  Diffusion models have garnered significant attention since they can
effectively learn complex multivariate Gaussian distributions, resulting in
diverse, high-quality outcomes. They introduce Gaussian noise into training
data and reconstruct the original data iteratively. Central to this iterative
process is a single Unet, adapting across time steps to facilitate generation.
Recent work revealed the presence of composition and denoising phases in this
generation process, raising questions about the Unets' varying roles. Our study
dives into the dynamic behavior of Unets within denoising diffusion
probabilistic models (DDPM), focusing on (de)convolutional blocks and skip
connections across time steps. We propose an analytical method to
systematically assess the impact of time steps and core Unet components on the
final output. This method eliminates components to study causal relations and
investigate their influence on output changes. The main purpose is to
understand the temporal dynamics and identify potential shortcuts during
inference. Our findings provide valuable insights into the various generation
phases during inference and shed light on the Unets' usage patterns across
these phases. Leveraging these insights, we identify redundancies in GLIDE (an
improved DDPM) and improve inference time by ~27% with minimal degradation in
output quality. Our ultimate goal is to guide more informed optimization
strategies for inference and influence new model designs.
