AffectAgent: Grounded Planning and Memory Calibration for Affective Image Editing

Affective image editing aims to align visual content with a target affect while preserving its underlying semantics. The same affective target can be realized through multiple visual modifications. However, existing end-to-end editors jointly perform affective reasoning and image generation, leaving editing strategies implicit and insufficiently grounded in affective semantics. Moreover, current optimization methods often apply task-agnostic supervision, failing to adapt learning strength to varying affective transitions. To address these limitations, we introduce AffectAgent, a planner–executor–evaluator framework that combines grounded planning with memory calibration for controllable affective image editing. Specifically, we propose Knowledge-Grounded Multi-Plan Exploration (KGME), which retrieves affective knowledge to explicitly construct multiple semantically meaningful editing plans and optimize the planner over a grounded strategy space. We further introduce Memory-Calibrated Preference Optimization (MCPO) to organize historical outcomes into an episodic memory and calibrate each sample’s optimization strength against the performance distribution of tasks with similar affective transitions. Extensive experiments show that AffectAgent consistently reduces valence and arousal errors compared with state-of-the-art methods while maintaining competitive semantic consistency, demonstrating more accurate target-affect control.