Summary of Qncd: Quantization Noise Correction For Diffusion Models, by Huanpeng Chu et al.
QNCD: Quantization Noise Correction for Diffusion Models
by Huanpeng Chu, Wei Wu, Chengjie Zang, Kun Yuan
First submitted to arxiv on: 28 Mar 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 paper introduces a novel approach to address the limitations of diffusion models in image synthesis. Specifically, it focuses on post-training quantization (PTQ), which accelerates sampling but compromises sample quality in low-bit settings. The authors propose a unified Quantization Noise Correction Scheme (QNCD) to mitigate quantization noise throughout the sampling process. QNCD tackles intra and inter quantization noise, caused by embeddings and cumulative deviations, respectively. Extensive experiments show that this method outperforms previous quantization methods for diffusion models, achieving lossless results in W4A8 and W8A8 quantization settings on ImageNet (LDM-4). The authors provide code availability at https://github.com/huanpengchu/QNCD. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper helps fix a problem with diffusion models that makes them too slow. These models are great for making new images, but they use up a lot of computer power. To make them faster, the authors suggest using something called post-training quantization (PTQ). PTQ is like a shortcut that makes the model work better and faster, but it also makes the pictures not as good in low-quality settings. The paper shows how to fix this problem by introducing a new way to reduce noise when using PTQ. This method works well and helps make the model more efficient. |
Keywords
» Artificial intelligence » Diffusion » Image synthesis » Quantization
