The unconditional generation of high fidelity images is a longstanding benchmark
 for testing the performance of image decoders. Autoregressive image models
 have been able to generate small images unconditionally, but the extension of
 these methods to large images where fidelity can be more readily assessed has
 remained an open problem. Among the major challenges are the capacity to encode
 the vast previous context and the sheer difficulty of learning a distribution that
 preserves both global semantic coherence and exactness of detail. To address the
 former challenge, we propose the Subscale Pixel Network (SPN), a conditional
 decoder architecture that generates an image as a sequence of image slices of equal
 size. The SPN compactly captures image-wide spatial dependencies and requires a
 fraction of the memory and the computation. To address the latter challenge, we
 propose to use multidimensional upscaling to grow an image in both size and depth
 via intermediate stages corresponding to distinct SPNs. We evaluate SPNs on the
 unconditional generation of CelebAHQ of size 256 and of ImageNet from size 32
 to 128. We achieve state-of-the-art likelihood results in multiple settings, set up
 new benchmark results in previously unexplored settings and are able to generate
 very high fidelity large scale samples on the basis of both datasets.
We show that autoregressive models can generate high fidelity images.  An architecture utilizing decoder, size-upscaling decoder, and depth-upscaling decoder components to tackle the problem of learning long-range dependencies in images in order to obtain high fidelity images. This paper addresses the problem of generation to high fidelity images, successfully showing convincing Imagenet samples with 128x128 resolution for a likelihood density model.