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Automatic font generation is an imitation task, which aims to create a fontlibrary that mimics the style of reference images while preserving the contentfrom source images. Although existing font generation methods have achievedsatisfactory performance, they still struggle with complex characters and largestyle variations. To address these issues, we propose FontDiffuser, adiffusion-based image-to-image one-shot font generation method, whichinnovatively models the font imitation task as a noise-to-denoise paradigm. Inour method, we introduce a Multi-scale Content Aggregation (MCA) block, whicheffectively 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 aStyle Contrastive Refinement (SCR) module, which is a novel structure for stylerepresentation learning. It utilizes a style extractor to disentangle stylesfrom images, subsequently supervising the diffusion model via a meticulouslydesigned style contrastive loss. Extensive experiments demonstrateFontDiffuser's state-of-the-art performance in generating diverse charactersand styles. It consistently excels on complex characters and large stylechanges compared to previous methods. The code is available athttps://github.com/yeungchenwa/FontDiffuser.

FontDiffuser: One-Shot Font Generation via Denoising Diffusion with Multi-Scale Content Aggregation and Style Contrastive Learning