  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.
