GitLab

Needed to convert `timesteps` to `float32` a bit sooner.

Fixes #1537

* Show error when loading safety_checker `from_flax`

* fix style

Make `key` optional so default pipelines don't fail.

* Fix typo in StableDiffusionInpaintPipeline

* Add embedded prompt handling

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

Related to #2124 
The current implementation is throwing a shape mismatch error. Which makes sense, as this line is obviously missing, comparing to XFormersCrossAttnProcessor and LoRACrossAttnProcessor.

I don't have formal tests, but I compared `LoRACrossAttnProcessor` and `LoRAXFormersCrossAttnProcessor` ad-hoc, and they produce the same results with this fix.

Flagged images would be set to the blank image instead of the original image that contained the NSF concept for optional viewing.
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>

* negative_prompt typo

* fix

* Fix timestep dtype in legacy inpaint

This matches the structure in the text2img, img2img, and inpaint ONNX pipelines

* Fix style in dtype patch

scheduling_ddpm: fix variance in the case of learned_range type.

In the case of learned_range variance type, there are missing logs
and exponent comparing to the theory (see "Improved Denoising Diffusion
Probabilistic Models" section 3.1 equation 15:
https://arxiv.org/pdf/2102.09672.pdf).

* up

* finish

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

-- 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

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

Fix typo in accelerate and transformers versions.

Possible typo introduced in 7c82a16f

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

* 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

* [Bump version] 0.13

* Bump model up

* up

* make tests deterministic

* run slow tests

* prepare for testing

* finish

* refactor

* add print statements

* finish more

* correct some test failures

* more fixes

* set up to correct tests

* more corrections

* up

* fix more

* more prints

* add

* up

* up

* up

* uP

* uP

* more fixes

* uP

* up

* up

* up

* up

* fix more

* up

* up

* clean tests

* up

* up

* up

* more fixes

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

* make

* correct

* finish

* finish
Co-authored-by: Suraj Patil <surajp815@gmail.com>

* add text embeds to sd

* add text embeds to sd

* finish tests

* finish

* finish

* make style

* fix tests

* make style

* make style

* up

* better docs

* fix

* fix

* new try

* up

* up

* finish

update example for pix2pix