Templates serve as a good starting point to implement a design (e.g., banner, slide) but it takes great effort from designers to manually create. In this paper, we present Desigen, an automatic template creation pipeline which generates background images as well as harmonious layout elements over the background. Different from natural images, a background image should preserve enough non-salient space for the overlaying layout elements. To equip existing advanced diffusion-based models with stronger spatial control, we propose two simple but effective techniques to constrain the saliency distribution and reduce the attention weight in desired regions during the background generation process. Then conditioned on the background, we synthesize the layout with a Transformer-based autoregressive generator. To achieve a more harmonious composition, we propose an iterative inference strategy to adjust the synthesized background and layout in multiple rounds. We constructed a design dataset with more than 40k advertisement banners to verify our approach. Extensive experiments demonstrate that the proposed pipeline generates high-quality templates comparable to human designers. More than a single-page design, we further show an application of presentation generation that outputs a set of theme-consistent slides. The data and code are available at https://whaohan.github.io/desigen.

本文提出了Desigen，一种自动模板创建流程，该流程生成背景图像以及与背景图像上的布局元素相协调的布局。通过两种简单而有效的技术对背景生成过程中的显著性分布进行约束，从而为现有的高级扩散模型提供更强的空间控制。然后，在背景的条件下，使用基于Transformer的自回归生成器合成布局。为了实现更加协调的构图，我们提出了一种迭代推理策略，以在多轮中调整合成的背景和布局。通过使用超过40,000个广告横幅构建了一个设计数据集来验证我们的方法。广泛的实验表明，所提出的流程生成的模板质量与人类设计师相媲美。除了单页设计外，我们进一步展示了演示文稿生成的应用，该应用能够输出一组具有主题一致性的幻灯片。数据和代码可在此 https URL 找到。

Desigen: 可控设计模板生成的流程