One Step Diffusion-based Super-Resolution with Time-Aware Distillation
Keywords: Efficient diffusion, Super-resolution, Knowledge distillation
Abstract: Diffusion-based image super-resolution (SR) methods have shown promise in reconstructing high-resolution images with fine details from low-resolution counterparts. However, these approaches typically require tens or even hundreds of iterative samplings, resulting in significant latency. Recently, techniques have been devised to enhance the sampling efficiency of diffusion-based SR models via knowledge distillation. Nonetheless, when aligning the knowledge of student and teacher models, these solutions either solely rely on pixel-level loss constraints or neglect the fact that diffusion models prioritize varying levels of information at different time steps. To accomplish effective and efficient image super-resolution, we propose a time-aware diffusion distillation method, named TAD-SR. 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. Furthermore, to mitigate performance limitations stemming from distillation, we fully leverage the knowledge in the teacher model and design a time-aware discriminator to differentiate between real and synthetic data. This discriminator effectively distinguishes the diffused distributions of real and generated images under varying levels of noise disturbance through the injection of time information. Extensive experiments on SR and blind face restoration (BFR) tasks demonstrate that the proposed method outperforms existing diffusion-based single-step techniques and achieves performance comparable to state-of-the-art diffusion models that rely on multi-step generation.
Primary Area: generative models
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Submission Number: 1713
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