Visual layout plays a critical role in graphic design fields such as
advertising, posters, and web UI design. The recent trend towards content-aware
layout generation through generative models has shown promise, yet it often
overlooks the semantic intricacies of layout design by treating it as a simple
numerical optimization. To bridge this gap, we introduce PosterLlama, a network
designed for generating visually and textually coherent layouts by reformatting
layout elements into HTML code and leveraging the rich design knowledge
embedded within language models. Furthermore, we enhance the robustness of our
model with a unique depth-based poster augmentation strategy. This ensures our
generated layouts remain semantically rich but also visually appealing, even
with limited data. Our extensive evaluations across several benchmarks
demonstrate that PosterLlama outperforms existing methods in producing
authentic and content-aware layouts. It supports an unparalleled range of
conditions, including but not limited to unconditional layout generation,
element conditional layout generation, layout completion, among others, serving
as a highly versatile user manipulation tool.

PosterLlama 是一种生成视觉上连贯且文本相关的布局的网络，通过重新格式化布局元素为 HTML 代码并利用语言模型中嵌入的丰富设计知识来进行设计，通过独特的基于深度的海报增强策略提高模型的鲁棒性。在多个基准测试中，PosterLlama 在生成真实且内容感知的布局方面优于现有方法，支持无条件布局生成、元素条件布局生成、布局完成等条件，并作为一种高度灵活的用户操作工具。