The Chosen One: Consistent Characters in Text-to-Image Diffusion Models
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
6556e6a97be68c09617c4e09
Omri Avrahami
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2023-12-11T14:00:54.330Z
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Omri Avrahami
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Amir Hertz
admin_assigned
2023-11-17T07:43:47.196Z
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Amir Hertz
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amirhertz
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Yael Vinker
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2023-11-17T07:43:55.978Z
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Yael Vinker
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Moab Arar
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2023-11-17T07:44:03.926Z
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Moab Arar
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moabarar
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Shlomi Fruchter
admin_assigned
2023-11-17T07:44:11.078Z
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Shlomi Fruchter
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shlomif
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Ohad Fried
admin_assigned
2023-11-17T07:44:18.445Z
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Ohad Fried
user
ohadf
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Daniel Cohen-Or
admin_assigned
2023-11-17T07:44:25.597Z
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Daniel Cohen-Or
user
cohenor
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Dani Lischinski
admin_assigned
2023-11-17T07:44:31.966Z
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/avatars/a728cefda3b95c39a3c9aa6485a11c60.svg
Dani Lischinski
user
danix3d
The Chosen One: Consistent Characters in Text-to-Image Diffusion Models
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
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