Concept Weaver: Enabling Multi-Concept Fusion in Text-to-Image Models

Autor:	Kwon, Gihyun, Jenni, Simon, Li, Dingzeyu, Lee, Joon-Young, Ye, Jong Chul, Heilbron, Fabian Caba
Rok vydání:	2024
Předmět:	Computer Science - Computer Vision and Pattern Recognition Computer Science - Artificial Intelligence Computer Science - Machine Learning
Druh dokumentu:	Working Paper
Popis:	While there has been significant progress in customizing text-to-image generation models, generating images that combine multiple personalized concepts remains challenging. In this work, we introduce Concept Weaver, a method for composing customized text-to-image diffusion models at inference time. Specifically, the method breaks the process into two steps: creating a template image aligned with the semantics of input prompts, and then personalizing the template using a concept fusion strategy. The fusion strategy incorporates the appearance of the target concepts into the template image while retaining its structural details. The results indicate that our method can generate multiple custom concepts with higher identity fidelity compared to alternative approaches. Furthermore, the method is shown to seamlessly handle more than two concepts and closely follow the semantic meaning of the input prompt without blending appearances across different subjects. Comment: CVPR 2024
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2404.03913 Zobrazit plný text záznamu View this record from Arxiv