  Layout generation is the keystone in achieving automated graphic design,
requiring arranging the position and size of various multi-modal design
elements in a visually pleasing and constraint-following manner. Previous
approaches are either inefficient for large-scale applications or lack
flexibility for varying design requirements. Our research introduces a unified
framework for automated graphic layout generation, leveraging the multi-modal
large language model (MLLM) to accommodate diverse design tasks. In contrast,
our data-driven method employs structured text (JSON format) and visual
instruction tuning to generate layouts under specific visual and textual
constraints, including user-defined natural language specifications. We
conducted extensive experiments and achieved state-of-the-art (SOTA)
performance on public multi-modal layout generation benchmarks, demonstrating
the effectiveness of our method. Moreover, recognizing existing datasets'
limitations in capturing the complexity of real-world graphic designs, we
propose two new datasets for much more challenging tasks (user-constrained
generation and complicated poster), further validating our model's utility in
real-life settings. Marking by its superior accessibility and adaptability,
this approach further automates large-scale graphic design tasks. The code and
datasets will be publicly available on
https://github.com/posterllava/PosterLLaVA.
