  Spatially-adaptive normalization (SPADE) is remarkably successful recently in
conditional semantic image synthesis \cite{park2019semantic}, which modulates
the normalized activation with spatially-varying transformations learned from
semantic layouts, to prevent the semantic information from being washed away.
Despite its impressive performance, a more thorough understanding of the
advantages inside the box is still highly demanded to help reduce the
significant computation and parameter overhead introduced by this novel
structure. In this paper, from a return-on-investment point of view, we conduct
an in-depth analysis of the effectiveness of this spatially-adaptive
normalization and observe that its modulation parameters benefit more from
semantic-awareness rather than spatial-adaptiveness, especially for
high-resolution input masks. Inspired by this observation, we propose
class-adaptive normalization (CLADE), a lightweight but equally-effective
variant that is only adaptive to semantic class. In order to further improve
spatial-adaptiveness, we introduce intra-class positional map encoding
calculated from semantic layouts to modulate the normalization parameters of
CLADE and propose a truly spatially-adaptive variant of CLADE, namely
CLADE-ICPE.Through extensive experiments on multiple challenging datasets, we
demonstrate that the proposed CLADE can be generalized to different SPADE-based
methods while achieving comparable generation quality compared to SPADE, but it
is much more efficient with fewer extra parameters and lower computational
cost. The code and pretrained models are available at
\url{https://github.com/tzt101/CLADE.git}.
