create TI output dir if needed

* Update environment-lin-cuda.yml (#2159)

Fixing line 42 to be the proper order to define the transformers requirement: ~= instead of =~

* diffusers: update sampler-to-scheduler mapping

based on https://github.com/huggingface/diffusers/issues/277#issuecomment-1371428672

* improve user exp for ckt to diffusers conversion

- !optimize_models command now operates on an existing ckpt file entry in models.yaml
- replaces existing entry, rather than adding a new one
- offers to delete the ckpt file after conversion

* web: adapt progress callback to deal with old generator or new diffusers pipeline

* clean-up model_manager code

- add_model() verified to work for .ckpt local paths,
  .ckpt remote URLs, diffusers local paths, and
  diffusers repo_ids

- convert_and_import() verified to work for local and
  remove .ckpt files

* handle edge cases for import_model() and convert_model()

* add support for safetensor .ckpt files

* fix name error

* code cleanup with pyflake

* improve model setting behavior

- If the user enters an invalid model name at startup time, will not
  try to load it, warn, and use default model
- CLI UI enhancement: include currently active model in the command
  line prompt.

* update test-invoke-pip.yml
- fix model cache path to point to runwayml/stable-diffusion-v1-5
- remove `skip-sd-weights` from configure_invokeai.py args

* exclude dev/diffusers from "fail for draft PRs"

* disable "fail on PR jobs"

* re-add `--skip-sd-weights` since no space

* update workflow environments
- include `INVOKE_MODEL_RECONFIGURE: '--yes'`

* clean up model load failure handling

- Allow CLI to run even when no model is defined or loadable.
- Inhibit stack trace when model load fails - only show last error
- Give user *option* to run configure_invokeai.py when no models
  successfully load.
- Restart invokeai after reconfiguration.

* further edge-case handling

1) only one model in models.yaml file, and that model is broken
2) no models in models.yaml
3) models.yaml doesn't exist at all

* fix incorrect model status listing

- "cached" was not being returned from list_models()
- normalize handling of exceptions during model loading:
   - Passing an invalid model name to generate.set_model() will return
     a KeyError
   - All other exceptions are returned as the appropriate Exception

* CI: do download weights (if not already cached)

* diffusers: fix scheduler loading in offline mode

* CI: fix model name (no longer has `diffusers-` prefix)

* Update txt2img2img.py (#2256)

* fixes to share models with HuggingFace cache system

- If HF_HOME environment variable is defined, then all huggingface models
  are stored in that directory following the standard conventions.
- For seamless interoperability, set HF_HOME to ~/.cache/huggingface
- If HF_HOME not defined, then models are stored in ~/invokeai/models.
  This is equivalent to setting HF_HOME to ~/invokeai/models

A future commit will add a migration mechanism so that this change doesn't
break previous installs.

* feat - make model storage compatible with hugging face caching system

This commit alters the InvokeAI model directory to be compatible with
hugging face, making it easier to share diffusers (and other models)
across different programs.

- If the HF_HOME environment variable is not set, then models are
  cached in ~/invokeai/models in a format that is identical to the
  HuggingFace cache.

- If HF_HOME is set, then models are cached wherever HF_HOME points.

- To enable sharing with other HuggingFace library clients, set
  HF_HOME to ~/.cache/huggingface to set the default cache location
  or to ~/invokeai/models to have huggingface cache inside InvokeAI.

* fixes to share models with HuggingFace cache system

    - If HF_HOME environment variable is defined, then all huggingface models
      are stored in that directory following the standard conventions.
    - For seamless interoperability, set HF_HOME to ~/.cache/huggingface
    - If HF_HOME not defined, then models are stored in ~/invokeai/models.
      This is equivalent to setting HF_HOME to ~/invokeai/models

    A future commit will add a migration mechanism so that this change doesn't
    break previous installs.

