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

翻译：近期文本到图像生成模型的突破为视觉创意释放了巨大潜力，但此类模型在生成一致角色方面仍面临挑战——这一能力对于故事可视化、游戏资产设计、广告等众多现实应用至关重要。现有方法通常依赖目标角色的多张预存图像，或涉及耗时的手动流程。本研究提出一种全自动解决方案，仅需文本提示即可实现一致角色生成。我们引入迭代流程：在每个阶段，算法自动识别共享相似身份的连贯图像集，并从中提取更一致的身份特征。定量分析表明，与基线方法相比，本方法在提示对齐度与身份一致性之间取得了更优平衡，该结论经用户研究进一步验证。最后，我们展示了该方法在多项实际场景中的应用。项目页面详见https://omriavrahami.com/the-chosen-one