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arXiv:2204.14217cs.CV

CogView2: Faster and Better Text-to-Image Generation via Hierarchical Transformers

Ming Ding, Wendi Zheng, Wenyi Hong, Jie Tang

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Abstract

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.

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DOI
Subjects
cs.CVComputer Vision and Pattern Recognition cs.LGMachine Learning

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No comments linking this paper were found in the collected sources. Found in 2 HN submissions from May 2022 to Jun 2022. Open submission discussions.

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