FontDiffuser: One-Shot Font Generation via Denoising Diffusion with Multi-Scale Content Aggregation and Style Contrastive Learning (2312.12142v1)

Abstract: 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.

Enhancing Font Generation with FontDiffuser: A Diffusion-Based One-Shot Approach

Introduction to FontDiffuser

The quest for advancing automatic font generation has led to the development of FontDiffuser, a novel approach leveraging a diffusion-based image-to-image one-shot font generation method. This method is distinct in addressing the challenges faced by previous techniques, particularly in generating complex characters and managing large style variations. By treating font generation as a noise-to-denoise process, FontDiffuser introduces a Multi-scale Content Aggregation (MCA) block and a Style Contrastive Refinement (SCR) module. These innovations collectively enable the generation of unseen characters and styles with remarkable accuracy and visual fidelity.

Key Contributions

FontDiffuser stands out with several significant contributions:

• The utilization of a diffusion model framework that skillfully addresses the font generation task, marking a shift away from the conventional GAN-based approaches that are often hindered by training stability issues.
• The introduction of the MCA block efficiently combines global and local content features, enhancing the capability to preserve intricate character details across different scales.
• The SCR module represents a sophisticated approach to style representation learning, ensuring the effective management of large style variations through a novel style contrastive loss.
• Experimental results comprehensively validate FontDiffuser's superior performance over existing methods across various complexity levels, showcasing its robustness and generalization capability, particularly on complex character generation and cross-lingual tasks.

Methodological Insights

FontDiffuser's methodology encapsulates its innovative strategies. The MCA block leverages multi-scale content features, ensuring that the fine-grained strokes of complex characters are preserved by capturing both detailed and global information. In parallel, the SCR module introduces a refined mechanism for style representation learning. By disentangling style features and employing a style contrastive loss, the diffusion model receives precise feedback, guiding it towards generating fonts that faithfully replicate the target style. Moreover, the Reference-Structure Interaction (RSI) block further underpins the model's ability to learn structural deformations, enhancing the overall font generation quality.

Evaluation and Impact

The extensive evaluation demonstrates FontDiffuser's state-of-the-art performance, particularly in generating characters with high complexity and under significant style variations. Notably, the method's generalization capacity is highlighted through its success in cross-lingual generation tasks, such as from Chinese to Korean, suggesting its potential applicability across diverse linguistic domains. This capability could significantly impact the fields of graphic design, digital humanities, and language preservation, offering a scalable solution to font generation challenges.

Looking Forward

While FontDiffuser marks a substantial advancement in automatic font generation, the exploration of its full potential and the extension to other domains represent exciting future directions. The method's ability to generalize across languages and styles opens avenues for research in multi-lingual text generation, personalized font creation, and the digital restoration of ancient scripts. Moreover, further efficiency improvements could expand its applicability in real-time applications, enhancing user experiences in graphic design software and digital content creation tools.

Conclusion

FontDiffuser emerges as a powerful tool in the automatic font generation arena, distinguishing itself through its innovative use of diffusion models and multi-scale content aggregation. By addressing the long-standing challenges in the field, this approach sets a new benchmark for future research and practical applications, paving the way for advances in digital typography and beyond.

The code availability at FontDiffuser GitHub further enriches the research community, facilitating ongoing innovation and collaboration in the pursuit of overcoming font generation challenges.