# Lightweight Diffusion Models for Resource-Constrained Semantic Communication

arXiv: https://arxiv.org/abs/2410.02491 · HTML: https://mlsanity.com/paper/2410.02491/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Placed in Diffusion for compression and wireless, one of 14 directions in Diffusion models.
- Builds on 21 of its 30 references that are in the map.
- First posted on arXiv 3 Oct 2024.

## Placed in

- Diffusion models / [Diffusion for compression and wireless](https://mlsanity.com/diffusion/diffusion-for-compression-and-wireless/)

## Builds on (most cited first, 21 of 21)

- [The Unreasonable Effectiveness of Deep Features as a Perceptual Metric](https://mlsanity.com/paper/1801.03924/) (2018)
- [Diffusion Models Beat GANs on Image Synthesis](https://mlsanity.com/paper/2105.05233/) (2021)
- [Improved Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2102.09672/) (2021)
- [Up or Down? Adaptive Rounding for Post-Training Quantization](https://mlsanity.com/paper/2004.10568/) (2020)
- [BRECQ: Pushing the Limit of Post-Training Quantization by Block Reconstruction](https://mlsanity.com/paper/2102.05426/) (2021)
- [Q-Diffusion: Quantizing Diffusion Models](https://mlsanity.com/paper/2302.04304/) (2023)
- [Post-training Quantization on Diffusion Models](https://mlsanity.com/paper/2211.15736/) (2022)
- [Learning Task-Oriented Communication for Edge Inference: An Information Bottleneck Approach](https://mlsanity.com/paper/2102.04170/) (2021)
- [Generative Joint Source-Channel Coding for Semantic Image Transmission](https://mlsanity.com/paper/2211.13772/) (2022)
- [Generative Semantic Communication: Diffusion Models Beyond Bit Recovery](https://mlsanity.com/paper/2306.04321/) (2023)
- [Latency-Aware Generative Semantic Communications with Pre-Trained Diffusion Models](https://mlsanity.com/paper/2403.17256/) (2024)
- [Diffusion-Driven Semantic Communication for Generative Models with Bandwidth Constraints](https://mlsanity.com/paper/2407.18468/) (2024)
- [Diffusion models for audio semantic communication](https://mlsanity.com/paper/2309.07195/) (2023)
- [Language-Oriented Communication with Semantic Coding and Knowledge Distillation for Text-to-Image Generation](https://mlsanity.com/paper/2309.11127/) (2023)
- [Semantic Communications Based on Adaptive Generative Models and Information Bottleneck](https://mlsanity.com/paper/2309.02387/) (2023)
- [A Unified Framework for Guiding Generative AI with Wireless Perception in Resource Constrained Mobile Edge Networks](https://mlsanity.com/paper/2309.01426/) (2023)
- [Language-Oriented Semantic Latent Representation for Image Transmission](https://mlsanity.com/paper/2405.09976/) (2024)
- [Generative AI Meets Semantic Communication: Evolution and Revolution of Communication Tasks](https://mlsanity.com/paper/2401.06803/) (2024)
- [Resource-efficient Generative Mobile Edge Networks in 6G Era: Fundamentals, Framework and Case Study](https://mlsanity.com/paper/2312.12063/) (2023)
- [Training-Free Multi-User Generative Semantic Communications via Null-Space Diffusion Sampling](https://mlsanity.com/paper/2405.09866/) (2024)
- [Semantic-Aware Power Allocation for Generative Semantic Communications with Foundation Models](https://mlsanity.com/paper/2407.03050/) (2024)

## Built on by (most cited first, 8 of 8)

- [Diffusion Models for Future Networks and Communications: A Comprehensive Survey](https://mlsanity.com/paper/2508.01586/) (2025)
- [Communicate Less, Synthesize the Rest: Latency-aware Intent-based Generative Semantic Multicasting with Diffusion Models](https://mlsanity.com/paper/2411.02334/) (2024)
- [Generative Semantic Communication: Architectures, Technologies, and Applications](https://mlsanity.com/paper/2412.08642/) (2024)
- [Low-Bitrate Video Compression through Semantic-Conditioned Diffusion](https://mlsanity.com/paper/2512.00408/) (2025)
- [Generative AI Meets 6G and Beyond: Diffusion Models for Semantic Communications](https://mlsanity.com/paper/2511.08416/) (2025)
- [A Causal Diffusion Model for Video Reconstruction from Ultra-Low-Bitrate Representations](https://mlsanity.com/paper/2602.13837/) (2026)
- [Optimally Bridging Semantics and Data: Generative Semantic Communication via Schr"odinger Bridge](https://mlsanity.com/paper/2604.17802/) (2026)
- [Selective Depthwise Separable Convolution for Lightweight Joint Source-Channel Coding in Wireless Image Transmission](https://mlsanity.com/paper/2604.22338/) (2026)

## Abstract (arXiv)

Recently, generative semantic communication models have proliferated as they are revolutionizing semantic communication frameworks, improving their performance, and opening the way to novel applications. Despite their impressive ability to regenerate content from the compressed semantic information received, generative models pose crucial challenges for communication systems in terms of high memory footprints and heavy computational load. In this paper, we present a novel Quantized GEnerative Semantic COmmunication framework, Q-GESCO. The core method of Q-GESCO is a quantized semantic diffusion model capable of regenerating transmitted images from the received semantic maps while simultaneously reducing computational load and memory footprint thanks to the proposed post-training quantization technique. Q-GESCO is robust to different channel noises and obtains comparable performance to the full precision counterpart in different scenarios saving up to 75% memory and 79% floating point operations. This allows resource-constrained devices to exploit the generative capabilities of Q-GESCO, widening the range of applications and systems for generative semantic communication frameworks. The code is available at https://github.com/ispamm/Q-GESCO.