diffusion serves as a more powerful generative de-
coder, and hence the input can be reduced to latent
representations with higher compression ratios.
3.1.4 Efficient and Enriched 1D U-Net
Another crucial module in our model is the effi-
cient 1D U-Net that we design. We identify that
the vanilla U-Net architecture (Ronneberger et al.,
2015), originally introduced for medial image seg-
mentation, has relatively limited efficiency and
speed, as it uses an hourglass convolutional-only
2D architecture with skip connections.
Moûsai