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.
Abstract (translated)
尽管最近的文本到图像模型在高保真度的文字渲染方面取得了进展，但它们仍然难以处理长文本或多段文字，因为全局注意力被稀释了。为此，我们提出了DCText，这是一种无需训练的视觉文本生成方法，采用“分而治之”的策略，利用多模态扩散变换器可靠的短文本生成能力。 我们的方法首先通过提取和分割目标文本将提示进行分解，然后为每个部分分配一个指定区域。为了在保持整体图像连贯性的同时准确渲染每个部分内的段落，我们引入了两个注意力掩码——Text-Focus（文本焦点）和Context-Expansion（上下文扩展），并按顺序应用于去噪过程中。此外，局部噪声初始化进一步提高了文字的准确性以及各部分与区域的一致性，同时不增加计算成本。 在单句和多句子基准测试中进行的广泛实验表明，DCText实现了最高的文本准确性，在不影响图像质量的前提下还提供了最低的生成延迟。
URL
https://arxiv.org/abs/2512.01302
