  Training a generative model on a single image has drawn significant attention
in recent years. Single image generative methods are designed to learn the
internal patch distribution of a single natural image at multiple scales. These
models can be used for drawing diverse samples that semantically resemble the
training image, as well as for solving many image editing and restoration tasks
that involve that particular image. Here, we introduce an extended framework,
which allows to simultaneously learn the internal distributions of several
images, by using a single model with spatially varying image-identity
conditioning. Our BlendGAN opens the door to applications that are not
supported by single-image models, including morphing, melding, and
structure-texture fusion between two or more arbitrary images.
