用一步扩散模型实现真实图像超分，又快又清晰。
TSD-SR: One-Step Diffusion with Target Score Distillation for Real-World Image Super-Resolution
- 引入目标得分蒸馏，利用先验和真实图像提升修复效果。
- 提出分布感知采样模块，更好恢复细节纹理。
- 比现有方法快40倍，超分质量领先，适合实际应用。
预训练的文本到图像扩散模型在真实世界图像超分辨率（Real-ISR）任务中应用日益广泛。由于扩散模型固有的迭代精炼特性，现有方法普遍计算开销大。尽管如SinSR和OSEDiff等方法通过蒸馏技术压缩推理步数，其在图像修复或细节恢复方面表现仍不理想。为此，本文提出TSD-SR，一种专为真实世界图像超分辨率设计的新颖蒸馏框架，旨在构建高效且有效的单步模型。首先，引入目标得分蒸馏（Target Score Distillation），利用扩散模型先验与真实图像参考，实现更真实的图像修复。其次，提出分布感知采样模块（Distribution-Aware Sampling Module），使细节导向梯度更易获取，解决精细细节恢复难题。大量实验表明，TSD-SR在各项指标上均表现最优，且推理速度远超以往基于预训练扩散先验的方法（例如比SeeSR快40倍）。
原文摘要 · Abstract (English)
Pre-trained text-to-image diffusion models are increasingly applied to real-world image super-resolution (Real-ISR) task. Given the iterative refinement nature of diffusion models, most existing approaches are computationally expensive. While methods such as SinSR and OSEDiff have emerged to condense inference steps via distillation, their performance in image restoration or details recovery is not satisfied. To address this, we propose TSD-SR, a novel distillation framework specifically designed for real-world image super-resolution, aiming to construct an efficient and effective one-step model. We first introduce the Target Score Distillation, which leverages the priors of diffusion models and real image references to achieve more realistic image restoration. Secondly, we propose a Distribution-Aware Sampling Module to make detail-oriented gradients more readily accessible, addressing the challenge of recovering fine details. Extensive experiments demonstrate that our TSD-SR has superior restoration results (most of the metrics perform the best) and the fastest inference speed (e.g. 40 times faster than SeeSR) compared to the past Real-ISR approaches based on pre-trained diffusion priors.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。