GitLab

* [Dtype] Align automatic dtype

* up

* up

* fix

* re-add accelerate

fix versatile

* initial

* type hints

* update scheduler type hint

* add to README

* add example generation to README

* v -> mix_factor

* load scheduler from pretrained

* unCLIP image variation

* remove prior comment re: @pcuenca

* stable diffusion -> unCLIP re: @pcuenca

* add copy froms re: @patil-suraj

* allow selecting precision to make DB class images

addresses #1831

* add prior_generation_precision argument

* correct prior_generation_precision's description
Co-authored-by: Suraj Patil <surajp815@gmail.com>
Co-authored-by: Suraj Patil <surajp815@gmail.com>

* Make xformers optional even if it is available

* Raise exception if xformers is used but not available

* Rename use_xformers to enable_xformers_memory_efficient_attention

* Add a note about xformers in README

* Reformat code style

* Width was typod as weight

* Run Black

* Device to use (e.g. cpu, cuda:0, cuda:1, etc.)

* "cuda" if torch.cuda.is_available() else "cpu"

* Make safety_checker optional in more pipelines.

* Remove inappropriate comment in inpaint pipeline.

* InPaint Test: set feature_extractor to None.

* Remove import

* img2img test: set feature_extractor to None.

* inpaint sd2 test: set feature_extractor to None.
Co-authored-by: Suraj Patil <surajp815@gmail.com>

Fix inconsistencies between code and comments in the function 'preprocess'

* unwrap_model text encoder before accessing weights

* fix another call

* fix the right call

* Remove hardcoded names from PT scripts

* Apply suggestions from code review
Co-authored-by: Suraj Patil <surajp815@gmail.com>

* reorder model wrap

* bug fix
Co-authored-by: Prathik Rao <prathikrao@microsoft.com>

Don't initialize Jax on startup.

* first proposal

* rename

* up

* Apply suggestions from code review

* better

* up

* finish

* up

* rename

* correct versatile

* up

* up

* up

* up

* fix

* Apply suggestions from code review

* make style

* Apply suggestions from code review
Co-authored-by: Pedro Cuenca <pedro@huggingface.co>

* add error message
Co-authored-by: Pedro Cuenca <pedro@huggingface.co>

* add flax img2img pipeline

* update pipeline

* black format file

* remove argg from get_timesteps

* update get_timesteps

* fix bug: make use of timesteps for for_loop

* black file

* black, isort, flake8

* update docstring

* update readme

* update flax img2img readme

* update sd pipeline init

* Update src/diffusers/pipelines/stable_diffusion/pipeline_flax_stable_diffusion_img2img.py
Co-authored-by: Pedro Cuenca <pedro@huggingface.co>

* Update src/diffusers/pipelines/stable_diffusion/pipeline_flax_stable_diffusion_img2img.py
Co-authored-by: Pedro Cuenca <pedro@huggingface.co>

* update inits

* revert change

* update var name to image, typo

* update readme

* return new t_start instead of modified timestep

* black format

* isort files

* update docs

* fix-copies

* update prng_seed typing
Co-authored-by: Pedro Cuenca <pedro@huggingface.co>

* 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>

* Section header for in-painting, inference from checkpoint.

* Inference: link to section to perform inference from checkpoint.

* Move Dreambooth in-painting instructions to the proper place.

* allow model download when no internet

* up

* make style

* [Flax] Stateless schedulers, fixes and refactors

* Remove scheduling_common_flax and some renames

* Update src/diffusers/schedulers/scheduling_pndm_flax.py
Co-authored-by: Pedro Cuenca <pedro@huggingface.co>
Co-authored-by: Pedro Cuenca <pedro@huggingface.co>

* expose polynomial:power and cosine_with_restarts:num_cycles using get_scheduler func, add it to train_dreambooth.py

* fix formatting

* fix style

* Update src/diffusers/optimization.py
Co-authored-by: Pedro Cuenca <pedro@huggingface.co>
Co-authored-by: Pedro Cuenca <pedro@huggingface.co>

* only check for xformers when xformers are enabled

* only test for xformers when enabling them

* reflect changes

* run make style
Co-authored-by: Prathik Rao <prathikrao@microsoft.com>
Co-authored-by: Prathik Rao <prathikrao@microsoft.com@orttrainingdev7.d32nl1ml4oruzj4qz3bqlggovf.px.internal.cloudapp.net>

* Fail if there are less images than the effective batch size.

* Remove lr-scheduler arg as it's currently ignored.

* Make guidance_scale work for batch_size > 1.

* update composable diffusion for an updated diffuser library

* fix style/quality for code

* Revert "fix style/quality for code"

This reverts commit 71f23497639fe69de00d93cf91edc31b08dcd7a4.

* update style

* reduce memory usage by computing score sequentially

* Transformers version req for UnCLIP

* add to the list

Update train_unconditional.py

Add logger flag to choose between tensorboard and wandb

* Add CPU offloading to UnCLIP

* use fp32 for testing the offload

duplicate maks for num_images_per_prompt

* Remove bogus file

* [Unclip] Add efficient attention

* [Unclip] Add efficient attention