This repository provides the PyTorch implementation of the discrete Variational Autoencoder (VAE) component used in OpenAI's DALL-E model. It enables researchers and developers to leverage the VAE for generating image tokens, a crucial step in the DALL-E pipeline, facilitating experimentation with image generation architectures.

How It Works

The package implements a discrete VAE, a generative model that learns a compressed, discrete latent representation of images. This VAE is trained to reconstruct images from these discrete latent codes, effectively learning a "visual vocabulary" of image tokens. This approach allows for efficient and high-quality generation of image components that can then be sequenced by a transformer model.

Quick Start & Requirements

• Install via pip: pip install DALL-E
• Requires PyTorch.
• See [Usage] for examples.

Highlighted Details

• Official PyTorch package for the DALL-E VAE.
• Focuses on the discrete VAE component, not the transformer.

Maintenance & Community

• Maintained by OpenAI.
• No community links (Discord/Slack) or roadmap provided in the README.

Licensing & Compatibility

• License not specified in the README.
• Compatibility for commercial use or closed-source linking is undetermined.

Limitations & Caveats

This release only includes the VAE component; the transformer model for text-to-image generation is not provided. The README does not specify the license, which may impact commercial use.