This paper introduces a conditional image generation model based on the PixelCNN architecture, called Conditional PixelCNN. The model can be conditioned on various types of input, including descriptive labels, tags, or latent embeddings generated by other networks. The model is able to generate diverse, realistic images when conditioned on class labels from the ImageNet database, and can also generate varied portraits of the same person when conditioned on an embedding derived from a single image. The model is also shown to be effective as a decoder in image autoencoders. The paper presents a gated variant of the PixelCNN, called Gated PixelCNN, which improves the model's performance and convergence speed. This model matches the performance of PixelRNN on CIFAR and ImageNet datasets while requiring significantly less training time. The Conditional PixelCNN is introduced as a model that can condition on latent vectors to generate images that match the description provided by the vector. This model is shown to be effective in generating images from different classes, such as dogs, lawn mowers, and coral reefs, by conditioning on a one-hot encoding of the class. The paper also explores the use of Conditional PixelCNN in autoencoders, where it is used as a decoder to reconstruct images from a low-dimensional representation. The model is shown to generate high-quality images with natural variations in objects and lighting conditions. The paper concludes that the Conditional PixelCNN is a powerful model for conditional image generation and can be used in various applications, including image compression, probabilistic planning, and content generation. The model is also shown to be effective in generating images from a single example, and has the potential to be used in future research with variational inference to create a variational autoencoder.This paper introduces a conditional image generation model based on the PixelCNN architecture, called Conditional PixelCNN. The model can be conditioned on various types of input, including descriptive labels, tags, or latent embeddings generated by other networks. The model is able to generate diverse, realistic images when conditioned on class labels from the ImageNet database, and can also generate varied portraits of the same person when conditioned on an embedding derived from a single image. The model is also shown to be effective as a decoder in image autoencoders. The paper presents a gated variant of the PixelCNN, called Gated PixelCNN, which improves the model's performance and convergence speed. This model matches the performance of PixelRNN on CIFAR and ImageNet datasets while requiring significantly less training time. The Conditional PixelCNN is introduced as a model that can condition on latent vectors to generate images that match the description provided by the vector. This model is shown to be effective in generating images from different classes, such as dogs, lawn mowers, and coral reefs, by conditioning on a one-hot encoding of the class. The paper also explores the use of Conditional PixelCNN in autoencoders, where it is used as a decoder to reconstruct images from a low-dimensional representation. The model is shown to generate high-quality images with natural variations in objects and lighting conditions. The paper concludes that the Conditional PixelCNN is a powerful model for conditional image generation and can be used in various applications, including image compression, probabilistic planning, and content generation. The model is also shown to be effective in generating images from a single example, and has the potential to be used in future research with variational inference to create a variational autoencoder.