| --- |
| license: apache-2.0 |
| datasets: |
| - ruchangyao/FlashVSR_Plus_Dataset |
| base_model: |
| - Wan-AI/Wan2.1-T2V-1.3B |
| pipeline_tag: video-to-video |
| tags: |
| - video-super-resolution |
| - video-restoration |
| - flashvsr |
| --- |
| |
| # FlashVSR+: Efficient Video Super-Resolution via Hybrid Attention and Reward-Yield Distribution Matching |
|
|
| <div align="center"> |
|
|
| [**Project Page**](https://flashvsr.tech) | [**Paper**](https://arxiv.org/abs/2610.17552) | [**GitHub**](https://github.com/OpenImagingLab/FlashVSR_Plus) | [**Dataset**](https://huggingface.co/datasets/ruchangyao/FlashVSR_Plus_Dataset) |
|
|
| </div> |
|
|
| ## Overview |
|
|
| **FlashVSR+** is an efficient one-step diffusion framework for high-quality video super-resolution, enabling **spatially untiled 4K inference on a single 24GB RTX 4090 GPU**. |
|
|
| FlashVSR+ introduces two key innovations: |
|
|
| - **Hybrid Attention:** Exploits spatial and temporal attention locality while retaining sparse global interactions, achieving up to **2× DiT acceleration** and **45% lower peak memory**. |
| - **Reward-Yield Distribution Matching Distillation (RY-DMD):** Enables preference alignment with black-box rewards without differentiating through the reward model, improving perceptual quality and generalizing to text-to-image generation. |
|
|
| For visual comparisons and additional results, please visit our [project page](https://flashvsr.tech). |
|
|
| ## Released Models |
|
|
| We provide two FlashVSR+ variants: |
|
|
| | Model | Description | |
| |---|---| |
| | **FlashVSR+ Turbo** | Faster inference, recommended for efficient high-resolution video super-resolution. | |
| | **FlashVSR+ Base** | Higher-quality restoration, recommended when reconstruction quality is the priority. | |
|
|
| Both models support one-step inference and share the same decoder. |
|
|
| ## Model Files |
|
|
| The released checkpoints are organized as follows: |
|
|
| ```text |
| ckpts/ |
| ├── turbo/ |
| │ ├── dit.safetensors |
| │ └── lq_proj_in.safetensors |
| ├── base/ |
| │ ├── dit.safetensors |
| │ └── lq_proj_in.safetensors |
| ├── TCDecoder.ckpt |
| └── Wan2.1_VAE.pth |
| ``` |
|
|
| - `dit.safetensors`: Main diffusion transformer weights. |
| - `lq_proj_in.safetensors`: Low-quality video conditioning weights. |
| - `TCDecoder.ckpt`: Shared decoder used by the default inference pipeline. |
| - `Wan2.1_VAE.pth`: Optional VAE decoder weights. |
|
|
| ## Citation |
|
|
| If you find FlashVSR+ useful, please cite: |
|
|
| ```bibtex |
| @misc{yao2026flashvsrefficientvideosuperresolution, |
| title={FlashVSR+: Efficient Video Super-Resolution via Hybrid Attention and Reward-Yield Distribution Matching}, |
| author={Ruchang Yao and Shi Guo and Junhao Zhuang and Mingde Yao and Xin Cai and Jiahao Zhan and Bin Fu and Xinyuan Chen and Yu Qiao and Tianfan Xue}, |
| year={2026}, |
| eprint={2610.17552}, |
| archivePrefix={arXiv}, |
| primaryClass={cs.CV}, |
| url={https://arxiv.org/abs/2610.17552} |
| } |
| ``` |
|
|
| ## Acknowledgements |
|
|
| FlashVSR+ builds upon the original [FlashVSR](https://arxiv.org/abs/2510.12747). We thank the authors of the open-source projects and pretrained models that support this work. |
