GitLab

* re-add RL model code

* match model forward api

* add register_to_config, pass training tests

* fix tests, update forward outputs

* remove unused code, some comments

* add to docs

* remove extra embedding code

* unify time embedding

* remove conv1d output sequential

* remove sequential from conv1dblock

* style and deleting duplicated code

* clean files

* remove unused variables

* clean variables

* add 1d resnet block structure for downsample

* rename as unet1d

* fix renaming

* rename files

* add get_block(...) api

* unify args for model1d like model2d

* minor cleaning

* fix docs

* improve 1d resnet blocks

* fix tests, remove permuts

* fix style

* add output activation

* rename flax blocks file

* Add Value Function and corresponding example script to Diffuser implementation (#884)

* valuefunction code

* start example scripts

* missing imports

* bug fixes and placeholder example script

* add value function scheduler

* load value function from hub and get best actions in example

* very close to working example

* larger batch size for planning

* more tests

* merge unet1d changes

* wandb for debugging, use newer models

* success!

* turns out we just need more diffusion steps

* run on modal

* merge and code cleanup

* use same api for rl model

* fix variance type

* wrong normalization function

* add tests

* style

* style and quality

* edits based on comments

* style and quality

* remove unused var

* hack unet1d into a value function

* add pipeline

* fix arg order

* add pipeline to core library

* community pipeline

* fix couple shape bugs

* style

* Apply suggestions from code review
Co-authored-by: Nathan Lambert <nathan@huggingface.co>

* update post merge of scripts

* add mdiblock / outblock architecture

* Pipeline cleanup (#947)

* valuefunction code

* start example scripts

* missing imports

* bug fixes and placeholder example script

* add value function scheduler

* load value function from hub and get best actions in example

* very close to working example

* larger batch size for planning

* more tests

* merge unet1d changes

* wandb for debugging, use newer models

* success!

* turns out we just need more diffusion steps

* run on modal

* merge and code cleanup

* use same api for rl model

* fix variance type

* wrong normalization function

* add tests

* style

* style and quality

* edits based on comments

* style and quality

* remove unused var

* hack unet1d into a value function

* add pipeline

* fix arg order

* add pipeline to core library

* community pipeline

* fix couple shape bugs

* style

* Apply suggestions from code review

* clean up comments

* convert older script to using pipeline and add readme

* rename scripts

* style, update tests

* delete unet rl model file

* remove imports in src
Co-authored-by: Nathan Lambert <nathan@huggingface.co>

* Update src/diffusers/models/unet_1d_blocks.py

* Update tests/test_models_unet.py

* RL Cleanup v2 (#965)

* valuefunction code

* start example scripts

* missing imports

* bug fixes and placeholder example script

* add value function scheduler

* load value function from hub and get best actions in example

* very close to working example

* larger batch size for planning

* more tests

* merge unet1d changes

* wandb for debugging, use newer models

* success!

* turns out we just need more diffusion steps

* run on modal

* merge and code cleanup

* use same api for rl model

* fix variance type

* wrong normalization function

* add tests

* style

* style and quality

* edits based on comments

* style and quality

* remove unused var

* hack unet1d into a value function

* add pipeline

* fix arg order

* add pipeline to core library

* community pipeline

* fix couple shape bugs

* style

* Apply suggestions from code review

* clean up comments

* convert older script to using pipeline and add readme

* rename scripts

* style, update tests

* delete unet rl model file

* remove imports in src

* add specific vf block and update tests

* style

* Update tests/test_models_unet.py
Co-authored-by: Nathan Lambert <nathan@huggingface.co>

* fix quality in tests

* fix quality style, split test file

* fix checks / tests

* make timesteps closer to main

* unify block API

* unify forward api

* delete lines in examples

* style

* examples style

* all tests pass

* make style

* make dance_diff test pass

* Refactoring RL PR (#1200)

* init file changes

* add import utils

* finish cleaning files, imports

* remove import flags

* clean examples

* fix imports, tests for merge

* update readmes

* hotfix for tests

* quality

* fix some tests

* change defaults

* more mps test fixes

* unet1d defaults

* do not default import experimental

* defaults for tests

* fix tests

* fix-copies

* fix

* changes per Patrik's comments (#1285)

