将扩散模型融入GAN，实现高效高质的布局生成。
DogLayout: Denoising Diffusion GAN for Discrete and Continuous Layout Generation
- 用扩散过程增强GAN，直接生成离散标签数据。
- 采样速度提升175倍，重叠率从16.43降至9.59。
- 适合需要快速生成高质量布局的应用场景。
布局生成旨在从给定元素中合成合理排布。当前主流方法为生成对抗网络（GAN）和扩散模型，各有局限：GAN难以处理离散数据，常将标签作为固定条件；扩散模型虽性能领先，但需大量采样步骤，耗时显著。为此，我们提出DogLayout（去噪扩散GAN布局模型），将扩散过程融入GAN，实现离散标签的直接生成，并大幅降低采样时间。实验表明，DogLayout将采样成本降低最多达175倍，重叠率由16.43降至9.59，优于现有扩散模型及基于GAN的其他方法。代码已开源。
原文摘要 · Abstract (English)
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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