- 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.
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.
- Remove current_text_encoder_initial_device and its updates
- Delete text_encoder_initial_device_patched and stop overriding mm.text_encoder_initial_device
- Simplify set_current_text_encoder_device and logging to track only current_text_encoder_device
Bump revision to 2.4.7
- Added preemptive model unloading and cache clearing in register_patched_safetensor_modelpatcher() to resolve potential memory issues when allocations are unavailable, prompting the usage of the standard loaders.
Advanced Checkpoint Loaders allow users to map each of the elements of the checkpoint to a different device, or in the case of DisTorch2, shard the UNet and CLiP .safetensors arbitrarily whilst ensuring actual computation remains on selected `compute` device.
Added example workflow for standard and DisTorch2 MultGPU checkpoint loaders.
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
Fix compute device inclusion in expert mode allocations
Include compute device in vram_string when expert_mode_allocations
is set but virtual_vram_gb is 0. This ensures the compute device is
properly specified in the full allocation string for expert mode
configurations without virtual VRAM.
Bump version to 2.2.1
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`.
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).
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.
If you were using nodes with DiffSynth in their name (like ...DiffSynthMultiGPU), please switch to the standard MultiGPU versions for now (e.g., ...MultiGPU). This change eliminates a device management issue that was affecting some Windows users.
See: https://github.com/pollockjj/ComfyUI-MultiGPU/issues/13
Most users won't be affected as this only impacts the DiffSynth variants of nodes.
If you need help modifying your workflows, please open an issue. Will revisit DiffSynth functionality once issue can be contained or worked-around
Update comfyregistry to 1.5.0 to reflect major change in functionality, in this case, a reduction.
John Pollock