RAPHAEL: Text-to-Image Generation via Large Mixture of Diffusion Paths
Authors: name: Zeyue Xue, name: Guanglu Song, name: Qiushan Guo, name: Boxiao Liu, name: Zhuofan Zong, name: Yu Liu, name: Ping Luo

Text-to-image generation has recently witnessed remarkable achievements. We
introduce a text-conditional image diffusion model, termed RAPHAEL, to generate
highly artistic images, which accurately portray the text prompts, encompassing
multiple nouns, adjectives, and verbs. This is achieved by stacking tens of
mixture-of-experts (MoEs) layers, i.e., space-MoE and time-MoE layers, enabling
billions of diffusion paths (routes) from the network input to the output. Each
path intuitively functions as a "painter" for depicting a particular textual
concept onto a specified image region at a diffusion timestep. Comprehensive
experiments reveal that RAPHAEL outperforms recent cutting-edge models, such as
Stable Diffusion, ERNIE-ViLG 2.0, DeepFloyd, and DALL-E 2, in terms of both
image quality and aesthetic appeal. Firstly, RAPHAEL exhibits superior
performance in switching images across diverse styles, such as Japanese comics,
realism, cyberpunk, and ink illustration. Secondly, a single model with three
billion parameters, trained on 1,000 A100 GPUs for two months, achieves a
state-of-the-art zero-shot FID score of 6.61 on the COCO dataset. Furthermore,
RAPHAEL significantly surpasses its counterparts in human evaluation on the
ViLG-300 benchmark. We believe that RAPHAEL holds the potential to propel the
frontiers of image generation research in both academia and industry, paving
the way for future breakthroughs in this rapidly evolving field. More details
can be found on a project webpage: https://raphael-painter.github.io/.