# Diffusion Models on the Edge: Challenges, Optimizations, and Applications

arXiv: https://arxiv.org/abs/2504.15298 · HTML: https://mlsanity.com/paper/2504.15298/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Apr 2025.

## Built on by (most cited first, 5 of 5)

- [Neural Network Quantization for Microcontrollers: A Comprehensive Survey of Methods, Platforms, and Applications](https://mlsanity.com/paper/2508.15008/) (2025)
- [Quantization with Unified Adaptive Distillation to enable multi-LoRA based one-for-all Generative Vision Models on edge](https://mlsanity.com/paper/2603.29535/) (2026)
- [Training-Free Generative Sampling via Moment-Matched Score Smoothing](https://mlsanity.com/paper/2605.14276/) (2026)
- [SocioGesture: Real-Time and Adaptive Social Gesture Perception for Human-Robot Interaction](https://mlsanity.com/paper/2609.04545/) (2026)
- [Diffusion Language Models for Mobile Edge Agentic AI: Foundations, Applications, and Challenges](https://mlsanity.com/paper/2609.04778/) (2026)

## Abstract (arXiv)

Diffusion models have shown remarkable capabilities in generating high-fidelity data across modalities such as images, audio, and video. However, their computational intensity makes deployment on edge devices a significant challenge. This survey explores the foundational concepts of diffusion models, identifies key constraints of edge platforms, and synthesizes recent advancements in model compression, sampling efficiency, and hardware-software co-design to make diffusion models viable on edge devices. We also review promising applications and suggest future research directions.