Update README.md

README.md

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-license: apache-2.0
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+---
+license: apache-2.0
+---
+# ⚡ FlashVSR
+
+**Towards Real-Time Diffusion-Based Streaming Video Super-Resolution**
+
+**Authors:** Junhao Zhuang, Shi Guo, Xin Cai, Xiaohui Li, Yihao Liu, Chun Yuan, Tianfan Xue
+
+<a href='http://zhuang2002.github.io/FlashVSR'><img src='https://img.shields.io/badge/Project-Page-Green'></a> &nbsp;
+<a href="https://github.com/OpenImagingLab/FlashVSR"><img src="https://img.shields.io/badge/GitHub-Repository-black?logo=github"></a> &nbsp;
+<a href="https://huggingface.co/JunhaoZhuang/FlashVSR"><img src="https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Model-blue"></a> &nbsp;
+<a href="https://huggingface.co/datasets/JunhaoZhuang/VSR-120K"><img src="https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Dataset-orange"></a> &nbsp;
+<a href="#"><img src="https://img.shields.io/badge/arXiv-TBD-b31b1b.svg"></a>
+
+**Your star means a lot for us to develop this project!** :star:
+
+<img src="https://raw.githubusercontent.com/OpenImagingLab/FlashVSR/main/examples/WanVSR/assert/teaser.png" />
+
+---
+
+### 🌟 Abstract
+
+Diffusion models have recently advanced video restoration, but applying them to real-world video super-resolution (VSR) remains challenging due to high latency, prohibitive computation, and poor generalization to ultra-high resolutions. Our goal in this work is to make diffusion-based VSR practical by achieving **efficiency, scalability, and real-time performance**. To this end, we propose **FlashVSR**, the first diffusion-based one-step streaming framework towards real-time VSR. **FlashVSR runs at ∼17 FPS for 768 × 1408 videos on a single A100 GPU** by combining three complementary innovations: (i) a train-friendly three-stage distillation pipeline that enables streaming super-resolution, (ii) locality-constrained sparse attention that cuts redundant computation while bridging the train–test resolution gap, and (iii) a tiny conditional decoder that accelerates reconstruction without sacrificing quality. To support large-scale training, we also construct **VSR-120K**, a new dataset with 120k videos and 180k images. Extensive experiments show that FlashVSR scales reliably to ultra-high resolutions and achieves **state-of-the-art performance with up to ∼12× speedup** over prior one-step diffusion VSR models.
+
+---
+
+### 📰 News
+
+- **Release Date:** October 2025 — Inference code and model weights are available now! 🎉  
+- **Coming Soon:** Dataset release (**VSR-120K**) for large-scale training.
+
+---
+
+### 📋 TODO
+
+- ✅ Release inference code and model weights  
+- ⬜ Release dataset (VSR-120K)
+
+---
+
+### 🚀 Getting Started
+
+Follow these steps to set up and run **FlashVSR** on your local machine:
+
+#### 1️⃣ Clone the Repository
+
+```bash
+git clone https://github.com/OpenImagingLab/FlashVSR
+cd FlashVSR
+````
+
+#### 2️⃣ Set Up the Python Environment
+
+Create and activate the environment (**Python 3.11.13**):
+
+```bash
+conda create -n flashvsr python=3.11.13
+conda activate flashvsr
+```
+
+Install project dependencies:
+
+```bash
+pip install -e .
+pip install -r requirements.txt
+```
+
+#### 3️⃣ Install Block-Sparse Attention (Required)
+
+FlashVSR **requires** the **Block-Sparse Attention** backend for inference:
+
+```bash
+git clone https://github.com/mit-han-lab/Block-Sparse-Attention
+cd Block-Sparse-Attention
+pip install packaging
+pip install ninja
+python setup.py install
+```
+
+#### 4️⃣ Download Model Weights from Hugging Face
+
+Weights are hosted on **Hugging Face** via **Git LFS**. Please install Git LFS first:
+
+```bash
+# From the repo root
+cd examples/WanVSR
+
+# Install Git LFS (once per machine)
+git lfs install
+
+# Clone the model repository into examples/WanVSR
+git lfs clone https://huggingface.co/JunhaoZhuang/FlashVSR
+```
+
+After cloning, you should have:
+
+```
+./examples/WanVSR/FlashVSR/
+│
+├── LQ_proj_in.ckpt                                   
+├── TCDecoder.ckpt                                    
+├── Wan2.1_VAE.pth                                    
+├── diffusion_pytorch_model_streaming_dmd.safetensors 
+└── README.md
+```
+
+> The inference scripts will load weights from `./examples/WanVSR/FlashVSR/` by default.
+
+#### 5️⃣ Run Inference
+
+```bash
+# From the repo root
+cd examples/WanVSR
+python infer_flashvsr_full.py      # Full model
+# or
+python infer_flashvsr_tiny.py      # Tiny model
+```
+
+---
+
+### 🛠️ Method
+
+The overview of **FlashVSR**. This framework features:
+
+* **Three-Stage Distillation Pipeline** for streaming VSR training.
+* **Locality-Constrained Sparse Attention** to cut redundant computation and bridge the train–test resolution gap.
+* **Tiny Conditional Decoder** for efficient, high-quality reconstruction.
+* **VSR-120K Dataset** consisting of **120k videos** and **180k images**, supports joint training on both images and videos.
+
+<img src="https://raw.githubusercontent.com/OpenImagingLab/FlashVSR/main/examples/WanVSR/assert/flowchart.jpg" width="1000" />
+
+---
+
+### 🤗 Feedback & Support
+
+We welcome feedback and issues. Thank you for trying **FlashVSR**!
+
+---
+
+### 📄 Acknowledgments
+
+We gratefully acknowledge the following open-source projects:
+
+* **DiffSynth Studio** — [https://github.com/modelscope/DiffSynth-Studio](https://github.com/modelscope/DiffSynth-Studio)
+* **Block-Sparse-Attention** — [https://github.com/mit-han-lab/Block-Sparse-Attention](https://github.com/mit-han-lab/Block-Sparse-Attention)
+* **taehv** — [https://github.com/madebyollin/taehv](https://github.com/madebyollin/taehv)
+
+---
+
+### 📞 Contact
+
+* **Junhao Zhuang**
+  Email: [zhuangjh23@mails.tsinghua.edu.cn](mailto:zhuangjh23@mails.tsinghua.edu.cn)
+
+---
+
+### 📜 Citation
+
+```bibtex
+@misc{zhuang2025flashvsr,
+      title={FLASHVSR: Towards Real-Time Diffusion-Based Streaming Video Super-Resolution},
+      author={Junhao Zhuang and Shi Guo and Xin Cai and Xiaohui Li and Yihao Liu and Chun Yuan and Tianfan Xue},
+      year={2025},
+      archivePrefix={arXiv},
+      primaryClass={cs.CV},
+      note={},
+      url={http://zhuang2002.github.io/FlashVSR}
+}
+```