This commit removes several utility modules used for debugging, memory inspection, and hardware information gathering. These tools are no longer required and their removal simplifies the codebase.
The following files have been deleted:
- `debug_utils.py`
- `device_memory_audit.py`
- `hardware_info.py`
- `model_sig.py`
Additionally, the call to log memory usage on startup has been removed from `__init__.py`.
Update the `get_device_list` function to detect and include the 'mps' (Metal Performance Shaders) backend if it's available through PyTorch.
This allows users on Apple Silicon hardware to see and select their GPU for accelerated computations.
This commit introduces a major reorganization of the `examples` directory to improve clarity and discoverability. Workflows are now grouped into subdirectories based on the features they demonstrate (e.g., `distorch`, `distorch2`, `gguf`, `multiGPU`).
Key changes:
- Moved existing example JSON files into new categorized folders.
- Added several new and updated workflows, particularly for DisTorch2.
- Removed outdated or redundant example files.
- Renamed an internal function from `..._gguf_v2` to `..._safetensor_v2` to better reflect its broader functionality in DisTorch2.
This commit introduces DisTorch v2.0.0, a major overhaul that extends multi-device model distribution to standard `.safetensors` models.
Key changes include:
- **Universal `.safetensors` Support:** The core distribution logic is no longer limited to GGUF models. It now fully supports `.safetensors`, allowing any UNet supported by native Comfy loaders to have its layers distributed across multiple devices (GPUs and CPU/RAM).
This commit refactors the model loading logic to properly integrate with ComfyUI's caching system.
- Implemented the `IS_CHANGED` class method, which creates a hash of the DisTorch-specific settings (e.g., `compute_device`, `virtual_vram_gb`).
- This allows ComfyUI to automatically detect when settings have changed and trigger a model reload, invalidating the cache correctly.
- Removed the previous manual and less reliable logic for unloading and reloading the model from within the `override` function.
- Set the default log level to "Engineering" to provide more detailed output during development.
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.
- Add distorch_safetensor.py module with safetensor allocation and model hashing utilities
- Update all DisTorch2 nodes to use new override_class_with_distorch_safetensor_v2
- Add FLUX-specific DisTorch2 nodes for checkpoint and UNET loading
- Import new safetensor functions for VRAM allocation analysis and model patching
This commit introduces a major update, "DisTorch v2", which integrates the new `BlockSwap` system for more efficient and dynamic memory management across multiple GPUs.
Key changes:
- **BlockSwap Integration:** GGUF model loading is completely refactored to use `BlockSwap`, enabling more intelligent VRAM allocation based on tensor analysis.
- **Node Renaming:** All SafeTensor loader nodes are renamed from `...DisTorchMultiGPU` to `...DisTorch2MultiGPU` to clearly distinguish the new implementation from the old one.
- **Legacy Support:** The previous GGUF loader is preserved as a legacy option for backward compatibility.
- **Improved Memory Calculation:** A more accurate memory calculation function (`get_total_memory_v2`) is implemented and used by the new system.
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.
- Simplified node naming from DisTorchBlockSwap to DisTorch
- Cleaned up accidentally added main_branch directory
- Updated all references in __init__.py and core/blockswap.py
- Created comprehensive architecture documentation (ARCHITECTURE_V2.0.0.md)
- Added DOE optimization planning document (DOE_OPTIMIZATION.md)
- Implemented DisTorchBlockSwap node for safetensor models
- Created core/blockswap.py with BlockSwapManager
- Unified VirtualVRAM interface design
- Based on analysis of WanVideo's block swap methodology
Populate 'type' options by sourcing from core nodes to avoid drift:\n- CLIPLoaderGGUF now derives 'type' from nodes.CLIPLoader.INPUT_TYPES()\n- DualCLIPLoaderGGUF now derives 'type' from nodes.DualCLIPLoader.INPUT_TYPES()\nThis fixes missing or outdated 'type' options in GGUF Single and Dual CLIP loaders.\n\nchore: bump version to 1.8.2
Add guarded Intel XPU support alongside CUDA:
- get_device_list now includes xpu:N when available
- device selection (model/text encoder) considers CUDA or XPU and validates devices
- DisTorch donor/offload selection includes xpu devices
Also: remove unused MergeFluxLoRAs node and mapping; delete tools/ and precompiled_binaries/; bump project version to 1.8.1.
Problem: WanVideoWrapper caches device at module load time, causing timesteps
and tensors to be created on wrong device when looping between models on
different GPUs.
Solution: WanVideoSamplerMultiGPU wrapper updates module-level device variable
to match current model's device before sampling.
Changes:
- Added comprehensive logging to trace device allocation through pipeline
- Identified module-level device caching as root cause
- Simplified WanVideoSamplerMultiGPU to only update device variable
- Verified fix works for multi-model workflows with looping
- Created custom implementations for all WanVideo nodes with explicit device selection
- Added WanVideoBlockSwap with dual device control (swap_device and model_offload_device)
- Created WanVideoModelLoader_TWO for multi-model workflows to avoid race conditions
- Discovered core ComfyUI bug: safetensors loader ignores device index (uses device.type instead of str(device))
- All wrapper nodes use runtime module patching to override WanVideoWrapper's cached device variables
- Extensive logging added for debugging device assignments
In Windows, module detection was failing because the method couldn't find the hard-coded custom_nodes/ folder in os.join.path.
We switch to using folder_paths which will return a correct path regardless of the platform.
John Pollock