用一致性模型实现高效真实图像超分辨率，解决结构失真问题
Joint Geometric and Trajectory Consistency Learning for One-Step Real-World Super-Resolution
- 通过轨迹对齐与双参考结构修正，提升生成一致性
- 在真实图像超分任务中达到领先性能，推理速度极快
- 适合追求高效高质图像重建的工业应用
基于扩散模型的真实世界图像超分辨率（Real-ISR）虽具备出色视觉质量，但因迭代采样导致计算开销大。近期利用大规模文本到图像（T2I）先验的蒸馏方法虽实现单步生成，却面临参数量庞大及教师模型能力上限的限制。作为轻量替代方案，一致性模型虽具备高效推理能力，但仍存在两个关键缺陷：训练过程中的累积一致性漂移，以及我们称之为“几何解耦”的现象——生成轨迹实现像素对齐，却丧失结构连贯性。为此，我们提出GTASR（几何轨迹对齐超分辨率）模型，采用轨迹对齐策略通过全路径投影校正切向场，并引入双参考结构修正机制施加严格的结构约束。大量实验表明，GTASR在保持极低延迟的同时，性能显著优于代表性基线。代码与模型将开源于https://github.com/Blazedengcy/GTASR。
原文摘要 · Abstract (English)
Diffusion-based Real-World Image Super-Resolution (Real-ISR) achieves impressive perceptual quality but suffers from high computational costs due to iterative sampling. While recent distillation approaches leveraging large-scale Text-to-Image (T2I) priors have enabled one-step generation, they are typically hindered by prohibitive parameter counts and the inherent capability bounds imposed by teacher models. As a lightweight alternative, Consistency Models offer efficient inference but struggle with two critical limitations: the accumulation of consistency drift inherent to transitive training, and a phenomenon we term "Geometric Decoupling" - where the generative trajectory achieves pixel-wise alignment yet fails to preserve structural coherence. To address these challenges, we propose GTASR (Geometric Trajectory Alignment Super-Resolution), a simple yet effective consistency training paradigm for Real-ISR. Specifically, we introduce a Trajectory Alignment (TA) strategy to rectify the tangent vector field via full-path projection, and a Dual-Reference Structural Rectification (DRSR) mechanism to enforce strict structural constraints. Extensive experiments verify that GTASR delivers superior performance over representative baselines while maintaining minimal latency. The code and model will be released at https://github.com/Blazedengcy/GTASR.
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