CogView: Mastering Text-to-Image Generation via Transformers

Ming Ding, Zhuoyi Yang, Wenyi Hong, Wendi Zheng, Chang Zhou, Da Yin, Junyang Lin, Xu Zou, Zhou Shao, Hongxia Yang, Jie Tang

Keywords: Transformer, pretraining, generative model, cross-modality

Abstract: Text-to-Image generation in the general domain has long been an open problem, which requires both a powerful generative model and cross-modal understanding. We propose CogView, a 4-billion-parameter Transformer with VQ-VAE tokenizer to advance this problem. We also demonstrate the finetuning strategies for various downstream tasks, e.g. style learning, super-resolution, text-image ranking and fashion design, and methods to stabilize pretraining, e.g. eliminating NaN losses. CogView achieves the state-of-the-art FID on the blurred MS COCO dataset, outperforming previous GAN-based models and a recent similar work DALL-E.

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TL;DR: CogView: Mastering Text-to-Image Generation via Transformers

Code: https://github.com/THUDM/CogView

Community Implementations: [ 3 code implementations](https://www.catalyzex.com/paper/arxiv:2105.13290/code)