HDGlyph: A Hierarchical Disentangled Glyph-Based Framework for Long-Tail Text Rendering in Diffusion Models
Journal:
arXiv
Published Date:
May 10, 2025
Abstract
Visual text rendering, which aims to accurately integrate specified textual
content within generated images, is critical for various applications such as
commercial design. Despite recent advances, current methods struggle with
long-tail text cases, particularly when handling unseen or small-sized text. In
this work, we propose a novel Hierarchical Disentangled Glyph-Based framework
(HDGlyph) that hierarchically decouples text generation from non-text visual
synthesis, enabling joint optimization of both common and long-tail text
rendering. At the training stage, HDGlyph disentangles pixel-level
representations via the Multi-Linguistic GlyphNet and the Glyph-Aware
Perceptual Loss, ensuring robust rendering even for unseen characters. At
inference time, HDGlyph applies Noise-Disentangled Classifier-Free Guidance and
Latent-Disentangled Two-Stage Rendering (LD-TSR) scheme, which refines both
background and small-sized text. Extensive evaluations show our model
consistently outperforms others, with 5.08% and 11.7% accuracy gains in English
and Chinese text rendering while maintaining high image quality. It also excels
in long-tail scenarios with strong accuracy and visual performance.
