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.

翻译：在生成式人工智能（AI）技术与算法的推动下，AI生成内容（AIGC）得以广泛应用，可生成多样化且高质量的内容。特别是基于扩散模型的AIGC技术已被广泛用于生成多种模态的内容。然而，AIGC模型的实际部署（尤其在手机等资源受限设备上）带来了能耗与隐私方面的显著挑战。为进一步推动普适AIGC服务的实现，本文提出一种新颖的协作式分布式扩散模型AIGC框架。该框架利用无线网络中设备间的协作，高效执行AIGC任务，并优化边缘计算资源利用率。此外，我们考察了去噪步骤在手机上的实际实现、所提方法对无线网络辅助AIGC格局的影响，以及其实际集成中蕴含的未来机遇。本文的贡献不仅为现有AIGC服务的局限性提供了有前景的解决方案，也为设备协作、资源优化及跨设备无缝提供AIGC服务等未来研究奠定了基础。我们的代码可于 https://github.com/HongyangDu/DistributedDiffusion 获取。