Add Wan2.2-Animate: Unified Character Animation and Replacement with Holistic Replication #12442
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tolgacangoz
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- Introduced WanAnimateTransformer3DModel and WanAnimatePipeline. - Updated get_transformer_config to handle the new model type. - Modified convert_transformer to instantiate the correct transformer based on model type. - Adjusted main execution logic to accommodate the new Animate model type.
…prove error handling for undefined parameters
…work for character animation and replacement - Added Wan 2.2 Animate 14B model to the documentation. - Introduced the Wan-Animate framework, detailing its capabilities for character animation and replacement. - Included example usage for the WanAnimatePipeline with preprocessing steps and guidance on input requirements.
- Introduced `WanAnimateGGUFSingleFileTests` to validate functionality. - Added dummy input generation for testing model behavior.
- Introduced `EncoderApp`, `Encoder`, `Direction`, `Synthesis`, and `Generator` classes for enhanced motion and appearance encoding. - Added `FaceEncoder`, `FaceBlock`, and `FaceAdapter` classes to integrate facial motion processing. - Updated `WanTimeTextImageMotionEmbedding` to utilize the new `Generator` for motion embedding. - Enhanced `WanAnimateTransformer3DModel` with additional face adapter and pose patch embedding for improved model functionality.
- Introduced `pad_video` method to handle padding of video frames to a target length. - Updated video processing logic to utilize the new padding method for `pose_video`, `face_video`, and conditionally for `background_video` and `mask_video`. - Ensured compatibility with existing preprocessing steps for video inputs.
…roved video processing - Added optional parameters: `conditioning_pixel_values`, `refer_pixel_values`, `refer_t_pixel_values`, `bg_pixel_values`, and `mask_pixel_values` to the `prepare_latents` method. - Updated the logic in the denoising loop to accommodate the new parameters, enhancing the flexibility and functionality of the pipeline.
…eneration - Updated the calculation of `num_latent_frames` and adjusted the shape of latent tensors to accommodate changes in frame processing. - Enhanced the `get_i2v_mask` method for better mask generation, ensuring compatibility with new tensor shapes. - Improved handling of pixel values and device management for better performance and clarity in the video processing pipeline.
…and mask generation - Consolidated the handling of `pose_latents_no_ref` to improve clarity and efficiency in latent tensor calculations. - Updated the `get_i2v_mask` method to accept batch size and adjusted tensor shapes accordingly for better compatibility. - Enhanced the logic for mask pixel values in the replacement mode, ensuring consistent processing across different scenarios.
…nced processing - Introduced custom QR decomposition and fused leaky ReLU functions for improved tensor operations. - Implemented upsampling and downsampling functions with native support for better performance. - Added new classes: `FusedLeakyReLU`, `Blur`, `ScaledLeakyReLU`, `EqualConv2d`, `EqualLinear`, and `RMSNorm` for advanced neural network layers. - Refactored `EncoderApp`, `Generator`, and `FaceBlock` classes to integrate new functionalities and improve modularity. - Updated attention mechanism to utilize `dispatch_attention_fn` for enhanced flexibility in processing.
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This PR is fixing #12441.
Project Page: https://humanaigc.github.io/wan-animate/
TODOs:
WanAnimatePipelineTry
WanAnimatePipeline!Who can review?
Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR.