Bridging Restoration and Generation in One-step Diffusion for Real-World Image Super-Resolution
Pretrained diffusion models have revolutionized real-world image super-resolution (Real-ISR), but their iterative sampling is computationally prohibitive, driving efforts to distill it into a single step. General one-step methods fine-tune the generative prior into a deterministic mapping, restoring efficiency but discarding its stochastic nature. Conversely, recent attempts re-engage generation by shifting the timestep or injecting random noise, adjusting either the position or the state while the other stays fixed. Because only one side is controlled, the two align at isolated preset timesteps but drift apart once steered, leaving generation unstable. To address this, we present one-ste...
| Authors | Shyang-En Weng, Yi-Cheng Liao, Yu-Syuan Xu et al. |
|---|---|
| Primary category | cs.CV |
| Published | 27 April 2026 |
| Last updated at source | 24 August 2026 |
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