OS-DiffVSR: Towards One-step Latent Diffusion Model for High-detailed Real-world Video Super-Resolution

• URL: http://arxiv.org/abs/2509.16507v1
• Date: Sat, 20 Sep 2025 03:04:41 GMT
• Title: OS-DiffVSR: Towards One-step Latent Diffusion Model for High-detailed Real-world Video Super-Resolution
• Authors: Hanting Li, Huaao Tang, Jianhong Han, Tianxiong Zhou, Jiulong Cui, Haizhen Xie, Yan Chen, Jie Hu,
• Abstract summary: We propose One-Step Diffusion model for real-world Video Super-Resolution, namely OS-DiffVSR.<n>Specifically, we devise a novel adjacent frame adversarial training paradigm, which can significantly improve the quality of synthetic videos.
• Score: 11.859297492802456
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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