FontDiffuser: One-Shot Font Generation via Denoising Diffusion with
  Multi-Scale Content Aggregation and Style Contrastive Learning
Zhenhua Yang Dezhi Peng Yuxin Kong Yuyi Zhang Cong Yao Lianwen Jin
Automatic font generation is an imitation task, which aims to create a font
library that mimics the style of reference images while preserving the content
from source images. Although existing font generation methods have achieved
satisfactory performance, they still struggle with complex characters and large
style variations. To address these issues, we propose FontDiffuser, a
diffusion-based image-to-image one-shot font generation method, which
innovatively models the font imitation task as a noise-to-denoise paradigm. In
our method, we introduce a Multi-scale Content Aggregation (MCA) block, which
effectively combines global and local content cues across different scales,
leading to enhanced preservation of intricate strokes of complex characters.
Moreover, to better manage the large variations in style transfer, we propose a
Style Contrastive Refinement (SCR) module, which is a novel structure for style
representation learning. It utilizes a style extractor to disentangle styles
from images, subsequently supervising the diffusion model via a meticulously
designed style contrastive loss. Extensive experiments demonstrate
FontDiffuser's state-of-the-art performance in generating diverse characters
and styles. It consistently excels on complex characters and large style
changes compared to previous methods. The code is available at
https://github.com/yeungchenwa/FontDiffuser.