Glyph-ByT5-v2: A Strong Aesthetic Baseline for Accurate Multilingual
  Visual Text Rendering
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.
666fc0d80224abdc1997630f
Zeyu Liu
claimed_verified
2025-03-25T14:36:37.220Z
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Zeyu Liu
user
shuidi0020
666fc0d80224abdc19976310
Weicong Liang
admin_assigned
2024-06-17T14:15:23.402Z
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Weicong Liang
user
kxqt
666fc0d80224abdc19976311
Yiming Zhao
claimed_verified
2025-02-27T09:18:43.711Z
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https://cdn-avatars.huggingface.co/v1/production/uploads/637a2be47ce76c3b8347aae2/rQdt1j35MA2OXJnkGQfHJ.jpeg
Yiming Zhao
user
ZYMPKU
666fc0d80224abdc19976312
Bohan Chen
admin_assigned
2024-06-17T14:15:33.769Z
64ba249e5c4deebf69aa17fd
https://cdn-avatars.huggingface.co/v1/production/uploads/noauth/h7YdQe9wEr1TTJfP1ALhb.jpeg
chen
user
bohanChen
666fc0d80224abdc19976313
Ji Li
666fc0d80224abdc19976314
Yuhui Yuan
claimed_verified
2025-03-28T08:39:15.236Z
631f108bb45367a05fe74260
/avatars/c20da6800b643728062712d9a2771648.svg
Researcher
user
YuanYuhui
Glyph-ByT5-v2: A Strong Aesthetic Baseline for Accurate Multilingual
  Visual Text Rendering
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.
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.
multilingual glyph-text graphic design dataset visual paragraph benchmark step-aware preference learning text encoder aesthetic graphic generation model