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* Create train_dreambooth_inpaint_lora.py

* Update train_dreambooth_inpaint_lora.py

* Update train_dreambooth_inpaint_lora.py

* Update train_dreambooth_inpaint_lora.py

* Update train_dreambooth_inpaint_lora.py

add citation.cff

Update xFormers docs.

* up

* finish

The dimension does not match when `inner_dim` is not equal to `in_channels`.

* add: guide on kerascv conversion tool.

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

* address additional suggestions from review.

* change links to documentation-images.

* add separate links for training and inference goodies from diffusers.

* address Patrick's comments.

---------
Co-authored-by: Pedro Cuenca <pedro@huggingface.co>
Co-authored-by: Kashif Rasul <kashif.rasul@gmail.com>
Co-authored-by: Suraj Patil <surajp815@gmail.com>

-- This commit adopts `requests` in place of `wget` to fetch config `.yaml`
   files as part of `load_pipeline_from_original_stable_diffusion_ckpt` API.
-- This was done because in Windows PowerShell one needs to explicitly ensure
   that `wget` binary is part of the PATH variable. If not present, this leads
   to the code not being able to download the `.yaml` config file.
Signed-off-by: Abhishek Varma <abhishek@nod-labs.com>
Co-authored-by: Abhishek Varma <abhishek@nod-labs.com>

* fix legacy inpaint noise and resize mask tensor

* updated legacy inpaint pipe test expected_slice

* Update README.md

* Update README.md

scheduling_ddpm: fix evaluate with lower timesteps count than train.
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>

* Don't copy when unwrapping model.

Otherwise an exception is raised when using fp16.

* Remove unused import

* Section on using LoRA alpha / scale.

* Accept suggestion
Co-authored-by: Sayak Paul <spsayakpaul@gmail.com>

* Clarify on merge.

---------
Co-authored-by: Sayak Paul <spsayakpaul@gmail.com>

* finish more

* finish philosophy

* Apply suggestions from code review
Co-authored-by: YiYi Xu <yixu310@gmail.com>
Co-authored-by: Will Berman <wlbberman@gmail.com>

---------
Co-authored-by: YiYi Xu <yixu310@gmail.com>
Co-authored-by: Will Berman <wlbberman@gmail.com>

Fix typo in accelerate and transformers versions.

Possible typo introduced in 7c82a16f

* Tmp.

* Adding more docs.

* Doc style.

* Remove the argument `use_safetensors=True`.

* doc-builder

Don't call the Hub if

Fix typo in loaders.py

* [LoRA] All to use in inference with pipeline

* [LoRA] allow cross attention kwargs passed to pipeline

* finish

Fix

* fuse attention mask

* lint

* use 0 beta when no attention mask re: @Birch-san

* fix PosixPath is not JSON serializable

* use PosixPath

* forgot elif like a dummy

* 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>

* Allow `UNet2DModel` to use arbitrary class embeddings.

We can currently use class conditioning in `UNet2DConditionModel`, but
not in `UNet2DModel`. However, `UNet2DConditionModel` requires text
conditioning too, which is unrelated to other types of conditioning.
This commit makes it possible for `UNet2DModel` to be conditioned on
entities other than timesteps. This is useful for training /
research purposes. We can currently train models to perform
unconditional image generation or text-to-image generation, but it's not
straightforward to train a model to perform class-conditioned image
generation, if text conditioning is not required.

We could potentiall use `UNet2DConditionModel` for class-conditioning
without text embeddings by using down/up blocks without
cross-conditioning. However:
- The mid block currently requires cross attention.
- We are required to provide `encoder_hidden_states` to `forward`.

* Style

* Align class conditioning, add docstring for `num_class_embeds`.

* Copy docstring to versatile_diffusion UNetFlatConditionModel

* [textual inversion] Allow validation images.

* Change key to `validation`

* Specify format instead of transposing.

As discussed with @sayakpaul.

* Style
Co-authored-by: isamu-isozaki <isamu.website@gmail.com>