  We make the following striking observation: fully convolutional VAE models
trained on 32x32 ImageNet can generalize well, not just to 64x64 but also to
far larger photographs, with no changes to the model. We use this property,
applying fully convolutional models to lossless compression, demonstrating a
method to scale the VAE-based 'Bits-Back with ANS' algorithm for lossless
compression to large color photographs, and achieving state of the art for
compression of full size ImageNet images. We release Craystack, an open source
library for convenient prototyping of lossless compression using probabilistic
models, along with full implementations of all of our compression results.
