SGD-font: Style and glyph decoupling for one-shot font generation

Abstract

Automatic font generation aims to generate a complete font library by learning the font style from reference samples. Font generation is challenging because it needs to generate a set of sheer quantity of characters with consistent style and complicated structures of glyphs with limited reference font images, especially when the character or font style is unseen during training. In this paper, we propose a glyph control-based diffusion model for one-shot font generation. Specifically, we employ a style encoder to extract multi-scale style features and incorporate them into the reverse denoising steps of the diffusion model via cross-attention-based style fusion blocks. Decoupling style and glyph enables the combination of arbitrary styles and glyphs in font creation and allows users to generate fonts with unseen styles and unseen glyphs. In the inference stage, we introduce a multi-condition sampling strategy to effectively align the desired style and target glyph. Comprehensive experiments and a user study show that our framework surpasses existing approaches for both seen and unseen fonts. We further demonstrate its capability for style interpolation and cross-lingual font generation. The code is available at https://github.com/ChenSiyi1/SGD-Font.