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Poster
in
Workshop: NeurIPS 2023 Workshop on Diffusion Models

Effective Quantization for Diffusion Models on CPUs

Hanwen Chang · Haihao Shen · Yiyang Cai · Xinyu Ye · Zhenzhong Xu · Wenhua Cheng · Weiwei Zhang · Yintong Lu · Heng Guo


Abstract:

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.

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