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AddSR: Accelerating Diffusion-based Blind Super-Resolution with Adversarial Diffusion Distillation

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arxiv 2404.01717 v4 pith:MJSZNUNN submitted 2024-04-02 cs.CV eess.IV

Rui Xie , Chen Zhao , Kai Zhang , Zhenyu Zhang , Jun Zhou , Jian Yang , Ying Tai This is my paper · ORCID
classification cs.CVeess.IV
keywords distillationdiffusionimagesaddressadversarialblindfastermodel
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abstract

Blind super-resolution methods based on stable diffusion showcase formidable generative capabilities in reconstructing clear high-resolution images with intricate details from low-resolution inputs. However, their practical applicability is often hampered by poor efficiency, stemming from the requirement of thousands or hundreds of sampling steps. Inspired by the efficient adversarial diffusion distillation (ADD), we design~\name~to address this issue by incorporating the ideas of both distillation and ControlNet. Specifically, we first propose a prediction-based self-refinement strategy to provide high-frequency information in the student model output with marginal additional time cost. Furthermore, we refine the training process by employing HR images, rather than LR images, to regulate the teacher model, providing a more robust constraint for distillation. Second, we introduce a timestep-adaptive ADD to address the perception-distortion imbalance problem introduced by original ADD. Extensive experiments demonstrate our~\name~generates better restoration results, while achieving faster speed than previous SD-based state-of-the-art models (e.g., $7$$\times$ faster than SeeSR).

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Cited by 5 Pith papers

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  3. One-Step Diffusion-based Real-World Image Super-Resolution with Visual Perception Distillation

    cs.CV 2025-06 conditional novelty 6.0 of 10

    One-step diffusion super-resolution via CLIP semantic alignment and DWT high-frequency perception distillation improves no-reference perceptual quality scores.

  4. Efficient Difficulty-Aware Dynamic Routing for Diffusion-Based Real-World Image Super-Resolution

    cs.CV 2026-07 reject novelty 4.0 of 10

    DDR-SR routes each real-world low-resolution image to one of two diffusion experts based on a high-frequency-loss difficulty score, using a low-compression VAE for hard images and a high-compression VAE for easy image...

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