Do Diffusion Models Learn Semantically Meaningful and Efficient
  Representations?
Qiyao Liang Ziming Liu Ila Fiete
Diffusion models are capable of impressive feats of image generation with
uncommon juxtapositions such as astronauts riding horses on the moon with
properly placed shadows. These outputs indicate the ability to perform
compositional generalization, but how do the models do so? We perform
controlled experiments on conditional DDPMs learning to generate 2D spherical
Gaussian bumps centered at specified $x$- and $y$-positions. Our results show
that the emergence of semantically meaningful latent representations is key to
achieving high performance. En route to successful performance over learning,
the model traverses three distinct phases of latent representations: (phase A)
no latent structure, (phase B) a 2D manifold of disordered states, and (phase
C) a 2D ordered manifold. Corresponding to each of these phases, we identify
qualitatively different generation behaviors: 1) multiple bumps are generated,
2) one bump is generated but at inaccurate $x$ and $y$ locations, 3) a bump is
generated at the correct $x$ and y location. Furthermore, we show that even
under imbalanced datasets where features ($x$- versus $y$-positions) are
represented with skewed frequencies, the learning process for $x$ and $y$ is
coupled rather than factorized, demonstrating that simple vanilla-flavored
diffusion models cannot learn efficient representations in which localization
in $x$ and $y$ are factorized into separate 1D tasks. These findings suggest
the need for future work to find inductive biases that will push generative
models to discover and exploit factorizable independent structures in their
inputs, which will be required to vault these models into more data-efficient
regimes.