  Diffusion models have been extensively utilized in AI-generated content
(AIGC) in recent years, thanks to the superior generation capabilities.
Combining with semantic communications, diffusion models are used for tasks
such as denoising, data reconstruction, and content generation. However,
existing diffusion-based generative models do not consider the stringent
bandwidth limitation, which limits its application in wireless communication.
This paper introduces a diffusion-driven semantic communication framework with
advanced VAE-based compression for bandwidth-constrained generative model. Our
designed architecture utilizes the diffusion model, where the signal
transmission process through the wireless channel acts as the forward process
in diffusion. To reduce bandwidth requirements, we incorporate a downsampling
module and a paired upsampling module based on a variational auto-encoder with
reparameterization at the receiver to ensure that the recovered features
conform to the Gaussian distribution. Furthermore, we derive the loss function
for our proposed system and evaluate its performance through comprehensive
experiments. Our experimental results demonstrate significant improvements in
pixel-level metrics such as peak signal to noise ratio (PSNR) and semantic
metrics like learned perceptual image patch similarity (LPIPS). These
enhancements are more profound regarding the compression rates and SNR compared
to deep joint source-channel coding (DJSCC).
