Summary of Mpq-diff: Mixed Precision Quantization For Diffusion Models, by Rocco Manz Maruzzelli et al.
MPQ-Diff: Mixed Precision Quantization for Diffusion Models
by Rocco Manz Maruzzelli, Basile Lewandowski, Lydia Y. Chen
First submitted to arxiv on: 28 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 This paper explores the application of mixed-precision quantization (MPQ) to diffusion models (DMs), specifically for accelerating image generation while maintaining quality. The authors propose a scheme called MPQ-Diff, which allocates varying bit-widths to weights and activations based on layer importance, measured by the network orthogonality metric. To avoid profiling overhead, they adopt a uniform sampling scheme across time steps. The proposed approach is evaluated on LSUN and ImageNet datasets, achieving significant improvements in FID scores (15.39 and 14.93 respectively) compared to fixed-precision quantization. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper talks about how to make computer programs that generate pictures faster and better. They use a special technique called mixed-precision quantization to do this. It works by using different levels of detail for different parts of the program, based on how important each part is. This helps the program run faster while still making good-looking pictures. The authors tested their method on two big datasets and found that it made the pictures look much better than before. |
Keywords
» Artificial intelligence » Image generation » Precision » Quantization
