  Can we customize a deep generative model which can generate images that can
match the texture of some given image? When you see an image of a church, you
may wonder if you can get similar pictures for that church. Here we present a
method, for adapting GANs with one reference image, and then we can generate
images that have similar textures to the given image. Specifically, we modify
the weights of the pre-trained GAN model, guided by the reference image given
by the user. We use a patch discriminator adversarial loss to encourage the
output of the model to match the texture on the given image, also we use a
laplacian adversarial loss to ensure diversity and realism, and alleviate the
contradiction between the two losses. Experiments show that the proposed method
can make the outputs of GANs match the texture of the given image as well as
keep diversity and realism.
