Textual image generation spans diverse fields like advertising, education,
product packaging, social media, information visualization, and branding.
Despite recent strides in language-guided image synthesis using diffusion
models, current models excel in image generation but struggle with accurate
text rendering and offer limited control over font attributes. In this paper,
we aim to enhance the synthesis of high-quality images with precise text
customization, thereby contributing to the advancement of image generation
models. We call our proposed method CustomText. Our implementation leverages a
pre-trained TextDiffuser model to enable control over font color, background,
and types. Additionally, to address the challenge of accurately rendering
small-sized fonts, we train the ControlNet model for a consistency decoder,
significantly enhancing text-generation performance. We assess the performance
of CustomText in comparison to previous methods of textual image generation on
the publicly available CTW-1500 dataset and a self-curated dataset for
small-text generation, showcasing superior results.

本文旨在提高高质量图像的合成与精确文字自定义，为图像生成模型的进步做出贡献。我们提出的方法名为 CustomText，利用预训练的 TextDiffuser 模型实现对字体颜色、背景和类型的控制。为了解决小字体渲染的挑战，我们训练了 ControlNet 模型用于一致性解码，从而显著提高了文字生成性能。我们通过与先前的文本图像生成方法在公开可用的 CTW-1500 数据集和自编数据集上的比较评估了 CustomText 的性能，并展示出优越的结果。