Recent advances in generative models like Stable Diffusion enable the
generation of highly photo-realistic images. Our objective in this paper is to
probe the diffusion network to determine to what extent it 'understands'
different properties of the 3D scene depicted in an image. To this end, we make
the following contributions: (i) We introduce a protocol to evaluate whether a
network models a number of physical 'properties' of the 3D scene by probing for
explicit features that represent these properties. The probes are applied on
datasets of real images with annotations for the property. (ii) We apply this
protocol to properties covering scene geometry, scene material, support
relations, lighting, and view dependent measures. (iii) We find that Stable
Diffusion is good at a number of properties including scene geometry, support
relations, shadows and depth, but less performant for occlusion. (iv) We also
apply the probes to other models trained at large-scale, including DINO and
CLIP, and find their performance inferior to that of Stable Diffusion