Paper · arXiv 2404.14239
MultiBooth: Towards Generating All Your Concepts in an Image from Text
Chenyang Zhu, Kai Li, Yue Ma, Chunming He, Li Xiu
MultiBooth enhances multi-concept image generation from text by separating the process into single-concept learning and multi-concept integration phases, using bounding boxes and cross-attention maps to improve concept fidelity and reduce inference costs.
Abstract
This paper introduces MultiBooth, a novel and efficient technique for multi-concept customization in image generation from text. Despite the significant advancements in customized generation methods, particularly with the success of diffusion models, existing methods often struggle with multi-concept scenarios due to low concept fidelity and high inference cost. MultiBooth addresses these issues by dividing the multi-concept generation process into two phases: a single-concept learning phase and a multi-concept integration phase. During the single-concept learning phase, we employ a multi-modal image encoder and an efficient concept encoding technique to learn a concise and discriminative representation for each concept. In the multi-concept integration phase, we use bounding boxes to define the generation area for each concept within the cross-attention map. This method enables the creation of individual concepts within their specified regions, thereby facilitating the formation of multi-concept images. This strategy not only improves concept fidelity but also reduces additional inference cost. MultiBooth surpasses various baselines in both qualitative and quantitative evaluations, showcasing its superior performance and computational efficiency. Project Page: https://multibooth.github.io/