Diffusion Models on the Edge: Challenges, Optimizations, and Applications
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arXiv
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866915253066924032 |
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| author | Zheng, Dongqi |
| author_facet | Zheng, Dongqi |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_15298 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Diffusion Models on the Edge: Challenges, Optimizations, and Applications Zheng, Dongqi Distributed, Parallel, and Cluster Computing Networking and Internet Architecture 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. |
| title | Diffusion Models on the Edge: Challenges, Optimizations, and Applications |
| topic | Distributed, Parallel, and Cluster Computing Networking and Internet Architecture |
| url | https://arxiv.org/abs/2504.15298 |