ScaleResfusion：基于残差向量场的残差整流流
ScaleResfusion: Residual Rectified Flow based on Residual Vector Field
- Nankai University（南开大学）
- University of Macau（澳门大学）
- Csiro Data 61（联邦科学与工业研究组织数据61部）
- Beihang University（北京航空航天大学）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
研究旨在解决现实世界图像恢复问题，提出ScaleResfusion框架，核心是残差整流流，从低质量图像出发学习残差向量场，结合知识蒸馏降低采样成本，实验证明其高效且性能优，为图像恢复提供实用可扩展方法。
AI中文摘要：
现实世界图像恢复旨在从复杂且未知的退化中恢复高质量图像。近期基于扩散的方法虽提升了感知质量，但仍有两个关键挑战未解决。本文提出ScaleResfusion，一种基于预训练文本到图像整流流模型的可扩展扩散框架。核心是残差整流流，引入残差项R，从有噪声的低质量图像开始，通过学习残差向量场，使输出分布等与预训练模型一致，还引入知识蒸馏降低采样成本。实验表明该方法高效且性能达最优，为适配大预训练扩散模型到现实图像恢复提供实用可扩展方式。
英文摘要：
Real-world Image Restoration (Real-IR) aims to recover high-quality (HQ) images from complex and unknown degradations. Recent diffusion-based methods have substantially improved perceptual quality, yet two obstacles remain: methods that sample from Gaussian noise require many steps and are often less faithful to the degraded input, whereas residual-based methods that start from the low-quality (LQ) image typically train task-specific models from scratch, with optimization objectives coupled to a particular noise scheduler, and therefore cannot reuse modern pre-trained generative priors. We present \textbf{ScaleResfusion}, which rewrites residual restoration as a scheduler-independent adaptation interface for pre-trained text-to-image rectified-flow models. Its core, \textbf{Residual Rectified Flow} (RRF), inserts the residual term $R$ into the linear transport path of Rectified Flow, so that sampling starts from noisy LQ at an exact acceleration point, where the signal-to-noise ratio of the starting state is continuously controlled by the residual ratio $γ$. The resulting optimization target, the \textbf{residual vector field}, contains no scheduler-specific coefficients and differs from the pre-trained rectified-flow target only by the residual offset $γR$; adapting a frozen billion-scale backbone therefore reduces to fitting this compact residual correction with LoRA-only training. A knowledge-distillation pipeline built around RRF further reduces sampling to as few as 4 steps. Experiments on real-world super-resolution across multiple benchmarks show that ScaleResfusion achieves state-of-the-art restoration quality and transfers consistently across pre-trained rectified-flow backbones from 2B to 9B parameters.
