AffectRAG: Continuous Affective Image Editing via Retrieval-Augmented Semantic Grounding and Residual Control

Affective image editing (AIE) focuses on modulating the emotions evoked by images, yet existing methods typically rely on discrete emotion categories and fail to capture fine-grained affective variations. In this paper, we study continuous affective image editing (CAIE), which aims to edit an image toward a target valence-arousal (V-A) state. Current methods face two key challenges in CAIE. First, V-A values lack explicit visual semantics, making the numeric-to-visual mapping difficult to learn. Second, existing editing mechanisms cannot precisely reach a specified V-A coordinate. To address these challenges, we propose **AffectRAG**, a retrieval-augmented planning framework that combines discrete semantic grounding with continuous residual control. Specifically, to compensate for the lack of visual semantics, we construct a large-scale **Affective Knowledge Base** to ground abstract V-A targets into editable semantic anchors. To further improve editing precision, we introduce **Retrieval-Augmented Residual Control Optimization**, which learns the relationship between anchor-target residuals and visual controls through verifiable rewards and retrieval-confidence gating. Experiments on multiple public affective datasets show that AffectRAG outperforms existing state-of-the-art AIE and general image editing methods in both V-A accuracy and semantic preservation.