* Add new text encoder
* add transformers depth
* More
* Correct conversion script
* Fix more
* Fix more
* Correct more
* correct text encoder
* Finish all
* proof that in works in run local xl
* clean up
* Get refiner to work
* Add red castle
* Fix batch size
* Improve pipelines more
* Finish text2image tests
* Add img2img test
* Fix more
* fix import
* Fix embeddings for classic models (#3888)
Fix embeddings for classic SD models.
* Allow multiple prompts to be passed to the refiner (#3895)
* finish more
* Apply suggestions from code review
* add watermarker
* Model offload (#3889)
* Model offload.
* Model offload for refiner / img2img
* Hardcode encoder offload on img2img vae encode
Saves some GPU RAM in img2img / refiner tasks so it remains below 8 GB.
---------
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
* correct
* fix
* clean print
* Update install warning for `invisible-watermark`
* add: missing docstrings.
* fix and simplify the usage example in img2img.
* fix setup for watermarking.
* Revert "fix setup for watermarking."
This reverts commit 491bc9f5a6.
* fix: watermarking setup.
* fix: op.
* run make fix-copies.
* make sure tests pass
* improve convert
* make tests pass
* make tests pass
* better error message
* fiinsh
* finish
* Fix final test
---------
Co-authored-by: Pedro Cuenca <pedro@huggingface.co>
Co-authored-by: Sayak Paul <spsayakpaul@gmail.com>
* Remove ONNX tests from PR.
They are already a part of push_tests.yml.
* Remove mps tests from PRs.
They are already performed on push.
* Fix workflow name for fast push tests.
* Extract mps tests to a workflow.
For better control/filtering.
* Remove --extra-index-url from mps tests
* Increase tolerance of mps test
This test passes in my Mac (Ventura 13.3) but fails in the CI hardware
(Ventura 13.2). I ran the local tests following the same steps that
exist in the CI workflow.
* Temporarily run mps tests on pr
So we can test.
* Revert "Temporarily run mps tests on pr"
Tests passed, go back to running on push.
* enable deterministic pytorch and cuda operations.
* disable manual seeding.
* make style && make quality for unet_2d tests.
* enable determinism for the unet2dconditional model.
* add CUBLAS_WORKSPACE_CONFIG for better reproducibility.
* relax tolerance (very weird issue, though).
* revert to torch manual_seed() where needed.
* relax more tolerance.
* better placement of the cuda variable and relax more tolerance.
* enable determinism for 3d condition model.
* relax tolerance.
* add: determinism to alt_diffusion.
* relax tolerance for alt diffusion.
* dance diffusion.
* dance diffusion is flaky.
* test_dict_tuple_outputs_equivalent edit.
* fix two more tests.
* fix more ddim tests.
* fix: argument.
* change to diff in place of difference.
* fix: test_save_load call.
* test_save_load_float16 call.
* fix: expected_max_diff
* fix: paint by example.
* relax tolerance.
* add determinism to 1d unet model.
* torch 2.0 regressions seem to be brutal
* determinism to vae.
* add reason to skipping.
* up tolerance.
* determinism to vq.
* determinism to cuda.
* determinism to the generic test pipeline file.
* refactor general pipelines testing a bit.
* determinism to alt diffusion i2i
* up tolerance for alt diff i2i and audio diff
* up tolerance.
* determinism to audioldm
* increase tolerance for audioldm lms.
* increase tolerance for paint by paint.
* increase tolerance for repaint.
* determinism to cycle diffusion and sd 1.
* relax tol for cycle diffusion 🚲
* relax tol for sd 1.0
* relax tol for controlnet.
* determinism to img var.
* relax tol for img variation.
* tolerance to i2i sd
* make style
* determinism to inpaint.
* relax tolerance for inpaiting.
* determinism for inpainting legacy
* relax tolerance.
* determinism to instruct pix2pix
* determinism to model editing.
* model editing tolerance.
* panorama determinism
* determinism to pix2pix zero.
* determinism to sag.
* sd 2. determinism
* sd. tolerance
* disallow tf32 matmul.
* relax tolerance is all you need.
* make style and determinism to sd 2 depth
* relax tolerance for depth.
* tolerance to diffedit.
* tolerance to sd 2 inpaint.
* up tolerance.
* determinism in upscaling.
* tolerance in upscaler.
* more tolerance relaxation.
* determinism to v pred.
* up tol for v_pred
* unclip determinism
* determinism to unclip img2img
* determinism to text to video.
* determinism to last set of tests
* up tol.
* vq cumsum doesn't have a deterministic kernel
* relax tol
* relax tol
* init for korean docs
* edit build yml file for multi language docs
* edit one more build yml file for multi language docs
* add title for get_frontmatter error
* use repeat_interleave
* fix repeat
* Trigger Build
* don't install accelerate from main
* install released accelrate for mps test
* Remove additional accelerate installation from main.
