PQCAD-DM: Progressive Quantization and Calibration-Assisted Distillation for Efficient Compression of Diffusion Models

Diffusion models (DMs) demonstrate outstanding performance in image generation, but their iterative sampling processes incur substantial computational costs and error accumulation, hindering deployment on resource-constrained IoT devices. In this article, we propose PQCAD-DM, a hybrid compression framework that tightly integrates progressive quantization (PQ) and calibration-assisted distillation (CAD). PQ employs a two-stage strategy with momentum-guided adaptive bit-width transitions to suppress instability, while CAD leverages a dual-calibration dataset to reconstruct robust full-precision guidance for low-bit students. Aligned with an industrial server-to-device deployment paradigm, PQCAD-DM optimizes models on high-resource servers for efficient edge inference. Extensive experiments, including validation on aerial and urban surveillance IoT datasets, demonstrate that PQCAD-DM consistently outperforms fixed-bit quantization baselines and architecture-optimized hybrid models. On a mobile smartphone, our framework achieves a 2.8 × higher throughput and a 48% reduction in model size while maintaining high visual fidelity, making it uniquely suited for latency-sensitive IoT applications.