@inproceedings{li-etal-2024-empowering-backbone,
title = "Empowering Backbone Models for Visual Text Generation with Input Granularity Control and Glyph-Aware Training",
author = "Li, Wenbo and
Li, Guohao and
Lan, Zhibin and
Xu, Xue and
Zhuang, Wanru and
Liu, Jiachen and
Xiao, Xinyan and
Su, Jinsong",
editor = "Al-Onaizan, Yaser and
Bansal, Mohit and
Chen, Yun-Nung",
booktitle = "Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing",
month = nov,
year = "2024",
address = "Miami, Florida, USA",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.emnlp-main.455/",
doi = "10.18653/v1/2024.emnlp-main.455",
pages = "8001--8014",
abstract = "Diffusion-based text-to-image models have demonstrated impressive achievements in diversity and aesthetics but struggle to generate images with legible visual texts. Existing backbone models have limitations such as misspelling, failing to generate texts, and lack of support for Chinese texts, but their development shows promising potential. In this paper, we propose a series of methods, aiming to empower backbone models to generate visual texts in English and Chinese. We first conduct a preliminary study revealing that BPE tokenization and insufficient learning of cross-attention modules restrict the performance of the backbone models. Based on these observations, we make the following improvements: (1) We design a mixed granularity input strategy to provide more suitable text representations; (2) We propose to augment the conventional training objective with three glyph-aware training losses, which enhance the learning of cross-attention modules and encourage the model to focus on visual texts. Through experiments, we demonstrate that our methods can effectively empower backbone models to generate semantic relevant, aesthetically appealing, and accurate visual text images, while maintaining their fundamental image generation quality."
}
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<abstract>Diffusion-based text-to-image models have demonstrated impressive achievements in diversity and aesthetics but struggle to generate images with legible visual texts. Existing backbone models have limitations such as misspelling, failing to generate texts, and lack of support for Chinese texts, but their development shows promising potential. In this paper, we propose a series of methods, aiming to empower backbone models to generate visual texts in English and Chinese. We first conduct a preliminary study revealing that BPE tokenization and insufficient learning of cross-attention modules restrict the performance of the backbone models. Based on these observations, we make the following improvements: (1) We design a mixed granularity input strategy to provide more suitable text representations; (2) We propose to augment the conventional training objective with three glyph-aware training losses, which enhance the learning of cross-attention modules and encourage the model to focus on visual texts. Through experiments, we demonstrate that our methods can effectively empower backbone models to generate semantic relevant, aesthetically appealing, and accurate visual text images, while maintaining their fundamental image generation quality.</abstract>
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%0 Conference Proceedings
%T Empowering Backbone Models for Visual Text Generation with Input Granularity Control and Glyph-Aware Training
%A Li, Wenbo
%A Li, Guohao
%A Lan, Zhibin
%A Xu, Xue
%A Zhuang, Wanru
%A Liu, Jiachen
%A Xiao, Xinyan
%A Su, Jinsong
%Y Al-Onaizan, Yaser
%Y Bansal, Mohit
%Y Chen, Yun-Nung
%S Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing
%D 2024
%8 November
%I Association for Computational Linguistics
%C Miami, Florida, USA
%F li-etal-2024-empowering-backbone
%X Diffusion-based text-to-image models have demonstrated impressive achievements in diversity and aesthetics but struggle to generate images with legible visual texts. Existing backbone models have limitations such as misspelling, failing to generate texts, and lack of support for Chinese texts, but their development shows promising potential. In this paper, we propose a series of methods, aiming to empower backbone models to generate visual texts in English and Chinese. We first conduct a preliminary study revealing that BPE tokenization and insufficient learning of cross-attention modules restrict the performance of the backbone models. Based on these observations, we make the following improvements: (1) We design a mixed granularity input strategy to provide more suitable text representations; (2) We propose to augment the conventional training objective with three glyph-aware training losses, which enhance the learning of cross-attention modules and encourage the model to focus on visual texts. Through experiments, we demonstrate that our methods can effectively empower backbone models to generate semantic relevant, aesthetically appealing, and accurate visual text images, while maintaining their fundamental image generation quality.
%R 10.18653/v1/2024.emnlp-main.455
%U https://aclanthology.org/2024.emnlp-main.455/
%U https://doi.org/10.18653/v1/2024.emnlp-main.455
%P 8001-8014
Markdown (Informal)
[Empowering Backbone Models for Visual Text Generation with Input Granularity Control and Glyph-Aware Training](https://aclanthology.org/2024.emnlp-main.455/) (Li et al., EMNLP 2024)
ACL
- Wenbo Li, Guohao Li, Zhibin Lan, Xue Xu, Wanru Zhuang, Jiachen Liu, Xinyan Xiao, and Jinsong Su. 2024. Empowering Backbone Models for Visual Text Generation with Input Granularity Control and Glyph-Aware Training. In Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing, pages 8001–8014, Miami, Florida, USA. Association for Computational Linguistics.