In this work, we consider the typography generation task that aims at producing diverse typographic styling for the given graphic document. We formulate typography generation as a fine-grained attribute generation for multiple text elements and build an autoregressive model to generate diverse typography that matches the input design context. We further propose a simple yet effective sampling approach that respects the consistency and distinction principle of typography so that generated examples share consistent typographic styling across text elements. Our empirical study shows that our model successfully generates diverse typographic designs while preserving a consistent typographic structure.

本文考虑对给定的图形文档进行生成多样的排版风格的排版生成任务，并将其形式化为针对多个文本元素的细粒度特征生成，建立自回归模型以生成与输入设计背景相匹配的多样排版风格。此外，我们提出了一种简单而有效的采样方法，以尊重排版的一致性和差异性原则，使生成样本在文本元素之间共享一致的排版风格。我们的实证研究表明，我们的模型成功生成多样的排版设计，同时保持了一致的排版结构。

迈向多样且一致的字体生成