LayerComposer: Interactive Personalized T2I via Spatially-Aware Layered
  Canvas
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.
68fada3af158a71c5a2f5812
Guocheng Gordon Qian
68fada3af158a71c5a2f5813
Ruihang Zhang
68fada3af158a71c5a2f5814
Tsai-Shien Chen
68fada3af158a71c5a2f5815
Yusuf Dalva
68fada3af158a71c5a2f5816
Anujraaj Argo Goyal
68fada3af158a71c5a2f5817
Willi Menapace
68fada3af158a71c5a2f5818
Ivan Skorokhodov
68fada3af158a71c5a2f5819
Meng Dong
68fada3af158a71c5a2f581a
Arpit Sahni
68fada3af158a71c5a2f581b
Daniil Ostashev
68fada3af158a71c5a2f581c
Ju Hu
68fada3af158a71c5a2f581d
Sergey Tulyakov
68fada3af158a71c5a2f581e
Kuan-Chieh Jackson Wang
LayerComposer: Interactive Personalized T2I via Spatially-Aware Layered
  Canvas
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.
LayerComposer provides interactive control over spatial composition and scalability in multi-subject text-to-image generation through a layered canvas and locking mechanism.
layered canvas locking mechanism positional embeddings data sampling strategy text-to-image generation spatial control identity preservation