This repository implements Pixel Recurrent Neural Networks, an autoregressive generative model that synthesizes images by predicting each pixel conditionally on previously generated pixels. It provides TensorFlow implementations of both PixelCNN using masked convolutions and PixelRNN using row LSTM and diagonal BiLSTM architectures. The project includes training scripts for MNIST and CIFAR-10 datasets, with support for generating new images from trained models.