Supplementary Material for PTQD: Accurate Post-Training Quantization for Diffusion Models Y efei He

ZIP Lab, Monash University, Australia We organize our supplementary material as follows: In section A, we provide a comprehensive explanation of extending PTQD to DDIM [10]. In section B, we show the statistical analysis of quantization noise. In section D, we provide additional visualization results on ImageNet and LSUN dataset. We first perform statistical tests to verify if the residual quantization noise adheres to a Gaussian distribution. This test is based on D'Agostino and Pearson's In Figure B, we present the variance of the residual uncorrelated quantization noise.