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Glyph-ByT5: A Customized Text Encoder for Accurate Visual Text Rendering (2403.09622v2)

Published 14 Mar 2024 in cs.CV

Abstract: Visual text rendering poses a fundamental challenge for contemporary text-to-image generation models, with the core problem lying in text encoder deficiencies. To achieve accurate text rendering, we identify two crucial requirements for text encoders: character awareness and alignment with glyphs. Our solution involves crafting a series of customized text encoder, Glyph-ByT5, by fine-tuning the character-aware ByT5 encoder using a meticulously curated paired glyph-text dataset. We present an effective method for integrating Glyph-ByT5 with SDXL, resulting in the creation of the Glyph-SDXL model for design image generation. This significantly enhances text rendering accuracy, improving it from less than $20\%$ to nearly $90\%$ on our design image benchmark. Noteworthy is Glyph-SDXL's newfound ability for text paragraph rendering, achieving high spelling accuracy for tens to hundreds of characters with automated multi-line layouts. Finally, through fine-tuning Glyph-SDXL with a small set of high-quality, photorealistic images featuring visual text, we showcase a substantial improvement in scene text rendering capabilities in open-domain real images. These compelling outcomes aim to encourage further exploration in designing customized text encoders for diverse and challenging tasks.

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Authors (7)
  1. Zeyu Liu (54 papers)
  2. Weicong Liang (6 papers)
  3. Zhanhao Liang (4 papers)
  4. Chong Luo (58 papers)
  5. Ji Li (186 papers)
  6. Gao Huang (178 papers)
  7. Yuhui Yuan (42 papers)
Citations (10)
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Summary

Glyph-ByT5: Advancing Visual Text Rendering with Customized Text Encoders

Introduction to Glyph-ByT5

Accurate visual text rendering remains a significant challenge for contemporary text-to-image generation models, despite their impressive ability to generate high-quality images. At the crux of this challenge is the deficiency of text encoders in handling the complexity of visual text accurately. Our recent work introduces a novel approach to address this issue by developing a customized text encoder, Glyph-ByT5, specifically designed for precise visual text rendering.

Customized Glyph-Aligned Character-Aware Text Encoder

The development of Glyph-ByT5 centers on the character-aware ByT5 encoder, fine-tuned with a meticulously curated paired glyph-text dataset. This customization aligns the text encoder not only with the character-level details but also with visual text signals, or glyphs, leading to significantly enhanced text rendering accuracy. Our approach leverages a scalable pipeline to generate a high-volume, high-quality glyph-text dataset, enabling effective fine-tuning of the ByT5 encoder. Furthermore, we introduce a novel glyph augmentation strategy to improve the character awareness of the text encoder, addressing a variety of common errors in visual text rendering.

Integration with SDXL: The Creation of Glyph-SDXL

Our paper does not stop at the development of a customized text encoder. We seamlessly integrate Glyph-ByT5 with the SDXL model through an efficient region-wise cross-attention mechanism, giving birth to a powerful design image generator, Glyph-SDXL. This model demonstrates remarkable spelling accuracy in text-rich design images, outperforming other state-of-the-art models significantly. Notably, Glyph-SDXL possesses the novel ability for text paragraph rendering, achieving high spelling accuracy for content ranging from tens to hundreds of characters.

Fine-Tuning for Scene Text Rendering

To extend the capabilities of Glyph-SDXL to scene text rendering, we fine-tuned it using a selection of high-quality, photorealistic images featuring visual text. The fine-tuning process relies on a small yet impactful dataset, resulting in substantial improvements in scene text rendering. This refinement allows Glyph-SDXL to render scene text accurately within open-domain real images, highlighting the model's flexibility and broad applicability.

Research Implications and Future Directions

Our work underscores the significance of customizing text encoders for specialized tasks, such as accurate visual text rendering. By training Glyph-ByT5 and integrating it with SDXL, we demonstrate the potential of customized text encoders in overcoming fundamental challenges in image generation models. Looking forward, we envisage further research into designing specialized text encoders and exploring innovative information injection mechanisms to enhance performance across a wider range of tasks.

Conclusion

In summary, the development and integration of Glyph-ByT5 represent a significant stride towards achieving precise visual text rendering in both design and scene images. This advancement not only addresses a longstanding challenge in the field but also opens up new avenues for research and application. As we continue to explore the potentials of customized text encoders, we anticipate uncovering more opportunities to push the boundaries of what's possible in generative AI and visual text rendering.

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