  Diffusion models, trained on large amounts of data, showed remarkable
performance for image synthesis. They have high error consistency with humans
and low texture bias when used for classification. Furthermore, prior work
demonstrated the decomposability of their bottleneck layer representations into
semantic directions. In this work, we analyze how well such representations are
aligned to human responses on a triplet odd-one-out task. We find that despite
the aforementioned observations: I) The representational alignment with humans
is comparable to that of models trained only on ImageNet-1k. II) The most
aligned layers of the denoiser U-Net are intermediate layers and not the
bottleneck. III) Text conditioning greatly improves alignment at high noise
levels, hinting at the importance of abstract textual information, especially
in the early stage of generation.
