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* 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

* [unCLIP docs] markdown

* [unCLIP docs] UnCLIPPipeline

* [fix] pipeline_unclip generator

pass generator to all schedulers

* fix fast tests test data

* [wip] attention block updates

* [wip] unCLIP unet decoder and super res

* [wip] unCLIP prior transformer

* [wip] scheduler changes

* [wip] text proj utility class

* [wip] UnCLIPPipeline

* [wip] kakaobrain unCLIP convert script

* [unCLIP pipeline] fixes re: @patrickvonplaten

remove callbacks

move denoising loops into call function

* UNCLIPScheduler re: @patrickvonplaten

Revert changes to DDPMScheduler. Make UNCLIPScheduler, a modified
DDPM scheduler with changes to support karlo

* mask -> attention_mask re: @patrickvonplaten

* [DDPMScheduler] remove leftover change

* [docs] PriorTransformer

* [docs] UNet2DConditionModel and UNet2DModel

* [nit] UNCLIPScheduler -> UnCLIPScheduler

matches existing unclip naming better

* [docs] SchedulingUnCLIP

* [docs] UnCLIPTextProjModel

* refactor

* finish licenses

* rename all to attention_mask and prep in models

* more renaming

* don't expose unused configs

* final renaming fixes

* remove x attn mask when not necessary

* configure kakao script to use new class embedding config

* fix copies

* [tests] UnCLIPScheduler

* finish x attn

* finish

* remove more

* rename condition blocks

* clean more

* Apply suggestions from code review

* up

* fix

* [tests] UnCLIPPipelineFastTests

* remove unused imports

* [tests] UnCLIPPipelineIntegrationTests

* correct

* make style
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>

* Fix/update LDM tests

* batched generators

* unset level

Co-authored-by: Peter <peterto@users.noreply.github.com>

[Batched Generators] This PR adds generators that are useful to make batched generation fully reproducible  (#1718)

* [Batched Generators] all batched generators

* up

* up

* up

* up

* up

* up

* up

* up

* up

* up

* up

* up

* up

* up

* up

* up

* hey

* up again

* fix tests

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

* correct tests
Co-authored-by: Pedro Cuenca <pedro@huggingface.co>

* [WIP] Nightly integration tests

* initial SD tests

* update SD slow tests

* style

* repaint

* ImageVariations

* style

* finish imgvar

* img2img tests

* debug

* inpaint 1.5

* inpaint legacy

* torch isn't happy about deterministic ops

* allclose -> max diff for shorter logs

* add SD2

* debug

* Update tests/pipelines/stable_diffusion_2/test_stable_diffusion.py
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>

* Update tests/pipelines/stable_diffusion/test_stable_diffusion.py
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>

* fix refs

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

* fix refs

* remove debug
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
Co-authored-by: Pedro Cuenca <pedro@huggingface.co>

* Fix links to flash attention.

* Add xformers installation instructions.

* Make link to xformers install more prominent.

* Link to xformers install from training docs.

* Fix ONNX img2img preprocessing and add fast tests coverage

* revert

* disable progressbars

* Latent Diffusion pipeline accept latents

* make style

* check for mps

randn does not work reproducibly on mps

Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
Co-authored-by: Pedro Cuenca <pedro@huggingface.co>

* Add examples with Intel optimizations (BF16 fine-tuning and inference)

* Remove unused package

* Add README for intel_opts and refine the description for research projects

* Add notes of intel opts for diffusers

* add conf.yaml

* enable bf16

enable amp bf16 for unet forward

fix style

fix readme

remove useless file

* change amp to full bf16

* align

* make stype

* fix format

* update inpaint_legacy to allow the use of predicted noise to construct intermediate diffused images

* Update src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_inpaint_legacy.py
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>