* fix error "no attribute CkptInpaint"

* model_manager.list_models() returns entire model config stanza+status

* Initial Draft - Model Manager Diffusers

* added hash function to diffusers

* implement sha256 hashes on diffusers models

* Add Model Manager Support for Diffusers

* fix various problems with model manager

- in cli import functions, fix not enough values to unpack from
  _get_name_and_desc()
- fix crash when using old-style vae: value with new-style diffuser

* rebuild frontend

* fix dictconfig-not-serializable issue

* fix NoneType' object is not subscriptable crash in model_manager

* fix "str has no attribute get" error in model_manager list_models()

* Add path and repo_id support for Diffusers Model Manager

Also fixes bugs

* Fix tooltip IT localization not working

* Add Version Number To WebUI

* Optimize Model Search

* Fix incorrect font on the Model Manager UI

* Fix image degradation on merge fixes - [Experimental]

This change should effectively fix a couple of things.

- Fix image degradation on subsequent merges of the canvas layers.
- Fix the slight transparent border that is left behind when filling the bounding box with a color.
- Fix the left over line of color when filling a bounding box with color.

So far there are no side effects for this. If any, please report.

* Add local model filtering for Diffusers / Checkpoints

* Go to home on modal close for the Add Modal UI

* Styling Fixes

* Model Manager Diffusers Localization Update

* Add Safe Tensor scanning to Model Manager

* Fix model edit form dispatching string values instead of numbers.

* Resolve VAE handling / edge cases for supplied repos

* defer injecting tokens for textual inversions until they're used for the first time

* squash a console warning

* implement model migration check

* add_model() overwrites previous config rather than merges

* fix model config file attribute merging

* fix precision handling in textual inversion script

* allow ckpt conversion script to work with safetensors .ckpts

Applied patch here:
beb932c5d1

* fix name "args" is not defined crash in textual_inversion_training

* fix a second NameError: name 'args' is not defined crash

* fix loading of the safety checker from the global cache dir

* add installation step to textual inversion frontend

- After a successful training run, the script will copy learned_embeds.bin
  to a subfolder of the embeddings directory.
- User given the option to delete the logs and intermediate checkpoints
  (which together use 7-8G of space)
- If textual inversion training fails, reports the error gracefully.

* don't crash out on incompatible embeddings

- put try: blocks around places where the system tries to load an embedding
  which is incompatible with the currently loaded model

* add support for checkpoint resuming

* textual inversion preferences are saved and restored between sessions

- Preferences are stored in a file named text-inversion-training/preferences.conf
- Currently the resume-from-checkpoint option is not working correctly. Possible
  bug in textual_inversion_training.py?

* copy learned_embeddings.bin into right location

* add front end for diffusers model merging

- Front end doesn't do anything yet!!!!
- Made change to model name parsing in CLI to support ability to have merged models
  with the "+" character in their names.

* improve inpainting experience

- recommend ckpt version of inpainting-1.5 to user
- fix get_noise() bug in ckpt version of omnibus.py

* update environment*yml

* tweak instructions to install HuggingFace token

* bump version number

* enhance update scripts

- update scripts will now fetch new INITIAL_MODELS.yaml so that
  configure_invokeai.py will know about the diffusers versions.

* enhance invoke.sh/invoke.bat launchers

- added configure_invokeai.py to menu
- menu defaults to browser-based invoke

* remove conda workflow (#2321)

* fix `token_ids has shape torch.Size([79]) - expected [77]`

* update CHANGELOG.md with 2.3.* info

- Add information on how formats have changed and the upgrade process.
- Add short bug list.

Co-authored-by: Damian Stewart <d@damianstewart.com>
Co-authored-by: Damian Stewart <null@damianstewart.com>
Co-authored-by: Lincoln Stein <lincoln.stein@gmail.com>
Co-authored-by: Wybartel-luxmc <37852506+Wybartel-luxmc@users.noreply.github.com>
Co-authored-by: mauwii <Mauwii@outlook.de>
Co-authored-by: mickr777 <115216705+mickr777@users.noreply.github.com>
Co-authored-by: blessedcoolant <54517381+blessedcoolant@users.noreply.github.com>
Co-authored-by: Eugene Brodsky <ebr@users.noreply.github.com>
Co-authored-by: Matthias Wild <40327258+mauwii@users.noreply.github.com>
2023-01-15 09:22:46 -05:00
Lincoln Stein
3929bd3e13
Lstein release candidate 2.2.5 (#2137)
* installer tweaks in preparation for v2.2.5

- pin numpy to 1.23.* to avoid requirements conflict with numba
- update.sh and update.bat now accept a tag or branch string, not a URL
- update scripts download latest requirements-base before updating.

