title: Beyond Words: Advancing Long-Text Image Generation via Multimodal
    Autoregressive Models
summary: 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.
paper_authors._id: 67e4b98039509b0149142daa
paper_authors.name: Alex Jinpeng Wang
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-03-27T10:16:22.358Z
paper_authors._id: 62333a88fd7bb4a39b92d387
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paper_authors.fullname: Alex Jinpeng Wang
paper_authors.type: user
paper_authors.user: Awiny
paper_authors._id: 67e4b98039509b0149142dab
paper_authors.name: Linjie Li
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-03-27T10:16:38.209Z
paper_authors._id: 63db16fff03c3d71ef397206
paper_authors.avatarUrl: /avatars/bfb7e0d730b7d03302799d5d2828d97d.svg
paper_authors.fullname: Linjie Li
paper_authors.type: user
paper_authors.user: linjieli222
paper_authors._id: 67e4b98039509b0149142dac
paper_authors.name: Zhengyuan Yang
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-03-27T10:16:44.117Z
paper_authors._id: 630713411801ecc7d2592a7c
paper_authors.avatarUrl: /avatars/fb36f69f03421c3a2a7f72ba0858fa60.svg
paper_authors.fullname: Zhengyuan Yang
paper_authors.type: user
paper_authors.user: zyang39
paper_authors._id: 67e4b98039509b0149142dad
paper_authors.name: Lijuan Wang
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-03-27T10:16:50.812Z
paper_authors._id: 6413521d4e5305c14f22e110
paper_authors.avatarUrl: /avatars/a6f8d0573e678f79bc3c0b7897b818ce.svg
paper_authors.fullname: Lijuan Wang
paper_authors.type: user
paper_authors.user: Lijuan
paper_authors._id: 67e4b98039509b0149142dae
paper_authors.name: Min Li
paper_title: Beyond Words: Advancing Long-Text Image Generation via Multimodal
    Autoregressive Models
paper_summary: 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.
paper_ai_summary: A multimodal autoregressive model with a novel binary tokenizer outperforms existing systems in generating high-quality long-text images with robust controllability.
paper_ai_keywords: autoregressive models diffusion models image tokenizer multimodal autoregressive model text-focused tokenizer high-quality long-text images font style font size color alignment DALL-E 3 SD3.5 Large GPT4o