  We propose an image synthesis approach that provides stratified navigation in
the latent code space. With a tiny amount of partial or very low-resolution
image, our approach can consistently out-perform state-of-the-art counterparts
in terms of generating the closest sampled image to the ground truth. We
achieve this through scale-independent editing while expanding scale-specific
diversity. Scale-independence is achieved with a nested scale disentanglement
loss. Scale-specific diversity is created by incorporating a progressive
diversification constraint. We introduce semantic persistency across the scales
by sharing common latent codes. Together they provide better control of the
image synthesis process. We evaluate the effectiveness of our proposed approach
through various tasks, including image outpainting, image superresolution, and
cross-domain image translation.
