Beyond Uniformity: Sample and Frequency Meta Weighting for Post-Training Quantization of Diffusion Models

Post-training quantization (PTQ) is an attractive approach for compressing diffusion models to speed up the sampling process and reduce the memory footprint. Most existing PTQ methods uniformly sample data from various time steps in the denoising process to construct a calibration set for quantization and consider calibration samples equally important during quantization process. However, treating all calibration samples equally may not be optimal. One notable property in the denoising process of diffusion models is low-frequency features are primarily recovered in early stages, while high-frequency features are recovered in later stages of the denoising process. However, none of previous works on quantization for diffusion models consider this property to enhance the effectiveness of quantized models. In this paper, we propose a novel meta-learning approach for PTQ of diffusion models that jointly optimizes the contributions of calibration samples and the weighting of frequency components at each time step for quantizing noise estimation networks. Specifically, our approach automatically learns to assign optimal weights to calibration samples while selectively focusing on mimicking specific frequency components of data generated by the full-precision noise estimation network at each denoising time step. Extensive experiments on CIFAR-10, LSUN-Bedrooms, FFHQ, and ImageNet datasets demonstrate that our approach consistently outperforms state-of-the-art PTQ methods for diffusion models.