title: The Chosen One: Consistent Characters in Text-to-Image Diffusion Models
summary: Recent advances in text-to-image generation models have unlocked vast
  potential for visual creativity. However, these models struggle with generation
  of consistent characters, a crucial aspect for numerous real-world applications
  such as story visualization, game development asset design, advertising, and
  more. Current methods typically rely on multiple pre-existing images of the
  target character or involve labor-intensive manual processes. In this work, we
  propose a fully automated solution for consistent character generation, with
  the sole input being a text prompt. We introduce an iterative procedure that,
  at each stage, identifies a coherent set of images sharing a similar identity
  and extracts a more consistent identity from this set. Our quantitative
  analysis demonstrates that our method strikes a better balance between prompt
  alignment and identity consistency compared to the baseline methods, and these
  findings are reinforced by a user study. To conclude, we showcase several
  practical applications of our approach. Project page is available at
  https://omriavrahami.com/the-chosen-one
paper_authors._id: 6556e6a97be68c09617c4e09
paper_authors.name: Omri Avrahami
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paper_authors.fullname: Omri Avrahami
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paper_authors.name: Amir Hertz
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paper_authors.fullname: Amir Hertz
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paper_authors.name: Yael Vinker
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paper_authors.fullname: Yael Vinker
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paper_authors.name: Moab Arar
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paper_authors.fullname: Moab Arar
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paper_authors.name: Shlomi Fruchter
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paper_authors.fullname: Shlomi Fruchter
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paper_authors.user: shlomif
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paper_authors.name: Ohad Fried
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paper_authors.fullname: Ohad Fried
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paper_authors.user: ohadf
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paper_authors.name: Daniel Cohen-Or
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paper_authors.fullname: Daniel Cohen-Or
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paper_authors.user: cohenor
paper_authors._id: 6556e6a97be68c09617c4e10
paper_authors.name: Dani Lischinski
paper_authors.status: admin_assigned
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paper_authors.avatarUrl: /avatars/a728cefda3b95c39a3c9aa6485a11c60.svg
paper_authors.fullname: Dani Lischinski
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paper_authors.user: danix3d
paper_title: The Chosen One: Consistent Characters in Text-to-Image Diffusion Models
paper_summary: Recent advances in text-to-image generation models have unlocked vast
  potential for visual creativity. However, these models struggle with generation
  of consistent characters, a crucial aspect for numerous real-world applications
  such as story visualization, game development asset design, advertising, and
  more. Current methods typically rely on multiple pre-existing images of the
  target character or involve labor-intensive manual processes. In this work, we
  propose a fully automated solution for consistent character generation, with
  the sole input being a text prompt. We introduce an iterative procedure that,
  at each stage, identifies a coherent set of images sharing a similar identity
  and extracts a more consistent identity from this set. Our quantitative
  analysis demonstrates that our method strikes a better balance between prompt
  alignment and identity consistency compared to the baseline methods, and these
  findings are reinforced by a user study. To conclude, we showcase several
  practical applications of our approach. Project page is available at
  https://omriavrahami.com/the-chosen-one
paper_ai_keywords: text-to-image generation models consistent characters story visualization game development asset design advertising fully automated solution iterative procedure coherent set of images consistent identity prompt alignment identity consistency quantitative analysis user study practical applications