Generating high-resolution, photo-realistic images has been a long-standing goal in machine learning. Recently, Nguyen et al. 2016 showed one interesting way to synthesize novel images by performing gradient descent in the latent space of a generator network to maximize the activations of one or multiple neurons in a separate classifier network. In this paper we extend this method by introducing an additional prior on the latent code, improving both sample quality and sample diversity, leading to a state-of-the-art generative model that produces high quality images at higher resolutions (227x227) than previous generative models, and does so for all 1000 ImageNet categories. In addition, we provide a unified probabilistic interpretation of related activation maximization methods and call the general class of models" Plug and Play Generative Networks". PPGNs are composed of (1) a generator network G that is capable of drawing a wide range of image types and (2) a replaceable" condition" network C that tells the generator what to draw. We demonstrate generation of images conditioned on a class-when C is an ImageNet classification network-and also conditioned on a caption-when C is an image captioning network. Our method also improves the state of the art of Deep Multifaceted Feature Visualization, which involves synthetically generating the set of inputs that activate a neuron in order to better understand how deep neural networks operate. Finally, we show that our model performs reasonably well at the task of image inpainting. While we operate on images in this paper, the approach is modality agnostic and can be applied to many types of data.