arXiv Computer Vision September 14, 2026 By Zeyang Liu, Le Wang, Sanping Zhou, Yuxuan Wu, Xiaolong Sun, Gang Hua, Haoxiang Li

UniLayDiff: A Unified Diffusion Transformer for Content-Aware Layout Generation

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UniLayDiff is a Unified Diffusion Transformer that tackles content‑aware layout generation across a wide range of tasks—unconditional, element‑type, size, and relationship‑conditioned generation—using a single end‑to‑end trainable model. It treats layout constraints as a distinct modality within a Multi‑Modal Diffusion Transformer framework, capturing interactions among background images, layout elements, and constraints. The model is further refined with LoRA fine‑tuning to incorporate relation constraints, achieving state‑of‑the‑art performance and, according to the authors, the first unified solution for all content‑aware layout generation tasks.

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