  We explore the use of Vector Quantized Variational AutoEncoder (VQ-VAE)
models for large scale image generation. To this end, we scale and enhance the
autoregressive priors used in VQ-VAE to generate synthetic samples of much
higher coherence and fidelity than possible before. We use simple feed-forward
encoder and decoder networks, making our model an attractive candidate for
applications where the encoding and/or decoding speed is critical.
Additionally, VQ-VAE requires sampling an autoregressive model only in the
compressed latent space, which is an order of magnitude faster than sampling in
the pixel space, especially for large images. We demonstrate that a multi-scale
hierarchical organization of VQ-VAE, augmented with powerful priors over the
latent codes, is able to generate samples with quality that rivals that of
state of the art Generative Adversarial Networks on multifaceted datasets such
as ImageNet, while not suffering from GAN's known shortcomings such as mode
collapse and lack of diversity.
