Diffusion models (DMs) have demonstrated remarkable success in real-world image super-resolution (SR), yet their reliance on time-consuming multi-step sampling largely hinders their practical applications. While recent efforts have introduced few- or single-step solutions, existing methods either inefficiently model the process from noisy input or fail to fully exploit iterative generative priors, compromising the fidelity and quality of the reconstructed images. To address this issue, we propose FlowSR, a novel approach that reformulates the SR problem as a rectified flow from low-resolution (LR) to high-resolution (HR) images. Our method leverages an improved consistency learning strategy to enable high-quality SR in a single step. Specifically, we refine the original consistency distillation process by incorporating HR regularization, ensuring that the learned SR flow not only enforces self-consistency but also converges precisely to the ground-truth HR target. Furthermore, we introduce a fast-slow scheduling strategy, where adjacent timesteps for consistency learning are sampled from two distinct schedulers: a fast scheduler with fewer timesteps to improve efficiency, and a slow scheduler with more timesteps to capture fine-grained texture details. Extensive experiments demonstrate that FlowSR achieves outstanding performance in both efficiency and image quality.

翻译：扩散模型（DMs）在真实图像超分辨率（SR）任务中取得了显著成功，但其依赖耗时的多步采样过程严重限制了实际应用。尽管近期研究提出了少步或单步解决方案，现有方法要么对含噪输入建模效率不足，要么未能充分挖掘迭代生成先验，导致重建图像的保真度和质量受损。针对此问题，我们提出FlowSR——一种将超分辨率问题重构为从低分辨率（LR）到高分辨率（HR）图像的修正流的新型方法。本方法通过改进的一致性学习策略实现单步高质量超分辨率。具体而言，我们通过引入高分辨率正则化优化原始一致性蒸馏流程，确保所学的超分辨率流不仅强制执行自一致性，还能精确收敛至真实高分辨率目标。此外，我们提出快慢调度策略：从两个不同调度器中采样相邻时间步进行一致性学习——快速调度器使用较少时间步以提升效率，慢速调度器使用较多时间步以捕捉精细纹理细节。大量实验表明，FlowSR在效率和图像质量方面均取得了卓越性能。