  Semantic image synthesis, translating semantic layouts to photo-realistic
images, is a one-to-many mapping problem. Though impressive progress has been
recently made, diverse semantic synthesis that can efficiently produce
semantic-level multimodal results, still remains a challenge. In this paper, we
propose a novel diverse semantic image synthesis framework from the perspective
of semantic class distributions, which naturally supports diverse generation at
semantic or even instance level. We achieve this by modeling class-level
conditional modulation parameters as continuous probability distributions
instead of discrete values, and sampling per-instance modulation parameters
through instance-adaptive stochastic sampling that is consistent across the
network. Moreover, we propose prior noise remapping, through linear
perturbation parameters encoded from paired references, to facilitate
supervised training and exemplar-based instance style control at test time.
Extensive experiments on multiple datasets show that our method can achieve
superior diversity and comparable quality compared to state-of-the-art methods.
Code will be available at \url{https://github.com/tzt101/INADE.git}
