The Chosen One: Consistent Characters in Text-to-Image Diffusion Models
Authors: name: Omri Avrahami, name: Amir Hertz, name: Yael Vinker, name: Moab Arar, name: Shlomi Fruchter, name: Ohad Fried, name: Daniel Cohen-Or, name: Dani Lischinski

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