- Cleaned up unnecessary whitespace and comments in model_management_mgpu.py, nodes.py, wanvideo.py, and wrappers.py for better code clarity.
- Replaced list comprehensions with direct list conversions in nodes.py for efficiency.
- Updated memory logging format in model_management_mgpu.py to streamline data capture.
- Enhanced device management in wanvideo.py by ensuring consistent device setting and loading.
- Added linting configurations in pyproject.toml to enforce code quality standards.
- Removed unused imports and optimized existing ones across multiple files.
- 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.
- Add comfyui_memory_load and create_model_identifier utilities (device_utils)
- Log GPU memory before/after UNet, VAE, and CLIP construction and after UNet weight load
- Include model identifiers in logs to correlate memory to specific patchers
- Guard logging calls with try/except to avoid impacting load flow
- Improves observability of memory usage for multi-GPU checkpoints and aids OOM/debugging
Refactor device detection into dedicated utility module
- Extract device enumeration and compatibility checks to device_utils.py
- Add support for additional device types (NPU, MLU, DirectML, CoreX)
- Update all modules to use centralized device utilities
- Implement caching for device list to improve performance
- Reduce code duplication across distorch, nodes, and wanvideo modules
- Adding a new `fantasyportrait_model` input to support FantasyPortrait models.
- Renaming the `vace_model` input to a more generic `extra_model` to allow loading other auxiliary models like VACE or MTV Crafter.
- Correcting the node type for the `fantasytalking_model` from `FANTASYTALKINGMODEL` to `FANTASYTALKMODEL`.
This commit introduces a major architectural refactoring, laying the groundwork for DisTorch V2. The changes focus on improving modularity, memory management, and diagnostics.
Key changes include:
- Renaming `distorch_safetensor.py` to `distorch_2.py` to house the new core logic.
- Deleting the legacy `block_swap.py` module.
- Adding `device_memory_audit.py` for more sophisticated analysis of GPU memory usage.
- Implementing a centralized and configurable logging system in `__init__.py` to provide standardized and level-controlled (DEBUG/INFO) output for better debugging.
This commit refactors the codebase by extracting major components from the main `__init__.py` file into their own dedicated modules. This improves code organization, readability, and maintainability.
- **`distorch.py`**: New file containing the `DisTorch` class, which manages multi-GPU device patching and distribution logic.
- **`block_swap.py`**: New file containing the generic `BlockSwap` class for UNet block swapping to manage VRAM.
- **`wanvideo.py`**: New file containing the `WanVideoBlockSwap` class, a specialized implementation for WanVideo models.
- **`__init__.py`**: Simplified to handle node registration and imports from the new modules.
John Pollock