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
paper_authors.type: user
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_summary: A new automated method generates consistent characters from text prompts by iteratively refining a set of similar images.
paper_ai_keywords: text-to-image generation models iterative procedure prompt alignment identity consistency user study