FaceRoute: Selective Reference Reweighting for Multi-Person Identity Preservation

Multi-person image editing must preserve several facial identities while using full-image reference context. Frozen diffusion-transformer editors already encode the reference images, yet their generated faces can lose distinctive features or take on attributes of another person. We ask (i) whether stronger access to reference evidence improves identity preservation and (ii) whether this strengthening should be spatially selective. We propose FaceRoute, a family of training-free routing variants that add a $\log \alpha$ bias from target-image queries to selected reference VAE keys, without changing backbone weights or adding a conditioning branch. On the two-person and three-/four-person splits of MultiID-Bench, whole-image routing (FaceRoute-W) at $\alpha =1.5$ improves reference and ground-truth identity similarity for both Qwen-Image-Edit-2511 and FLUX.2-klein-base-9B, with all eight paired bootstrap intervals above zero. Face-local routing (FaceRoute-F) also improves over both base editors in paired tests, and its two-person gains replicate across three seeds. Both variants also raise copy-paste preference and slightly lower aggregate aesthetic scores relative to the base editors; for whole-image routing, both costs grow when $\alpha$ is raised from $1.5$ to $2.5$. The answer to (ii) depends on reference face scale: on ordinary references, whole-image routing has higher paired identity similarity, and a mass-matched control links this advantage to the larger injected reference attention mass; on a small-face stress test, face-local routing scores higher on both identity metrics, with higher aesthetic scores and lower copy-paste preference.