Diffusion-based image super-resolution (SR) models have attracted substantial interest due to their powerful image restoration capabilities. However, prevailing diffusion models often struggle to strike an optimal balance between efficiency and performance. Typically, they either neglect to exploit the potential of existing extensive pretrained models, limiting their generative capacity, or they necessitate a dozens of forward passes starting from random noises, compromising inference efficiency. In this paper, we present DoSSR, a Domain Shift diffusion-based SR model that capitalizes on the generative powers of pretrained diffusion models while significantly enhancing efficiency by initiating the diffusion process with low-resolution (LR) images. At the core of our approach is a domain shift equation that integrates seamlessly with existing diffusion models. This integration not only improves the use of diffusion prior but also boosts inference efficiency. Moreover, we advance our method by transitioning the discrete shift process to a continuous formulation, termed as DoS-SDEs. This advancement leads to the fast and customized solvers that further enhance sampling efficiency. Empirical results demonstrate that our proposed method achieves state-of-the-art performance on synthetic and real-world datasets, while notably requiring only 5 sampling steps. Compared to previous diffusion prior based methods, our approach achieves a remarkable speedup of 5-7 times, demonstrating its superior efficiency. Code: https://github.com/QinpengCui/DoSSR.

翻译：基于扩散的图像超分辨率模型因其强大的图像修复能力而受到广泛关注。然而，现有的扩散模型往往难以在效率与性能之间取得最佳平衡。通常，它们要么未能充分利用现有大规模预训练模型的潜力，限制了其生成能力；要么需要从随机噪声开始进行数十次前向传播，从而损害了推理效率。本文提出DoSSR，一种基于域漂移的扩散超分辨率模型，该模型充分利用预训练扩散模型的生成能力，同时通过以低分辨率图像启动扩散过程显著提升效率。我们方法的核心是一个与现有扩散模型无缝集成的域漂移方程。这种集成不仅优化了扩散先验的利用，还提升了推理效率。此外，我们通过将离散漂移过程转化为连续形式（称为DoS-SDEs）来推进该方法。这一进展催生了快速定制求解器，从而进一步提升了采样效率。实验结果表明，所提方法在合成与真实数据集上均达到了最先进的性能，且仅需5个采样步骤。与先前基于扩散先验的方法相比，我们的方法实现了5-7倍的显著加速，展现了其卓越的效率。代码：https://github.com/QinpengCui/DoSSR。