GitLab

* add

* clean

* up

* clean up more

* fix more tests

* Improve docs further

* improve

* more fixes docs

* Improve docs more

* Update src/diffusers/models/unet_2d_condition.py

* fix

* up

* update doc links

* make fix-copies

* add safety checker and watermarker to stage 3 doc page code snippets

* speed optimizations docs

* memory optimization docs

* make style

* add watermarking snippets to doc string examples

* make style

* use pt_to_pil helper functions in doc strings

* skip mps tests

* Improve safety

* make style

* new logic

* fix

* fix bad onnx design

* make new stable diffusion upscale pipeline model arguments optional

* define has_nsfw_concept when non-pil output type

* lowercase linked to notebook name

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Co-authored-by: William Berman <WLBberman@gmail.com>

* Fix docs text inversion

* Apply suggestions from code review

sync cache version check from transformers

When the token used for textual inversion does not have any special symbols (e.g. it is not surrounded by <>), the tokenizer does not properly split the replacement tokens.  Adding a space for the padding tokens fixes this.

Add v1.1 docs

adding enable_vae_tiling and disable_val_tiling functions

cast to weight dtype

Revert "[Community Pipelines] Update lpw_stable_diffusion pipeline (#3197)"

This reverts commit 9965cb50.

* Make sure attention works with Torch 2_0

* make style

* Fix more

* Update lpw_stable_diffusion.py

* fix cpu offload

* Update train_dreambooth_lora.py

fix bug

* Update train_dreambooth_lora.py

* Add karras pattern to discrete heun scheduler

* Add integration test

* Fix failing CI on pytorch test on M1 (mps)

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

* Multi Vector

* Improve

* fix multi token

* improve test

* make style

* Update examples/test_examples.py

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

* update

* Finish

* Apply suggestions from code review

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

* clarify training arg

* apply feedback

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

pass silence_dtype_warnings as kwarg
Signed-off-by: Asfiya Baig <asfiyab@nvidia.com>
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>

* Update custom_diffusion.mdx

* fix: unnecessary list comprehension.

deterministic algos

* Add model offload to x4 upscaler

* fix

* Update Habana Gaudi doc

* Fix tables

* add: LoRA text encoder support for DreamBooth example.

* fix initialization.

* fix: modification call.

* add: entry in the readme.

* use dog dataset from hub.

* fix: params to clip.

* add entry to the LoRA doc.

* add: tests for lora.

* remove unnecessary list comprehension./

* Update Pix2PixZero Auto-correlation Loss

* Add fast inversion tests

* Clarify purpose and mark as deprecated

Fix inversion prompt broadcasting

* Register modules set to `None` in config for `test_save_load_optional_components`

* Update new tests to coordinate with #2953

* Added distillation for quantization example on textual inversion.
Signed-off-by: Ye, Xinyu <xinyu.ye@intel.com>

* refined readme and code style.
Signed-off-by: Ye, Xinyu <xinyu.ye@intel.com>

* Update text2images.py

* refined code of model load and added compatibility check.
Signed-off-by: Ye, Xinyu <xinyu.ye@intel.com>

* fixed code style.
Signed-off-by: Ye, Xinyu <xinyu.ye@intel.com>

* fix C403 [*] Unnecessary `list` comprehension (rewrite as a `set` comprehension)
Signed-off-by: Ye, Xinyu <xinyu.ye@intel.com>

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Signed-off-by: Ye, Xinyu <xinyu.ye@intel.com>

Add missing newlines for rendering the links correctly

* diffusers==0.14.0 update

* custom diffusion update

* custom diffusion update

* custom diffusion update

* custom diffusion update

* custom diffusion update

* custom diffusion update

* custom diffusion

* custom diffusion

* custom diffusion

* custom diffusion

* custom diffusion

* apply formatting and get rid of bare except.

* refactor readme and other minor changes.

* misc refactor.

* fix: repo_id issue and loaders logging bug.

* fix: save_model_card.

* fix: save_model_card.

* fix: save_model_card.

* add: doc entry.

* refactor doc,.

* custom diffusion

* custom diffusion

* custom diffusion

* apply style.

