Text-to-image diffusion models have recently taken center stage as pivotal
tools in promoting visual creativity across an array of domains such as comic
book artistry, children's literature, game development, and web design. These
models harness the power of artificial intelligence to convert textual
descriptions into vivid images, thereby enabling artists and creators to bring
their imaginative concepts to life with unprecedented ease. However, one of the
significant hurdles that persist is the challenge of maintaining consistency in
character generation across diverse contexts. Variations in textual prompts,
even if minor, can yield vastly different visual outputs, posing a considerable
problem in projects that require a uniform representation of characters
throughout. In this paper, we introduce a novel framework designed to produce
consistent character representations from a single text prompt across diverse
settings. Through both quantitative and qualitative analyses, we demonstrate
that our framework outperforms existing methods in generating characters with
consistent visual identities, underscoring its potential to transform creative
industries. By addressing the critical challenge of character consistency, we
not only enhance the practical utility of these models but also broaden the
horizons for artistic and creative expression.

通过介绍一种新的方法，本文研究了如何从单一的文本提示产生具有一致的视觉特征的角色表达，通过定量和定性分析，证明了该方法在生成具有一致视觉特征的角色方面优于现有方法，扩展了艺术和创作表达的可能性。