  We present a novel algorithm to reduce tensor compute required by a
conditional image generation autoencoder without sacrificing quality of
photo-realistic image generation. Our method is device agnostic, and can
optimize an autoencoder for a given CPU-only, GPU compute device(s) in about
normal time it takes to train an autoencoder on a generic workstation. We
achieve this via a two-stage novel strategy where, first, we condense the
channel weights, such that, as few as possible channels are used. Then, we
prune the nearly zeroed out weight activations, and fine-tune the autoencoder.
To maintain image quality, fine-tuning is done via student-teacher training,
where we reuse the condensed autoencoder as the teacher. We show performance
gains for various conditional image generation tasks: segmentation mask to face
images, face images to cartoonization, and finally CycleGAN-based model over
multiple compute devices. We perform various ablation studies to justify the
claims and design choices, and achieve real-time versions of various
autoencoders on CPU-only devices while maintaining image quality, thus enabling
at-scale deployment of such autoencoders.
