Diffusion models have revealed powerful potential in all-in-one image restoration (AiOIR), which is talented in generating abundant texture details. The existing AiOIR methods either retrain a diffusion model or fine-tune the pretrained diffusion model with extra conditional guidance. However, they often suffer from high inference costs and limited adaptability to diverse degradation types. In this paper, we propose an efficient AiOIR method, Diffusion Once and Done (DOD), which aims to achieve superior restoration performance with only one-step sampling of Stable Diffusion (SD) models. Specifically, multi-degradation feature modulation is first introduced to capture different degradation prompts with a pretrained diffusion model. Then, parameter-efficient conditional low-rank adaptation integrates the prompts to enable the fine-tuning of the SD model for adapting to different degradation types. Besides, a high-fidelity detail enhancement module is integrated into the decoder of SD to improve structural and textural details. Experiments demonstrate that our method outperforms existing diffusion-based restoration approaches in both visual quality and inference efficiency.

Blind super-resolution (SR) aims to restore real lowresolution (LR) images. However, most current methods focus on global uniform blur but neglect motion blur, and the few motion deblurring SR methods tend to produce too smooth images. In this letter, we introduce a novel diffusion-based SR method, which can effectively handle the motion blur effect in LR images and retain fine-grained texture information. Our method uses a deblurred feature extraction module and a texture feature extraction module to obtain deblurred features and texture features of the LR image respectively. These two features are then fed into the diffusion model, which samples the image from a learned distribution and outputs a clear and realistic HR image. Moreover, to speed up the sampling process of the diffusion model, we combine it with a conditional generative adversarial network (GAN) to implement stride sampling. Extensive experiments show that our method outperforms state-ofthe-art methods in terms of perceptual metrics, and can generate more natural and realistic images. The code is available athttps://github.com/tonia86/motion-blur-SR.