| --- |
| license: apache-2.0 |
| pipeline_tag: video-to-video |
| --- |
| |
| # ⚡ 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> |
| <a href="https://github.com/OpenImagingLab/FlashVSR"><img src="https://img.shields.io/badge/GitHub-Repository-black?logo=github"></a> |
| <a href="https://huggingface.co/JunhaoZhuang/FlashVSR"><img src="https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Model%20(v1)-blue"></a> |
| <a href="https://huggingface.co/JunhaoZhuang/FlashVSR-v1.1"><img src="https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Model%20(v1.1)-blue"></a> |
| <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> |
| <a href="https://arxiv.org/abs/2510.12747"><img src="https://img.shields.io/badge/arXiv-2510.12747-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/assets/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 |
|
|
| - **Nov 2025 — 🎉 [FlashVSR v1.1](https://huggingface.co/JunhaoZhuang/FlashVSR-v1.1) released:** enhanced stability + fidelity |
| - **Oct 2025 — [FlashVSR v1](https://huggingface.co/JunhaoZhuang/FlashVSR) (initial release)**: Inference code and model weights are available now! 🎉 |
| - **Bug Fix (October 21, 2025):** Fixed `local_attention_mask` update logic to prevent artifacts when switching between different aspect ratios during continuous inference. |
| - **Coming Soon:** Dataset release (**VSR-120K**) for large-scale training. |
|
|
| --- |
|
|
| ### 📢 Important Quality Note (ComfyUI & other third-party implementations) |
|
|
| First of all, huge thanks to the community for the fast adoption, feedback, and contributions to FlashVSR! 🙌 |
| During community testing, we noticed that some third-party implementations of FlashVSR (e.g. early ComfyUI versions) do **not include our Locality-Constrained Sparse Attention (LCSA)** module and instead fall back to **dense attention**. This may lead to **noticeable quality degradation**, especially at higher resolutions. |
| Community discussion: https://github.com/kijai/ComfyUI-WanVideoWrapper/issues/1441 |
|
|
| Below is a comparison example provided by a community member: |
|
|
| | Fig.1 – LR Input Video | Fig.2 – 3rd-party (no LCSA) | Fig.3 – Official FlashVSR | |
| |------------------|-----------------------------------------------|--------------------------------------| |
| | <video src="https://github.com/user-attachments/assets/ea12a191-48d5-47c0-a8e5-e19ad13581a9" controls width="260"></video> | <video src="https://github.com/user-attachments/assets/c8e53bd5-7eca-420d-9cc6-2b9c06831047" controls width="260"></video> | <video src="https://github.com/user-attachments/assets/a4d80618-d13d-4346-8e37-38d2fabf9827" controls width="260"></video> | |
|
|
| ✅ The **official FlashVSR pipeline (this repository)**: |
| - **Better preserves fine structures and details** |
| - **Effectively avoids texture aliasing and visual artifacts** |
|
|
| We are also working on a **version that does not rely on the Block-Sparse Attention library** while keeping **the same output quality**; this alternative may run slower than the optimized original implementation. |
|
|
| Thanks again to the community for actively testing and helping improve FlashVSR together! 🚀 |
|
|
| --- |
|
|
| ### 📋 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: |
|
|
| > ⚠️ **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 |
|
|
| ```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 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. |
| > * Based on our testing, the Block-Sparse Attention backend works correctly on **NVIDIA A100 and A800** (Ampere) with **ideal acceleration performance**, and it also runs correctly on **H200** (Hopper) but the acceleration is limited due to hardware scheduling differences and sparse kernel behavior. **Compatibility and performance on other GPUs (e.g., RTX 40/50 series or H800) are currently unknown**. For more details, please refer to the official documentation: https://github.com/mit-han-lab/Block-Sparse-Attention |
|
|
|
|
| ```bash |
| # ✅ Recommended: clone and install in a separate clean folder (outside the FlashVSR repo) |
| 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 |
|
|
| FlashVSR provides both **v1** and **v1.1** model weights 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 v1 (original) or v1.1 (recommended) |
| git lfs clone https://huggingface.co/JunhaoZhuang/FlashVSR # v1 |
| # or |
| git lfs clone https://huggingface.co/JunhaoZhuang/FlashVSR-v1.1 # v1.1 |
| ``` |
|
|
| After cloning, you should have one of the following folders: |
|
|
| ``` |
| ./examples/WanVSR/FlashVSR/ # v1 |
| ./examples/WanVSR/FlashVSR-v1.1/ # v1.1 |
| │ |
| ├── LQ_proj_in.ckpt |
| ├── TCDecoder.ckpt |
| ├── Wan2.1_VAE.pth |
| ├── diffusion_pytorch_model_streaming_dmd.safetensors |
| └── README.md |
| ``` |
|
|
| > Inference scripts automatically load weights from the corresponding folder. |
|
|
| --- |
|
|
| #### 5️⃣ Run Inference |
|
|
| ```bash |
| # From the repo root |
| cd examples/WanVSR |
| |
| # v1 (original) |
| python infer_flashvsr_full.py |
| # or |
| python infer_flashvsr_tiny.py |
| # or |
| python infer_flashvsr_tiny_long_video.py |
| |
| # v1.1 (recommended) |
| python infer_flashvsr_v1.1_full.py |
| # or |
| python infer_flashvsr_v1.1_tiny.py |
| # or |
| python infer_flashvsr_v1.1_tiny_long_video.py |
| ``` |
|
|
| --- |
|
|
| ### 🛠️ 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/assets/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 |
| @article{zhuang2025flashvsr, |
| title={FlashVSR: Towards Real-Time Diffusion-Based Streaming Video Super-Resolution}, |
| author={Zhuang, Junhao and Guo, Shi and Cai, Xin and Li, Xiaohui and Liu, Yihao and Yuan, Chun and Xue, Tianfan}, |
| journal={arXiv preprint arXiv:2510.12747}, |
| year={2025} |
| } |
| ``` |
|
|
