* Adapt ddpm, ddpmsolver to prediction_type.
* Deprecate predict_epsilon in __init__.
* Bring FlaxDDIMScheduler up to date with DDIMScheduler.
* Set prediction_type as an ivar for consistency.
* Convert pipeline_ddpm
* Adapt tests.
* Adapt unconditional training script.
* Adapt BitDiffusion example.
* Add missing kwargs in dpmsolver_multistep
* Ugly workaround to accept deprecated predict_epsilon when loading
schedulers using from_pretrained.
* make style
* Remove import no longer in use.
* Apply suggestions from code review
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
* Use config.prediction_type everywhere
* Add a couple of Flax prediction type tests.
* make style
* fix register deprecated arg
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
* 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>
* 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
* [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>
* 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>
* 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>
* 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>
Anton Lozhkov