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.
Update the "Bytes" mode documentation in the README to specify that the CPU acts as the default wildcard device.
This change clarifies that if no `*` is explicitly used in the device allocation string, the remainder of the model will be automatically assigned to the CPU. This helps users better understand the default behavior and prevent confusion.
Update the documentation to include the new 'bytes' and 'ratio' expert modes for model allocation.
These new modes provide more intuitive, model-driven ways for users to control how models are split across multiple devices.
- Adds 'bytes' mode for direct allocation in GB/MB, similar to Huggingface's `device_map`.
- Adds 'ratio' mode for proportional splitting, inspired by llama.cpp.
- Rebrands the original expert mode as 'fraction' mode for clarity.
- Provides clear examples for all three expert modes.
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