SNR-LDM: An Efficient SNR-Guided Latent Diffusion Model for Generative AI Services Over Dynamic Wireless Networks

Abstract

The proliferation of generative AI services, from cloud-based synthesis to on-device content creation, is increasingly bottlenecked by the need for efficient communication over dynamic wireless channels. However, deploying these models at the network edge is impeded by two fundamental challenges, namely the weak coupling between the model and physical channel conditions and the prohibitive computational latency of diffusion-based inference. Existing approaches often treat diffusion as a static module trained at a fixed noise level and indexed by abstract timesteps without physical interpretation, which yields suboptimal performance under time-varying channels. To address this, we propose an signal-to-noise ratio guided latent diffusion model (SNR-LDM). The diffusion process is reparameterized with a physically interpretable mapping between denoising timesteps and channel SNR, aligning the denoising trajectory with the channel state and enabling SNR-adaptive inference. To enhance semantic fidelity, we introduce multimodal guidance that fuses textual and visual prompts to steer content generation. We further develop two inference schemes, namely a dynamic multi-step procedure for highest reconstruction quality and a single-step analytic inversion for ultra-low latency, which makes deployment on resource-constrained edge devices feasible. Experiments show that the proposed model achieves about 4 dB higher PSNR than adaptive baselines and yields up to 8.2% lower LPIPS than diffusionbased benchmarks. The framework links large-scale generative modeling with the communication-constrained network edge and enables robust and efficient deployment of generative AI services.