The Chosen One: Consistent Characters in Text-to-Image Diffusion Models

• URL: http://arxiv.org/abs/2311.10093v4
• Date: Wed, 5 Jun 2024 14:34:30 GMT
• Title: The Chosen One: Consistent Characters in Text-to-Image Diffusion Models
• Authors: Omri Avrahami, Amir Hertz, Yael Vinker, Moab Arar, Shlomi Fruchter, Ohad Fried, Daniel Cohen-Or, Dani Lischinski,
• Abstract summary: We propose a fully automated solution for consistent character generation with the sole input being a text prompt. Our method strikes a better balance between prompt alignment and identity consistency compared to the baseline methods.
• Score: 71.15152184631951
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Recent advances in text-to-image generation models have unlocked vast potential for visual creativity. However, the users that use these models struggle with the 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.

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