  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.
