Abstract
Recent advancements in autoregressive and diffusion models have led to strong performance in image generation with short scene text words. However, generating coherent, long-form text in images, such as paragraphs in slides or documents, remains a major challenge for current generative models. We present the first work specifically focused on long text image generation, addressing a critical gap in existing text-to-image systems that typically handle only brief phrases or single sentences. Through comprehensive analysis of state-of-the-art autoregressive generation models, we identify the image tokenizer as a critical bottleneck in text generating quality. To address this, we introduce a novel text-focused, binary tokenizer optimized for capturing detailed scene text features. Leveraging our tokenizer, we develop \ModelName, a multimodal autoregressive model that excels in generating high-quality long-text images with unprecedented fidelity. Our model offers robust controllability, enabling customization of text properties such as font style, size, color, and alignment. Extensive experiments demonstrate that \ModelName~significantly outperforms SD3.5 Large~\cite{sd3} and GPT4o~\cite{gpt4o} with DALL-E 3~\cite{dalle3} in generating long text accurately, consistently, and flexibly. Beyond its technical achievements, \ModelName~opens up exciting opportunities for innovative applications like interleaved document and PowerPoint generation, establishing a new frontier in long-text image generating.
Abstract (translated)
最近在自回归和扩散模型方面取得的进展已经使得使用简短场景文本词生成图像时表现出色。然而，当前的生成模型仍然难以克服一个重大挑战：即在图像中生成连贯且长篇幅的文字，例如幻灯片或文档中的段落。我们首次提出了专注于长文本图像生成的工作，填补了现有文字到图像系统的一个关键空白，这些系统通常只能处理简短语句或单个句子。 通过对最先进的自回归生成模型进行全面分析，我们发现图像标记器是影响文本生成质量的关键瓶颈。为了解决这一问题，我们引入了一种新颖的以文本为中心、二进制化的标记器，该标记器专为捕捉详细的场景文字特征而优化。利用我们的标记器，我们开发了\ModelName（假设这里的\ModelName是指一个具体的模型名称），这是一种多模态自回归模型，在生成高质量且细节丰富的长篇幅文字图像方面表现卓越，并以前所未有的精确度实现了这一目标。 该模型提供强大的可控制性，能够根据需求定制文本属性，如字体样式、大小、颜色和对齐方式。广泛的实验表明，\ModelName在准确、一致和灵活地生成长文本方面显著优于SD3.5 Large~\cite{sd3}、GPT4o~\cite{gpt4o}与DALL-E 3~\cite{dalle3}的组合。 除了技术成就之外，\ModelName还为创新应用开辟了令人兴奋的机会，例如交错生成文档和PowerPoint演示文稿，从而在长篇幅文字图像生成领域开创了一个新的前沿。
URL
https://arxiv.org/abs/2503.20198