* remove tralining whitespace.

* fix: toctree entry.

* remove unnecessary print.

* custom diffusion

* custom diffusion

* custom diffusion test

* custom diffusion xformer update

* custom diffusion xformer update

* custom diffusion xformer update

---------
Co-authored-by: Nupur Kumari <nupurkumari@Nupurs-MacBook-Pro.local>
Co-authored-by: Sayak Paul <spsayakpaul@gmail.com>
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
Co-authored-by: Nupur Kumari <nupurkumari@nupurs-mbp.wifi.local.cmu.edu>

controlnet training center crop input images to multiple of 8

The pipeline code resizes inputs to multiples of 8.
Not doing this resizing in the training script is causing
the encoded image to have different height/width dimensions
than the encoded conditioning image (which uses a separate
encoder that's part of the controlnet model).

We resize and center crop the inputs to make sure they're the
same size (as well as all other images in the batch). We also
check that the initial resolution is a multiple of 8.

* Modified altdiffusion pipline to support altdiffusion-m18

* Modified altdiffusion pipline to support altdiffusion-m18

* Modified altdiffusion pipline to support altdiffusion-m18

* Modified altdiffusion pipline to support altdiffusion-m18

* Modified altdiffusion pipline to support altdiffusion-m18

* Modified altdiffusion pipline to support altdiffusion-m18

* Modified altdiffusion pipline to support altdiffusion-m18

---------
Co-authored-by: root <fulong_ye@163.com>

* Update pipeline_stable_diffusion_inpaint_legacy.py

* fix preprocessing of Pil images with adequate batch size

* revert map

* add tests

* reformat

* Update test_stable_diffusion_inpaint_legacy.py

* Update test_stable_diffusion_inpaint_legacy.py

* Update test_stable_diffusion_inpaint_legacy.py

* Update test_stable_diffusion_inpaint_legacy.py

* next try to fix the style

* wth is this

* Update testing_utils.py

* Update testing_utils.py

* Update test_stable_diffusion_inpaint_legacy.py

* Update test_stable_diffusion_inpaint_legacy.py

* Update test_stable_diffusion_inpaint_legacy.py

* Update test_stable_diffusion_inpaint_legacy.py

* Update test_stable_diffusion_inpaint_legacy.py

* Update test_stable_diffusion_inpaint_legacy.py

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

⚙️chore(transformer_2d) update function signature for encoder_hidden_states

* Add SD/txt2img Community Pipeline to diffusers along with TensorRT utils
Signed-off-by: Asfiya Baig <asfiyab@nvidia.com>

* update installation command
Signed-off-by: Asfiya Baig <asfiyab@nvidia.com>

* update tensorrt installation
Signed-off-by: Asfiya Baig <asfiyab@nvidia.com>

* changes
1. Update setting of cache directory
2. Address comments: merge utils and pipeline code.
3. Address comments: Add section in README
Signed-off-by: Asfiya Baig <asfiyab@nvidia.com>

* apply make style
Signed-off-by: Asfiya Baig <asfiyab@nvidia.com>

---------
Signed-off-by: Asfiya Baig <asfiyab@nvidia.com>
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>

* add mixin class for pipeline from original sd ckpt

* Improve

* make style

* merge main into

* Improve more

* fix more

* up

* Apply suggestions from code review

* finish docs

* rename

* make style

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

* [ckpt loader] Allow loading the Inpaint and Img2Img pipelines, while loading a ckpt model

* Address review comment from PR

* PyLint formatting

* Some more pylint fixes, unrelated to our change

* Another pylint fix

* Styling fix

This mimics the dtype cast for the standard time embeddings

Adding act fn config to the unet timestep class embedding and conv
activation.

The custom activation defaults to silu which is the default
activation function for both the conv act and the timestep class
embeddings so default behavior is not changed.

The only unet which use the custom activation is the stable diffusion
latent upscaler https://huggingface.co/stabilityai/sd-x2-latent-upscaler/blob/main/unet/config.json
(I ran a script against the hub to confirm).
The latent upscaler does not use the conv activation nor the timestep
class embeddings so we don't change its behavior.

* Update README.md

* Apply suggestions from code review