Create Anything Anywhere: Layout-Controllable Personalized Diffusion Model for Multiple Subjects

• URL: http://arxiv.org/abs/2505.20909v1
• Date: Tue, 27 May 2025 08:57:07 GMT
• Title: Create Anything Anywhere: Layout-Controllable Personalized Diffusion Model for Multiple Subjects
• Authors: Wei Li, Hebei Li, Yansong Peng, Siying Wu, Yueyi Zhang, Xiaoyan Sun,
• Abstract summary: LCP-Diffusion is a novel framework that integrates subject identity preservation with flexible layout guidance in a tuning-free approach.<n>Experiments validate that LCP-Diffusion excels in both identity preservation and layout controllability.
• Score: 13.980211126764349
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Diffusion models have significantly advanced text-to-image generation, laying the foundation for the development of personalized generative frameworks. However, existing methods lack precise layout controllability and overlook the potential of dynamic features of reference subjects in improving fidelity. In this work, we propose Layout-Controllable Personalized Diffusion (LCP-Diffusion) model, a novel framework that integrates subject identity preservation with flexible layout guidance in a tuning-free approach. Our model employs a Dynamic-Static Complementary Visual Refining module to comprehensively capture the intricate details of reference subjects, and introduces a Dual Layout Control mechanism to enforce robust spatial control across both training and inference stages. Extensive experiments validate that LCP-Diffusion excels in both identity preservation and layout controllability. To the best of our knowledge, this is a pioneering work enabling users to "create anything anywhere".

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