Degradation-Guided One-Step Image Super-Resolution with Diffusion Priors
Authors: name: Aiping Zhang, name: Zongsheng Yue, name: Renjing Pei, name: Wenqi Ren, name: Xiaochun Cao

Diffusion-based image super-resolution (SR) methods have achieved remarkable
success by leveraging large pre-trained text-to-image diffusion models as
priors. However, these methods still face two challenges: the requirement for
dozens of sampling steps to achieve satisfactory results, which limits
efficiency in real scenarios, and the neglect of degradation models, which are
critical auxiliary information in solving the SR problem. In this work, we
introduced a novel one-step SR model, which significantly addresses the
efficiency issue of diffusion-based SR methods. Unlike existing fine-tuning
strategies, we designed a degradation-guided Low-Rank Adaptation (LoRA) module
specifically for SR, which corrects the model parameters based on the
pre-estimated degradation information from low-resolution images. This module
not only facilitates a powerful data-dependent or degradation-dependent SR
model but also preserves the generative prior of the pre-trained diffusion
model as much as possible. Furthermore, we tailor a novel training pipeline by
introducing an online negative sample generation strategy. Combined with the
classifier-free guidance strategy during inference, it largely improves the
perceptual quality of the super-resolution results. Extensive experiments have
demonstrated the superior efficiency and effectiveness of the proposed model
compared to recent state-of-the-art methods.