Allo{SR}2: Rectifying One-Step Super-Resolution to Stay Real via Allomorphic Generative Flows

Real-world image super-resolution (Real-SR) has been rev-olutionized by leveraging the powerful generative priors from DiffusionModels (DMs) and Flow Matching (FM). However, existing one-stepmethods typically replace Gaussian noise with degraded low-resolution(LR) latents at initialization, introducing a substantial distribution shiftthat further leads to trajectory deviation and prior collapse under ex-treme acceleration. To overcome these limitations, we propose Allo{SR}2 ,a novel FM-based framework that rectifies one-step SR flows via allomor-phic generative flows to maintain high-fidelity generative realism. Specifi-cally, we utilize SNR-Guided Trajectory Initialization to identify a statis-tically aligned intermediate state along the pre-trained path to integrateLR representations into the generative flow. To ensure a stable, low-curvature path for one-step inference, we propose Flow-Anchored Trajec-tory Consistency (FATC), which explicitly regularizes the velocity fieldof the underlying probability flow. Furthermore, we develop AllomorphicTrajectory Matching (ATM), a self-adversarial distillation strategy thatjointly models the SR flow and the generative flow within a unified ve-locity field, enabling one-step Real-SR while preserving the generativeprior. Extensive experiments on both synthetic and real-world bench-marks demonstrate that Allo{SR}2 achieves state-of-the-art performancein one-step Real-SR, offering a superior balance between fidelity and re-alism while maintaining extreme efficiency.