  PixelCNN achieves state-of-the-art results in density estimation for natural
images. Although training is fast, inference is costly, requiring one network
evaluation per pixel; O(N) for N pixels. This can be sped up by caching
activations, but still involves generating each pixel sequentially. In this
work, we propose a parallelized PixelCNN that allows more efficient inference
by modeling certain pixel groups as conditionally independent. Our new PixelCNN
model achieves competitive density estimation and orders of magnitude speedup -
O(log N) sampling instead of O(N) - enabling the practical generation of
512x512 images. We evaluate the model on class-conditional image generation,
text-to-image synthesis, and action-conditional video generation, showing that
our model achieves the best results among non-pixel-autoregressive density
models that allow efficient sampling.
