title: MultiBooth: Towards Generating All Your Concepts in an Image from Text
summary: 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/
paper_authors._id: 66271474311a0f41f9735c60
paper_authors.name: Chenyang Zhu
paper_authors._id: 66271474311a0f41f9735c61
paper_authors.name: Kai Li
paper_authors._id: 66271474311a0f41f9735c62
paper_authors.name: Yue Ma
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-04-23T08:24:04.665Z
paper_authors._id: 63871e72189d6915f1d3a87b
paper_authors.avatarUrl: /avatars/4978b3ad59b5712c73fa692193837ecd.svg
paper_authors.fullname: Jack Ma
paper_authors.type: user
paper_authors.user: YueMafighting
paper_authors._id: 66271474311a0f41f9735c63
paper_authors.name: Chunming He
paper_authors._id: 66271474311a0f41f9735c64
paper_authors.name: Li Xiu
paper_title: MultiBooth: Towards Generating All Your Concepts in an Image from Text
paper_summary: 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/
paper_ai_keywords: multi-concept customization diffusion models low concept fidelity inference cost multi-modal image encoder efficient concept encoding cross-attention map bounding boxes qualitative evaluations quantitative evaluations