PIT: Preserving Identity through Trajectories for Layout-Aware Multi-Reference Image Generation

Layout-aware multi-reference image generation aims to place multiple reference subjects in prescribed regions while preserving their identities and adapting them to a coherent text-specified scene. Recent methods strengthen reference-to-region binding through layout-conditioned inputs or structured attention, typically relying on a single evolving latent trajectory for both identity preservation and scene adaptation. However, reference-specific evidence may become less recoverable from the evolving scene-adaptive state, even though reference conditioning remains available at every step. Once identity deviations arise, they may persist across subsequent steps, while no alternative identity history is retained for correction. To address this limitation, we propose PIT, a framework that maintains a persistent identity trajectory alongside a primary trajectory. Both trajectories share the same text and reference conditions but evolve under different guidance rules. Accordingly, the primary trajectory adapts the generation to the target scene, whereas the identity trajectory maintains an alternative state history with greater emphasis on reference guidance. To exploit this alternative history, PIT compares the two trajectory states at the same timestep and uses the difference between their clean predictions to correct the primary trajectory within the estimated subject regions. Extensive experiments show that PIT achieves a strong overall balance across identity preservation, spatial accuracy, and text consistency.