* fix: update SkyReels-V2 documentation and moving into attn dispatcher
* Refactors SkyReelsV2's attention implementation
* style
* up
* Fixes formatting in SkyReels-V2 documentation
Wraps the visual demonstration section in a Markdown code block.
This change corrects the rendering of ASCII diagrams and examples, improving the overall readability of the document.
* Docs: Condense example arrays in skyreels_v2 guide
Improves the readability of the `step_matrix` examples by replacing long sequences of repeated numbers with a more compact `value×count` notation.
This change makes the underlying data patterns in the examples easier to understand at a glance.
* Add _repeated_blocks attribute to SkyReelsV2Transformer3DModel
* Refactor rotary embedding calculations in SkyReelsV2 to separate cosine and sine frequencies
* Enhance SkyReels-V2 documentation: update model loading for GPU support and remove outdated notes
* up
* up
* Update model_id in SkyReels-V2 documentation
* up
* refactor: remove device_map parameter for model loading and add pipeline.to("cuda") for GPU allocation
* fix: update copyright year to 2025 in skyreels_v2.md
* docs: enhance parameter examples and formatting in skyreels_v2.md
* docs: update example formatting and add notes on LoRA support in skyreels_v2.md
* refactor: remove copied comments from transformer_wan in SkyReelsV2 classes
* Clean up comments in skyreels_v2.md
Removed comments about acceleration helpers and Flash Attention installation.
* Add deprecation warning for `SkyReelsV2AttnProcessor2_0` class
* Fix PyTorch 2.3.1 compatibility: add version guard for torch.library.custom_op
- Add hasattr() check for torch.library.custom_op and register_fake
- These functions were added in PyTorch 2.4, causing import failures in 2.3.1
- Both decorators and functions are now properly guarded with version checks
- Maintains backward compatibility while preserving functionality
Fixes#12195
* Use dummy decorators approach for PyTorch version compatibility
- Replace hasattr check with version string comparison
- Add no-op decorator functions for PyTorch < 2.4.0
- Follows pattern from #11941 as suggested by reviewer
- Maintains cleaner code structure without indentation changes
* Update src/diffusers/models/attention_dispatch.py
Update all the decorator usages
Co-authored-by: Aryan <contact.aryanvs@gmail.com>
* Update src/diffusers/models/attention_dispatch.py
Co-authored-by: Aryan <contact.aryanvs@gmail.com>
* Update src/diffusers/models/attention_dispatch.py
Co-authored-by: Aryan <contact.aryanvs@gmail.com>
* Update src/diffusers/models/attention_dispatch.py
Co-authored-by: Aryan <contact.aryanvs@gmail.com>
* Move version check to top of file and use private naming as requested
* Apply style fixes
---------
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Co-authored-by: Aryan <aryan@huggingface.co>
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* Add Bria model and pipeline to diffusers
- Introduced `BriaTransformer2DModel` and `BriaPipeline` for enhanced image generation capabilities.
- Updated import structures across various modules to include the new Bria components.
- Added utility functions and output classes specific to the Bria pipeline.
- Implemented tests for the Bria pipeline to ensure functionality and output integrity.
* with working tests
* style and quality pass
* adding docs
* add to overview
* fixes from "make fix-copies"
* Refactor transformer_bria.py and pipeline_bria.py: Introduce new EmbedND class for rotary position embedding, and enhance Timestep and TimestepProjEmbeddings classes. Add utility functions for handling negative prompts and generating original sigmas in pipeline_bria.py.
* remove redundent and duplicates tests and fix bf16
slow test
* style fixes
* small doc update
* Enhance Bria 3.2 documentation and implementation
- Updated the GitHub repository link for Bria 3.2.
- Added usage instructions for the gated model access.
- Introduced the BriaTransformerBlock and BriaAttention classes to the model architecture.
- Refactored existing classes to integrate Bria-specific components, including BriaEmbedND and BriaPipeline.
- Updated the pipeline output class to reflect Bria-specific functionality.
- Adjusted test cases to align with the new Bria model structure.
* Refactor Bria model components and update documentation
- Removed outdated inference example from Bria 3.2 documentation.
- Introduced the BriaTransformerBlock class to enhance model architecture.
- Updated attention handling to use `attention_kwargs` instead of `joint_attention_kwargs`.
- Improved import structure in the Bria pipeline to handle optional dependencies.
- Adjusted test cases to reflect changes in model dtype assertions.
* Update Bria model reference in documentation to reflect new file naming convention
* Update docs/source/en/_toctree.yml
* Refactor BriaPipeline to inherit from DiffusionPipeline instead of FluxPipeline, updating imports accordingly.
* move the __call__ func to the end of file
* Update BriaPipeline example to use bfloat16 for precision sensitivity for better result
* make style && make quality && make fix-copiessource
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Co-authored-by: Linoy Tsaban <57615435+linoytsaban@users.noreply.github.com>
Co-authored-by: Aryan <contact.aryanvs@gmail.com>
- Modify offload_models function to handle DiffusionPipeline correctly
- Ensure compatibility with both single and multiple module inputs
Co-authored-by: Sayak Paul <spsayakpaul@gmail.com>
* CogView4: remove SiLU in final AdaLN (match Megatron); add switch to AdaLayerNormContinuous; split temb_raw/temb_blocks
* CogView4: remove SiLU in final AdaLN (match Megatron); add switch to AdaLayerNormContinuous; split temb_raw/temb_blocks
* CogView4: remove SiLU in final AdaLN (match Megatron); add switch to AdaLayerNormContinuous; split temb_raw/temb_blocks
* CogView4: use local final AdaLN (no SiLU) per review; keep generic AdaLN unchanged
* re-add configs as normal files (no LFS)
* Apply suggestions from code review
* Apply style fixes
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Co-authored-by: 武嘉涵 <lambert@wujiahandeMacBook-Pro.local>
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