LayoutGD: Content-Aware Layout Generation via Graph-Enhanced Diffusion Model

Content-aware layout generation is crucial in poster design for automatically arranging layout elements. With the data scarcity problem, existing methods mainly employ retrieval augmentation or leverage Large Language Models (LLMs). However, these approaches still face persistent issues such as element overlap, misalignment, and high resource consumption (especially for LLMs). Additionally, these methods ignore and hardly handle layout generation based on pre-existing text canvases (i.e., text-rich images), which are common in real-world poster design. To address these issues, we propose LayoutGD, a graph-based diffusion method that aims to optimize overlap and misalignment while simultaneously enhancing performance on text-rich images. Our method represents all layout elements and image patches as independent nodes, constructs graphs based on specific topologies, and applies Graph Neural Networks (GNNs) to capture their high-dimensional spatial relationships. Furthermore, LayoutGD can process both text-clean and text-rich canvases in a unified framework, benefiting from our Enhance-Branch architecture. Extensive experiments demonstrate that our method achieves the state-of-the-art on various benchmarks. To further validate our performance and facilitate future research in text-rich canvas layout generation, we also construct a challenging text-rich dataset named TRich500¹, which contains a wide variety of pre-existing text images from the real-world.