build(ui): fix husky path
build(ui): fix hmr issue, remove emotion cache
build(ui): clean up package.json
build(ui): update gh action and npm scripts
feat(ui): wip port lightbox to chakra theme
feat(ui): wip use chakra theme tokens
feat(ui): Add status text to main loading spinner
feat(ui): wip chakra theme tweaking
feat(ui): simply iaisimplemenu button
feat(ui): wip chakra theming
feat(ui): Theme Management
feat(ui): Add Ocean Blue Theme
feat(ui): wip lightbox
fix(ui): fix lightbox mouse
feat(ui): set default theme variants
feat(ui): model manager chakra theme
chore(ui): lint
feat(ui): remove last scss
feat(ui): fix switch theme
feat(ui): Theme Cleanup
feat(ui): Stylize Search Models Found List
feat(ui): hide scrollbars
feat(ui): fix floating button position
feat(ui): Scrollbar Styling
fix broken scripts
This PR fixes the following scripts:
1) Scripts that can be executed within the repo's scripts directory.
Note that these are for development testing and are not intended
to be exposed to the user.
configure_invokeai.py - configuration
dream.py - the legacy CLI
images2prompt.py - legacy "dream prompt" retriever
invoke-new.py - new nodes-based CLI
invoke.py - the legacy CLI under another name
make_models_markdown_table.py - a utility used during the release/doc process
pypi_helper.py - another utility used during the release process
sd-metadata.py - retrieve JSON-formatted metadata from a PNG file
2) Scripts that are installed by pip install. They get placed into the venv's
PATH and are intended to be the official entry points:
invokeai-node-cli - new nodes-based CLI
invokeai-node-web - new nodes-based web server
invokeai - legacy CLI
invokeai-configure - install time configuration script
invokeai-merge - model merging script
invokeai-ti - textual inversion script
invokeai-model-install - model installer
invokeai-update - update script
invokeai-metadata" - retrieve JSON-formatted metadata from PNG files
protect invocations against black autoformatting
deps: upgrade to diffusers 0.14, safetensors 0.3, transformers 4.26, accelerate 0.16
- filter paths for `build-container.yml` and `test-invoke-pip.yml`
- add workflow to pass required checks on PRs with `paths-ignore`
- this triggers if `test-invoke-pip.yml` does not
- fix "CI checks on main link" in `/README.md`
- dont build frontend since complications with QEMU
- set pip cache dir
- add pip cache to all pip related build steps
- dont lock pip cache
- update dockerignore to exclude uneeded files
* remove non maintained Dockerfile
* adapt Docker related files to latest changes
- also build the frontend when building the image
- skip user response if INVOKE_MODEL_RECONFIGURE is set
- split INVOKE_MODEL_RECONFIGURE to support more than one argument
* rename `docker-build` dir to `docker`
* update build-container.yml
- rename image to invokeai
- add cpu flavor
- add metadata to build summary
- enable caching
- remove build-cloud-img.yml
* fix yarn cache path, link copyjob
other changes which where required:
- move configure_invokeai.py into ldm.invoke
- update files which imported configure_invokeai to use new location:
- ldm/invoke/CLI.py
- scripts/load_models.py
- scripts/preload_models.py
- update test-invoke-pip.yml:
- remove pr type "converted_to_draft"
- remove reference to dev/diffusers
- remove no more needed requirements from matrix
- add pytorch to matrix
- install via `pip3 install --use-pep517 .`
- use the created executables
- this should also fix configure_invoke not executed in windows
To install use `pip install --use-pep517 -e .` where `-e` is optional
* 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: 6e4dad60023df37e
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>
* set workflow PR triggers to filter PR-types
- `review_requested`
- `ready_for_review`
* fail tests if draft pr
* add more types to test pr triggers
* remove unneeded condition
* readd condition
* leave PR-types default, only verify PRs to main
and fail for draft-PRs
* set types to cancel when converted to draft
* (config) handle huggingface token more gracefully
* (docs) document HuggingFace token requirement for Concepts
* (cli) deprecate the --(no)-interactive CLI flag
It was previously only used to skip the SD weights download, and therefore
the prompt for Huggingface token (the "interactive" part).
Now that we don't need a Huggingface token
to download the SD weights at all, we can replace this flag with
"--skip-sd-weights", to clearly describe its purpose
The `--(no)-interactive` flag still functions the same, but shows a deprecation message
* (cli) fix emergency_model_reconfigure argument parsing
* (config) fix installation issues on systems with non-UTF8 locale
Co-authored-by: Matthias Wild <40327258+mauwii@users.noreply.github.com>
* Auto-format frontend
* Update lint-frontend GA workflow node and checkout
* Fix linter error in ThemeChanger
* Add a `on: pull_request` to lint-frontend workflow
Co-authored-by: Lincoln Stein <lincoln.stein@gmail.com>
* update build scripts and dockerignore
updates to build and run script:
- read repository name
- include flavor in container name
- read arch via arch command
- use latest tag instead of arch
- don't bindmount `$HOME/.huggingface`
- make sure HUGGINGFACE_TOKEN is set
updates to .dockerignore
- include environment-and-requirements
- exclude binary_installer
- exclude docker-build
- exclude docs
* disable push and pr triggers of cloud image
also disable pushing.
This was decided since:
- it is not multiarch useable
- the default image is already cloud aproved
* integrate patchmatch in container
* pin verisons of recently introduced dependencies
* remove now unecesarry part from build.sh
move huggingface token to run script, so it can download missing models
* move GPU_FLAGS to run script
since not needed at build time
* update env.sh
- read REPOSITORY_NAME from env if available
- add comment to explain the intension of this file
- remove unecesarry exports
* get rid of repository_name_lc
* capitalize variables
* update INSTALL_DOCKER with new variables
* add comments pointing to the docs
Co-authored-by: Lincoln Stein <lincoln.stein@gmail.com>
* Push dockerfile (#18)
* update build-container.yml
* add login step to build-container.yml
* update job name
* update matrix: add registry and platforms
also set latest only for cuda image
* quote string
* use latest for amd and cuda image
* separate images for cuda and amd
* change latest from auto to true
* configure_invoke -y instead of --interactive
* fix argument to --yes
* update matrix:
- use flavor instead of pip-requirements
- add flavor `cloud`
- add `dockerfile`
* introduce INVOKE_MODEL_RECONFIGURE
* add `--cap-add=sys_nice` to run.sh
* update Dockerfile: install wheel
* only have main branch in action again
* disable push of cloud image for now
since it still has it's own workflow, but PoC succeeded
* remove now untrue comments in top
* install pip, setuptools and wheel in sep. step
* add labels to the image
* remove doubled installation of wheel
* updated Dockerfile
- use `python:3.10-slim` as baseimage
- separate builder and runtime stages again
- get rid of uneeded packages
- pin packages for persistence
- remove outdir from entrypoint since invoke.init is available in /data
- shrinked image size to <2GB
- way better security score than before
* small output update to build.sh and run.sh
* update matrix in build-container.yml
* remove branches from build-container.yml
