* begin text2image script
* loading the datasets, preprocessing & transforms
* handle input features correctly
* add gradient checkpointing support
* fix output names
* run unet in train mode not text encoder
* use no_grad instead of freezing params
* default max steps None
* pad to longest
* don't pad when tokenizing
* fix encode on multi gpu
* fix stupid bug
* add random flip
* add ema
* fix ema
* put ema on cpu
* improve EMA model
* contiguous_format
* don't warp vae and text encode in accelerate
* remove no_grad
* use randn_like
* fix resize
* improve few things
* log epoch loss
* set log level
* don't log each step
* remove max_length from collate
* style
* add report_to option
* make scale_lr false by default
* add grad clipping
* add an option to use 8bit adam
* fix logging in multi-gpu, log every step
* more comments
* remove eval for now
* adress review comments
* add requirements file
* begin readme
* begin readme
* fix typo
* fix push to hub
* populate readme
* update readme
* remove use_auth_token from the script
* address some review comments
* better mixed precision support
* remove redundant to
* create ema model early
* Apply suggestions from code review
Co-authored-by: Pedro Cuenca <pedro@huggingface.co>
* better description for train_data_dir
* add diffusers in requirements
* update dataset_name_mapping
* update readme
* add inference example
Co-authored-by: anton-l <anton@huggingface.co>
Co-authored-by: Pedro Cuenca <pedro@huggingface.co>
* Support deepspeed
* Dreambooth DeepSpeed documentation
* Remove unnecessary casts, documentation
Due to recent commits some casts to half precision are not necessary
anymore.
Mention that DeepSpeed's version of Adam is about 2x faster.
* Review comments
* 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>
* 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
