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

Hongyang Du, Ruichen Zhang, Dusit Niyato, Jiawen Kang, Zehui Xiong, Dong In Kim, Xuemin, Shen, H. Vincent Poor

arXiv:2304.03446v2cs.DC

TL;DR

Resource-constrained devices make diffusion-based AIGC challenging because denoising consumes energy and users may have privacy concerns about server execution. The paper proposes collaborative denoising across devices and explores its implementation and wireless-network implications. In an example, five shared denoising steps yielded a visually appealing result, while more shared steps degraded or constrained the user-specific output.

  • Problem

    Resource-constrained devices face energy and computation demands from diffusion denoising, while users may prefer local execution for privacy.

    Source: §I · p. 2

  • Method

    The framework assigns shared denoising steps to one device, transmits intermediate results wirelessly, and lets other devices complete task-specific steps.

    Source: §I · p. 2

  • Results

    With 5 shared denoising steps, user 2 achieved a visually appealing result; 7 negatively affected image quality, while 9 left only two local steps, insufficient for the task.

    Source: B. Performance · p. 6

  • Takeaways & Limitations

    Stable Diffusion v1-4 was successfully implemented on a mobile phone without an Internet connection.

    Source: §I · p. 2

    Substantial semantic differences between users’ prompts may reduce shared-step efficacy and yield suboptimal results.

    Source: B. Performance · p. 6

Abstract

from arXiv · show

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.

I. INTRODUCTION

Diffusion-based AIGC can generate content across modalities, but its deployment on resource-constrained devices raises energy, computation, and privacy challenges. The paper proposes collaborative denoising across devices and examines its wireless-network implementation and potential.

I. INTRODUCTION

  • Resource-constrained devices face demanding diffusion denoising energy and computation needs, while users may prefer local or nearby execution for privacy.The introduction notes that each denoising step consumes substantial energy and that server-side offloading may not suit users handling sensitive content.Source: §I · p. 2
  • The proposed framework divides denoising between devices: one device performs shared steps, transmits intermediate results, and others complete task-specific steps.The shared steps may run on an edge server or an end device; the approach is also presented as offloading that preserves user control over content.Source: §I · p. 2
  • The paper analyzes diffusion-based AIGC deployment in wireless networks and the principles for partitioning the diffusion process.Source: §I · p. 2
  • The framework integrates central and edge inference to address diffusion models’ computational-resource limitations and provide an efficient, scalable, personalized user experience.Source: §I · p. 2
  • Stable Diffusion v1-4 was implemented on a mobile phone without an Internet connection.The paper also discusses the framework’s potential impact on wireless-network-enabled AIGC.Source: §I · p. 2

II. COLLABORATIVE DISTRIBUTED DIFFUSION-BASED AI-GENERATED CONTENT (AIGC) FRAMEWORK

The section introduces diffusion models and the proposed collaborative diffusion-based AIGC framework. It describes diffusion and denoising as successive processes and uses Stable Diffusion to illustrate the model workflow and collaborative setting.

II. COLLABORATIVE DISTRIBUTED DIFFUSION-BASED AI-GENERATED CONTENT (AIGC) FRAMEWORK

  • The framework section introduces diffusion-model principles and collaborative distributed computing of the diffusion process.Source: §II · p. 2

A. Overview of Diffusion Models

  • Stable Diffusion is presented as a high-performance text-to-image example with high-quality images, faster computation, lower resource consumption, and a smaller memory footprint.The section also names Disco-Diffusion, Mid-Journey, and DALL-E2 as diffusion-based text-image models.Source: A. Overview of Diffusion Models · p. 2

1) Principles of Diffusion Models:

  • Diffusion models generate data resembling training data by progressively adding Gaussian noise, then learning to reverse the process through denoising and reconstruction.They use neural networks to model data relationships and transform a simple Gaussian distribution toward the target distribution.Source: 1) Principles of Diffusion Models · p. 2
  • Figure 1 depicts two-user collaborative AIGC using the prompts “A bird on a table” and “A cat on a table.”The figure caption describes tokenizer, embedding, and text-transformer stages in the workflow.Source: Figure 1 · p. 3

2) Workflow of Diffusion Model-based AIGC:

The workflow conditions generation on text, starts from a random latent-noise tensor, and repeatedly predicts and subtracts noise. A VAE decoder then converts the refined latent representation into the final image.

