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：随着生成式人工智能技术与算法的进步，AI生成内容（AIGC）的广泛应用已崭露头角，能够生成多样且高质量的内容。特别是基于扩散模型的AIGC技术，已被广泛应用于多模态内容的生成。然而，AIGC模型在实际部署中，尤其是在手机等资源受限设备上，面临着能耗与隐私保护等重大挑战。为促进泛在化AIGC服务的实现，我们提出了一种新颖的协作式分布式扩散AIGC框架。通过利用无线网络设备间的协作，该框架能够高效执行AIGC任务，优化边缘计算资源利用。此外，我们探讨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，以及其在实际集成中的未来机遇。本文贡献不仅为现有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交付AIGC服务的未来研究铺平了道路。我们的代码已在 https://github.com/HongyangDu/DistributedDiffusion 公开。