  We introduce a new generator architecture, aimed at fast and efficient
high-resolution image-to-image translation. We design the generator to be an
extremely lightweight function of the full-resolution image. In fact, we use
pixel-wise networks; that is, each pixel is processed independently of others,
through a composition of simple affine transformations and nonlinearities. We
take three important steps to equip such a seemingly simple function with
adequate expressivity. First, the parameters of the pixel-wise networks are
spatially varying so they can represent a broader function class than simple
1x1 convolutions. Second, these parameters are predicted by a fast
convolutional network that processes an aggressively low-resolution
representation of the input; Third, we augment the input image with a
sinusoidal encoding of spatial coordinates, which provides an effective
inductive bias for generating realistic novel high-frequency image content. As
a result, our model is up to 18x faster than state-of-the-art baselines. We
achieve this speedup while generating comparable visual quality across
different image resolutions and translation domains.
