Abstract
Video super-resolution (VSR) seeks to reconstruct high-resolution frames from low-resolution inputs. While diffusion-based methods have substantially improved perceptual quality, extending them to video remains challenging for two reasons: strong generative priors can introduce temporal instability, and multi-frame diffusion pipelines are often too expensive for practical deployment. To address both challenges simultaneously, we propose InstaVSR, a lightweight diffusion framework for efficient video super-resolution. InstaVSR combines three ingredients: (1) a pruned one-step diffusion backbone that removes several costly components from conventional diffusion-based VSR pipelines, (2) recurrent training with flow-guided temporal regularization to improve frame-to-frame stability, and (3) dual-space adversarial learning in latent and pixel spaces to preserve perceptual quality after backbone simplification. On an NVIDIA RTX 4090, InstaVSR processes a 30-frame video at 2K$\times$2K resolution in under one minute with only 7 GB of memory usage, substantially reducing the computational cost compared to existing diffusion-based methods while maintaining favorable perceptual quality with significantly smoother temporal transitions.
Abstract (translated)
视频超分辨率（VSR）旨在从低分辨率输入重建高分辨率帧。尽管基于扩散的方法已显著提升了感知质量，但将其扩展到视频领域仍面临两大挑战：强大的生成先验可能引入时间不稳定性，而多帧扩散流程通常因计算成本过高难以实际部署。为同时应对这两项挑战，我们提出了InstaVSR——一个轻量级的高效视频超分辨率扩散框架。InstaVSR融合三项核心技术：（1）通过剪枝单步扩散主干网络，移除了传统基于扩散的VSR流程中多个高成本组件；（2）采用基于光流引导的时间正则化循环训练，以提升帧间稳定性；（3）在隐空间与像素空间实施双空间对抗学习，确保主干简化后仍能保持感知质量。在NVIDIA RTX 4090显卡上，InstaVSR处理30帧2K×2K分辨率视频耗时不足一分钟，内存占用仅7GB，相较于现有基于扩散的方法大幅降低计算成本，同时保持了更优的感知质量与显著更平滑的时间过渡效果。
URL
https://arxiv.org/abs/2603.26134
