Pixel Recurrent Neural Networks (PixelRNNs) are deep neural networks designed to model the distribution of natural images by sequentially predicting pixels in two spatial dimensions. The model captures the discrete probability of raw pixel values and encodes all dependencies in the image. Key innovations include fast two-dimensional recurrent layers and effective use of residual connections in deep recurrent networks. PixelRNNs achieve significantly better log-likelihood scores on natural images compared to previous methods and provide benchmarks on the ImageNet dataset. Generated images are crisp, varied, and globally coherent. The model processes images row by row, predicting each pixel's conditional distribution based on previously generated pixels. It uses a discrete distribution with a softmax layer, modeling pixel values as discrete variables. This approach offers advantages in representation and training compared to continuous distributions. The model incorporates two types of LSTM layers: Row LSTM and Diagonal BiLSTM, which enable efficient modeling of dependencies. Residual connections improve training for deep networks, while masked convolutions ensure proper conditioning. PixelCNN is a fully convolutional network that models pixel dependencies without introducing independence assumptions. Both PixelRNN and PixelCNN capture full pixel interdependencies. The Multi-Scale PixelRNN combines an unconditional and conditional network to generate images at different scales. Experiments show that PixelRNNs outperform previous results on MNIST, CIFAR-10, and ImageNet datasets. The models effectively capture local and long-range spatial correlations, producing sharp and coherent images. As models grow larger, they improve in performance, suggesting that further development could yield even better results.Pixel Recurrent Neural Networks (PixelRNNs) are deep neural networks designed to model the distribution of natural images by sequentially predicting pixels in two spatial dimensions. The model captures the discrete probability of raw pixel values and encodes all dependencies in the image. Key innovations include fast two-dimensional recurrent layers and effective use of residual connections in deep recurrent networks. PixelRNNs achieve significantly better log-likelihood scores on natural images compared to previous methods and provide benchmarks on the ImageNet dataset. Generated images are crisp, varied, and globally coherent. The model processes images row by row, predicting each pixel's conditional distribution based on previously generated pixels. It uses a discrete distribution with a softmax layer, modeling pixel values as discrete variables. This approach offers advantages in representation and training compared to continuous distributions. The model incorporates two types of LSTM layers: Row LSTM and Diagonal BiLSTM, which enable efficient modeling of dependencies. Residual connections improve training for deep networks, while masked convolutions ensure proper conditioning. PixelCNN is a fully convolutional network that models pixel dependencies without introducing independence assumptions. Both PixelRNN and PixelCNN capture full pixel interdependencies. The Multi-Scale PixelRNN combines an unconditional and conditional network to generate images at different scales. Experiments show that PixelRNNs outperform previous results on MNIST, CIFAR-10, and ImageNet datasets. The models effectively capture local and long-range spatial correlations, producing sharp and coherent images. As models grow larger, they improve in performance, suggesting that further development could yield even better results.