  Semantic image synthesis (SIS) aims to produce photorealistic images aligning
to given conditional semantic layout and has witnessed a significant
improvement in recent years. Although the diversity in image-level has been
discussed heavily, class-level mode collapse widely exists in current
algorithms. Therefore, we declare a new requirement for SIS to achieve more
photorealistic images, variation-aware, which consists of inter- and
intra-class variation. The inter-class variation is the diversity between
different semantic classes while the intra-class variation stresses the
diversity inside one class. Through analysis, we find that current algorithms
elusively embrace the inter-class variation but the intra-class variation is
still not enough. Further, we introduce two simple methods to achieve
variation-aware semantic image synthesis (VASIS) with a higher intra-class
variation, semantic noise and position code. We combine our method with several
state-of-the-art algorithms and the experimental result shows that our models
generate more natural images and achieves slightly better FIDs and/or mIoUs
than the counterparts. Our codes and models will be publicly available.
