- 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.

…l guidance

…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.

…annotations

- Removed extra-abstractioned-functions such as `custom_qr`, `fused_leaky_relu`, and `make_kernel` to streamline the codebase.
- Updated class constructors and method signatures to include type hints for better clarity and type checking.
- Refactored the `FusedLeakyReLU`, `Blur`, `EqualConv2d`, and `EqualLinear` classes to enhance readability and maintainability.
- Simplified the `Generator` and `Encoder` classes by removing redundant parameters and improving initialization logic.

…otion encoder's upcast_to_fp32 arg

…sor subclass

…e transformer

…ses more memory

…well as vae_scale_factor when calculating default height and width