Recent diffusion-based generative models show promise in their ability to generate text images, but limitations in specifying the styles of the generated texts render them insufficient in the realm of typographic design. This paper proposes a typographic text generation system to add and modify text on typographic designs while specifying font styles, colors, and text effects. The proposed system is a novel combination of two off-the-shelf methods for diffusion models, ControlNet and Blended Latent Diffusion. The former functions to generate text images under the guidance of edge conditions specifying stroke contours. The latter blends latent noise in Latent Diffusion Models (LDM) to add typographic text naturally onto an existing background. We first show that given appropriate text edges, ControlNet can generate texts in specified fonts while incorporating effects described by prompts. We further introduce text edge manipulation as an intuitive and customizable way to produce texts with complex effects such as ``shadows'' and ``reflections''. Finally, with the proposed system, we successfully add and modify texts on a predefined background while preserving its overall coherence.

翻译：基于扩散的生成模型在文本图像生成方面展现出潜力，但生成文本样式指定能力的局限性使其难以满足字体设计领域的需求。本文提出一种字体设计文本生成系统，能够在指定字体样式、颜色和文本特效的同时，对字体设计作品进行文本添加与修改。该系统创新性地融合了两种现成的扩散模型方法：ControlNet与混合潜在扩散。前者通过指定笔画轮廓的边缘条件引导文本图像生成，后者通过混合潜在扩散模型中的潜在噪声，将字体设计文本自然融入现有背景。我们首先证明，在给定合适文本边缘的条件下，ControlNet能够生成指定字体的文本，并融入提示词描述的特效。进一步引入文本边缘操控方法，以直观可定制的方式生成包含"阴影"、"反射"等复杂特效的文本。最后，利用所提系统，我们在保持背景整体连贯性的前提下，成功实现了对预设背景的文本添加与修改。