* 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
* [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>
* [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>
* 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
* Add state checkpointing to other training scripts
* Fix first_epoch
* Apply suggestions from code review
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
* Update Dreambooth checkpoint help message.
* Dreambooth docs: checkpoints, inference from a checkpoint.
* make style
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
* add check_min_version for examples
* move __version__ to the top
* Apply suggestions from code review
Co-authored-by: Pedro Cuenca <pedro@huggingface.co>
* fix comment
* fix error_message
* adapt the install message
Co-authored-by: Pedro Cuenca <pedro@huggingface.co>
[textual_inversion] Add an option to only save embeddings
Add an command line option --only_save_embeds to the example script, for
not saving the full model. Then only the learned embeddings are saved,
which can be added to the original model at runtime in a similar way as
they are created in the training script.
Saving the full model is forced when --push_to_hub is used. (Implements #759)
* [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>
* pytorch only schedulers
* fix style
* remove match_shape
* pytorch only ddpm
* remove SchedulerMixin
* remove numpy from karras_ve
* fix types
* remove numpy from lms_discrete
* remove numpy from pndm
* fix typo
* remove mixin and numpy from sde_vp and ve
* remove remaining tensor_format
* fix style
* sigmas has to be torch tensor
* removed set_format in readme
* remove set format from docs
* remove set_format from pipelines
* update tests
* fix typo
* continue to use mixin
* fix imports
* removed unsed imports
* match shape instead of assuming image shapes
* remove import typo
* update call to add_noise
* use math instead of numpy
* fix t_index
* removed commented out numpy tests
* timesteps needs to be discrete
* cast timesteps to int in flax scheduler too
* fix device mismatch issue
* small fix
* Update src/diffusers/schedulers/scheduling_pndm.py
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
* Fix typos
* Add a typo check action
* Fix a bug
* Changed to manual typo check currently
Ref: https://github.com/huggingface/diffusers/pull/483#pullrequestreview-1104468010
Co-authored-by: Anton Lozhkov <aglozhkov@gmail.com>
* Removed a confusing message
* Renamed "nin_shortcut" to "in_shortcut"
* Add memo about NIN
Co-authored-by: Anton Lozhkov <aglozhkov@gmail.com>
* beta never changes removed from state
* fix typos in docs
* removed unused var
* initial ddim flax scheduler
* import
* added dummy objects
* fix style
* fix typo
* docs
* fix typo in comment
* set return type
* added flax ddom
* fix style
* remake
* pass PRNG key as argument and split before use
* fix doc string
* use config
* added flax Karras VE scheduler
* make style
* fix dummy
* fix ndarray type annotation
* replace returns a new state
* added lms_discrete scheduler
* use self.config
* add_noise needs state
* use config
* use config
* docstring
* added flax score sde ve
* fix imports
* fix typos
* add textual inversion script
* make the loop work
* make coarse_loss optional
* save pipeline after training
* add arg pretrained_model_name_or_path
* fix saving
* fix gradient_accumulation_steps
* style
* fix progress bar steps
* scale lr
* add argument to accept style
* remove unused args
* scale lr using num gpus
* load tokenizer using args
* add checks when converting init token to id
* improve commnets and style
* document args
* more cleanup
* fix default adamw arsg
* TextualInversionWrapper -> CLIPTextualInversionWrapper
* fix tokenizer loading
* Use the CLIPTextModel instead of wrapper
* clean dataset
* remove commented code
* fix accessing grads for multi-gpu
* more cleanup
* fix saving on multi-GPU
* init_placeholder_token_embeds
* add seed
* fix flip
* fix multi-gpu
* add utility methods in wrapper
* remove ipynb
* don't use wrapper
* dont pass vae an dunet to accelerate prepare
* bring back accelerator.accumulate
* scale latents
* use only one progress bar for steps
* push_to_hub at the end of training
* remove unused args
* log some important stats
* store args in tensorboard
* pretty comments
* save the trained embeddings
* mobe the script up
* add requirements file
* more cleanup
* fux typo
* begin readme
* style -> learnable_property
* keep vae and unet in eval mode
* address review comments
* address more comments
* removed unused args
* add train command in readme
* update readme
Pedro Cuenca