PQD: Post-training Quantization for Efficient Diffusion Models

Jiaojiao Ye, Zhen Wang, Linnan Jiang; Proceedings of the Winter Conference on Applications of Computer Vision (WACV) Workshops, 2025, pp. 150-156

Diffusion models (DMs) have demonstrated remarkable achievements in synthesizing images of high fidelity and diversity. However the extensive computational requirements and slow generative speed of diffusion models have limited their widespread adoption. In this paper we propose a novel post-training quantization for diffusion models (PQD) which is a time-aware optimization framework for diffusion models based on post-training quantization. The proposed framework optimizes the inference process by conducting time-aware calibration. Experimental results show that our proposed method is able to directly quantize full-precision diffusion models into 8-bit or 4-bit models while maintaining comparable performance in a training-free manner achieving a few FID change on ImageNet for unconditional image generation. Our approach demonstrates compatibility and can also be applied to 512x512 text-guided image generation for the first time.