The development of the transformer-based text-to-image models are impeded by its slow generation and complexity for high-resolution images. In this work, we put forward a solution based on hierarchical transformers and local parallel auto-regressive generation. We pretrain a 6B-parameter transformer with a simple and flexible self-supervised task, Cross-modal general language model (CogLM), and finetune it for fast super-resolution. The new text-to-image system, CogView2, shows very competitive generation compared to concurrent state-of-the-art DALL-E-2, and naturally supports interactive text-guided editing on images.
1@article{ding2022cogview2,
2 title={CogView2: Faster and Better Text-to-Image Generation via Hierarchical Transformers},
3 author={Ding, Ming and Zheng, Wendi and Hong, Wenyi and Tang, Jie},
4 journal={arXiv preprint arXiv:2204.14217},
5 year={2022}
6}
