Summary of Mixdq: Memory-efficient Few-step Text-to-image Diffusion Models with Metric-decoupled Mixed Precision Quantization, by Tianchen Zhao et al.
MixDQ: Memory-Efficient Few-Step Text-to-Image Diffusion Models with Metric-Decoupled Mixed Precision Quantization
by Tianchen Zhao, Xuefei Ning, Tongcheng Fang, Enshu Liu, Guyue Huang, Zinan Lin, Shengen Yan, Guohao Dai, Yu Wang
First submitted to arxiv on: 28 May 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- 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 The proposed paper introduces a novel mixed-precision quantization framework called MixDQ to optimize few-step diffusion models for deployment on resource-constrained devices. The authors leverage Post Training Quantization (PTQ) techniques to reduce memory consumption, while preserving image quality and text alignment. They develop specialized methods for text embedding quantization and conduct metric-decoupled sensitivity analysis to measure layer sensitivity. A bit-width allocation method is also introduced to achieve the best trade-off between performance and efficiency. The resulting MixDQ framework can achieve W8A8 without performance loss, or W4A8 with negligible visual degradation, while reducing model size and memory cost by 3-4x compared to FP16. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper proposes a new way to make computer models work better on devices with limited resources. It’s like compressing a big file to send it over the internet – you keep the important parts and throw away some of the extra information. This helps the model be more efficient, so it can run faster or use less energy. The authors also test their new method to make sure it doesn’t hurt the quality of the images generated by the model. They found that it worked well even when using a lower level of detail, and they achieved significant speed and memory improvements. |
Keywords
» Artificial intelligence » Alignment » Embedding » Precision » Quantization
