MFSR: MeanFlow Distillation for One Step Real-World Image Super Resolution
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 t...
| Authors | Ruiqing Wang, Kai Zhang, Yuanzhi Zhu et al. |
|---|---|
| Primary category | cs.CV |
| Published | 21 March 2026 |
| Last updated at source | 21 March 2026 |
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