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workspace/ComfyUI/models/FlashVSR-v1.1/README.md

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+---
+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> &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%20(v1)-blue"></a> &nbsp;
+<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> &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="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}
+}
+```