LayerComposer: Interactive Personalized T2I via Spatially-Aware Layered Canvas

• URL: http://arxiv.org/abs/2510.20820v2
• Date: Mon, 27 Oct 2025 17:53:30 GMT
• Title: LayerComposer: Interactive Personalized T2I via Spatially-Aware Layered Canvas
• Authors: Guocheng Gordon Qian, Ruihang Zhang, Tsai-Shien Chen, Yusuf Dalva, Anujraaj Argo Goyal, Willi Menapace, Ivan Skorokhodov, Meng Dong, Arpit Sahni, Daniil Ostashev, Ju Hu, Sergey Tulyakov, Kuan-Chieh Jackson Wang,
• Abstract summary: We present LayerComposer, an interactive framework for personalized, multi-subject text-to-image generation.<n>The proposed layered canvas allows users to place, resize, or lock input subjects through intuitive layer manipulation.<n>Our locking mechanism requires no architectural changes, relying instead on inherent positional embeddings combined with a new complementary data sampling strategy.
• Score: 47.5187068545227
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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