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.

提出了FontDiffuser，一种基于扩散的图像生成方法，通过将字体模仿任务创新地建模为一个噪声去噪范式，有效地组合了全局和局部内容线索以提高复杂字符的精细笔画保留，并通过引入一个精心设计的风格对比损失利用风格提取器解耦图像中的风格，以更好地处理风格转移中的大规模变化。与之前的方法相比，FontDiffuser在生成多样字符和风格方面表现出卓越的性能，特别是在处理复杂字符和大的风格变化时。

FontDiffuser：基于多尺度内容聚合和风格对比学习的一次性字体生成