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

Despite recent text-to-image models achieving high-fidelity text rendering, they still struggle with long or multiple texts due to diluted global attention. We propose DC-Text, 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 multi-sentence benchmarks show that DCText achieves the best text accuracy without compromising image quality while also delivering the lowest generation latency.