* update.bat.in debugged and working

* update pulls from "latest" now

* bump version number

* fix permissions on create_installer.sh

* give Linux user option of installing ROCm or CUDA

* rc2.2.5 (install.sh) relative path fixes (#2155)

* (installer) fix bug in resolution of relative paths in linux install script

point installer at 2.2.5-rc1

selecting ~/Data/myapps/ as location  would create a ./~/Data/myapps
instead of expanding the ~/ to the value of ${HOME}

also, squash the trailing slash in path, if it was entered by the user

* (installer) add option to automatically start the app after install

also: when exiting, print the command to get back into the app

* remove extraneous whitespace

* model_cache applies rootdir to config path

* bring installers up to date with 2.2.5-rc2

* bump rc version

* create_installer now adds version number

* rebuild frontend

* bump rc#

* add locales to frontend dist package

- bump to patchlevel 6

* bump patchlevel

* use invoke-ai version of GFPGAN

- This version is very slightly modified to allow weights files
  to be pre-downloaded by the configure script.

* fix formatting error during startup

* bump patch level

* workaround #2 for GFPGAN facexlib() weights downloading

* bump patch

* ready for merge and release

* remove extraneous comment

* set PYTORCH_ENABLE_MPS_FALLBACK directly in invoke.py

Co-authored-by: Eugene Brodsky <ebr@users.noreply.github.com>
2023-01-01 17:54:45 +00:00
Lincoln Stein
4fd97ceddd
remove redundant code line (#2068)
* remove redundant code line

install.bat was copying the requirements file into the install folder
twice, causing an error message on the second try. This fixes the
issue.

* add further improvements to installer

- Windows version will unzip to have requirements.txt already in
  the right location, to prevent problems when users try to run
  the .bat script from within a mounted read-only zip file manager.

- Do not assume that "pip" is on the path in either the .bat or shell
  versions of the update script.
2022-12-19 14:57:41 +00:00
Lincoln Stein
0439b51a26
Simple Installer for Unified Directory Structure, Initial Implementation (#1819)
* partially working simple installer

* works on linux

* fix linux requirements files

* read root environment variable in right place

* fix cat invokeai.init in test workflows

* fix classical cp error in test-invoke-pip.yml

* respect --root argument now

* untested bat installers added

* windows install.bat now working

fix logic to find frontend files

* rename simple_install to "installer"

1. simple_install => 'installer'
2. source and binary install directories are removed

* enable update scripts to update requirements

- Also pin requirements to known working commits.
- This may be a breaking change; exercise with caution
- No functional testing performed yet!

* update docs and installation requirements

NOTE: This may be a breaking commit! Due to the way the installer
works, I have to push to a public branch in order to do full end-to-end
testing.

- Updated installation docs, removing binary and source installers and
  substituting the "simple" unified installer.
- Pin requirements for the "http:" downloads to known working commits.
- Removed as much as possible the invoke-ai forks of others' repos.

* fix directory path for installer

* correct requirement/environment errors

* exclude zip files in .gitignore

* possible fix for dockerbuild

* ready for torture testing

- final Windows bat file tweaks
- copy environments-and-requirements to the runtime directory so that
  the `update.sh` script can run.

  This is not ideal, since we lose control over the
  requirements. Better for the update script to pull the proper
  updated requirements script from the repository.

* allow update.sh/update.bat to install arbitrary InvokeAI versions

- Can pass the zip file path to any InvokeAI release, branch, commit or tag,
  and the installer will try to install it.
- Updated documentation
- Added Linux Python install hints.

* use binary installer's :err_exit function

* user diffusers 0.10.0

* added logic for CPPFLAGS on mac

* improve windows install documentation

- added information on a couple of gotchas I experienced during
  windows installation, including DLL loading errors experienced
  when Visual Studio C++ Redistributable was not present.

* tagged to pull from 2.2.4-rc1

- also fix error of shell window closing immediately if suitable
  python not found

Co-authored-by: mauwii <Mauwii@outlook.de>
2022-12-11 00:37:08 -05:00