SparseDM: Toward Sparse Efficient Diffusion Models

• URL: http://arxiv.org/abs/2404.10445v2
• Date: Fri, 31 May 2024 02:56:14 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. Experiments on four datasets conducted on a state-of-the-art Transformer-based diffusion model demonstrate that our method reduces MACs by $50%$ while increasing FID by only 1.5 on average.
• Score: 20.783533300147866
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Diffusion models have been extensively used in data generation tasks and are recognized as one of the best generative models. However, their time-consuming deployment, long inference time, and requirements on large memory limit their application on mobile 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 use design progressive sparsity for model training in the fine-tuning stage, and switch the inference mask on and off, which supports a flexible choice of sparsity during inference according to the FID and MACs requirements. Experiments on four datasets conducted on a state-of-the-art Transformer-based diffusion model demonstrate that our method reduces MACs by $50\%$ while increasing FID by only 1.5 on average. Under other MACs conditions, the FID is also lower than 1$\sim$137 compared to other methods.

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