Create Anything Anywhere: Layout-Controllable Personalized Diffusion Model for Multiple Subjects
Fuente:
arXiv
Enregistré dans:
| Auteurs principaux: | , , , , , |
|---|---|
| Format: | Preprint |
| Publié: |
2025
|
| Sujets: | |
| Accès en ligne: | |
| Tags: |
Ajouter un tag
Pas de tags, Soyez le premier à ajouter un tag!
|
| _version_ | 1866918036378746880 |
|---|---|
| author | Li, Wei Li, Hebei Peng, Yansong Wu, Siying Zhang, Yueyi Sun, Xiaoyan |
| author_facet | Li, Wei Li, Hebei Peng, Yansong Wu, Siying Zhang, Yueyi Sun, Xiaoyan |
| contents | 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". |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_20909 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Create Anything Anywhere: Layout-Controllable Personalized Diffusion Model for Multiple Subjects Li, Wei Li, Hebei Peng, Yansong Wu, Siying Zhang, Yueyi Sun, Xiaoyan Computer Vision and Pattern Recognition 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". |
| title | Create Anything Anywhere: Layout-Controllable Personalized Diffusion Model for Multiple Subjects |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2505.20909 |