Summary of Svdquant: Absorbing Outliers by Low-rank Components For 4-bit Diffusion Models, By Muyang Li et al.
SVDQuant: Absorbing Outliers by Low-Rank Components for 4-Bit Diffusion Models
by Muyang Li, Yujun Lin, Zhekai Zhang, Tianle Cai, Xiuyu Li, Junxian Guo, Enze Xie, Chenlin Meng, Jun-Yan Zhu, Song Han
First submitted to arxiv on: 7 Nov 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG)
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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 The proposed SVDQuant method accelerates diffusion models by quantizing their weights and activations to 4 bits, addressing memory demands and latency issues. The technique absorbs outliers using a low-rank branch, first shifting them from activations to weights, then using Singular Value Decomposition (SVD) to handle weight outliers while a low-bit quantized branch handles residuals. An inference engine called Nunchaku fuses kernels to reduce redundant memory access, supporting off-the-shelf adapters without re-quantization. The method preserves image quality, reducing memory usage and achieving speedups on various datasets and benchmarks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Diffusion models can create high-quality images but are limited by their large size. This paper solves this problem by shrinking the model’s weight and activation values from 8 bits to just 4 bits. To do this, they developed a new technique called SVDQuant that helps the model work better with low-bit values. They also created an engine called Nunchaku that makes it faster to use the model on different devices. The results show that their method can make the model much smaller and faster without losing its ability to create high-quality images. |
Keywords
» Artificial intelligence » Diffusion » Inference » Quantization
