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Model Description

This is a Flax/JAX implementation of VQGAN, which learns a codebook of context-rich visual parts by leveraging both the use of convolutional methods and transformers. It was introduced in Taming Transformers for High-Resolution Image Synthesis (CVPR paper).

The model allows the encoding of images as a fixed-length sequence of tokens taken from the codebook.

This version of the model uses a reduction factor f=16 and a vocabulary of 16,384 tokens.

As an example of how the reduction factor works, images of size 256x256 are encoded to sequences of 256 tokens: 256/16 * 256/16. Images of 512x512 would result in sequences of 1024 tokens.

This model was ported to JAX using a checkpoint trained on ImageNet.

How to Use

The checkpoint can be loaded using Suraj Patil's implementation of VQModel.


This model can be used as part of the implementation of DALL·E mini. Our report contains more details on how to leverage it in an image encoding / generation pipeline.

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