Beyond Words: Advancing Long-Text Image Generation via Multimodal
  Autoregressive Models
Authors: name: Alex Jinpeng Wang, name: Linjie Li, name: Zhengyuan Yang, name: Lijuan Wang, name: Min Li

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~sd3 and GPT4o~gpt4o with DALL-E
3~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.