GitLab

* 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

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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

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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

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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>

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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

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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

extract pipeline from log_validation

* feat: verfication of multi-gpu support for select examples.

* add: multi-gpu training sections to the relvant doc pages.

* Better deprecation message

* Better deprecation message

* Better doc string

* Fixes

* fix more

* fix more

* Improve __getattr__

* correct more

* fix more

* fix

* Improve more

* more improvements

* fix more

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

* make style

* Fix all rest & add tests & remove old deprecation fns

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Co-authored-by: Pedro Cuenca <pedro@huggingface.co>

Make sure correct timesteps are chosen for img2img

Fix img2img processor with safety checker

fix breaking change

add custom timesteps test

add custom timesteps descending order check

docs

timesteps -> custom_timesteps

can only pass one of num_inference_steps and timesteps

remvoe one line

* fix default

* add guess mode (WIP)

* fix uncond/cond order

* support guidance_scale=1.0 and batch != 1

* remove magic coeff

* add docstring

* add intergration test

* add document to controlnet.mdx

* made the comments a bit more explanatory

* fix table

* update community pipeline docs

* fix formatting

* explain sharing workflows

Allow stable diffusion attend and excite pipeline to work with any size output image. Re: #2476, #2603