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

• URL: http://arxiv.org/abs/2404.01717v3
• Date: Thu, 23 May 2024 12:49:54 GMT
• Title: AddSR: Accelerating Diffusion-based Blind Super-Resolution with Adversarial Diffusion Distillation
• Authors: Rui Xie, Ying Tai, Chen Zhao, Kai Zhang, Zhenyu Zhang, Jun Zhou, Xiaoqian Ye, Qian Wang, Jian Yang,
• Abstract summary: 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. 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 designnameto address this issue by incorporating the ideas of both distillation and ControlNet.
• Score: 43.62480338471837
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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