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CLIPVisionLoaderDisTorch2MultiGPU.md

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CLIPVisionLoaderDisTorch2MultiGPU

The CLIPVisionLoaderDisTorch2MultiGPU node is used to load CLIP Vision models with DisTorch2 distributed tensor allocation, enabling advanced multi-device VRAM management to handle larger vision encoder models across multiple GPUs.

This node automatically detects models located in the ComfyUI/models/clip_vision folder, and it will also read models from additional paths configured in the extra_model_paths.yaml file. Sometimes, you may need to refresh the ComfyUI interface to allow it to read the model files from the corresponding folder.

Inputs

Parameter Data Type Description
clip_vision STRING The name of the CLIP Vision model to load.
device STRING Target device for vision encoder compute operations (e.g., 'cuda:0', 'cuda:1', 'cpu'). Selected from available devices on your system.
virtual_vram_gb FLOAT Amount of virtual VRAM in gigabytes to allocate for distributed tensor management (default: 4.0, range: 0.0-128.0).
donor_device STRING Device to donate VRAM from when allocating virtual memory (default: 'cpu').
expert_mode_allocations STRING Advanced allocation string for expert users to manually specify device/ratio distributions (e.g., 'cuda:0,50%;cpu,*').
eject_models BOOLEAN Whether to unload ALL models from the target device before loading this model, enabling deterministic model eviction for testing and memory management (default: true).

Outputs

Output Name Data Type Description
CLIP_VISION CLIP_VISION The loaded CLIP Vision model with DisTorch2 distributed allocation applied.

DisTorch2 Distributed Loading

DisTorch2 is an advanced memory management system that enables loading and running large diffusion models across multiple GPUs by intelligently distributing tensor allocations. Instead of loading an entire model on a single device, DisTorch2 splits the model's layers across available devices while maintaining computational efficiency.

Key Concepts

Virtual VRAM Allocation: Artificially increases the available VRAM on the compute device by borrowing memory capacity from donor devices through intelligent tensor distribution.

Expert Mode Allocations: Advanced users can manually specify exactly how much of the model should be placed on each device using ratio or byte-based allocation strings.

Allocation Examples

Basic Virtual VRAM Mode:

  • device: cuda:0
  • virtual_vram_gb: 8.0
  • donor_device: cuda:1
  • Result: Loads model as if cuda:0 had 8GB more VRAM available, using cuda:1 as memory donor.

Expert Ratio Allocation:

  • expert_mode_allocations: cuda:0,60%;cuda:1,30%;cpu,10%
  • Distributes model layers with 60% on GPU 0, 30% on GPU 1, and 10% on CPU.

Expert Byte Allocation:

  • expert_mode_allocations: cuda:0,4gb;cuda:1,2gb;cpu,*
  • Allocates exactly 4GB to cuda:0, 2GB to cuda:1, and remaining to CPU.

Mixed Mode: Combines virtual VRAM with expert allocations for complex multi-device scenarios.