One-Step Diffusion Transformer for Controllable Real-World Image Super-Resolution

• URL: http://arxiv.org/abs/2511.17138v3
• Date: Thu, 27 Nov 2025 03:58:51 GMT
• Title: One-Step Diffusion Transformer for Controllable Real-World Image Super-Resolution
• Authors: Yushun Fang, Yuxiang Chen, Shibo Yin, Qiang Hu, Jiangchao Yao, Ya Zhang, Xiaoyun Zhang, Yanfeng Wang,
• Abstract summary: We present ODTSR, a one-step diffusion transformer that performs Real-ISR considering fidelity and controllability simultaneously.<n>ODTSR employs Fidelity-aware Adversarial Training (FAA) to enhance controllability and achieve one-step inference.<n>Experiments demonstrate that ODTSR achieves state-of-the-art (SOTA) performance on generic Real-ISR.
• Score: 54.485573602613535
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recent advances in diffusion-based real-world image super-resolution (Real-ISR) have demonstrated remarkable perceptual quality, yet the balance between fidelity and controllability remains a problem: multi-step diffusion-based methods suffer from generative diversity and randomness, resulting in low fidelity, while one-step methods lose control flexibility due to fidelity-specific finetuning. In this paper, we present ODTSR, a one-step diffusion transformer based on Qwen-Image that performs Real-ISR considering fidelity and controllability simultaneously: a newly introduced visual stream receives low-quality images (LQ) with adjustable noise (Control Noise), and the original visual stream receives LQs with consistent noise (Prior Noise), forming the Noise-hybrid Visual Stream (NVS) design. ODTSR further employs Fidelity-aware Adversarial Training (FAA) to enhance controllability and achieve one-step inference. Extensive experiments demonstrate that ODTSR not only achieves state-of-the-art (SOTA) performance on generic Real-ISR, but also enables prompt controllability on challenging scenarios such as real-world scene text image super-resolution (STISR) of Chinese characters without training on specific datasets. Codes are available at https://github.com/RedMediaTech/ODTSR.

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