Stable Layout Image Diffusion for Content-Aware Layout Generation

Content-aware layout generation is a critical technique for automating poster design, as it ensures the effective spatial organization of multimodal elements for enhanced clarity and impact. Existing methods face notable limitations: data-driven models depend on scarce clean-canvas datasets or noisy pseudo-canvases, while large language model–based approaches incur high cost and lack fine-grained visual sensitivity. We propose Stable Layout Image Diffusion (SLID), a diffusion-based framework that reformulates layout generation as image synthesis with color-coded representations, avoiding pseudo-canvas artifacts and enabling stable training. Content-aware conditioning with subject mask, edge map, and elements image further enhances spatial reasoning and supports diverse, irregular layouts. We also introduce DC-Poster, a dataset of 37k annotated posters. Experiments show SLID surpasses state-of-the-art methods, producing visually appealing and semantically aligned layouts suitable for real-application.