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。

利用互信息在扩散模型中提供一致字符生成的 ORACLE

ORACLE: Leveraging Mutual Information for Consistent Character  Generation with LoRAs in Diffusion Models

In this paper, we collect an anthology of 100 visual stories from authors who
participated in our systematic creative process of improvised story-building
based on image sequences. Following close reading and thematic analysis of our
anthology, we present five themes that characterize the variations found in
this creative visual storytelling process: (1) Narrating What is in Vision vs.
Envisioning; (2) Dynamically Characterizing Entities/Objects; (3) Sensing
Experiential Information About the Scenery; (4) Modulating the Mood; (5)
Encoding Narrative Biases. In understanding the varied ways that people derive
stories from images, we offer considerations for collecting story-driven
training data to inform automatic story generation. In correspondence with each
theme, we envision narrative intelligence criteria for computational visual
storytelling as: creative, reliable, expressive, grounded, and responsible.
From these criteria, we discuss how to foreground creative expression, account
for biases, and operate in the bounds of visual storyworlds.

通过主题分析创作过程中的变异性，本研究呈现了创造性视觉叙事过程中的五个主题：根据视觉叙事与构想进行叙述、动态描绘实体 / 物体特征、感知场景的经验性信息、调节情绪、编码叙事偏见。从对人们从图像中获取故事的不同方式的理解出发，我们提出了收集以故事驱动的训练数据来支持自动故事生成的注意事项。同时，我们针对每个主题提出了计算机视觉叙述智能标准：创造性、可靠性、表达性、基于实证的、负责任的，从这些标准出发，我们讨论了如何突出创意表达、考虑偏见，并对视觉叙事世界的范围进行了操作。

构想叙事智能：创造性视觉讲故事选集

Envisioning Narrative Intelligence: A Creative Visual Storytelling  Anthology

We investigated the potential and limitations of generative artificial
intelligence (AI) in reflecting the authors' cognitive processes through
creative expression. The focus is on the AI-generated artwork's ability to
understand human intent (alignment) and visually represent emotions based on
criteria such as creativity, aesthetic, novelty, amusement, and depth. Results
show a preference for images based on the descriptions of the authors' emotions
over the main events. We also found that images that overrepresent specific
elements or stereotypes negatively impact AI alignment. Our findings suggest
that AI could facilitate creativity and the self-expression of emotions. Our
research framework with generative AIs can help design AI-based interventions
in related fields (e.g., mental health education, therapy, and counseling).

该研究探讨了生成人工智能在反映作者认知过程、进行创意表达方面的潜力和限制，发现根据创意、美学、新颖性、娱乐性和深度等标准，AI 生成的艺术作品能够理解人类意图和基于情感进行视觉表现，并发现过度表现某些元素或刻板印象的图像对于 AI 的对齐性有负面影响。研究结果表明，AI 在促进创造力和自我情感表达方面具有潜力，而采用生成 AI 的框架可以帮助设计相关领域的人工智能干预措施（例如心理健康教育、治疗和咨询）。