Allo{SR}$^2$: 通过全形生成流纠正一步超分辨率以保持真实
Allo{SR}$^2$: Rectifying One-Step Super-Resolution to Stay Real via Allomorphic Generative Flows
AI总结：
本文提出Allo{SR}$^2$，通过全形生成流纠正一步超分辨率轨迹，以维持高保真生成现实。方法利用SNR引导轨迹初始化和FATC确保稳定路径，同时通过ATM策略减少分布差异，实验证明其在真实世界超分辨率中表现优异。
摘要：
真实世界图像超分辨率（Real-SR）通过利用大规模扩散和流基模型的强大生成先验得到了革命性发展。然而，这些模型在有限的LR-HR配对上微调时往往导致
英文摘要：
Real-world image super-resolution (Real-SR) has been revolutionized by leveraging the powerful generative priors from Diffusion Models (DMs) and Flow Matching (FM). However, existing one-step methods typically replace Gaussian noise with degraded low-resolution (LR) latents at initialization, introducing a substantial distribution shift that further leads to trajectory deviation and prior collapse under extreme acceleration. To overcome these limitations, we propose Allo{SR}$^2$, a novel FM-based framework that rectifies one-step SR flows via allomorphic generative flows to maintain high-fidelity generative realism. Specifically, we utilize SNR-Guided Trajectory Initialization to identify a statistically aligned intermediate state along the pre-trained path to integrate LR representations into the generative flow. To ensure a stable, low-curvature path for one-step inference, we propose Flow-Anchored Trajectory Consistency (FATC), which explicitly regularizes the velocity field of the underlying probability flow. Furthermore, we develop Allomorphic Trajectory Matching (ATM), a self-adversarial distillation strategy that jointly models the SR flow and the generative flow within a unified velocity field, enabling one-step Real-SR while preserving the generative prior. Extensive experiments on both synthetic and real-world benchmarks demonstrate that Allo{SR}$^2$ achieves state-of-the-art performance in one-step Real-SR, offering a superior balance between fidelity and realism while maintaining extreme efficiency.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
