ScaleResfusion: Residual Rectified Flow based on Residual Vector Field

Abstract

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 $\gamma$. 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 $\gamma R$; 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.

Authors 7

Zhenning Shi, Chen Xu, Junhao Zhang, Kefei Zhang, Linjie Liu, Zhedong Zheng, Tao Li

Specification

Official page

arxiv.org/abs/2607.25275

Source:arXiv (Atom API + RSS)·T1·observed 2 h ago·high

Arxiv announce type

replace

Source:arXiv (Atom API + RSS)·T1·observed 2 h ago·high

arXiv id

2607.25275

Source:arXiv (Atom API + RSS)·T1·observed 2 h ago·high

Categories

cs.CV, cs.AI

Source:arXiv (Atom API + RSS)·T1·observed 2 h ago·high

PDF

arxiv.org/pdf/2607.25275

Source:arXiv (Atom API + RSS)·T1·observed 2 h ago·high

Primary category

cs.CV

Source:arXiv (Atom API + RSS)·T1·observed 2 h ago·high

Published

11 Sept 2026

Source:arXiv (Atom API + RSS)·T1·observed 2 h ago·high

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