分治策略提升长文本图像生成准确率
DCText: Scheduled Attention Masking for Visual Text Generation via Divide-and-Conquer Strategy
- 将长文本拆解为短段，在指定区域分别生成
- 使用双注意力掩码，文本准确率显著提升
- 无需训练，适合需要快速生成的场景
尽管近期文本到图像模型在高保真文本渲染方面取得进展，但仍面临长文本或多文本生成时全局注意力稀释的问题。我们提出 DCText，一种无需训练的视觉文本生成方法，采用分治策略，利用多模态扩散变换器可靠的短文本生成能力。该方法首先通过提取并划分提示中的目标文本，再将其分配至指定区域。为在保持整体图像连贯性的同时精准渲染各片段，我们在去噪过程中依次引入两种注意力掩码——文本聚焦与上下文扩展。此外，局部噪声初始化进一步提升文本准确性与区域对齐度，且不增加计算成本。在单句和多句基准测试上的大量实验表明，DCText 在不牺牲图像质量的前提下实现了最佳文本准确率，并达到最低生成延迟。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。