🎨 Intelligent Image Inpainting: Powered by OneReward model based on FLUX architecture
🖼️ Image Restoration: Smart fill of blank areas with prompt-guided content
🧽 Object Removal: Automatically remove unwanted elements from images
🔄 CFG Support: Built-in Classifier-Free Guidance for enhanced generation quality
⚙️ Memory Optimization: Quantization and CPU offload support for consumer-grade GPUs (~16GB VRAM)
🚀 Flexible Control: True CFG parameter control for fine-tuning results

🔧 Node List

Core Nodes

RH_OneReward_Loader: Load and initialize OneReward models with optimization options
RH_OneReward_Sampler: Prompt-guided image inpainting and restoration generator
RH_OneReward_Eraser: Intelligent object removal tool

🚀 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_OneReward

# Install dependencies
cd ComfyUI_RH_OneReward
pip install -r requirements.txt

Step 2: Download Required Models

# Download FLUX.1-Fill-dev model (Required base model)
huggingface-cli download black-forest-labs/FLUX.1-Fill-dev --local-dir models/black-forest-labs/FLUX.1-Fill-dev

# Download OneReward models
# Download standard OneReward model
huggingface-cli download black-forest-labs/flux.1-fill-dev-OneReward-transformer --local-dir models/OneReward/flux.1-fill-dev-OneReward-transformer

# Download dynamic OneReward model (optional)
huggingface-cli download black-forest-labs/flux.1-fill-dev-OneRewardDynamic-transformer --local-dir models/OneReward/flux.1-fill-dev-OneRewardDynamic-transformer

# Final model structure should look like:
models/
├── black-forest-labs/
│   └── FLUX.1-Fill-dev/
│       ├── text_encoder/
│       ├── text_encoder_2/
│       ├── tokenizer/
│       ├── tokenizer_2/
│       ├── transformer/
│       ├── vae/
│       └── scheduler/
└── OneReward/
    ├── flux.1-fill-dev-OneReward-transformer/
    └── flux.1-fill-dev-OneRewardDynamic-transformer/
    
# Restart ComfyUI

📖 Usage

Basic Workflow

[RH_OneReward_Loader] → [RH_OneReward_Sampler] → [Save Image]

Generation Types

Image Inpainting

Load image that needs filling
Provide mask layer marking areas to be filled
Input text prompt describing the fill content
Generate intelligently filled images

Image Restoration

Load damaged or incomplete images
Use mask to mark areas needing repair
Input prompt describing restoration content
Generate naturally restored images

Object Removal

Use RH_OneReward_Eraser node
Load image containing unwanted elements
Provide mask marking areas to remove
Automatically remove and fill background

🛠️ Technical Requirements

GPU: 16GB+ VRAM (with quantization optimization)
RAM: 32GB+ recommended
Storage: ~30GB for all models
- FLUX.1-Fill-dev: ~24GB
- OneReward models: ~6GB
CUDA: Required for optimal performance

⚠️ Important Notes

Model Paths: Models must be placed in specific directories:
- FLUX.1-Fill-dev → models/black-forest-labs/FLUX.1-Fill-dev/
- OneReward models → models/OneReward/
Low-memory GPUs automatically enable quantization and CPU offload optimization
All model files must be downloaded before first use
Supports true_cfg parameter for quality control

📄 License

This project is licensed under Apache 2.0 License.

🔗 References

🤝 Contributing

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

⭐ Citation

If you find this project useful, please consider citing the related papers:

@article{onereward2024,
    title={OneReward: Unified Image Inpainting and Outpainting},
    author={OneReward Team},
    year={2024},
    journal={arXiv preprint},
}