Recently, latent diffusion models has demonstrated promising performance in real-world video super-resolution (VSR) task, which can reconstruct high-quality videos from distorted low-resolution input through multiple diffusion steps. Compared to image super-resolution (ISR), VSR methods needs to process each frame in a video, which poses challenges to its inference efficiency. However, video quality and inference efficiency have always been a trade-off for the diffusion-based VSR methods. In this work, we propose One-Step Diffusion model for real-world Video Super-Resolution, namely OS-DiffVSR. Specifically, we devise a novel adjacent frame adversarial training paradigm, which can significantly improve the quality of synthetic videos. Besides, we devise a multi-frame fusion mechanism to maintain inter-frame temporal consistency and reduce the flicker in video. Extensive experiments on several popular VSR benchmarks demonstrate that OS-DiffVSR can even achieve better quality than existing diffusion-based VSR methods that require dozens of sampling steps.
@article{arxiv.2509.16507,
title = {OS-DiffVSR: Towards One-step Latent Diffusion Model for High-detailed Real-world Video Super-Resolution},
author = {Hanting Li and Huaao Tang and Jianhong Han and Tianxiong Zhou and Jiulong Cui and Haizhen Xie and Yan Chen and Jie Hu},
journal= {arXiv preprint arXiv:2509.16507},
year = {2025}
}