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：由于生成式人工智能（AI）技术和算法的进步，生成多样和高质量内容的人工智能生成内容（AIGC）得以广泛采用。特别是，扩散模型为基础的AIGC技术已广泛用于生成各个模态的内容。然而，在资源受限的设备（如移动电话）上实现AIGC模型，尤其是在能源消耗和隐私问题方面，带来了重大挑战。为了更进一步促进普遍实现AIGC服务，我们提出了一种新颖的协作分布式扩散基础上的AIGC框架。通过利用无线网络中的设备协作，所提出的框架有助于有效执行AIGC任务，优化边缘计算资源利用率。此外，我们研究了在移动电话上实际实现去噪步骤的影响，提出的方法对无线网络辅助AIGC形势的影响以及与其实际结合的未来机会。本文的贡献不仅提供了AIGC服务现有限制的有前途的解决方案，还为未来在设备协作、资源优化和跨各种设备无缝交付AIGC服务方面的研究铺平了道路。我们的代码可在 https://github.com/HongyangDu/DistributedDiffusion 找到。