Abstract
Diffusion transformer (DiT) architectures show great potential for real-world image super-resolution (Real-ISR). However, their computationally expensive iterative sampling necessitates one-step distillation. Existing one-step distillation methods struggle with Real-ISR on DiT. They suffer from fundamental trajectory mismatch and generate severe grid-like periodic artifacts. To tackle these challenges, we propose StrSR, a novel one-step adversarial distillation framework featuring spectral and trajectory regularization. Specifically, we propose an asymmetric discriminative distillation architecture to bridge the trajectory gap. Additionally, we design a frequency distribution matching strategy to effectively suppress DiT-specific periodic artifacts caused by high-frequency spectral leakage. Extensive experiments demonstrate that StrSR achieves state-of-the-art performance in Real-ISR, across both quantitative metrics and visual perception. The code and models will be released at this https URL .
Abstract (translated)
扩散变换器（DiT）架构在现实世界中的图像超分辨率（Real-ISR）方面表现出巨大的潜力。然而，其计算成本高昂的迭代采样需要采用一次性蒸馏方法来优化效率。现有的单一步骤蒸馏方法在处理基于DiT的Real-ISR任务时存在困难，它们遇到了根本性的轨迹不匹配问题，并且生成了严重的网格状周期性伪影。 为了应对这些挑战，我们提出了一种新的单一步骤对抗性蒸馏框架StrSR，该框架具有频谱和轨迹正则化特性。具体而言，我们设计了一个不对称的判别式蒸馏架构来弥合轨迹差距。此外，我们制定了一种频率分布匹配策略，以有效抑制由高频谱泄漏引发的DiT特有的周期性伪影。 广泛的实验表明，StrSR在Real-ISR任务中实现了当前最先进的性能，在定量度量和视觉感知方面均表现出色。代码和模型将在[这个链接](https://此链接应为实际URL)上发布。
URL
https://arxiv.org/abs/2603.06275
