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.

该研究提出了一种排版文本生成系统，结合了扩散模型的控制方法和混合潜在扩散模型，实现了在排版设计上添加和修改文本，并指定字体风格、颜色和文本效果。通过合适的边缘条件，该系统能够生成指定字体的文本，同时应用提前设定的效果，如阴影和倒影，成功地将文本添加和修改在预设的背景上，保持整体的一致性。

使用现成的扩散模型生成印刷文本

Typographic Text Generation with Off-the-Shelf Diffusion Model

In this work, we explore the problem of generating fantastic special-effects
for the typography. It is quite challenging due to the model diversities to
illustrate varied text effects for different characters. To address this issue,
our key idea is to exploit the analytics on the high regularity of the spatial
distribution for text effects to guide the synthesis process. Specifically, we
characterize the stylized patches by their normalized positions and the optimal
scales to depict their style elements. Our method first estimates these two
features and derives their correlation statistically. They are then converted
into soft constraints for texture transfer to accomplish adaptive multi-scale
texture synthesis and to make style element distribution uniform. It allows our
algorithm to produce artistic typography that fits for both local texture
patterns and the global spatial distribution in the example. Experimental
results demonstrate the superiority of our method for various text effects over
conventional style transfer methods. In addition, we validate the effectiveness
of our algorithm with extensive artistic typography library generation.

本文研究探讨了在字体设计中生成出色特效的问题，并提出了一种基于空间分布统计分析的自适应多尺度纹理合成算法，通过软约束实现对样例全局空间分布和局部纹理图案的逐层合成，使样例的艺术字体与本地纹理和全局形态之间的匹配更加自然，相较于传统样式转移方法，本方法在各种文本效果上表现出更高的优越性。