RefFlow: Training-Free Reference-Guided Image Editing via Residual Instance Transport

Recent training-free image editing methods with pretrained generative models enable flexible text-driven manipulation, but text guidance alone cannot reproduce the appearance of a specific visual instance. Moreover, even with a visual reference, it remains challenging to simultaneously achieve task diversity and reference fidelity. We introduce RefFlow, a training-free framework for reference-guided image editing via residual instance transport. RefFlow augments semantic editing with a reference-induced velocity residual, implemented through Relationally Consistent Reference Value Transport that adaptively transfers reference appearance via target-adaptive attention retrieval and relational refinement. This design preserves the target editing trajectory while enabling instance-specific visual control without any additional training. RefFlow is compatible with various pretrained models and supports diverse tasks, including object replacement, typography editing, texture transfer, and multi-reference composition. Experiments on RefEdit-Eval and DreamBooth demonstrate improved reference fidelity, editing success, and source preservation over existing training-free and trained baselines.