Update the README to specify that the "up to 10% faster GGUF inference" claim for DisTorch2 is a direct comparison against the previous DisTorch V1 implementation.
This clarification helps manage user expectations and provides a more accurate performance context.
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).
Code Changes (__init__.py):
- Switches from deepcopy to standard copy for better efficiency
- Initializes current_device from model_management instead of hardcoded "cuda:0"
- Simplifies node class mapping by removing intermediate TARGET_NODE_CLASS_MAPPINGS
- Updates node naming convention: removes underscore from MultiGPU suffix
- Adds new supported nodes: "CheckpointLoaderSimple", "ControlNetLoader", "LoadFluxControlNet"
- Improves code organization with better comment clarity
COMPATIBILITY NOTE: This version restores backward compatibility with workflows
using the previous node naming scheme. Both old and new node names will work.
Documentation Changes (README.md):
- Updates node list to reflect automatic detection system
- Adds proper attribution links for required dependencies (ComfyUI-GGUF, x-flux-comfy)
- Links to example quantized models like flux1-dev-gguf
- Reorganizes loader sections with clear dependency requirements
- Updates support links to new maintainer
- Removes business/commercial references
- Updates credits section to reflect current project status
John Pollock