Diffusion-based image super-resolution (SR) methods are mainly limited by the low inference speed due to the requirements of hundreds or even thousands of sampling steps. Existing acceleration sampling techniques inevitably sacrifice performance to some extent, leading to over-blurry SR results. To address this issue, we propose a novel and efficient diffusion model for SR that significantly reduces the number of diffusion steps, thereby eliminating the need for post-acceleration during inference and its associated performance deterioration. Our method constructs a Markov chain that transfers between the high-resolution image and the low-resolution image by shifting the residual between them, substantially improving the transition efficiency. Additionally, an elaborate noise schedule is developed to flexibly control the shifting speed and the noise strength during the diffusion process. Extensive experiments demonstrate that the proposed method obtains superior or at least comparable performance to current state-of-the-art methods on both synthetic and real-world datasets, even only with 15 sampling steps. Our code and model are available at https://github.com/zsyOAOA/ResShift.

翻译：基于扩散模型的图像超分辨率（SR）方法主要受限于推理速度慢，这源于其需要数百甚至数千次采样步骤。现有的加速采样技术不可避免地会在一定程度上牺牲性能，导致生成过度模糊的SR结果。为解决此问题，我们提出了一种新颖且高效的SR扩散模型，该模型显著减少了扩散步骤的数量，从而消除了推理过程中后加速步骤的必要性及其带来的性能退化。我们的方法通过在高分辨率图像与低分辨率图像之间平移其残差来构建马尔可夫链，从而大幅提升转移效率。此外，我们设计了一种精细的噪声调度机制，以灵活控制扩散过程中的平移速度与噪声强度。大量实验表明，即使在仅使用15个采样步骤的情况下，所提方法在合成数据集与真实世界数据集上均能获得优于或至少可媲美当前最优方法的性能。我们的代码和模型已开源至https://github.com/zsyOAOA/ResShift。