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 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
this https URL

提出了一种名为 SinSR 的简单而有效的方法，通过仅需要一步推断即可生成超分辨率图像，同时利用新的一致性保持损失，以实现比之前最先进方法和教师模型更优的性能和高达 10 倍的推断加速。