title: Glyph-ByT5-v2: A Strong Aesthetic Baseline for Accurate Multilingual
    Visual Text Rendering
summary: Recently, Glyph-ByT5 has achieved highly accurate visual text rendering
  performance in graphic design images. However, it still focuses solely on
  English and performs relatively poorly in terms of visual appeal. In this work,
  we address these two fundamental limitations by presenting Glyph-ByT5-v2 and
  Glyph-SDXL-v2, which not only support accurate visual text rendering for 10
  different languages but also achieve much better aesthetic quality. To achieve
  this, we make the following contributions: (i) creating a high-quality
  multilingual glyph-text and graphic design dataset consisting of more than 1
  million glyph-text pairs and 10 million graphic design image-text pairs
  covering nine other languages, (ii) building a multilingual visual paragraph
  benchmark consisting of 1,000 prompts, with 100 for each language, to assess
  multilingual visual spelling accuracy, and (iii) leveraging the latest
  step-aware preference learning approach to enhance the visual aesthetic
  quality. With the combination of these techniques, we deliver a powerful
  customized multilingual text encoder, Glyph-ByT5-v2, and a strong aesthetic
  graphic generation model, Glyph-SDXL-v2, that can support accurate spelling in
  10 different languages. We perceive our work as a significant advancement,
  considering that the latest DALL-E3 and Ideogram 1.0 still struggle with the
  multilingual visual text rendering task.
paper_authors._id: 666fc0d80224abdc1997630f
paper_authors.name: Zeyu Liu
paper_authors.status: claimed_verified
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paper_authors.fullname: Zeyu Liu
paper_authors.type: user
paper_authors.user: shuidi0020
paper_authors._id: 666fc0d80224abdc19976310
paper_authors.name: Weicong Liang
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paper_authors.fullname: Weicong Liang
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paper_authors.user: kxqt
paper_authors._id: 666fc0d80224abdc19976311
paper_authors.name: Yiming Zhao
paper_authors.status: claimed_verified
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paper_authors.fullname: Yiming Zhao
paper_authors.type: user
paper_authors.user: ZYMPKU
paper_authors._id: 666fc0d80224abdc19976312
paper_authors.name: Bohan Chen
paper_authors.status: admin_assigned
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paper_authors.fullname: chen
paper_authors.type: user
paper_authors.user: bohanChen
paper_authors._id: 666fc0d80224abdc19976313
paper_authors.name: Ji Li
paper_authors._id: 666fc0d80224abdc19976314
paper_authors.name: Yuhui Yuan
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-03-28T08:39:15.236Z
paper_authors._id: 631f108bb45367a05fe74260
paper_authors.avatarUrl: /avatars/c20da6800b643728062712d9a2771648.svg
paper_authors.fullname: Researcher
paper_authors.type: user
paper_authors.user: YuanYuhui
paper_title: Glyph-ByT5-v2: A Strong Aesthetic Baseline for Accurate Multilingual
    Visual Text Rendering
paper_summary: Recently, Glyph-ByT5 has achieved highly accurate visual text rendering
  performance in graphic design images. However, it still focuses solely on
  English and performs relatively poorly in terms of visual appeal. In this work,
  we address these two fundamental limitations by presenting Glyph-ByT5-v2 and
  Glyph-SDXL-v2, which not only support accurate visual text rendering for 10
  different languages but also achieve much better aesthetic quality. To achieve
  this, we make the following contributions: (i) creating a high-quality
  multilingual glyph-text and graphic design dataset consisting of more than 1
  million glyph-text pairs and 10 million graphic design image-text pairs
  covering nine other languages, (ii) building a multilingual visual paragraph
  benchmark consisting of 1,000 prompts, with 100 for each language, to assess
  multilingual visual spelling accuracy, and (iii) leveraging the latest
  step-aware preference learning approach to enhance the visual aesthetic
  quality. With the combination of these techniques, we deliver a powerful
  customized multilingual text encoder, Glyph-ByT5-v2, and a strong aesthetic
  graphic generation model, Glyph-SDXL-v2, that can support accurate spelling in
  10 different languages. We perceive our work as a significant advancement,
  considering that the latest DALL-E3 and Ideogram 1.0 still struggle with the
  multilingual visual text rendering task.
paper_ai_summary: Glyph-ByT5-v2 and Glyph-SDXL-v2 enhance multilingual visual text rendering accuracy and aesthetic quality using a large multilingual dataset and an advanced preference learning approach.
paper_ai_keywords: multilingual glyph-text graphic design dataset visual paragraph benchmark step-aware preference learning text encoder aesthetic graphic generation model