MultiBooth: Towards Generating All Your Concepts in an Image from Text
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/
66271474311a0f41f9735c60
Chenyang Zhu
66271474311a0f41f9735c61
Kai Li
66271474311a0f41f9735c62
Yue Ma
admin_assigned
2024-04-23T08:24:04.665Z
63871e72189d6915f1d3a87b
/avatars/4978b3ad59b5712c73fa692193837ecd.svg
Jack Ma
user
YueMafighting
66271474311a0f41f9735c63
Chunming He
66271474311a0f41f9735c64
Li Xiu
MultiBooth: Towards Generating All Your Concepts in an Image from Text
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/
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.
multi-modal image encoder concept encoding technique cross-attention map bounding boxes concept fidelity inference cost