ComfyUI-multiGPU-upscaler

Multi-GPU batch-parallel upscaling nodes for ComfyUI.

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Table of Contents

• Features
• Requirements
• Performance
• Installation
• Nodes
  • multiGPU_upscaler: Multi-GPU Batch Parallel
• Recommended Settings
• Tips & Debugging
• License

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Features

This extension is designed to:

• Use 1–10 GPUs efficiently.
• By default, auto-detect and use up to 2 GPUs.
• Split batched images across GPUs and upscale them in parallel.
• Use robust tiled upscaling with OOM-safe fallback.
• Work great with RealESRGAN / ESRGAN style models (e.g. RealESRGAN_4xplus).

Tested with:

• Dual RTX 3060 setup
• SDXL generation + 4x RealESRGAN upscaling, batch 4–8
• Achieved measurable speedups vs single-GPU upscaling.

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Requirements

• NVIDIA GPUs only. This extension relies on CUDA for device management and communication.

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Performance

Here are some sample benchmarks comparing a standard Upscale Image (using Model) node against the multiGPU_upscaler node.

• Test Setup: Dual RTX 3060
• Workflow: SDXL Generation + 4x RealESRGAN Upscaling
• Resolution: 1024x1024 upscaled to 4K (4096x4096)
• Batch Size: 8 (split as 4 images per GPU in the multi-GPU test)

Run Type	Standard Upscaler (1 GPU)	multiGPU Upscaler (2 GPUs)	Speedup
Cold Run (gen0)	261.81s	233.53s	~10.8%
Run 1 (gen1)	259.57s	223.22s	~14.0%
Run 2 (gen2)	251.65s	226.82s	~9.9%

Results show a measurable speedup, especially on repetitive runs, by parallelizing the upscale task across both GPUs.

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Installation

1. Go to your ComfyUI custom_nodes directory.

   Example:

   cd ComfyUI/custom_nodes

2. Clone this repository:

   git clone [https://github.com/alludus/ComfyUI-multiGPU-upscaler.git](https://github.com/alludus/ComfyUI-multiGPU-upscaler.git)

   Or download the ZIP from GitHub and extract it to:

   ComfyUI/custom_nodes/ComfyUI-multiGPU-upscaler/
   

3. Ensure the structure looks like this:

   ComfyUI/custom_nodes/ComfyUI-multiGPU-upscaler/__init__.py
   ComfyUI/custom_nodes/ComfyUI-multiGPU-upscaler/multiGPU_upscaler.py
   

4. Restart ComfyUI.

All nodes will appear under the category:

• multiGPU_upscaler

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Nodes

multiGPU_upscaler: Multi-GPU Batch Parallel

Main node. Splits the batch across multiple GPUs and upscales in parallel.

Best for:

• Batch size ≥ 4

• 2 or more GPUs

• Post-generation upscaling (e.g. SDXL → 8 images → 4x RealESRGAN upscale)

• Inputs:

  • upscale_model: Note: Load this using a standard Load Upscale Model node.
  • image: Batched input tensor from ComfyUI.
  • device_list:
    • How to select GPUs.
    • auto (default): Uses up to auto_max_devices GPUs with the most free VRAM.
    • Custom list (Examples: cuda:0,cuda:1 or 0,1,2).
    • Up to 10 GPUs supported.
  • auto_max_devices:
    • Default: 2
    • Used only when device_list = "auto".
    • Limits the number of GPUs auto mode uses.
  • primary_share:
    • Default: 0.5
    • Approximate fraction of the batch assigned to the first (best) GPU.
    • If one GPU is stronger or has more free VRAM, increase (e.g. 0.7).
  • tile_size:
    • Default: 512
    • Starting tile size on all GPUs. Automatically reduced on OOM.
  • min_tile_size:
    • Default: 128
    • Smallest allowed tile size before failing.
  • overlap:
    • Default: 32
    • Tile overlap in pixels.

• Behavior:

  • Determines GPUs:
    • If device_list is set: Uses exactly that set (filtered by availability).
    • If device_list = "auto": Uses up to auto_max_devices GPUs with the most free VRAM.
  • Splits Batch:
    • First GPU receives about primary_share of the images.
    • Remaining GPUs share the rest.
  • Executes:
    • Spawns a worker thread for each GPU.
    • Each worker instantiates its own copy of the model on that GPU.
    • Each worker runs tiled upscaling on its subset with OOM-safe tiling.
  • Finishes:
    • Outputs are concatenated in the original batch order.
    • If any worker errors or OOMs, it falls back to a single-GPU tiled upscale on the best available GPU.

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Recommended Settings

For a setup like:

• 2x RTX 3060
• SDXL generation
• RealESRGAN_4xplus 4x upscaling
• Batch size 4–8

Recommended Node: multiGPU_upscaler: Multi-GPU Batch Parallel

• Settings:
  • device_list: auto
  • auto_max_devices: 2
  • primary_share: 0.5
  • tile_size: 512
  • min_tile_size: 128
  • overlap: 32

This configuration lets the extension pick the two best GPUs, splits work evenly, and uses robust tiling.

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Tips & Debugging

• If you encounter OOM:
  • Lower tile_size (e.g. to 256).
  • Optionally increase min_tile_size to reduce retries.
• If one GPU is stronger:
  • Increase primary_share (e.g. 0.6–0.8) so it does more work.
• Debugging:
  • Watch the ComfyUI console/log for [multiGPU] messages.
  • Use nvidia-smi to confirm multiple GPUs are active during upscaling.

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License

This project is released under the Apache 2.0 License.

See the LICENCE file for details.