TextFlux: An OCR-Free DiT Model for High-Fidelity Multilingual Scene Text Synthesis

Understand

Diffusion-based scene text synthesis has progressed rapidly, yet existing methods commonly rely on additional visual conditioning modules and require large-scale annotated data to support multilingual generation.

• In this work, we revisit the necessity of complex auxiliary modules and further explore an approach that simultaneously ensures glyph accuracy and achieves high-fidelity scene integration, by leveraging diffusion models' inherent capabilities for contextual reasoning.
• To this end, we introduce TextFlux, a DiT-based framework that enables multilingual scene text synthesis.
• The advantages of TextFlux can be summarized as follows: (1) OCR-free model architecture.