提出一步式扩散模型，兼顾图像清晰度与可控性。
One-Step Diffusion Transformer for Controllable Real-World Image Super-Resolution
- 设计噪声混合视觉流，分别处理可控噪声与先验噪声
- 一步推理实现领先清晰度，且在中文文本场景保持高可控性
- 无需特定数据集训练，适合真实场景文本图像增强
基于扩散的现实世界图像超分辨率（Real-ISR）虽显著提升感知质量，但保真度与可控性难以兼顾：多步方法生成多样且随机，保真度低；一步方法因保真度特化微调而失去控制灵活性。本文提出ODTSR，一种基于Qwen-Image的一步扩散变换器，同时考虑保真度与可控性：新引入的视觉流接收带可调噪声（控制噪声）的低质量图像，原视觉流接收带固定噪声（先验噪声）的低质量图像，构成噪声混合视觉流（NVS）；ODTSR进一步采用保真度感知对抗训练（FAA）以增强可控性并实现一步推理。大量实验表明，ODTSR不仅在通用Real-ISR上达到当前最优性能，且在中文场景文字图像超分辨率（STISR）等挑战性场景中实现提示可控性，且无需在特定数据集上训练。代码已开源于https://github.com/RedMediaTech/ODTSR。
原文摘要 · Abstract (English)
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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