  We present fast, realistic image generation on high-resolution, multimodal
datasets using hierarchical variational autoencoders (VAEs) trained on a
deterministic autoencoder's latent space. In this two-stage setup, the
autoencoder compresses the image into its semantic features, which are then
modeled with a deep VAE. With this method, the VAE avoids modeling the
fine-grained details that constitute the majority of the image's code length,
allowing it to focus on learning its structural components. We demonstrate the
effectiveness of our two-stage approach, achieving a FID of 9.34 on the
ImageNet-256 dataset which is comparable to BigGAN. We make our implementation
available online.
