Spectral and Trajectory Regularization for Diffusion Transformer Super-Resolution

Diffusion transformer (DiT) architectures show great poten-tial for real-world image super-resolution (Real-ISR). However, theircomputationally expensive iterative sampling necessitates one-step dis-tillation. Existing one-step distillation methods struggle with Real-ISRon DiT. They suffer from fundamental trajectory mismatch and gen-erate severe grid-like periodic artifacts. To tackle these challenges, wepropose StrSR, a novel one-step adversarial distillation framework fea-turing spectral and trajectory regularization. Specifically, we proposean asymmetric discriminative distillation architecture to bridge the tra-jectory gap. Additionally, we design a frequency distribution matchingstrategy to effectively suppress DiT-specific periodic artifacts caused byhigh-frequency spectral leakage. Extensive experiments demonstrate thatStrSR achieves state-of-the-art performance in Real-ISR, across bothquantitative metrics and visual perception. The code and models will bereleased at https://github.com/jkwang28/StrSR.