Pre-trained text-to-image diffusion models are increasingly applied to real-world image super-resolution (Real-ISR) task. Given the iterative refinement nature of diffusion models, most existing approaches are computationally expensive. While methods such as SinSR and OSEDiff have emerged to condense inference steps via distillation, their performance in image restoration or details recovery is not satisfied. To address this, we propose TSD-SR, a novel distillation framework specifically designed for real-world image super-resolution, aiming to construct an efficient and effective one-step model. We first introduce the Target Score Distillation, which leverages the priors of diffusion models and real image references to achieve more realistic image restoration. Secondly, we propose a Distribution-Aware Sampling Module to make detail-oriented gradients more readily accessible, addressing the challenge of recovering fine details. Extensive experiments demonstrate that our TSD-SR has superior restoration results (most of the metrics perform the best) and the fastest inference speed (e.g. 40 times faster than SeeSR) compared to the past Real-ISR approaches based on pre-trained diffusion priors.

该研究解决了现有真实世界图像超分辨率方法在计算效率和细节恢复上的不足。通过提出目标评分蒸馏和分布感知采样模块，TSD-SR实现了一种高效的一步图像重建模型。实验结果表明，TSD-SR在恢复效果和推断速度上均优于基于预训练扩散模型的先前方法，具有更显著的实用价值。

TSD-SR：用于真实世界图像超分辨率的一步扩散与目标评分蒸馏