  Diffusion models have gained popularity for generating images from textual
descriptions. Nonetheless, the substantial need for computational resources
continues to present a noteworthy challenge, contributing to time-consuming
processes. Quantization, a technique employed to compress deep learning models
for enhanced efficiency, presents challenges when applied to diffusion models.
These models are notably more sensitive to quantization compared to other model
types, potentially resulting in a degradation of image quality. In this paper,
we introduce a novel approach to quantize the diffusion models by leveraging
both quantization-aware training and distillation. Our results show the
quantized models can maintain the high image quality while demonstrating the
inference efficiency on CPUs. The code is publicly available at:
https://github.com/intel/intel-extension-for-transformers.
