  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.
