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This reverts commit 024c4376.

* before running make style

* remove left overs from flake8

* finish

* make fix-copies

* final fix

* more fixes

* make scaling factor cnfig arg of vae

* fix

* make flake happy

* fix ldm

* fix upscaler

* qualirty

* Apply suggestions from code review
Co-authored-by: Anton Lozhkov <anton@huggingface.co>
Co-authored-by: Pedro Cuenca <pedro@huggingface.co>
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>

* solve conflicts, addres some comments

* examples

* examples min version

* doc

* fix type

* typo

* Update src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_upscale.py
Co-authored-by: Pedro Cuenca <pedro@huggingface.co>

* remove duplicate line

* Apply suggestions from code review
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
Co-authored-by: Anton Lozhkov <anton@huggingface.co>
Co-authored-by: Pedro Cuenca <pedro@huggingface.co>
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>

* [Bump version] 0.13

* Bump model up

* up

* Create repo before cloning in examples

* code quality

* 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.

* [Flax] Stateless schedulers, fixes and refactors

* Remove scheduling_common_flax and some renames

* Update src/diffusers/schedulers/scheduling_pndm_flax.py
Co-authored-by: Pedro Cuenca <pedro@huggingface.co>
Co-authored-by: Pedro Cuenca <pedro@huggingface.co>

* Fail if there are less images than the effective batch size.

* Remove lr-scheduler arg as it's currently ignored.

* Make guidance_scale work for batch_size > 1.

* 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>

load text encoder from subfolder

fix sample batch size

* [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

make input_args optional

* 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>

* Add --pretrained_model_name_revision option to train_dreambooth.py

* Renamed --pretrained_model_name_revision to --revision

* Add an underscore to filename if it already exists

* Use sha1sum hash instead of adding underscores

dont' use safety check when generating prior images

* allow fine-tuning text encoder

* fix a few things

* update readme

Fix dreambooth loss type with prior preservation

* 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

* Fix push_to_hub for dreambooth and textual_inversion

* Use repo.push_to_hub instead of push_to_hub

up

remove use_auth_token

using already created `Path` in dataset

* Fix typos

* Update examples/dreambooth/train_dreambooth.py
Co-authored-by: Pedro Cuenca <pedro@huggingface.co>
Co-authored-by: Pedro Cuenca <pedro@huggingface.co>

fix applying clip grad norm

don't pass tensor_format

* Add training example for DreamBooth.

* Fix bugs.

* Update readme and default hyperparameters.

* Reformatting code with black.

* Update for multi-gpu trianing.

* Apply suggestions from code review

* improgve sampling

* fix autocast

* improve sampling more

* fix saving

* actuallu fix saving

* fix saving

* improve dataset

* fix collate fun

* fix collate_fn

* fix collate fn

* fix key name

* fix dataset

* fix collate fn

* concat batch in collate fn

* add grad ckpt

* add option for 8bit adam

* do two forward passes for prior preservation

* Revert "do two forward passes for prior preservation"

This reverts commit 661ca4677e6dccc4ad596c2ee6ca4baad4159e95.

* add option for prior_loss_weight

* add option for clip grad norm

* add more comments

* update readme

* update readme

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

* add docstr for dataset

* update the saving logic

* Update examples/dreambooth/README.md

* remove unused imports
Co-authored-by: Suraj Patil <surajp815@gmail.com>
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>