Accelerating Diffusion Model Inference: A Blockchain-Aided Trusted Cloud-Edge Collaborative Framework

Abstract

Artificial Intelligence-Generated Content (AIGC) has developed rapidly, with Diffusion Models (DMs) gaining wide attention for their superior image generation capabilities. However, the high computational cost of inference limits their deployment in resource-constrained environments such as edge computing. Cloud-edge collaboration is considered a feasible solution to improve inference efficiency, but existing studies have not fully addressed the issue of trust propagation among multiple entities. To address these, we propose a blockchain-aided trusted inference framework for cloud–edge collaborative DMs. Smart contracts are designed to automate image generation task management and ensure traceability throughout the inference process. Leveraging the step-by-step denoising nature of DMs, we introduce a Siamese Network-based Semantic Matching (SNSM) model to identify whether a new task can reuse intermediate results from historical inferences, thereby reducing redundant computation and improving efficiency. We formulate an objective function to minimize total inference latency by jointly considering queuing, transmission, and computation delays, with image quality metrics as constraints. To solve these, we propose Diffusion-Attention integrated Multi-Agent Reinforcement Learning (DAMARL), which dynamically optimizes task partitioning and scheduling between cloud and edge to reduce latency while preserving generation quality. Extensive experiments show that SNSM achieves 85.3% accuracy in reuse decisions, and DAMARL improves average reward by 17.5% $\sim ~35$ % over existing methods, demonstrating the effectiveness of our approach in enhancing DM inference efficiency and performance.