ComfyUI Extension: ComfyUI USO Node

Authored by HM-RunningHub

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A custom node for ComfyUI that integrates USO (Unified Style and Subject-Driven Generation) for high-quality image generation with style and subject control.

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    README

    ComfyUI USO Node

    A custom node for ComfyUI that integrates USO (Unified Style and Subject-Driven Generation) for high-quality image generation with style and subject control.

    ✨ Features

    • šŸŽØ Unified Style & Subject Generation: Powered by USO model based on FLUX architecture
    • šŸŽÆ Style-Driven Generation: Generate images with specific artistic styles
    • šŸ‘¤ Subject-Driven Generation: Maintain subject consistency across generations
    • šŸ”„ Multi-Style Support: Combine multiple styles in a single generation
    • āš™ļø Memory Optimization: FP8 precision support for consumer-grade GPUs (~16GB VRAM)
    • šŸš€ Flexible Control: Advanced parameter control for fine-tuning results

    šŸ”§ Node List

    Core Nodes

    • RH_USO_Loader: Load and initialize USO models with optimization options
    • RH_USO_Generator: Generate images with style and subject control

    šŸš€ Quick Installation

    Step 1: Install the Node

    # Navigate to ComfyUI custom_nodes directory
    cd ComfyUI/custom_nodes
    
    # Clone the repository
    git clone https://github.com/HM-RunningHub/ComfyUI_RH_USO
    
    # Install dependencies
    cd ComfyUI_RH_USO
    pip install -r requirements.txt
    

    Step 2: Download Required Models

    # Download FLUX.1-dev model (Required base model)
    huggingface-cli download black-forest-labs/FLUX.1-dev flux1-dev.safetensors --local-dir models/diffusers/FLUX.1-dev
    huggingface-cli download black-forest-labs/FLUX.1-dev ae.safetensors --local-dir models/diffusers/FLUX.1-dev
    
    # Download USO model
    huggingface-cli download bytedance-research/USO --local-dir models/uso
    
    # Download SigLIP model
    huggingface-cli download google/siglip-so400m-patch14-384 --local-dir models/clip/siglip-so400m-patch14-384
    
    # Final model structure should look like:
    models/
    ā”œā”€ā”€ diffusers/
    │   └── FLUX.1-dev/
    │       ā”œā”€ā”€ flux1-dev.safetensors
    │       └── ae.safetensors
    │       └── ....
    ā”œā”€ā”€ uso/
    │   ā”œā”€ā”€ assets/
    │   │   └── uso.webp
    │   ā”œā”€ā”€ config.json
    │   ā”œā”€ā”€ download_repo_enhanced.py
    │   ā”œā”€ā”€ README.md
    │   └── uso_flux_v1.0/
    │       ā”œā”€ā”€ dit_lora.safetensors
    │       └── projector.safetensors
    └── clip/
        └── siglip-so400m-patch14-384/
        
    # Restart ComfyUI
    

    šŸ“– Usage

    Basic Workflow

    [RH_USO_Loader] → [RH_USO_Generator] → [Save Image]
    

    Generation Types

    Style-Driven Generation

    • Load style reference images
    • Input text prompt describing the content
    • Generate images in the specified style

    Subject-Driven Generation

    • Load subject reference image
    • Input text prompt with scene description
    • Generate images maintaining subject identity

    Style + Subject Generation

    • Load both style and subject reference images
    • Combine style transfer with subject consistency
    • Generate images with unified style and preserved subjects

    šŸ› ļø Technical Requirements

    • GPU: 16GB+ VRAM (with FP8 optimization)
    • RAM: 32GB+ recommended
    • Storage: ~35GB for all models
      • FLUX.1-dev: ~24GB (flux1-dev.safetensors + ae.safetensors)
      • USO models: ~6GB
      • SigLIP: ~1.5GB
    • CUDA: Required for optimal performance

    āš ļø Important Notes

    • Model Paths: Models must be placed in specific directories:
      • FLUX.1-dev → models/diffusers/FLUX.1-dev/
      • USO models → models/uso/
      • SigLIP → models/clip/siglip-so400m-patch14-384/
    • FP8 mode recommended for consumer GPUs (reduces VRAM usage)
    • All model files must be downloaded before first use

    šŸ“„ License

    This project is licensed under Apache 2.0 License.

    šŸ”— References

    šŸ”— Example

    <img width="1788" height="866" alt="image" src="https://github.com/user-attachments/assets/3b462f37-b874-45c8-9f30-9c7d0d963d81" /> <img width="1833" height="821" alt="image" src="https://github.com/user-attachments/assets/54ab0142-ba49-45a4-8e57-32404904ce20" /> <img width="1837" height="836" alt="image" src="https://github.com/user-attachments/assets/1a4120f4-2258-4216-b7f8-f4a6a8a36169" />

    šŸ¤ Contributing

    Contributions are welcome! Please feel free to submit issues and pull requests.

    ⭐ Citation

    If you find this project useful, please consider citing the original USO paper:

    @article{wu2025uso,
        title={USO: Unified Style and Subject-Driven Generation via Disentangled and Reward Learning},
        author={Shaojin Wu and Mengqi Huang and Yufeng Cheng and Wenxu Wu and Jiahe Tian and Yiming Luo and Fei Ding and Qian He},
        year={2025},
        eprint={2508.18966},
        archivePrefix={arXiv},
        primaryClass={cs.CV},
    }