BizGen: Advancing Article-level Visual Text Rendering for Infographics Generation (Glyph-ByT5-v3)
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Abstract
Recently, state-of-the-art text-to-image generation models, such as Flux and Ideogram 2.0, have made
significant progress in sentence-level visual text rendering. In this paper, we focus on the more
challenging scenarios of article-level visual text rendering and address a novel task of generating
high-quality business content, including infographics and slides, based on user provided article-level
descriptive prompts and ultra-dense layouts. The fundamental challenges are twofold: significantly
longer context lengths and the scarcity of high-quality business content data.
In contrast to most previous works that focus on a limited number of sub-regions and sentence-level
prompts, ensuring precise adherence to ultra-dense layouts with tens or even hundreds of sub-regions in
business content is far more challenging. We make two key technical contributions: (i) the construction
of scalable, high-quality business content dataset, i.e., Infographics-650K, equipped with
ultra-dense layouts and prompts by implementing a layer-wise retrieval-augmented infographic generation
scheme; and (ii) a layout-guided cross attention scheme, which injects tens of region-wise prompts into
a set of cropped region latent space according to the ultra-dense layouts, and refine each sub-regions
flexibly during inference using a layout conditional CFG.
We demonstrate the strong results of our system compared to previous SOTA systems such as Flux and SD3
on our BizEval prompt set. Additionally, we conduct thorough ablation experiments to verify the
effectiveness of each component. We hope our constructed Infographics-650K and BizEval can encourage
the broader community to advance the progress of business content generation.
Model Description
The ByT5 model is finetuned from Glyph-ByT5-v2, which supports accurate visual text rendering in ten different languages.
The SPO model is a substitute for the original sdxl-base-1.0 for aesthetic improvement. The lora/infographic and lora/slides are respectively tuned on our infographics and slides datasets.
You can follow our github to organize and run the model.
Citation
If you find our work or codebase useful, please consider giving us a star and citing our work.
@misc{peng2025bizgenadvancingarticlelevelvisual,
title={BizGen: Advancing Article-level Visual Text Rendering for Infographics Generation},
author={Yuyang Peng and Shishi Xiao and Keming Wu and Qisheng Liao and Bohan Chen and Kevin Lin and Danqing Huang and Ji Li and Yuhui Yuan},
year={2025},
eprint={2503.20672},
archivePrefix={arXiv},
primaryClass={cs.CV},
url={https://arxiv.org/abs/2503.20672},
}
@article{liu2024glyphv2,
title={Glyph-ByT5-v2: A Strong Aesthetic Baseline for Accurate Multilingual Visual Text Rendering},
author={Liu, Zeyu and Liang, Weicong and Zhao, Yiming and Chen, Bohan and Li, Ji and Yuan, Yuhui},
journal={arXiv preprint arXiv:2406.10208},
year={2024}
}
@article{liu2024glyph,
title={Glyph-byt5: A customized text encoder for accurate visual text rendering},
author={Liu, Zeyu and Liang, Weicong and Liang, Zhanhao and Luo, Chong and Li, Ji and Huang, Gao and Yuan, Yuhui},
journal={arXiv preprint arXiv:2403.09622},
year={2024}
}
