LayoutDiffuse: Adapting Foundational Diffusion Models for Layout-to-Image Generation

• URL: http://arxiv.org/abs/2302.08908v1
• Date: Thu, 16 Feb 2023 14:20:25 GMT
• Title: LayoutDiffuse: Adapting Foundational Diffusion Models for Layout-to-Image Generation
• Authors: Jiaxin Cheng, Xiao Liang, Xingjian Shi, Tong He, Tianjun Xiao and Mu Li
• Abstract summary: Our method trains efficiently, generates images with both high perceptual quality and layout alignment. Our method significantly outperforms other 10 generative models based on GANs, VQ-VAE, and diffusion models.
• Score: 24.694298869398033
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Layout-to-image generation refers to the task of synthesizing photo-realistic images based on semantic layouts. In this paper, we propose LayoutDiffuse that adapts a foundational diffusion model pretrained on large-scale image or text-image datasets for layout-to-image generation. By adopting a novel neural adaptor based on layout attention and task-aware prompts, our method trains efficiently, generates images with both high perceptual quality and layout alignment, and needs less data. Experiments on three datasets show that our method significantly outperforms other 10 generative models based on GANs, VQ-VAE, and diffusion models.

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