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--- |
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license: apache-2.0 |
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pipeline_tag: image-to-image |
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--- |
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# Generative Refocusing: Flexible Defocus Control from a Single Image |
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This model, **Generative Refocusing**, presented in the paper [Generative Refocusing: Flexible Defocus Control from a Single Image](https://huggingface.co/papers/2512.16923), offers a novel two-step process for depth-of-field control from a single image. It uses DeblurNet to recover all-in-focus images from various inputs and BokehNet for creating controllable bokeh. The method leverages semi-supervised training, combining synthetic paired data with unpaired real bokeh images, and achieves state-of-the-art performance in defocus deblurring, bokeh synthesis, and refocusing benchmarks, allowing text-guided adjustments and custom aperture shapes. |
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- [Paper (Hugging Face)](https://huggingface.co/papers/2512.16923) |
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- [Project Page](https://generative-refocusing.github.io/) |
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- [GitHub Repository](https://github.com/rayray9999/Genfocus) |
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- [Hugging Face Demo](https://huggingface.co/spaces/nycu-cplab/Genfocus-Demo) |
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- [YouTube Tutorial](https://youtu.be/CMh_jGDl-RE) |
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<div align="center"> |
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<img src="https://github.com/rayray9999/Genfocus/raw/main/assets/demo_vid.gif" width="50%" alt="Demo Video"> |
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</div> |
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--- |
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## ⚡ Quick Start |
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Follow the steps below to set up the environment and run the inference demo. |
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### 1. Installation |
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Clone the repository: |
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```bash |
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git clone git@github.com:rayray9999/Genfocus.git |
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cd Genfocus |
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```` |
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Environment setup: |
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```bash |
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conda create -n Genfocus python=3.12 |
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conda activate Genfocus |
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``` |
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Install requirements: |
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```bash |
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pip install -r requirements.txt |
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``` |
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### 2. Download Weights |
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You can download the pre-trained models using the following commands. Ensure you are in the `Genfocus` root directory. |
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```bash |
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# 1. Download main models to the root directory |
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wget https://huggingface.co/nycu-cplab/Genfocus-Model/resolve/main/bokehNet.safetensors |
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wget https://huggingface.co/nycu-cplab/Genfocus-Model/resolve/main/deblurNet.safetensors |
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# 2. Setup checkpoints directory and download auxiliary model |
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mkdir -p checkpoints |
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cd checkpoints |
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wget https://huggingface.co/nycu-cplab/Genfocus-Model/resolve/main/checkpoints/depth_pro.pt |
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cd .. |
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``` |
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### 3. Run Gradio Demo |
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Launch the interactive web interface locally: |
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> **Note:** The project uses [FLUX.1-dev](https://huggingface.co/black-forest-labs/FLUX.1-dev). You must request access and authenticate locally before running the demo. |
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```bash |
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python demo.py |
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``` |
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The demo will be accessible at `http://127.0.0.1:7860` in your browser. |
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----- |
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## Citation |
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If you find this project useful for your research, please consider citing: |
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```bibtex |
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@article{Genfocus2025, |
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title={Generative Refocusing: Flexible Defocus Control from a Single Image}, |
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author={Tuan Mu, Chun-Wei and Huang, Jia-Bin and Liu, Yu-Lun}, |
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journal={arXiv preprint arXiv:2512.16923}, |
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year={2025} |
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} |
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``` |
