metadata
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
Project Page | Paper | GitHub | Dataset
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.
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:
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:
@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. We thank the authors of the open-source projects and pretrained models that support this work.