The ControlNetLoaderDisTorch2MultiGPU node is used to load ControlNet models with DisTorch2 distributed tensor allocation, enabling advanced multi-device VRAM management to handle larger conditional generation models across multiple GPUs.
This node automatically detects models located in the ComfyUI/models/controlnet 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.
| Parameter | Data Type | Description |
|---|---|---|
control_net_name |
STRING |
The name of the ControlNet model to load. |
compute_device |
STRING |
Target device for compute operations (e.g., 'cuda:0', 'cuda:1', 'cpu'). Selected from available devices on your system. |
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). |
| Output Name | Data Type | Description |
|---|---|---|
CONTROL_NET |
CONTROL_NET |
The loaded ControlNet model with DisTorch2 distributed allocation applied. |
DisTorch2 places the model's weights automatically when it loads. The compute device keeps the layers it must compute with directly; the rest are spread over every other device, system RAM included, in proportion to their free memory, and weights in system RAM are pinned so they never page in from disk. Large diffusion models on supported AMD GPUs also run streamed: activations stay on the compute device and weights are streamed in. See the repository README for details.