Delta-适配器：基于单对监督的可扩展范例图像编辑

Delta-Adapter: Scalable Exemplar-Based Image Editing with Single-Pair Supervision

May 8, 2026

作者: Jiacheng Chen, Songze Li, Han Fu, Baoquan Zhao, Wei Liu, Yanyan Liang, Li Qing, Xudong Mao

cs.AI

摘要

基于范例的图像编辑方法通过源-目标图像对定义的变换作用于新的查询图像。现有方法依赖双配对监督范式，需要两对共享相同编辑语义的图像对来学习目标变换。这一约束导致训练数据难以大规模构建，且限制了模型在不同编辑类型间的泛化能力。我们提出Delta-Adapter方法，在单配对监督下学习可迁移的编辑语义，无需任何文本引导。该方法并非直接将范例对暴露给模型，而是利用预训练视觉编码器提取编码两幅图像间视觉变换的语义差异量（semantic delta）。通过基于感知器的适配器将该语义差异量注入预训练图像编辑模型。由于目标图像对模型始终不可见，它可作为预测目标，从而实现无需额外范例对的单配对监督。这种设计使我们能够利用现有大规模编辑数据集进行训练。为进一步促进变换的忠实迁移，我们引入语义差异一致性损失，确保生成输出的语义变化与从范例对中提取的真实语义差异量保持一致。大量实验表明，Delta-Adapter在四个强基线方法的基础上，持续提升了已知编辑任务的编辑准确性与内容一致性，同时在未见编辑任务上展现出更优的泛化能力。代码将发布于https://delta-adapter.github.io。

English

Exemplar-based image editing applies a transformation defined by a source-target image pair to a new query image. Existing methods rely on a pair-of-pairs supervision paradigm, requiring two image pairs sharing the same edit semantics to learn the target transformation. This constraint makes training data difficult to curate at scale and limits generalization across diverse edit types. We propose Delta-Adapter, a method that learns transferable editing semantics under single-pair supervision, requiring no textual guidance. Rather than directly exposing the exemplar pair to the model, we leverage a pre-trained vision encoder to extract a semantic delta that encodes the visual transformation between the two images. This semantic delta is injected into a pre-trained image editing model via a Perceiver-based adapter. Since the target image is never directly visible to the model, it can serve as the prediction target, enabling single-pair supervision without requiring additional exemplar pairs. This formulation allows us to leverage existing large-scale editing datasets for training. To further promote faithful transformation transfer, we introduce a semantic delta consistency loss that aligns the semantic change of the generated output with the ground-truth semantic delta extracted from the exemplar pair. Extensive experiments demonstrate that Delta-Adapter consistently improves both editing accuracy and content consistency over four strong baselines on seen editing tasks, while also generalizing more effectively to unseen editing tasks. Code will be available at https://delta-adapter.github.io.