IARouter: Identity–Attribute Routing for Compositional Text-to-Image Personalization

Multi-reference personalization must preserve each identity and assign every requested attribute to the correct person. Shared attention often mixes these assignments. We introduce IARouter, a training-free method that routes reference identity information to the corresponding heads and textual attributes to the corresponding body regions. First, a reference-free pass locates the people and freezes their head and body ownership maps. Personalized generation first establishes a text-conditioned layout and then activates the two routes. The attribute route redistributes the existing attribute-attention budget across the person-specific body regions. Ordinary text conditioning, the frozen backbone, and self-attention remain unchanged. On our two-subject benchmark ($N=144$ images per method), our variants achieve the best value in every reported metric. This includes the highest target-versus-decoy binding (0.663) and valid-face rate (0.997) among the evaluated baselines. Paired ablations show that attribute routing improves both binding and complete prompt response. These results support identity–attribute routing for improving two-subject compositional personalization.