Autoregressive Text-to-Image Generation

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Autoregressive Text-to-Image Generation

Zero-Shot Text-to-Image Generation

Text-to-image generation has traditionally focused on finding better modeling assumptions for training on a fixed dataset.

These assumptions might involve complex architectures, auxiliary losses, or side information such as object part labels or segmentation masks supplied during training.

We describe a simple approach for this task based on a transformer that autoregressively models the text and image tokens as a single stream of data.

With sufficient data and scale, our approach is competitive with previous domain-specificmodels when evaluated in a zero-shot fashion.

Authors

Aditya Ramesh, Mikhail Pavlov, Gabriel Goh, Scott Gray, Chelsea Voss, Alec Radford, Mark Chen, Ilya Sutskever

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