Graphic design, which has been evolving since the 15th century, plays a
crucial role in advertising. The creation of high-quality designs demands
creativity, innovation, and lateral thinking. This intricate task involves
understanding the objective, crafting visual elements such as the background,
decoration, font, color, and shape, formulating diverse professional layouts,
and adhering to fundamental visual design principles. In this paper, we
introduce COLE, a hierarchical generation framework designed to comprehensively
address these challenges. This COLE system can transform a straightforward
intention prompt into a high-quality graphic design, while also supporting
flexible editing based on user input. Examples of such input might include
directives like ``design a poster for Hisaishi's concert.'' The key insight is
to dissect the complex task of text-to-design generation into a hierarchy of
simpler sub-tasks, each addressed by specialized models working
collaboratively. The results from these models are then consolidated to produce
a cohesive final output. Our hierarchical task decomposition can streamline the
complex process and significantly enhance generation reliability. Our COLE
system consists of multiple fine-tuned Large Language Models (LLMs), Large
Multimodal Models (LMMs), and Diffusion Models (DMs), each specifically
tailored for a design-aware text or image generation task. Furthermore, we
construct the DESIGNERINTENTION benchmark to highlight the superiority of our
COLE over existing methods in generating high-quality graphic designs from user
intent. We perceive our COLE as an important step towards addressing more
complex visual design generation tasks in the future.

介绍了一个名为 COLE 的分层生成框架，用于将简单的意图提示转化为高质量的平面设计，并支持根据用户输入进行灵活编辑，通过分解任务、合作模型和整合结果，提供了生成可靠性的显著提升。

COLE: 图形设计的层次生成框架

COLE: A Hierarchical Generation Framework for Graphic Design

Models for affective text generation have shown a remarkable progress, but
they commonly rely only on basic emotion theories or valance/arousal values as
conditions. This is appropriate when the goal is to create explicit emotion
statements ("The kid is happy."). Emotions are, however, commonly communicated
implicitly. For instance, the emotional interpretation of an event ("Their dog
died.") does often not require an explicit emotion statement. In psychology,
appraisal theories explain the link between a cognitive evaluation of an event
and the potentially developed emotion. They put the assessment of the situation
on the spot, for instance regarding the own control or the responsibility for
what happens. We hypothesize and subsequently show that including appraisal
variables as conditions in a generation framework comes with two advantages.
(1) The generation model is informed in greater detail about what makes a
specific emotion and what properties it has. This leads to text generation that
better fulfills the condition. (2) The variables of appraisal allow a user to
perform a more fine-grained control of the generated text, by stating
properties of a situation instead of only providing the emotion category. Our
Bart and T5-based experiments with 7 emotions (Anger, Disgust, Fear, Guilt,
Joy, Sadness, Shame), and 7 appraisals (Attention, Responsibility, Control,
Circumstance, Pleasantness, Effort, Certainty) show that (1) adding appraisals
during training improves the accurateness of the generated texts by 10 pp in
F1. Further, (2) the texts with appraisal variables are longer and contain more
details. This exemplifies the greater control for users.

通过加入评估变量作为条件，在生成框架中包含评价理论能够提高情感文本生成的准确性，并使用户可以实现更加精细的控制生成的文本。