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.
By Zeyang Liu, Le Wang, Sanping Zhou, Yuxuan Wu, Xiaolong Sun, Gang Hua, Haoxiang Li
arXiv:2607. 16409v1 Announce Type: cross Abstract: Unified Multimodal Large Language Models (MLLMs) offer a promising paradigm for unifying visual understanding and generation, yet they still struggle to follow complex spatial instructions and logical constraints in controllable image generation.
By Junhao Liu, Jian-Wei Zhang, Tao Huang, Miles Yang, Zhao Zhong, Liefeng Bo
arXiv:2509. 12046v2 Announce Type: replace-cross Abstract: Although autoregressive (AR) models have demonstrated remarkable success in image generation, extending these models to layout-conditioned generation remains challenging due to the sparse nature of layout conditions and the risk of feature entanglement.
By Zirui Zheng, Takashi Isobe, Tong Shen, Xu Jia, Jianbin Zhao, Xiaomin Li, Mengmeng Ge, Baolu Li, Qinghe Wang, Dong Li, Dong Zhou, Yunzhi Zhuge, Huchuan Lu, Emad Barsoum
Reference-based diffusion models enable highly controllable image generation by leveraging elements from input images to guide prompt-driven synthesis. However, these models are computationally expensive in runtime, and their cost scales severely with the number of input references.
arXiv:2602.17690v3 Announce Type: replace-cross
Abstract: Graphic design generation demands a delicate balance between high visual fidelity and fine-grained structural editability. However, existing...
By Ziyuan Liu, Shizhao Sun, Danqing Huang, Yingdong Shi, Meisheng Zhang, Ji Li, Jingsong Yu, Jiang Bian
Image outpainting extends an image beyond its original borders, requiring seamless style integration and globally coherent scene completion. Building on the success of diffusion models, recent methods have achieved substantial improvements in visual quality.