SinSR: Diffusion-Based Image Super-Resolution in a Single Step
Authors: Yufei Wang, Wenhan Yang, Xinyuan Chen, Yaohui Wang, Lanqing Guo, Lap-Pui Chau, Ziwei Liu, Yu Qiao, Alex C. Kot, Bihan Wen

While super-resolution (SR) methods based on diffusion models exhibit promising results their practical application is hindered by the substantial number of required inference steps. Recent methods utilize the degraded images in the initial state thereby shortening the Markov chain. Nevertheless these solutions either rely on a precise formulation of the degradation process or still necessitate a relatively lengthy generation path (e.g. 15 iterations). To enhance inference speed we propose a simple yet effective method for achieving single-step SR generation named SinSR. Specifically we first derive a deterministic sampling process from the most recent state-of-the-art (SOTA) method for accelerating diffusion-based SR. This allows the mapping between the input random noise and the generated high-resolution image to be obtained in a reduced and acceptable number of inference steps during training. We show that this deterministic mapping can be distilled into a student model that performs SR within only one inference step. Additionally we propose a novel consistency-preserving loss to simultaneously leverage the ground-truth image during the distillation process ensuring that the performance of the student model is not solely bound by the feature manifold of the teacher model resulting in further performance improvement. Extensive experiments conducted on synthetic and real-world datasets demonstrate that the proposed method can achieve comparable or even superior performance compared to both previous SOTA methods and the teacher model in just one sampling step resulting in a remarkable up to x10 speedup for inference. Our code will be released at https://github.com/wyf0912/SinSR/.