One Step Diffusion-Based Super-Resolution With Time-Aware Distillation

Diffusion-based image super-resolution (SR) has shown strong potential in recovering high-fidelity details from low-resolution inputs. However, the need for tens or hundreds of sampling steps leads to substantial inference latency. Recent works attempt to accelerate this process via knowledge distillation, but often rely solely on pixel-level loss or overlook the fact that diffusion models capture different information across time steps. To address this, we propose TAD-SR, a time-aware diffusion distillation framework. Specifically, we introduce a novel score distillation strategy to align the score functions between the outputs of the student and teacher models after minor noise perturbation. This distillation strategy eliminates the inherent bias in score distillation sampling (SDS) and enables the student models to focus more on high-frequency image details by sampling at smaller time steps. We further introduce a time-aware discriminator that exploits the teacher's knowledge to differentiate real and synthetic samples across different noise scales, using explicit temporal conditioning. Extensive experiments on SR tasks demonstrate that TAD-SR outperforms existing single-step diffusion methods and achieves performance on par with multi-step state-of-the-art models.