Joint Geometric and Trajectory Consistency Learning for One-Step Real-World Super-Resolution

TL;DR

This paper introduces GTASR, a novel training paradigm for real-world image super-resolution that combines geometric and trajectory consistency to improve structural fidelity and efficiency in one-step super-resolution models.

Contribution

The paper proposes GTASR, a new consistency training method that addresses geometric decoupling and drift in super-resolution, enhancing structural preservation and efficiency.

Findings

GTASR outperforms baseline methods in super-resolution quality.

GTASR maintains minimal latency during inference.

Extensive experiments validate the effectiveness of GTASR.

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 https://github.com/Blazedengcy/GTASR.