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