As a result, it is a massive bottleneck for the users to fully utilize the information stored inthe EHR.

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.

Diffusion models have recently dominated image synthesis and other related generative tasks. However, the iterative denoising process is expensive in computations at inference time, making diffusion models less practical for low-latency and scalable real-world applications.