ComfyUI USO Node

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

• 🎨 Unified Style & Subject Generation: Powered by USO model based on FLUX architecture
• 🎯 Style-Driven Generation: Generate images with specific artistic styles
• 👤 Subject-Driven Generation: Maintain subject consistency across generations
• 🔄 Multi-Style Support: Combine multiple styles in a single generation
• ⚙️ Memory Optimization: FP8 precision support for consumer-grade GPUs (~16GB VRAM)
• 🚀 Flexible Control: Advanced parameter control for fine-tuning results

🔧 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

🚀 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/
├── diffusers/
│   └── FLUX.1-dev/
│       ├── flux1-dev.safetensors
│       └── ae.safetensors
│       └── ....
├── uso/
│   ├── assets/
│   │   └── uso.webp
│   ├── config.json
│   ├── download_repo_enhanced.py
│   ├── README.md
│   └── uso_flux_v1.0/
│       ├── dit_lora.safetensors
│       └── projector.safetensors
└── clip/
    └── siglip-so400m-patch14-384/
    
# Restart ComfyUI

📖 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

🛠️ 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

⚠️ 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

📄 License

This project is licensed under Apache 2.0 License.

🔗 References

• USO Project Page
• USO Paper
• USO HuggingFace
• ComfyUI

🔗 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" />

🤝 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},
}