FontAdapter: Instant Font Adaptation in Visual Text Generation
Myungkyu Koo, Subin Kim, Sangkyung Kwak, Jaehyun Nam, Seojin Kim, Jinwoo Shin

TL;DR
FontAdapter is a rapid, reference-based font adaptation framework for visual text generation that achieves high-quality, real-time font customization and editing without fine-tuning.
Contribution
It introduces a two-stage curriculum learning approach and synthetic datasets to enable instant adaptation to unseen fonts conditioned on glyph images.
Findings
Enables font customization in seconds without fine-tuning
Supports visual text editing and font style blending
Achieves high-quality, robust font transfer across languages
Abstract
Text-to-image diffusion models have significantly improved the seamless integration of visual text into diverse image contexts. Recent approaches further improve control over font styles through fine-tuning with predefined font dictionaries. However, adapting unseen fonts outside the preset is computationally expensive, often requiring tens of minutes, making real-time customization impractical. In this paper, we present FontAdapter, a framework that enables visual text generation in unseen fonts within seconds, conditioned on a reference glyph image. To this end, we find that direct training on font datasets fails to capture nuanced font attributes, limiting generalization to new glyphs. To overcome this, we propose a two-stage curriculum learning approach: FontAdapter first learns to extract font attributes from isolated glyphs and then integrates these styles into diverse natural…
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Taxonomy
TopicsGenerative Adversarial Networks and Image Synthesis · Computer Graphics and Visualization Techniques · Digital Humanities and Scholarship
