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 512 × 512 images. We evaluate the model on class-conditional image generation, text-toimage synthesis, and action-conditional video generation, showing that our model achieves the best results among non-pixel-autoregressive density models that allow efficient sampling.
To deal with the issue, a parallelized pixelCNN is proposed in REF where the authors have made inference procedure faster by modeling certain pixel groups as conditionally independent.
Parallel Multiscale Autoregressive Density Estimation
ArXiv
ArXiv
Computer Science