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Typographic Text Generation with Off-the-Shelf Diffusion Model

2024-02-22 06:15:51
KhayTze Peong, Seiichi Uchida, Daichi Haraguchi


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.

Abstract (translated)

最近基于扩散的生成模型在生成文本图像方面显示出潜力,但指定生成文本的风格在排版设计领域存在局限性。本文提出了一种可以在排版设计中添加和修改文本的字体样式、颜色和文本效果的字体生成系统。所提出的系统是将扩散模型的两个经典方法——ControlNet和Blended Latent Diffusion相结合的全新组合。前一个方法根据指定边缘条件生成文本图像,控制轮廓;后一个方法将潜在噪音在Latent Diffusion Models(LDM)中混合,以自然地将字体文本添加到现有背景上。我们首先证明了,只要给定适当的文本边缘,ControlNet可以在指定的字体中生成指定的文本,同时包含由提示描述的效果。我们进一步引入了文本边缘操作作为直观且可定制的生成具有复杂效果(如“阴影”和“反射”)的文本的方法。最后,在所提出的系统中,我们在预定义的背景上成功添加和修改文本,同时保留其整体连贯性。



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