@inproceedings{liu-etal-2023-rapid,
    title = "Rapid Diffusion: Building Domain-Specific Text-to-Image Synthesizers with Fast Inference Speed",
    author = "Liu, Bingyan  and
      Lin, Weifeng  and
      Duan, Zhongjie  and
      Wang, Chengyu  and
      Ziheng, Wu  and
      Zipeng, Zhang  and
      Jia, Kui  and
      Jin, Lianwen  and
      Chen, Cen  and
      Huang, Jun",
    editor = "Sitaram, Sunayana  and
      Beigman Klebanov, Beata  and
      Williams, Jason D",
    booktitle = "Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 5: Industry Track)",
    month = jul,
    year = "2023",
    address = "Toronto, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2023.acl-industry.28",
    doi = "10.18653/v1/2023.acl-industry.28",
    pages = "295--304",
    abstract = "Text-to-Image Synthesis (TIS) aims to generate images based on textual inputs. Recently, several large pre-trained diffusion models have been released to create high-quality images with pre-trained text encoders and diffusion-based image synthesizers. However, popular diffusion-based models from the open-source community cannot support industrial domain-specific applications due to the lack of entity knowledge and low inference speed. In this paper, we propose Rapid Diffusion, a novel framework for training and deploying super-resolution, text-to-image latent diffusion models with rich entity knowledge injected and optimized networks. Furthermore, we employ BladeDISC, an end-to-end Artificial Intelligence (AI) compiler, and FlashAttention techniques to optimize computational graphs of the generated models for online deployment. Experiments verify the effectiveness of our approach in terms of image quality and inference speed. In addition, we present industrial use cases and integrate Rapid Diffusion to an AI platform to show its practical values.",
}

Text-to-Image Synthesis (TIS) aims to generate images based on textual inputs. Recently, several large pre-trained diffusion models have been released to create high-quality images with pre-trained text encoders and diffusion-based image synthesizers. However, popular diffusion-based models from the open-source community cannot support industrial domain-specific applications due to the lack of entity knowledge and low inference speed. In this paper, we propose Rapid Diffusion, a novel framework for training and deploying super-resolution, text-to-image latent diffusion models with rich entity knowledge injected and optimized networks. Furthermore, we employ BladeDISC, an end-to-end Artificial Intelligence (AI) compiler, and FlashAttention techniques to optimize computational graphs of the generated models for online deployment. Experiments verify the effectiveness of our approach in terms of image quality and inference speed. In addition, we present industrial use cases and integrate Rapid Diffusion to an AI platform to show its practical values.
Liu, Bingyan Lin, Weifeng Duan, Zhongjie Wang, Chengyu Ziheng, Wu Zipeng, Zhang Jia, Kui Jin, Lianwen Chen, Cen Huang, Jun
Rapid Diffusion: Building Domain-Specific Text-to-Image Synthesizers with Fast Inference Speed