Diffusion models represent a powerful family of generative models widely used for image and video generation. However, the time-consuming deployment, long inference time, and requirements on large memory hinder their applications on resource constrained devices. In this paper, we propose a method based on the improved Straight-Through Estimator to improve the deployment efficiency of diffusion models. Specifically, we add sparse masks to the Convolution and Linear layers in a pre-trained diffusion model, then transfer learn the sparse model during the fine-tuning stage and turn on the sparse masks during inference. Experimental results on a Transformer and UNet-based diffusion models demonstrate that our method reduces MACs by 50% while maintaining FID. Sparse models are accelerated by approximately 1.2x on the GPU. Under other MACs conditions, the FID is also lower than 1 compared to other methods.
@article{arxiv.2404.10445,
title = {SparseDM: Toward Sparse Efficient Diffusion Models},
author = {Kafeng Wang and Jianfei Chen and He Li and Zhenpeng Mi and Jun Zhu},
journal= {arXiv preprint arXiv:2404.10445},
year = {2025}
}