Diffusion-Driven Semantic Communication for Generative Models with Bandwidth Constraints

TL;DR

This paper proposes a diffusion-driven semantic communication framework with VAE-based compression to enable high-quality generative model transmission over bandwidth-limited wireless channels, improving metrics like PSNR and LPIPS.

Contribution

It introduces a novel diffusion-based semantic communication architecture incorporating VAE compression and a unique loss function for bandwidth efficiency in wireless generative tasks.

Findings

Significant improvements in PSNR and LPIPS metrics.

Enhanced performance at various compression rates and SNR levels.

Outperforms deep joint source-channel coding (DJSCC) in experiments.

Abstract

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). We release the code at https://github.com/import-sudo/Diffusion-Driven-Semantic-Communication.