Add comprehensive model card with metadata, links, and usage

This PR enhances the model card for 'Generative Refocusing: Flexible Defocus Control from a Single Image' by adding:
- The `pipeline_tag: image-to-image` for improved discoverability.
- Links to the paper ([Generative Refocusing: Flexible Defocus Control from a Single Image](https://huggingface.co/papers/2512.16923)), project page ([https://generative-refocusing.github.io/](https://generative-refocusing.github.io/)), GitHub repository ([https://github.com/rayray9999/Genfocus](https://github.com/rayray9999/Genfocus)), Hugging Face Demo, and YouTube tutorial.
- A concise description of the model's functionality.
- A detailed 'Quick Start' section from the GitHub README, providing installation and Gradio demo instructions.
- The official citation information.

Please review and merge if everything looks good.

README.md

@@ -1,3 +1,87 @@
----
-license: apache-2.0
----
+---
+license: apache-2.0
+pipeline_tag: image-to-image
+---
+
+# Generative Refocusing: Flexible Defocus Control from a Single Image
+
+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.
+
+-   [Paper (Hugging Face)](https://huggingface.co/papers/2512.16923)
+-   [Project Page](https://generative-refocusing.github.io/)
+-   [GitHub Repository](https://github.com/rayray9999/Genfocus)
+-   [Hugging Face Demo](https://huggingface.co/spaces/nycu-cplab/Genfocus-Demo)
+-   [YouTube Tutorial](https://youtu.be/CMh_jGDl-RE)
+
+<div align="center">
+  <img src="https://generative-refocusing.github.io/assets/demo_vid.gif" width="50%" alt="Demo Video">
+</div>
+
+---
+
+## ⚡ Quick Start
+
+Follow the steps below to set up the environment and run the inference demo.
+
+### 1. Installation
+
+Clone the repository:
+
+```bash
+git clone git@github.com:rayray9999/Genfocus.git
+cd Genfocus
+````
+
+Environment setup:
+
+```bash
+conda create -n Genfocus python=3.12
+conda activate Genfocus
+```
+
+Install requirements:
+
+```bash
+pip install -r requirements.txt
+```
+
+### 2. Download Weights
+
+You can download the pre-trained models using the following commands. Ensure you are in the `Genfocus` root directory.
+
+```bash
+# 1. Download main models to the root directory
+wget https://huggingface.co/nycu-cplab/Genfocus-Model/resolve/main/bokehNet.safetensors
+wget https://huggingface.co/nycu-cplab/Genfocus-Model/resolve/main/deblurNet.safetensors
+
+# 2. Setup checkpoints directory and download auxiliary model
+mkdir -p checkpoints
+cd checkpoints
+wget https://huggingface.co/nycu-cplab/Genfocus-Model/resolve/main/checkpoints/depth_pro.pt
+cd ..
+```
+### 3. Run Gradio Demo
+
+Launch the interactive web interface locally:
+> **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.
+
+```bash
+python demo.py
+```
+
+The demo will be accessible at `http://127.0.0.1:7860` in your browser.
+
+-----
+
+## Citation
+
+If you find this project useful for your research, please consider citing:
+
+```bibtex
+@article{Genfocus2025,
+  title={Generative Refocusing: Flexible Defocus Control from a Single Image},
+  author={Tuan Mu, Chun-Wei and Huang, Jia-Bin and Liu, Yu-Lun},
+  journal={arXiv preprint arXiv:2512.16923},
+  year={2025}
+}
+```