Summary of Sparsedm: Toward Sparse Efficient Diffusion Models, by Kafeng Wang et al.
SparseDM: Toward Sparse Efficient Diffusion Models
by Kafeng Wang, Jianfei Chen, He Li, Zhenpeng Mi, Jun Zhu
First submitted to arxiv on: 16 Apr 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper proposes a novel approach to improve the deployment efficiency of diffusion models for data generation tasks. The method, which builds upon the Straight-Through Estimator, introduces sparse masks in convolutional and linear layers of a pre-trained diffusion model. This allows for flexible control over sparsity during inference, meeting FID and MACs requirements. Experimental results on four datasets using a Transformer-based diffusion model show that this approach reduces MACs by 50% while maintaining a high FID score (average increase of only 1.5). The method outperforms other approaches under various MACs conditions. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper makes it easier to use powerful computer models for generating data. These models, called diffusion models, are very good at creating fake but realistic data. However, they take a long time to run and need a lot of memory, which is a problem when you want to use them on small devices like phones. The researchers found a way to make these models work better on smaller devices by adding special masks to the model’s layers. This makes the model more efficient and can help create data that looks almost as good as real data. |
Keywords
» Artificial intelligence » Diffusion » Diffusion model » Inference » Transformer
