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arXiv · 2105.13290

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
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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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Ming Ding, Zhuoyi Yang, Wenyi Hong, Wendi Zheng, Chang Zhou, Da Yin, Junyang Lin, Xu Zou, Zhou Shao, Hongxia Yang, Jie Tang. 2021-05-26. CogView: Mastering Text-to-Image Generation via Transformers. https://arxiv.org/abs/2105.13290

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