Abstract
Diffusion- and flow-based models have advanced Real-world Image Super-Resolution (Real-ISR), but their multi-step sampling makes inference slow and hard to deploy. One-step distillation alleviates the cost, yet often degrades restoration quality and removes the option to refine with more steps. We present Mean Flows for Super-Resolution (MFSR), a new distillation framework that produces photorealistic results in a single step while still allowing an optional few-step path for further improvement. Our approach uses MeanFlow as the learning target, enabling the student to approximate the average velocity between arbitrary states of the Probability Flow ODE (PF-ODE) and effectively capture the teacher's dynamics without explicit rollouts. To better leverage pretrained generative priors, we additionally improve original MeanFlow's Classifier-Free Guidance (CFG) formulation with teacher CFG distillation strategy, which enhances restoration capability and preserves fine details. Experiments on both synthetic and real-world benchmarks demonstrate that MFSR achieves efficient, flexible, and high-quality super-resolution, delivering results on par with or even better than multi-step teachers while requiring much lower computational cost.
Abstract (translated)
扩散模型与流模型推动了真实世界图像超分辨率（Real-ISR）的发展，但其多步采样过程导致推理速度缓慢且难以部署。一步蒸馏虽能降低计算成本，却常损害修复质量并丧失多步精细调整的可能性。本文提出超分辨率平均流（MFSR）这一新型蒸馏框架，既能单步生成照片级真实结果，又保留可选的多步优化路径以实现进一步提升。该方法以平均流作为学习目标，使学生模型能够逼近概率流常微分方程（PF-ODE）任意状态间的平均速度，无需显式展开即可有效捕捉教师模型动态。为更好利用预训练生成先验，我们进一步改进原始平均流的无分类器引导（CFG）公式，通过教师CFG蒸馏策略增强修复能力并保留精细细节。在合成与真实世界基准测试中，MFSR均展现出高效、灵活且高质量的超分辨率性能，其效果与多步教师模型相当甚至更优，而计算成本显著降低。
URL
https://arxiv.org/abs/2603.20690
