GitLab

* up

* convert dual unet

* revert dual attn

* adapt for vd-official

* test the full pipeline

* mixed inference

* mixed inference for text2img

* add image prompting

* fix clip norm

* split text2img and img2img

* fix format

* refactor text2img

* mega pipeline

* add optimus

* refactor image var

* wip text_unet

* text unet end to end

* update tests

* reshape

* fix image to text

* add some first docs

* dual guided pipeline

* fix token ratio

* propose change

* dual transformer as a native module

* DualTransformer(nn.Module)

* DualTransformer(nn.Module)

* correct unconditional image

* save-load with mega pipeline

* remove image to text

* up

* uP

* fix

* up

* final fix

* remove_unused_weights

* test updates

* save progress

* uP

* fix dual prompts

* some fixes

* finish

* style

* finish renaming

* up

* fix

* fix

* fix

* finish
Co-authored-by: anton-l <anton@huggingface.co>

* Add pipeline_stable_diffusion_safe.py to pipelines

* Fix repository consistency

Ran make fix-copies after adding new pipline

* Add Paper/Equation reference for parameters to doc string

* Ensure code style and quality

* Perform code refactoring

* Fix copies inherited from merge with huggingface/main

* Add docs

* Fix code style

* Fix errors in documentation

* Fix refactoring error

* remove debugging print statement

* added Safe Latent Diffusion tests

* Fix style

* Fix style

* Add pre-defined safety configurations

* Fix line-break

* fix some tests

* finish

* Change safety checker

* Add missing safety_checker.py file

* Remove unused imports
Co-authored-by: PatrickSchrML <patrick_schramowski@hotmail.de>
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>

* Better error message for transformers dummy

* [PIL] Better deprecation functionality

* up

* add conversion script for vae

* up

* up

* some fixes

* add text model

* use the correct config

* add docs

* move model in it's own file

* move model in its own file

* pass attenion mask to text encoder

* pass attn mask to uncond inputs

* quality

* fix image2image

* add imag2image in init

* fix import

* fix one more import

* fix import, dummy objetcs

* fix copied from

* up

* finish
Co-authored-by: patil-suraj <surajp815@gmail.com>

* Add CycleDiffusion pipeline for Stable Diffusion

* Add the option of passing noise to DDIMScheduler

Add the option of providing the noise itself to DDIMScheduler, instead of the random seed generator.

* Update README.md

* Update README.md

* Update pipeline_stable_diffusion_cycle_diffusion.py

* Update pipeline_stable_diffusion_cycle_diffusion.py

* Update pipeline_stable_diffusion_cycle_diffusion.py

* Update pipeline_stable_diffusion_cycle_diffusion.py

* Update scheduling_ddim.py

* Update import format

* Update pipeline_stable_diffusion_cycle_diffusion.py

* Update scheduling_ddim.py

* Update src/diffusers/schedulers/scheduling_ddim.py
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>

* Update src/diffusers/schedulers/scheduling_ddim.py
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>

* Update src/diffusers/schedulers/scheduling_ddim.py
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>

* Update src/diffusers/schedulers/scheduling_ddim.py
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>

* Update src/diffusers/schedulers/scheduling_ddim.py
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>

* Update scheduling_ddim.py

* Update scheduling_ddim.py

* Update scheduling_ddim.py

* add two tests

* Update pipeline_stable_diffusion_cycle_diffusion.py

* Update pipeline_stable_diffusion_cycle_diffusion.py

* Update README.md

* Rename pipeline name as suggested in the latest reviewer comment

* Update test_pipelines.py

* Update test_pipelines.py

* Update test_pipelines.py

* Update pipeline_stable_diffusion_cycle_diffusion.py

* Remove the generator

This generator does not control all randomness during sampling, which can be misleading.

* Update optimal hyperparameters

* Update src/diffusers/pipelines/stable_diffusion/README.md
Co-authored-by: Suraj Patil <surajp815@gmail.com>

* Update src/diffusers/pipelines/stable_diffusion/README.md
Co-authored-by: Suraj Patil <surajp815@gmail.com>

* Update src/diffusers/pipelines/stable_diffusion/README.md
Co-authored-by: Suraj Patil <surajp815@gmail.com>

* Apply suggestions from code review

* uP

* Update src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_cycle_diffusion.py
Co-authored-by: Suraj Patil <surajp815@gmail.com>

* up

* up

* Replace assert with ValueError

* finish docs
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
Co-authored-by: Suraj Patil <surajp815@gmail.com>

* Changes for VQ-diffusion VQVAE

Add specify dimension of embeddings to VQModel:
`VQModel` will by default set the dimension of embeddings to the number
of latent channels. The VQ-diffusion VQVAE has a smaller
embedding dimension, 128, than number of latent channels, 256.

Add AttnDownEncoderBlock2D and AttnUpDecoderBlock2D to the up and down
unet block helpers. VQ-diffusion's VQVAE uses those two block types.

* Changes for VQ-diffusion transformer

Modify attention.py so SpatialTransformer can be used for
VQ-diffusion's transformer.

SpatialTransformer:
- Can now operate over discrete inputs (classes of vector embeddings) as well as continuous.
- `in_channels` was made optional in the constructor so two locations where it was passed as a positional arg were moved to kwargs
- modified forward pass to take optional timestep embeddings

ImagePositionalEmbeddings:
- added to provide positional embeddings to discrete inputs for latent pixels

BasicTransformerBlock:
- norm layers were made configurable so that the VQ-diffusion could use AdaLayerNorm with timestep embeddings
- modified forward pass to take optional timestep embeddings

CrossAttention:
- now may optionally take a bias parameter for its query, key, and value linear layers

FeedForward:
- Internal layers are now configurable

ApproximateGELU:
- Activation function in VQ-diffusion's feedforward layer

AdaLayerNorm:
- Norm layer modified to incorporate timestep embeddings

* Add VQ-diffusion scheduler

* Add VQ-diffusion pipeline

* Add VQ-diffusion convert script to diffusers

* Add VQ-diffusion dummy objects

* Add VQ-diffusion markdown docs

* Add VQ-diffusion tests

* some renaming

* some fixes

* more renaming

* correct

* fix typo

* correct weights

* finalize

* fix tests

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

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

* finish

* finish

* up
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
Co-authored-by: Anton Lozhkov <aglozhkov@gmail.com>
Co-authored-by: Pedro Cuenca <pedro@huggingface.co>

Signed-off-by: Ryan Russell <git@ryanrussell.org>
Signed-off-by: Ryan Russell <git@ryanrussell.org>

* fix small typos

* capitalize Diffusers

* up

* up

* finish

* first pass at docs structure

* minor reformatting, add github actions for docs

* populate docs (primarily from README, some writing)