NeurIPS 2022accept359 citations
CogView2: Faster and Better Text-to-Image Generation via Hierarchical Transformers
Ming Ding, Wendi Zheng, Wenyi Hong, Jie Tang
Abstract
Development of transformer-based text-to-image models is 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 autoregressive generation. We pretrain a 6B-parameter transformer with a simple and flexible self-supervised task, a cross-modal general language model (CogLM), and fine-tune 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.
text-to-image generationpretrainingtransformer
BibTeX
@inproceedings{
ding2022cogview,
title={CogView2: Faster and Better Text-to-Image Generation via Hierarchical Transformers},
author={Ming Ding and Wendi Zheng and Wenyi Hong and Jie Tang},
booktitle={Advances in Neural Information Processing Systems},
editor={Alice H. Oh and Alekh Agarwal and Danielle Belgrave and Kyunghyun Cho},
year={2022},
url={https://openreview.net/forum?id=GkDbQb6qu_r}
}