FlashVSR: Towards Real-Time Diffusion-Based Streaming Video
  Super-Resolution
Authors: name: Junhao Zhuang; name: Shi Guo; name: Xin Cai; name: Xiaohui Li; name: Yihao Liu; name: Chun Yuan; name: Tianfan Xue
Published: 2025-10-14 17:25:54

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 approximately 17 FPS for 768x1408
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 12x speedup over prior one-step
diffusion VSR models. We will release the code, pretrained models, and dataset
to foster future research in efficient diffusion-based VSR.