  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.
