Deep generative models, such as Generative Adversarial Networks (GANs) in particular, can sample realistic images from Gaussian noise. However, are they good for image editing? Image editing requires the output to retain some resemblance to the user-provided input image. In this talk, I will discuss a different formulation in which the generator network is trained to transform one image to another. I will explore some interesting ways to constrain the generator to respect the input images, and show that they are indeed useful for image editing and other practical tasks.