  Graphic design, which has been evolving since the 15th century, plays a
crucial role in advertising. The creation of high-quality designs demands
design-oriented planning, reasoning, and layer-wise generation. Unlike the
recent CanvaGPT, which integrates GPT-4 with existing design templates to build
a custom GPT, this paper introduces the COLE system - a hierarchical generation
framework designed to comprehensively address these challenges. This COLE
system can transform a vague intention prompt into a high-quality multi-layered
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 comprises multiple fine-tuned Large
Language Models (LLMs), Large Multimodal Models (LMMs), and Diffusion Models
(DMs), each specifically tailored for design-aware layer-wise captioning,
layout planning, reasoning, and the task of generating images and text.
Furthermore, we construct the DESIGNINTENTION benchmark to demonstrate the
superiority of our COLE system over existing methods in generating high-quality
graphic designs from user intent. Last, we present a Canva-like multi-layered
image editing tool to support flexible editing of the generated multi-layered
graphic design images. We perceive our COLE system as an important step towards
addressing more complex and multi-layered graphic design generation tasks in
the future.
