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arXiv · 2512.01302

DCText: Scheduled Attention Masking for Visual Text Generation via Divide-and-Conquer Strategy

Jaewoo Song·Jooyoung Choi·Kanghyun Baek·Sangyub Lee·Daemin Park·Sungroh Yoon
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Abstract

Despite recent text-to-image models achieving highfidelity text rendering, they still struggle with long or multiple texts due to diluted global attention. We propose DCText, a training-free visual text generation method that adopts a divide-and-conquer strategy, leveraging the reliable short-text generation of Multi-Modal Diffusion Transformers. Our method first decomposes a prompt by extracting and dividing the target text, then assigns each to a designated region. To accurately render each segment within their regions while preserving overall image coherence, we introduce two attention masks - Text-Focus and Context-Expansion - applied sequentially during denoising. Additionally, Localized Noise Initialization further improves text accuracy and region alignment without increasing computational cost. Extensive experiments on single- and multisentence benchmarks show that DCText achieves the best text accuracy without compromising image quality while also delivering the lowest generation latency.

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Jaewoo Song, Jooyoung Choi, Kanghyun Baek, Sangyub Lee, Daemin Park, Sungroh Yoon. 2025-12-01. DCText: Scheduled Attention Masking for Visual Text Generation via Divide-and-Conquer Strategy. https://arxiv.org/abs/2512.01302

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