- Implemented WanVideoVACEEncode class for encoding with VACE, including input parameters for width, height, number of frames, and strength.
- Enhanced WanVideoEncode class to support multi-GPU encoding, with additional parameters for noise augmentation and latent strength.
- Updated device management to ensure proper device context during encoding processes.
So this is a change from something just newly-released in 2.5.0, but most should either see an improvement or no change to behavior. This was the weakest, and jankiest part of 2.5.0 and my decision to manage a CPU memory leak turned into a too-aggressive solution with unwanted side effects.
This solution should provide a better way to manage `compute` VRAM as the most asked-for feature is a way to remove everything else from VRAM prior to main UNet inference, which this accomplishes nicely, as well as reporting back accurate information DisTorch2 on-device shard sizes.
Refactor memory management in distorch_2.py to patch load_models_gpu instead of LoadedModel.model_memory_required. Implement correct memory reporting based on model flags (eject_models and is_distorch_model), ensuring proper eviction logic and improved handling of virtual VRAM. This drives behavior purely by either comfy core matching or DisTorch flag, fixing potential issues in multi-GPU setups.
Add comprehensive guide for accessing and using documentation on core MultiGPU and DisTorch2 nodes, covering 36+ nodes with detailed parameters, outputs, and usage examples. This enhances user experience by providing easy reference for standard ComfyUI loaders and DisTorch2 features, while clarifying coverage excludes third-party nodes.
Add a new constant WEB_DIRECTORY set to "./web" to define the directory path for web-related assets during package initialization. This improves organization by centralizing the path configuration. Additionally, removed trailing newline at file end to maintain consistent code formatting.
John Pollock