GitLab

* adjust to get CI test cases passed on XPU
Signed-off-by: Liu, Kaixuan <kaixuan.liu@intel.com>

* fix format issue
Signed-off-by: Liu, Kaixuan <kaixuan.liu@intel.com>

* Apply style fixes

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Signed-off-by: Liu, Kaixuan <kaixuan.liu@intel.com>
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Co-authored-by: Aryan <aryan@huggingface.co>

skip instead of returning.

* update

* update

* update

* raise as early as possible in group offloading

* remove check from ModuleGroup

update

up

* minor cleanups in the lora docs.

* Apply suggestions from code review
Co-authored-by: Steven Liu <59462357+stevhliu@users.noreply.github.com>

* format docs

* fix copies

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Co-authored-by: Steven Liu <59462357+stevhliu@users.noreply.github.com>

Co-authored-by: Linoy Tsaban <57615435+linoytsaban@users.noreply.github.com>

update

model test updates

enable cpu offloading of new pipelines on XPU & use device agnostic empty to make pipelines work on XPU (#11671)

* commit 1
Signed-off-by: YAO Matrix <matrix.yao@intel.com>

* patch 2
Signed-off-by: YAO Matrix <matrix.yao@intel.com>

* Update pipeline_pag_sana.py

* Update pipeline_sana.py

* Update pipeline_sana_controlnet.py

* Update pipeline_sana_sprint_img2img.py

* Update pipeline_sana_sprint.py

* fix style
Signed-off-by: YAO Matrix <matrix.yao@intel.com>

* fix fat-thumb while merge conflict
Signed-off-by: YAO Matrix <matrix.yao@intel.com>

* fix ci issues
Signed-off-by: YAO Matrix <matrix.yao@intel.com>

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Signed-off-by: YAO Matrix <matrix.yao@intel.com>
Co-authored-by: Ilyas Moutawwakil <57442720+IlyasMoutawwakil@users.noreply.github.com>

* enable deterministic in bnb 4 bit tests
Signed-off-by: jiqing-feng <jiqing.feng@intel.com>

* fix 8bit test
Signed-off-by: jiqing-feng <jiqing.feng@intel.com>

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Signed-off-by: jiqing-feng <jiqing.feng@intel.com>

Fix dimensionality in `apply_rotary_emb` functions' comments.

draft

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

* draft

* feedback

* update

* feedback

* fix

* feedback

* feedback

* fix

* feedback

update

* start

* updates

* update

* update

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

* start implementing disk offloading in group.

* delete diff file.

* updates.patch

* offload_to_disk_path

* check if safetensors already exist.

* add test and clarify.

* updates

* update todos.

* update more docs.

* update docs

update

update

update

* factor out stuff from load_lora_adapter().

* simplifying text encoder lora loading.

* fix peft.py

* fix logging locations.

* formatting

* fix

* update

* update

* update

Bumps [urllib3](https://github.com/urllib3/urllib3) from 2.2.3 to 2.5.0.
- [Release notes](https://github.com/urllib3/urllib3/releases)
- [Changelog](https://github.com/urllib3/urllib3/blob/main/CHANGES.rst)
- [Commits](https://github.com/urllib3/urllib3/compare/2.2.3...2.5.0

)

---
updated-dependencies:
- dependency-name: urllib3
  dependency-version: 2.5.0
  dependency-type: indirect
...
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update

update

add is_compileable property to quantizers.

* update

* update

* update

* update

* update

* update

* update

* update

* update

* update

* update

* update

* update

* update

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* updte

* update

* update

* update

change to 2025 licensing for remaining

* device_map tests for all models.

* updates

* Update tests/models/test_modeling_common.py
Co-authored-by: Aryan <aryan@huggingface.co>

* fix device_map in test

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Co-authored-by: Aryan <aryan@huggingface.co>

* [training] add ds support to lora hidream

* Apply style fixes

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Co-authored-by: J石页 <jiangshuo9@h-partners.com>
Co-authored-by: Sayak Paul <spsayakpaul@gmail.com>
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* ⚡

️ Speed up method `AutoencoderKLWan.clear_cache` by 886%

**Key optimizations:**
- Compute the number of `WanCausalConv3d` modules in each model (`encoder`/`decoder`) **only once during initialization**, store in `self._cached_conv_counts`. This removes unnecessary repeated tree traversals at every `clear_cache` call, which was the main bottleneck (from profiling).
- The internal helper `_count_conv3d_fast` is optimized via a generator expression with `sum` for efficiency.

All comments from the original code are preserved, except for updated or removed local docstrings/comments relevant to changed lines.  
**Function signatures and outputs remain unchanged.**

* Apply style fixes

* Apply suggestions from code review
Co-authored-by: Aryan <contact.aryanvs@gmail.com>

* Apply style fixes

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Co-authored-by: Aseem Saxena <aseem.bits@gmail.com>

* lora alpha

* Apply style fixes

* Update examples/advanced_diffusion_training/README_flux.md
Co-authored-by: Sayak Paul <spsayakpaul@gmail.com>

* fix readme format

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

update

* Add Pruna optimization framework documentation

- Introduced a new section for Pruna in the table of contents.
- Added comprehensive documentation for Pruna, detailing its optimization techniques, installation instructions, and examples for optimizing and evaluating models

* Enhance Pruna documentation with image alt text and code block formatting

- Added alt text to images for better accessibility and context.
- Changed code block syntax from diff to python for improved clarity.

* Add installation section to Pruna documentation

- Introduced a new installation section in the Pruna documentation to guide users on how to install the framework.
- Enhanced the overall clarity and usability of the documentation for new users.

* Update pruna.md

* Update pruna.md

* Update Pruna documentation for model optimization and evaluation

- Changed section titles for consistency and clarity, from "Optimizing models" to "Optimize models" and "Evaluating and benchmarking optimized models" to "Evaluate and benchmark models".
- Enhanced descriptions to clarify the use of `diffusers` models and the evaluation process.
- Added a new example for evaluating standalone `diffusers` models.
- Updated references and links for better navigation within the documentation.

* Refactor Pruna documentation for clarity and consistency

- Removed outdated references to FLUX-juiced and streamlined the explanation of benchmarking.
- Enhanced the description of evaluating standalone `diffusers` models.
- Cleaned up code examples by removing unnecessary imports and comments for better readability.

* Apply suggestions from code review
Co-authored-by: Steven Liu <59462357+stevhliu@users.noreply.github.com>

* Enhance Pruna documentation with new examples and clarifications

- Added an image to illustrate the optimization process.
- Updated the explanation for sharing and loading optimized models on the Hugging Face Hub.
- Clarified the evaluation process for optimized models using the EvaluationAgent.
- Improved descriptions for defining metrics and evaluating standalone diffusers models.

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Co-authored-by: Steven Liu <59462357+stevhliu@users.noreply.github.com>

* show how metadata stuff should be incorporated in training scripts.

* typing

* fix

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Co-authored-by: Linoy Tsaban <57615435+linoytsaban@users.noreply.github.com>