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.

翻译：基于扩散的真实世界图像超分辨率在感知质量方面取得了令人瞩目的成果，但由于迭代采样导致计算成本高昂。虽然近期利用大规模文本到图像先验的蒸馏方法实现了一步式生成，但它们通常受到参数量过大以及教师模型固有能力边界的限制。作为一种轻量级替代方案，一致性模型提供了高效的推理能力，但面临两个关键局限：传递性训练固有的累积一致性漂移，以及我们称之为"几何解耦"的现象——即生成轨迹实现了像素级对齐却未能保持结构连贯性。为应对这些挑战，我们提出了GTASR（几何轨迹对齐超分辨率），一种简单而有效的一致性训练范式。具体而言，我们引入了轨迹对齐策略，通过全路径投影修正切向量场；以及双参考结构校正机制，以施加严格的结构约束。大量实验验证，GTASR在保持极低延迟的同时，性能优于代表性基线方法。代码与模型将在 https://github.com/Blazedengcy/GTASR 发布。