DriftSR: One-Step Real-World Image Super-Resolution via Distribution Drifting
One-step real-world image super-resolution (Real-ISR) offers efficient inference, but recovering realistic and perceptually rich details often relies on score distillation or adversarial learning, introducing additional trainable components and making optimization more cumbersome. To this end, we propose DriftSR, a one-step Real-ISR framework that leverages pretrained diffusion priors through distribution drifting. Specifically, we perform drifting in the frozen intermediate representation space of a pretrained diffusion model, without introducing an additional task-specific feature encoder. Building on this space, we introduce Spatial Feature Drifting, which treats spatial features rathe...
| Authors | Wei Zhu, Kai Zhang, Yu Zheng et al. |
|---|---|
| Primary category | cs.CV |
| Published | 3 October 2026 |
| Last updated at source | 3 October 2026 |
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