Glyph-ByT5: A Customized Text Encoder for Accurate Visual Text Rendering
Zeyu Liu Weicong Liang Zhanhao Liang Chong Luo Ji Li Gao Huang Yuhui Yuan
Visual text rendering poses a fundamental challenge for contemporary
text-to-image generation models, with the core problem lying in text encoder
deficiencies. To achieve accurate text rendering, we identify two crucial
requirements for text encoders: character awareness and alignment with glyphs.
Our solution involves crafting a series of customized text encoder, Glyph-ByT5,
by fine-tuning the character-aware ByT5 encoder using a meticulously curated
paired glyph-text dataset. We present an effective method for integrating
Glyph-ByT5 with SDXL, resulting in the creation of the Glyph-SDXL model for
design image generation. This significantly enhances text rendering accuracy,
improving it from less than $20\%$ to nearly $90\%$ on our design image
benchmark. Noteworthy is Glyph-SDXL's newfound ability for text paragraph
rendering, achieving high spelling accuracy for tens to hundreds of characters
with automated multi-line layouts. Finally, through fine-tuning Glyph-SDXL with
a small set of high-quality, photorealistic images featuring visual text, we
showcase a substantial improvement in scene text rendering capabilities in
open-domain real images. These compelling outcomes aim to encourage further
exploration in designing customized text encoders for diverse and challenging
tasks.