* changes per Patrik's comments

* update conversion script

* fix renaming

* skip more mps tests

* last test fix

* Update examples/rl/README.md
Co-authored-by: Ben Glickenhaus <benglickenhaus@gmail.com>

* Match the generator device to the pipeline for DDPM and DDIM

* style

* fix

* update values

* fix fast tests

* trigger slow tests

* deprecate

* last value fixes

* mps fixes

add ddim in clip guided

* [Scheduler] Move predict epsilon to init

* up

* uP

* uP

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

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

* Make errors for invalid options without "--with_prior_preservation"

* Make --instance_prompt required

* Removed needless check because --instance_data_dir is marked with required

* Updated messages

* Use logger.warning instead of raise errors
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>

load text encoder from subfolder

* Add multilingual_stable_diffusion.py file

* Add multilingual stable diffusion to examples README file

* Update examples/community/README.md
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>

* adds image to image inpainting with `PIL.Image.Image` inputs
the base implementation claims to support `torch.Tensor` but seems it
would also fail in this case.

* `make style` and `make quality`

* updates community examples readme
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>

fix typo

[Community Pipeline] lpw_stable_diffusion: add xformers_memory_efficient_attention and sequential_cpu_offload  (#1130)

lpw_stable_diffusion: xformers and cpu_offload

fix 404 link in README.mb

fix sample batch size

* remove batch size from repeat

* repeat empty string if uncond_tokens is none

* fix inpaint pipes

* return back whitespace to pass code quality

* Apply suggestions from code review

* Fix typos.
Co-authored-by: Had <had-95@yandex.ru>

* changed training example to add option to train model that predicts x0 (instead of eps), changed DDPM pipeline accordingly

* Revert "changed training example to add option to train model that predicts x0 (instead of eps), changed DDPM pipeline accordingly"

This reverts commit c5efb525648885f2e7df71f4483a9f248515ad61.

* changed training example to add option to train model that predicts x0 (instead of eps), changed DDPM pipeline accordingly

* fixed code style
Co-authored-by: lukovnikov <lukovnikov@users.noreply.github.com>

Update README.md

Update README.md

fixed typo

* [Community Pipelines] lpw_stable_diffusion: Add is_cancelled_callback

* [Community pipelines] lpw_stable_diffusion_onnx: Add is_cancelled_callback

* initial commit to add imagic to stable diffusion community pipelines

* remove some testing changes

* comments from PR review for imagic stable diffusion

* remove changes from pipeline_stable_diffusion as part of imagic pipeline

* clean up example code and add line back in to pipeline_stable_diffusion for imagic pipeline

* remove unused functions

* small code quality changes for imagic pipeline

* clean up readme

* remove hardcoded logging values for imagic community example

* undo change for DDIMScheduler

* [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>

* add seed resizing to community examples

* actually add the file responsible for seed resizing

* Update training and fine-tuning docs.

* Update examples README.

* Update README.

* Add Flax fine-tuning section.

* Accept suggestion
Co-authored-by: Anton Lozhkov <anton@huggingface.co>

* Accept suggestion
Co-authored-by: Anton Lozhkov <anton@huggingface.co>
Co-authored-by: Anton Lozhkov <anton@huggingface.co>

fix jnp dtype

tensorboard import in readme, otherwise accelerator.trackers[0] out of range
Co-authored-by: lukovnikov <lukovnikov@users.noreply.github.com>

Deprecate `init_git_repo` and `push_to_hub`, refactor `train_unconditional.py`

* [Flax] Add DreamBooth

* fix sample rng

* style

* not reuse rng

* add dtype for mixed precision training

* Add Flax example

Set train mode for text encoder

* [Flax] Add finetune Stable Diffusion

* temporary fix

* drop_last and seed

* add dtype for mixed precision training

* style

* Add Flax example

make input_args optional

* add textual inversion flax

* make style

* make style

* replicate vae and unet params

* make style

* minor

* save after end of training

* style

* Temporary fix
Co-authored-by: Suraj Patil <surajp815@gmail.com>

* Add Flax instruction
Co-authored-by: Suraj Patil <surajp815@gmail.com>

* Make training code usable by external scripts

Add parameter inputs to training and argument parsing function to allow this script to be used by an external call.

* Apply suggestions from code review
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>

remove tensor_format in the new version

* Add --pretrained_model_name_revision option to train_dreambooth.py

* Renamed --pretrained_model_name_revision to --revision