2) Workflow of Diffusion Model-based AIGC:

  • Text prompts are tokenized, embedded, and processed by a text transformer before conditioning the noise predictor through cross-attention.The transformer can incorporate different conditioning modalities, and its output is used repeatedly by the noise predictor.Source: 2) Workflow of Diffusion Model-based… · p. 3
  • Stable Diffusion initializes generation with a random latent-space tensor whose stochastic content can be reproduced using the same random-number-generator seed.The initial tensor is pure noise rather than a coherent image.Source: 2) Workflow of Diffusion Model-based… · p. 3
  • The noise predictor takes a latent noisy image and text prompt as input, predicts latent-space noise, and that prediction is subtracted from the initial latent tensor.Source: 2) Workflow of Diffusion Model-based… · p. 3
  • Repeated sampling steps refine the latent image, which the VAE decoder converts back into pixel space to produce the final image.Source: 2) Workflow of Diffusion Model-based… · p. 3

3) Wireless Network Architecture:

The section describes collaborative architectures that distribute denoising across edge servers and devices. Their designs vary in where shared steps run, how intermediate outputs are exchanged, and whether collaboration is centralized or direct.

3) Wireless Network Architecture:

  • An edge server can perform shared denoising for users with semantically similar tasks, transmit intermediate outputs, and leave task-specific denoising to each device.The described architecture is intended to reduce latency and balance processing and load across devices.Source: 3) Wireless Network Architecture · p. 3
  • Two devices can jointly select shared denoising steps, execute them on one device, exchange intermediate outputs, and individually finish their remaining steps.Source: 3) Wireless Network Architecture · p. 3
  • Direct device communication can avoid additional central processing and is associated with energy savings; independent execution without a central server reduces potential content-leakage risk.Source: 3) Wireless Network Architecture · p. 3
  • The section also identifies forming a cluster among multiple devices, with or without an edge server, as an architecture option.Source: 3) Wireless Network Architecture · p. 3

A B C

The illustrated setup labels prompt upload and semantic analysis for scheduling, with two example prompts and an edge device described as having limited computation capacity.

  • The diagram labels prompt uploading and semantic analysis for scheduling.Source: A B C · p. 4
  • The two example prompts are “A Bird on The Desk” and “A Dog on The Desk.”Source: A B C · p. 4
  • An edge device is labeled as having limited computation capacity.Source: A B C · p. 4
  • A further example prompt is “A Man on The Road.”Source: A B C · p. 4

D Local Inference

The section describes collaborative clustering and a local-inference setup in which devices complete denoising after shared work. It identifies adaptability, scalability, and resource optimization as cluster benefits.

D Local Inference

  • The illustrated comparison uses 8 local denoising steps on user devices and contrasts collaboration with no collaborative distributed AIGC.Source: Figure 2 · p. 4
  • Clustering lets devices perform shared denoising steps, distribute intermediate outputs, and complete task-specific steps individually.Source: D Local Inference · p. 4
  • Clustering is described as adaptable to varied AIGC requirements, including separate clusters for animal-image and car-image generation.Source: D Local Inference · p. 4
  • Dynamically adjusting cluster formations is described as accommodating growing numbers of devices and tasks.Source: D Local Inference · p. 4
  • The section associates device resource sharing and joint AIGC task execution with improved overall system efficiency.Source: D Local Inference · p. 4

B. A Collaborative Distributed Diffusion-Based AIGC Approach

The framework groups semantically similar requests for shared denoising, then sends intermediate outputs to devices for task-specific inference. The paper presents this division as balancing computation and latency while maintaining high-quality generation.

