InfVSR: Breaking Length Limits of Generic Video Super-Resolution

Ziqing Zhang, Kai Liu, Zheng Chen, Xi Li, Yucong Chen, Bingnan Duan, Linghe Kong, Yulun Zhang

Keywords: Video Super-Resolution, One-Step Diffusion, Auto-Regression

TL;DR: A one-step diffusion based AR method for video super-resolution

Abstract: Real-world videos often extend over thousands of frames, posing unique demands far beyond current short benchmarks. Existing video super-resolution (VSR) approaches, however, face two persistent challenges when processing long sequences: (1) Efficiency due to the heavy cost of multi-step denoising for full-length sequences; and (2) Scalability hindered by temporal decomposition that causes artifacts and discontinuities. To break these limits, we propose InfVSR, which novelly reformulate VSR as an autoregressive-one-step-diffusion paradigm. This enables streaming inference while fully leveraging pre-trained video diffusion priors. First, we adapt the pre-trained DiT into a causal structure, maintaining both local and global coherence via rolling KV-cache and joint visual guidance. Second, we distill diffusion process into a single step efficiently, with patch-wise pixel supervision and cross-chunk distribution matching. Together, these designs enable efficient and scalable VSR for unbounded-length videos. To fill the gap in long-form video evaluation, we build a new benchmark tailored for extended sequences, and further introduce semantic-level metrics to comprehensively assess temporal consistency. Our method pushes the frontier of long-form VSR, achieves state-of-the-art quality with enhanced semantic consistency, and delivers up to 58x speed-up over existing methods such as MGLD-VSR. Code will be released soon.

Primary Area: applications to computer vision, audio, language, and other modalities

Submission Number: 1517