  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.