B. A Collaborative Distributed Diffusion-Based AIGC Approach

III. NUMERICAL RESULTS AND DISCUSSION

The numerical-results section is organized around implementation and performance discussion.

III. NUMERICAL RESULTS AND DISCUSSION

  • The section organizes its numerical results and discussion into implementation and performance topics.Source: §III · p. 5

A. Implementation

The paper implements Stable Diffusion v1-4 offline on a mobile phone and discusses how device limits and wireless conditions affect distributed execution. It also reports that local denoising can preserve image quality despite transmission errors.

A. Implementation

  • Stable Diffusion v1-4 was deployed offline on a Redmi K40 using ncnn, with 512×512 output images and an Euler-A sampler.The model and CLIP were transformed for offline use with pnnx.Source: A. Implementation · p. 5, Figure 4 · p. 5
  • Mobile computational capacity can constrain inference speed and the complexity of executable models and tasks.The paper names pruning, quantization, and hardware acceleration as possible optimization techniques.Source: A. Implementation · pp. 5–6
  • Transmit power affects signal-to-noise ratio and transmission reliability, while edge-side denoising during deep fading can postpone transmission until channel quality improves.The paper also notes that mobility can rapidly change channel conditions and require communication-system adaptation.Source: A. Implementation · p. 6
  • At a 2% bit-error probability, user 2 still produced high-quality images, and local denoising could partly correct transmission damage.The paper evaluates image quality using MSE, PSNR, and SSIM.Source: A. Implementation · p. 6

B. Performance

Shared denoising can improve resource use, but the number of shared steps affects the quality of each user’s output. The framework also depends on semantic similarity between users’ prompts for effective sharing.

B. Performance

  • More shared denoising steps can let multiple tasks reuse a diffusion result and reduce individual workloads, but too few terminal steps can make image quality inadequate.The paper frames the choice as a trade-off between resource utilization and image quality.Source: B. Performance · p. 6
  • Substantial semantic differences between users’ prompts can make shared denoising ineffective and lead to suboptimal outputs.The illustrated failure uses 11 total steps, with 4 shared and 7 local.Source: B. Performance · pp. 6–7, Figure 6 · p. 7
  • Clustering users with similar semantic requirements or caching intermediate outputs from similar earlier tasks are proposed ways to improve sharing efficiency.The paper describes both as approaches to reduce computational overhead or resource use.Source: B. Performance · p. 7

IV. FUTURE DIRECTIONS

The paper identifies incentives, adaptive communication design, and security as research directions for distributed AIGC. It concludes that collaboration across devices may improve the efficiency and scalability of AIGC task execution.

A. Incentive Mechanism Designs for Distributed AIGC Computing

  • Incentive mechanisms for distributed AIGC should account for latency, synchronization, and resource availability while encouraging device participation and resource sharing.The paper notes that AIGC’s iterative and distributed nature requires close collaboration and synchronization.Source: A. Incentive Mechanism Designs for… · p. 7

B. Joint Diffusion and Channel Coding With Adaptive Modulation

  • Jointly optimizing diffusion computing and channel coding, with modulation and coding adapted to channel conditions, is proposed to balance throughput and reliability.The suggested design includes feedback to respond to channel-quality variation.Source: B. Joint Diffusion and Channel Coding… · p. 7

C. Secure Scheme Designs for Distributed AIGC Computing

  • Blockchain-based consensus and auditing methods such as anomaly detection or cryptographic proofs are proposed to protect shared intermediate results and check transmission correctness.The paper presents these as research directions for privacy and security in distributed AIGC.Source: C. Secure Scheme Designs for… · p. 7

V. CONCLUSION

  • The conclusion presents device collaboration as a way to improve the efficiency and scalability of AIGC task execution across devices.It also notes that real-world integration still involves hurdles.Source: §V · p. 7
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