  Hierarchical VAEs have emerged in recent years as a reliable option for
maximum likelihood estimation. However, instability issues and demanding
computational requirements have hindered research progress in the area. We
present simple modifications to the Very Deep VAE to make it converge up to
$2.6\times$ faster, save up to $20\times$ in memory load and improve stability
during training. Despite these changes, our models achieve comparable or better
negative log-likelihood performance than current state-of-the-art models on all
$7$ commonly used image datasets we evaluated on. We also make an argument
against using 5-bit benchmarks as a way to measure hierarchical VAE's
performance due to undesirable biases caused by the 5-bit quantization.
Additionally, we empirically demonstrate that roughly $3\%$ of the hierarchical
VAE's latent space dimensions is sufficient to encode most of the image
information, without loss of performance, opening up the doors to efficiently
leverage the hierarchical VAEs' latent space in downstream tasks. We release
our source code and models at https://github.com/Rayhane-mamah/Efficient-VDVAE .