Co-authored-by: Pedro Cuenca <pedro@huggingface.co>
* add AudioDiffusionPipeline and LatentAudioDiffusionPipeline
* add docs to toc
* fix tests
* fix tests
* fix tests
* fix tests
* fix tests
* Update pr_tests.yml
Fix tests
* parent 499ff34b3e
author teticio <teticio@gmail.com> 1668765652 +0000
committer teticio <teticio@gmail.com> 1669041721 +0000
parent 499ff34b3e
author teticio <teticio@gmail.com> 1668765652 +0000
committer teticio <teticio@gmail.com> 1669041704 +0000
add colab notebook
[Flax] Fix loading scheduler from subfolder (#1319)
[FLAX] Fix loading scheduler from subfolder
Fix/Enable all schedulers for in-painting (#1331)
* inpaint fix k lms
* onnox as well
* up
Correct path to schedlure (#1322)
* [Examples] Correct path
* uP
Avoid nested fix-copies (#1332)
* Avoid nested `# Copied from` statements during `make fix-copies`
* style
Fix img2img speed with LMS-Discrete Scheduler (#896)
Casting `self.sigmas` into a different dtype (the one of original_samples) is not advisable. In my img2img pipeline this leads to a long running time in the `integrate.quad` call later on- by long I mean more than 10x slower.
Co-authored-by: Anton Lozhkov <anton@huggingface.co>
Fix the order of casts for onnx inpainting (#1338)
Legacy Inpainting Pipeline for Onnx Models (#1237)
* Add legacy inpainting pipeline compatibility for onnx
* remove commented out line
* Add onnx legacy inpainting test
* Fix slow decorators
* pep8 styling
* isort styling
* dummy object
* ordering consistency
* style
* docstring styles
* Refactor common prompt encoding pattern
* Update tests to permanent repository home
* support all available schedulers until ONNX IO binding is available
Co-authored-by: Anton Lozhkov <aglozhkov@gmail.com>
* updated styling from PR suggested feedback
Co-authored-by: Anton Lozhkov <aglozhkov@gmail.com>
Jax infer support negative prompt (#1337)
* support negative prompts in sd jax pipeline
* pass batched neg_prompt
* only encode when negative prompt is None
Co-authored-by: Juan Acevedo <jfacevedo@google.com>
Update README.md: Minor change to Imagic code snippet, missing dir error (#1347)
Minor change to Imagic Readme
Missing dir causes an error when running the example code.
make style
change the sample model (#1352)
* Update alt_diffusion.mdx
* Update alt_diffusion.mdx
Add bit diffusion [WIP] (#971)
* Create bit_diffusion.py
Bit diffusion based on the paper, arXiv:2208.04202, Chen2022AnalogBG
* adding bit diffusion to new branch
ran tests
* tests
* tests
* tests
* tests
* removed test folders + added to README
* Update README.md
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
* move Mel to module in pipeline construction, make librosa optional
* fix imports
* fix copy & paste error in comment
* fix style
* add missing register_to_config
* fix class docstrings
* fix class docstrings
* tweak docstrings
* tweak docstrings
* update slow test
* put trailing commas back
* respect alphabetical order
* remove LatentAudioDiffusion, make vqvae optional
* move Mel from models back to pipelines :-)
* allow loading of pretrained audiodiffusion models
* fix tests
* fix dummies
* remove reference to latent_audio_diffusion in docs
* unused import
* inherit from SchedulerMixin to make loadable
* Apply suggestions from code review
* Apply suggestions from code review
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
* [Better scheduler docs] Improve usage examples of schedulers
* finish
* fix warnings and add test
* finish
* more replacements
* adapt fast tests hf token
* correct more
* Apply suggestions from code review
Co-authored-by: Pedro Cuenca <pedro@huggingface.co>
* Integrate compatibility with euler
Co-authored-by: Pedro Cuenca <pedro@huggingface.co>
Tests: upgrade PyTorch cuda to 11.7.
Otherwise the cuda versions of torch and torchvision mismatch, and
examples tests fail. We were requesting cuda 11.6 for PyTorch, and the
default torchvision (via setup.py).
Another option would be to include torchvision in the same pip install
line as torch.
* add method to enable cuda with minimal gpu usage to stable diffusion
* add test to minimal cuda memory usage
* ensure all models but unet are onn torch.float32
* move to cpu_offload along with minor internal changes to make it work
* make it test against accelerate master branch
* coming back, its official: I don't know how to make it test againt the master branch from accelerate
* make it install accelerate from master on tests
* go back to accelerate>=0.11
* undo prettier formatting on yml files
* undo prettier formatting on yml files againn
* [CI] Add Apple M1 tests
* setup-python
* python build
* conda install
* remove branch
* only 3.8 is built for osx-arm
* try fetching prebuilt tokenizers
* use user cache
* update shells
* Reports and cleanup
* -> MPS
* Disable parallel tests
* Better naming
* investigate worker crash
* return xdist
* restart
* num_workers=2
* still crashing?
* faulthandler for segfaults
* faulthandler for segfaults
* remove restarts, stop on segfault
* torch version
* change installation order
* Use pre-RC version of PyTorch.
To be updated when it is released.
* Skip crashing test on MPS, add new one that works.
* Skip cuda tests in mps device.
* Actually use generator in test.
I think this was a typo.
* make style
Co-authored-by: Pedro Cuenca <pedro@huggingface.co>
