Keywords: text-to-image generation, pretraining, transformer
TL;DR: Faster and Better Text-to-Image Generation via Hierarchical Transformers
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.
Supplementary Material: pdf
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