Exploring Collaborative Distributed Diffusion-Based AI-Generated Content (AIGC) in Wireless Networks

TL;DR

This paper introduces a collaborative distributed diffusion-based framework for AI-generated content in wireless networks, addressing energy and privacy challenges on mobile devices to enable efficient, ubiquitous AIGC services.

Contribution

It proposes a novel collaborative framework that enhances resource utilization and addresses implementation challenges of diffusion-based AIGC on mobile devices.

Findings

Improved energy efficiency in mobile AIGC generation

Enhanced privacy preservation through distributed processing

Potential for seamless AIGC service delivery across devices

Abstract

Driven by advances in generative artificial intelligence (AI) techniques and algorithms, the widespread adoption of AI-generated content (AIGC) has emerged, allowing for the generation of diverse and high-quality content. Especially, the diffusion model-based AIGC technique has been widely used to generate content in a variety of modalities. However, the real-world implementation of AIGC models, particularly on resource-constrained devices such as mobile phones, introduces significant challenges related to energy consumption and privacy concerns. To further promote the realization of ubiquitous AIGC services, we propose a novel collaborative distributed diffusion-based AIGC framework. By capitalizing on collaboration among devices in wireless networks, the proposed framework facilitates the efficient execution of AIGC tasks, optimizing edge computation resource utilization. Furthermore, we examine the practical implementation of the denoising steps on mobile phones, the impact of the proposed approach on the wireless network-aided AIGC landscape, and the future opportunities associated with its real-world integration. The contributions of this paper not only offer a promising solution to the existing limitations of AIGC services but also pave the way for future research in device collaboration, resource optimization, and the seamless delivery of AIGC services across various devices. Our code is available at https://github.com/HongyangDu/DistributedDiffusion.