DogLayout: Denoising Diffusion GAN for Discrete and Continuous Layout Generation

• URL: http://arxiv.org/abs/2412.00381v1
• Date: Sat, 30 Nov 2024 07:12:39 GMT
• Title: DogLayout: Denoising Diffusion GAN for Discrete and Continuous Layout Generation
• Authors: Zhaoxing Gan, Guangnan Ye,
• Abstract summary: We present textbfDog (textbfDentextbfoising textbfGAN textbf textbf model), which integrates a diffusion process into GANs to enable the generation of discrete label data.<n>Experiments demonstrate that Dog considerably reduces sampling costs by up to 175 times and cuts overlap from 16.43 to 9.59 compared to existing diffusion models.
• Score: 0.6138671548064356
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Layout Generation aims to synthesize plausible arrangements from given elements. Currently, the predominant methods in layout generation are Generative Adversarial Networks (GANs) and diffusion models, each presenting its own set of challenges. GANs typically struggle with handling discrete data due to their requirement for differentiable generated samples and have historically circumvented the direct generation of discrete labels by treating them as fixed conditions. Conversely, diffusion-based models, despite achieving state-of-the-art performance across several metrics, require extensive sampling steps which lead to significant time costs. To address these limitations, we propose \textbf{DogLayout} (\textbf{D}en\textbf{o}ising Diffusion \textbf{G}AN \textbf{Layout} model), which integrates a diffusion process into GANs to enable the generation of discrete label data and significantly reduce diffusion's sampling time. Experiments demonstrate that DogLayout considerably reduces sampling costs by up to 175 times and cuts overlap from 16.43 to 9.59 compared to existing diffusion models, while also surpassing GAN based and other layout methods. Code is available at https://github.com/deadsmither5/DogLayout.

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