LayerComposer: Interactive Personalized T2I via Spatially-Aware Layered
  Canvas
Authors: name: Guocheng Gordon Qian; name: Ruihang Zhang; name: Tsai-Shien Chen; name: Yusuf Dalva; name: Anujraaj Argo Goyal; name: Willi Menapace; name: Ivan Skorokhodov; name: Meng Dong; name: Arpit Sahni; name: Daniil Ostashev; name: Ju Hu; name: Sergey Tulyakov; name: Kuan-Chieh Jackson Wang
Published: 2025-10-23 17:59:55

Abstract: Despite their impressive visual fidelity, existing personalized generative
models lack interactive control over spatial composition and scale poorly to
multiple subjects. To address these limitations, we present LayerComposer, an
interactive framework for personalized, multi-subject text-to-image generation.
Our approach introduces two main contributions: (1) a layered canvas, a novel
representation in which each subject is placed on a distinct layer, enabling
occlusion-free composition; and (2) a locking mechanism that preserves selected
layers with high fidelity while allowing the remaining layers to adapt flexibly
to the surrounding context. Similar to professional image-editing software, the
proposed layered canvas allows users to place, resize, or lock input subjects
through intuitive layer manipulation. Our versatile locking mechanism requires
no architectural changes, relying instead on inherent positional embeddings
combined with a new complementary data sampling strategy. Extensive experiments
demonstrate that LayerComposer achieves superior spatial control and identity
preservation compared to the state-of-the-art methods in multi-subject
personalized image generation.