PAI-Diffusion: Constructing and Serving a Family of Open Chinese
  Diffusion Models for Text-to-image Synthesis on the Cloud
Chengyu Wang Zhongjie Duan Bingyan Liu Xinyi Zou Cen Chen Kui Jia Jun Huang
Text-to-image synthesis for the Chinese language poses unique challenges due
to its large vocabulary size, and intricate character relationships. While
existing diffusion models have shown promise in generating images from textual
descriptions, they often neglect domain-specific contexts and lack robustness
in handling the Chinese language. This paper introduces PAI-Diffusion, a
comprehensive framework that addresses these limitations. PAI-Diffusion
incorporates both general and domain-specific Chinese diffusion models,
enabling the generation of contextually relevant images. It explores the
potential of using LoRA and ControlNet for fine-grained image style transfer
and image editing, empowering users with enhanced control over image
generation. Moreover, PAI-Diffusion seamlessly integrates with Alibaba Cloud's
Machine Learning Platform for AI, providing accessible and scalable solutions.
All the Chinese diffusion model checkpoints, LoRAs, and ControlNets, including
domain-specific ones, are publicly available. A user-friendly Chinese WebUI and
the diffusers-api elastic inference toolkit, also open-sourced, further
facilitate the easy deployment of PAI-Diffusion models in various environments,
making it a valuable resource for Chinese text-to-image synthesis.