SparseDM: Toward Sparse Efficient Diffusion Models

• URL: http://arxiv.org/abs/2404.10445v4
• Date: Thu, 17 Apr 2025 16:05:20 GMT
• Title: SparseDM: Toward Sparse Efficient Diffusion Models
• Authors: Kafeng Wang, Jianfei Chen, He Li, Zhenpeng Mi, Jun Zhu,
• Abstract summary: We propose a method based on the improved Straight-Through Estimator to improve the deployment efficiency of diffusion models.<n> Experimental results on a Transformer and UNet-based diffusion models demonstrate that our method reduces MACs by 50% while maintaining FID.
• Score: 20.783533300147866
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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