* Fix resuming state when using gradient checkpointing.
Also, allow --resume_from_checkpoint to be used when the checkpoint does
not yet exist (a normal training run will be started).
* style
* Dreambooth: use `optimizer.zero_grad(set_to_none=True)` to reduce VRAM usage
* Allow the user to control `optimizer.zero_grad(set_to_none=True)` with --set_grads_to_none
* Update Dreambooth readme
* Fix link in readme
* Fix header size in readme
* example on fine-tuning with LoRA.
* apply make quality.
* fix: pipeline loading.
* Apply suggestions from code review
Co-authored-by: Suraj Patil <surajp815@gmail.com>
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
* apply suggestions for PR review.
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
* apply make style and make quality.
* chore: remove mention of dreambooth from text2image.
* add: weight path and wandb run link.
* Apply suggestions from code review
* apply make style.
* make style
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
Co-authored-by: Suraj Patil <surajp815@gmail.com>
* improve EMA
* style
* one EMA model
* quality
* fix tests
* fix test
* Apply suggestions from code review
Co-authored-by: Pedro Cuenca <pedro@huggingface.co>
* re organise the unconditional script
* backwards compatibility
* default to init values for some args
* fix ort script
* issubclass => isinstance
* update state_dict
* docstr
* doc
* Apply suggestions from code review
Co-authored-by: Pedro Cuenca <pedro@huggingface.co>
* use .to if device is passed
* deprecate device
* make flake happy
* fix typo
Co-authored-by: patil-suraj <surajp815@gmail.com>
Co-authored-by: Pedro Cuenca <pedro@huggingface.co>
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
* [Lora] first upload
* add first lora version
* upload
* more
* first training
* up
* correct
* improve
* finish loaders and inference
* up
* up
* fix more
* up
* finish more
* finish more
* up
* up
* change year
* revert year change
* Change lines
* Add cloneofsimo as co-author.
Co-authored-by: Simo Ryu <cloneofsimo@gmail.com>
* finish
* fix docs
* Apply suggestions from code review
Co-authored-by: Pedro Cuenca <pedro@huggingface.co>
Co-authored-by: Suraj Patil <surajp815@gmail.com>
* upload
* finish
Co-authored-by: Simo Ryu <cloneofsimo@gmail.com>
Co-authored-by: Pedro Cuenca <pedro@huggingface.co>
Co-authored-by: Suraj Patil <surajp815@gmail.com>
* implemented multi subject dreambooth in research_projects
* minor edits to readme
* added style and quality fixes
Co-authored-by: Krista Opsahl-Ong <kristaopsahlong@gmail.com>
* Various Fixes for Flax Dreambooth
- Correctly update the progress bar every epoch
- Allow specifying a pretrained VAE
- Allow specifying a revision to pretrained models
- Cache compiled models between invocations (speeds up TPU execution a lot!)
- Save intermediate checkpoints by specifying `save_steps`
* Don't die when save_steps is not set.
* Address CR
* Address comments
* Apply suggestions from code review
Co-authored-by: Pedro Cuenca <pedro@huggingface.co>
Co-authored-by: Suraj Patil <surajp815@gmail.com>
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
Co-authored-by: Pedro Cuenca <pedro@huggingface.co>
* Support training SD V2 with Flax
Mostly involves supporting a v_prediction scheduler.
The implementation in #1777 doesn't take into account a recent refactor of `scheduling_utils_flax`, so this should be used instead.
* Add to other top-level files.
* [Deterministic torch randn] Allow tensors to be generated on CPU
* fix more
* up
* fix more
* up
* Update src/diffusers/utils/torch_utils.py
Co-authored-by: Anton Lozhkov <anton@huggingface.co>
* Apply suggestions from code review
* up
* up
* Apply suggestions from code review
Co-authored-by: Pedro Cuenca <pedro@huggingface.co>
Co-authored-by: Anton Lozhkov <anton@huggingface.co>
Co-authored-by: Pedro Cuenca <pedro@huggingface.co>
* misc fixes
* more comments
* Update examples/textual_inversion/textual_inversion.py
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
* set transformers verbosity to warning
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
* allow using non-ema weights for training
* Apply suggestions from code review
Co-authored-by: Pedro Cuenca <pedro@huggingface.co>
* address more review comment
* reorganise a few lines
* always pad text to max_length to match original training
* ifx collate_fn
* remove unused code
* don't prepare ema_unet, don't register lr scheduler
* style
* assert => ValueError
* add allow_tf32
* set log level
* fix comment
Co-authored-by: Pedro Cuenca <pedro@huggingface.co>
* move files a bit
* more refactors
* fix more
* more fixes
* fix more onnx
* make style
* upload
* fix
* up
* fix more
* up again
* up
* small fix
* Update src/diffusers/__init__.py
Co-authored-by: Pedro Cuenca <pedro@huggingface.co>
* correct
Co-authored-by: Pedro Cuenca <pedro@huggingface.co>
* initial
* type hints
* update scheduler type hint
* add to README
* add example generation to README
* v -> mix_factor
* load scheduler from pretrained
* 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
