Glyph-ByT5 is a customized text encoder designed to enhance the accuracy of visual text rendering in text-to-image generation. The core challenge in visual text rendering lies in the limitations of text encoders, which are not adequately aligned with visual text signals. To address this, the authors propose Glyph-ByT5, a text encoder fine-tuned using a meticulously curated dataset of paired glyph-text data. This encoder is integrated with SDXL to create Glyph-SDXL, a model that significantly improves text rendering accuracy, achieving nearly 90% accuracy on design image benchmarks. Glyph-SDXL also demonstrates the ability to render text paragraphs with automated multi-line layouts and improves scene text rendering when fine-tuned with high-quality images. The approach involves creating a scalable glyph-text dataset, employing glyph augmentation strategies, and using a box-level contrastive loss to align text with glyph images. The results show that Glyph-SDXL outperforms existing models in generating text-rich design images and scene-text images. The study highlights the importance of customized text encoders in overcoming the limitations of diffusion models in visual text rendering tasks.Glyph-ByT5 is a customized text encoder designed to enhance the accuracy of visual text rendering in text-to-image generation. The core challenge in visual text rendering lies in the limitations of text encoders, which are not adequately aligned with visual text signals. To address this, the authors propose Glyph-ByT5, a text encoder fine-tuned using a meticulously curated dataset of paired glyph-text data. This encoder is integrated with SDXL to create Glyph-SDXL, a model that significantly improves text rendering accuracy, achieving nearly 90% accuracy on design image benchmarks. Glyph-SDXL also demonstrates the ability to render text paragraphs with automated multi-line layouts and improves scene text rendering when fine-tuned with high-quality images. The approach involves creating a scalable glyph-text dataset, employing glyph augmentation strategies, and using a box-level contrastive loss to align text with glyph images. The results show that Glyph-SDXL outperforms existing models in generating text-rich design images and scene-text images. The study highlights the importance of customized text encoders in overcoming the limitations of diffusion models in visual text rendering tasks.