A Systematic Framework for Compressing Generative Diffusion Models for Resource-Constrained IoT Devices

Generative diffusion models deliver remarkable synthesis quality but remain impractical for resource-limited Internet of Things (IoT) devices due to their substantial computational and memory demands. To bridge this critical gap, we present a comprehensive, multi-stage optimization framework that systematically reduces model size while meticulously preserving generative fidelity. The framework integrates an efficient backbone architecture designed for inherent lightness, a sensitivity-guided fine-grained pruning strategy that strategically removes redundant parameters to achieve high sparsity, and a novel distribution-aware quantization algorithm based on Gaussian Mixture Models (GMMs) to compress weights and activations with minimal quality degradation. Extensive validation across multiple diffusion architectures (DDPM, DDIM, SGM) and diverse datasets demonstrates the framework’s strong generalizability, achieving up to 79% model sparsity while preserving generative fidelity. To showcase practical utility, we demonstrate that our framework produces a compressed model compatible with standard mobile deployment toolchains, realizing a significant reduction in the on-device memory footprint required for inference. This work offers a robust and generalizable methodology for enabling advanced generative AI on a wide spectrum of edge and IoT platforms. Code is available at: https://github.com/mitchell-cheng/compress_diffusion.