ARTIST: Improving the Generation of Text-rich Images with Disentangled Diffusion Models
<|reference_start|>ARTIST: Improving the Generation of Text-rich Images with Disentangled Diffusion Models: Diffusion models have demonstrated exceptional capabilities in generating a broad spectrum of visual content, yet their proficiency in rendering text is still limited: they often generate inaccurate characters or words that fail to blend well with the underlying image. To address these shortcomings, we introduce a new framework named ARTIST. This framework incorporates a dedicated textual diffusion model to specifically focus on the learning of text structures. Initially, we pretrain this textual model to capture the intricacies of text representation. Subsequently, we finetune a visual diffusion model, enabling it to assimilate textual structure information from the pretrained textual model. This disentangled architecture design and the training strategy significantly enhance the text rendering ability of the diffusion models for text-rich image generation. Additionally, we leverage the capabilities of pretrained large language models to better interpret user intentions, contributing to improved generation quality. Empirical results on the MARIO-Eval benchmark underscore the effectiveness of the proposed method, showing an improvement of up to 15\% in various metrics.<|reference_end|>
@article{zhang2024artist:,
    title={ARTIST: Improving the Generation of Text-rich Images with Disentangled
  Diffusion Models},
    author={Jianyi Zhang, Yufan Zhou, Jiuxiang Gu, Curtis Wigington, Tong Yu,
  Yiran Chen, Tong Sun, Ruiyi Zhang},
    journal={arXiv preprint arXiv:2406.12044},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.12044},
    primaryClass={cs.CV}
}