⚡ 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

     

Your star means a lot for us to develop this project! ⭐

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🌟 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.

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📰 News

• Release Date: October 2025 — Inference code and model weights are available now! 🎉
• Coming Soon: Dataset release (VSR-120K) for large-scale training.

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📋 TODO

• ✅ Release inference code and model weights
• ⬜ Release dataset (VSR-120K)

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🚀 Getting Started

Follow these steps to set up and run FlashVSR on your local machine:

⚠️ Note: This project is primarily designed and optimized for 4× video super-resolution.
We strongly recommend using the 4× SR setting to achieve better results and stability. ✅

1️⃣ Clone the Repository

git clone https://github.com/OpenImagingLab/FlashVSR
cd FlashVSR

2️⃣ Set Up the Python Environment

Create and activate the environment (Python 3.11.13):

conda create -n flashvsr python=3.11.13
conda activate flashvsr

Install project dependencies:

pip install -e .
pip install -r requirements.txt

3️⃣ Install Block-Sparse Attention (Required)

FlashVSR relies on the Block-Sparse Attention backend to enable flexible and dynamic attention masking for efficient inference.

⚠️ Note:

• The Block-Sparse Attention build process can be memory-intensive, especially when compiling in parallel with multiple ninja jobs. It is recommended to keep sufficient memory available during compilation to avoid OOM errors. Once the build is complete, runtime memory usage is stable and not an issue.
• The Block-Sparse Attention backend currently achieves ideal acceleration only on NVIDIA A100 or A800 GPUs (Ampere architecture). On H100/H800 (Hopper) GPUs, due to differences in hardware scheduling and sparse kernel behavior, the > expected speedup may not be realized, and in some cases performance can even be slower than dense attention.

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:

# 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

# From the repo root
cd examples/WanVSR
python infer_flashvsr_full.py      # Full model
# or
python infer_flashvsr_tiny.py      # Tiny model

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🛠️ 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.

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🤗 Feedback & Support

We welcome feedback and issues. Thank you for trying FlashVSR!

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📄 Acknowledgments

We gratefully acknowledge the following open-source projects:

• DiffSynth Studio — https://github.com/modelscope/DiffSynth-Studio
• Block-Sparse-Attention — https://github.com/mit-han-lab/Block-Sparse-Attention
• taehv — https://github.com/madebyollin/taehv

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📞 Contact

• Junhao Zhuang Email: zhuangjh23@mails.tsinghua.edu.cn

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📜 Citation

@misc{zhuang2025flashvsrrealtimediffusionbasedstreaming,
      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},
      eprint={2510.12747},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
      url={https://arxiv.org/abs/2510.12747}, 
}