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arXiv · 2503.10127

PlanGen: Towards Unified Layout Planning and Image Generation in Auto-Regressive Vision Language Models

Runze He·Bo Cheng·Yuhang Ma·Qingxiang Jia·Shanyuan Liu·Ao Ma·Xiaoyu Wu·Liebucha Wu·Dawei Leng·Yuhui Yin
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Abstract

In this paper, we propose a unified layout planning and image generation model, PlanGen, which can pre-plan spatial layout conditions before generating images. Unlike previous diffusion-based models that treat layout planning and layout-to-image as two separate models, PlanGen jointly models the two tasks into one autoregressive transformer using only next-token prediction. PlanGen integrates layout conditions into the model as context without requiring specialized encoding of local captions and bounding box coordinates, which provides significant advantages over the previous embed-and-pool operations on layout conditions, particularly when dealing with complex layouts. Unified prompting allows PlanGen to perform multitasking training related to layout, including layout planning, layout-to-image generation, image layout understanding, etc. In addition, PlanGen can be seamlessly expanded to layout-guided image manipulation thanks to the well-designed modeling, with teacher-forcing content manipulation policy and negative layout guidance. Extensive experiments verify the effectiveness of our PlanGen in multiple layoutrelated tasks, showing its great potential. Code is available at: https://360cvgroup.github.io/PlanGen.

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Runze He, Bo Cheng, Yuhang Ma, Qingxiang Jia, Shanyuan Liu, Ao Ma, Xiaoyu Wu, Liebucha Wu, Dawei Leng, Yuhui Yin. 2025-03-13. PlanGen: Towards Unified Layout Planning and Image Generation in Auto-Regressive Vision Language Models. https://arxiv.org/abs/2503.10127

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