  This paper outlines an end-to-end optimized lossy image compression framework
using diffusion generative models. The approach relies on the transform coding
paradigm, where an image is mapped into a latent space for entropy coding and,
from there, mapped back to the data space for reconstruction. In contrast to
VAE-based neural compression, where the (mean) decoder is a deterministic
neural network, our decoder is a conditional diffusion model. Our approach thus
introduces an additional ``content'' latent variable on which the reverse
diffusion process is conditioned and uses this variable to store information
about the image. The remaining ``texture'' variables characterizing the
diffusion process are synthesized at decoding time. We show that the model's
performance can be tuned toward perceptual metrics of interest. Our extensive
experiments involving multiple datasets and image quality assessment metrics
show that our approach yields stronger reported FID scores than the GAN-based
model, while also yielding competitive performance with VAE-based models in
several distortion metrics. Furthermore, training the diffusion with
$\mathcal{X}$-parameterization enables high-quality reconstructions in only a
handful of decoding steps, greatly affecting the model's practicality. Our code
is available at: \url{https://github.com/buggyyang/CDC_compression}
