ViType: High-Fidelity Visual Text Rendering via Glyph-Aware Multimodal Diffusion
Lishuai Gao, Jun-Yan He, Yingsen Zeng, Yujie Zhong, Xiaopeng Sun, Jie Hu, Zan Gao, Xiaoming Wei
Abstract
Current text-to-image models face challenges in visual text rendering: text encoders like CLIP and T5 lack glyph-level understanding and often struggle to distinguish between the specific words to be rendered and their intended semantic meaning within prompts. In addition, inconsistencies between the base model and its plugins further compromise the quality of synthesized images. In this paper, we enhance the existing text-to-image method by addressing the following aspects: (1) Text-Glyph Alignmentin a Visual Question Answering (VQA) manner to enable glyph understanding for the text encoder. This involves establishing an explicit alignment between the representations of the glyphs and their detailed attribute descriptions, which boosts the model
BibTeX
@inproceedings{aaai2026_vitypehighfideli,
title = {ViType: High-Fidelity Visual Text Rendering via Glyph-Aware Multimodal Diffusion},
author = {Lishuai Gao and Jun-Yan He and Yingsen Zeng and Yujie Zhong and Xiaopeng Sun and Jie Hu and Zan Gao and Xiaoming Wei},
booktitle = {AAAI 2026},
year = {2026}
}