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* initial ddpm

for issue #293

* initial ddpm pipeline doc

* added docstrings

* Update docs/source/api/pipelines/ddpm.mdx
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>

* make style

* fix docs

* make style

* fix doc strings
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>

* initial description

* add doc strings

Add contribution.mdx by copy/pasting and adapting.

* up

* up

* up

* up

* up

* up

* up

fix saving embeds

* up

* add tests

* correct

* up

* finish

* better naming

* Update README.md
Co-authored-by: Pedro Cuenca <pedro@huggingface.co>
Co-authored-by: Pedro Cuenca <pedro@huggingface.co>

Use `expand` instead of ones to broadcast tensor.

As suggested by @bes-dev. According the documentation this shouldn't
take any memory - it just plays with the strides.

move to fp16, update ddim

* [Type Hint] VAE models

* Update src/diffusers/models/vae.py

* apply suggestions from code review
Co-authored-by: Anton Lozhkov <aglozhkov@gmail.com>

* [Type hint] scheduling karras ve

* [Type hint] scheduling lms discrete

* type hints: models/vae.py

* modify typings in vae.py
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
Co-authored-by: Anton Lozhkov <anton@huggingface.co>

* type hints

* Apply suggestions from code review
Co-authored-by: Anton Lozhkov <anton@huggingface.co>

* add outputs for models

* add for pipelines

* finish schedulers

* better naming

* adapt tests as well

* replace dict access with . access

* make schedulers works

* finish

* correct readme

* make  bcp compatible

* up

* small fix

* finish

* more fixes

* more fixes

* Apply suggestions from code review
Co-authored-by: Suraj Patil <surajp815@gmail.com>
Co-authored-by: Pedro Cuenca <pedro@huggingface.co>

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

* Adapt model outputs

* Apply more suggestions

* finish examples

* correct
Co-authored-by: Suraj Patil <surajp815@gmail.com>
Co-authored-by: Pedro Cuenca <pedro@huggingface.co>

* package `version` on main should have `.dev0` suffix

package `version` on main should have `.dev0` suffix, which is the convention followed in transformers [here](https://github.com/huggingface/transformers/blob/main/setup.py#L403)

which will also make the docs built into `main` folder in [doc-build diffusers](https://github.com/huggingface/doc-build/tree/main/diffusers)

* dev version should be incremented

* Update version in `__init__.py`

use add_tokens

* [Type hint] scheduling ddim

* apply suggestions from code review

apply suggestions to also return the return type
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>

* [Type Hints] VAE models

* apply suggestions from code review

apply suggestions to also return the return type

* [Type hint] PNDM Schedulers

* ran make style

* updated timesteps type hint

* apply suggestions from code review

* ran make style

* removed unused import

Update unet_blocks.py

fix typo

* [Commands] Add env command

* Apply suggestions from code review

uP

* add void check

* remove void, add types for params

* Use ONNX / Core ML compatible method to broadcast.

Unfortunately `tile` could not be used either, it's still not compatible
with ONNX.

See #284.

* Add comment about why broadcast_to is not used.

Also, apply style to changed files.

* Make sure broadcast remains in same device.

* [Type hint] PNDM pipeline

* ran make style

* Revert "ran make style" wrong black version

Remove joke

* up

* thanks Clau

* finish

* finish

* up

* Fix tqdm and OOM

* tqdm auto

* tqdm is still spamming try to disable it altogether

* rather just set the pipe config, to keep the global tqdm clean

* style

Prose: stable diffusion version v1-3 -> v1-4

The code examples use `v1-4`, but the license text was referring to
`v1-3`.

* 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

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