Abstract
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 this https URL.
Abstract (translated)
基于扩散的现实世界图像超分辨率（Real-ISR）在感知质量方面表现出色，但由于迭代采样导致计算成本高昂。尽管最近利用大规模文本到图像（T2I）先验知识的蒸馏方法已实现了一步式生成，但这些方法通常受限于庞大的参数数量和教师模型内在的能力限制。作为轻量级替代方案，一致性模型提供了高效的推理，但存在两个关键问题：由于传递训练固有的连贯性漂移累积以及我们称之为“几何解耦”的现象——即生成轨迹在像素级别上对齐但仍无法保持结构一致性。 为解决这些问题，我们提出了GTASR（几何轨迹对准超分辨率），这是一种简单而有效的现实世界图像超分辨的一致性训练范式。具体来说，我们引入了轨迹对准(TA)策略，通过全路径投影修正切向矢量场，并提出了一种双参考结构校正(DRSR)机制以强制严格的结构约束。 广泛的实验验证显示，GTASR在代表性的基线模型上提供了优越的性能并保持最低延迟。代码和模型将在[此链接](https://this https URL/)发布。
URL
https://arxiv.org/abs/2602.24240
