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 https://github.com/jkwang28/StrSR .
Cite
@article{arxiv.2603.06275,
title = {Spectral and Trajectory Regularization for Diffusion Transformer Super-Resolution},
author = {Jingkai Wang and Yixin Tang and Jue Gong and Jiatong Li and Shu Li and Libo Liu and Jianliang Lan and Yutong Liu and Yulun Zhang},
journal= {arXiv preprint arXiv:2603.06275},
year = {2026}
}