  In semantic image synthesis the state of the art is dominated by methods that
use customized variants of the SPatially-Adaptive DE-normalization (SPADE)
layers, which allow for good visual generation quality and editing versatility.
By design, such layers learn pixel-wise modulation parameters to de-normalize
the generator activations based on the semantic class each pixel belongs to.
Thus, they tend to overlook global image statistics, ultimately leading to
unconvincing local style editing and causing global inconsistencies such as
color or illumination distribution shifts. Also, SPADE layers require the
semantic segmentation mask for mapping styles in the generator, preventing
shape manipulations without manual intervention. In response, we designed a
novel architecture where cross-attention layers are used in place of SPADE for
learning shape-style correlations and so conditioning the image generation
process. Our model inherits the versatility of SPADE, at the same time
obtaining state-of-the-art generation quality, as well as improved global and
local style transfer. Code and models available at
https://github.com/TFonta/CA2SIS.
