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.

As knowledge distillation technology evolves, it has bifurcated into three distinct methodologies: logic-based, feature-based, and attention-based knowledge distillation. Although the principle of attention-based knowledge distillation is more intuitive, its performance lags behind the other two methods. To address this, we systematically analyze the advantages and limitations of traditional attention-based methods. In order to optimize these limitations and explore more effective attention information, we expand attention-based knowledge distillation in the channel and class dimensions, proposing Spatial Attention-based Knowledge Distillation with Channel Attention (SAKD-Channel) and Spatial Attention-based Knowledge Distillation with Class Attention (SAKD-Class). On CIFAR-100, with ResNet8 × 4 as the student model, SAKD-Channel improves Top-1 validation accuracy by 1.98%, and SAKD-Class improves it by 3.35% compared to traditional distillation methods. On ImageNet, using ResNet18, these two methods improve Top-1 validation accuracy by 0.55% and 0.17%, respectively, over traditional methods. We also conduct extensive experiments to investigate the working mechanisms and application conditions of channel and class dimensions knowledge distillation, providing new theoretical insights for attention-based knowledge transfer.