In this paper we investigate applying two deep generative models to digital halftoning with the aim of generating halftones with comparable quality to those generated with the direct binary search (DBS) algorithm. For the first framework, we apply conditional generative adversarial networks (cGANs) using two discriminators with different receptive field size and a generator consisting of densely connected blocks. For the second framework, deep autoregressive (AR) models, we propose mapping input images into a feature space using a single forward pass of a deep neural network and then applying a shallow autoregressive model at the end output. Our methods show promising results; halftones generated with our algorithms are less noisy than those generated with DBS screen and do not contain artifacts commonly associated with error diffusion type algorithms.