Summary of Dilatequant: Accurate and Efficient Diffusion Quantization Via Weight Dilation, by Xuewen Liu et al.
DilateQuant: Accurate and Efficient Diffusion Quantization via Weight Dilation
by Xuewen Liu, Zhikai Li, Qingyi Gu
First submitted to arxiv on: 22 Sep 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 Diffusion models have achieved impressive results on various image generation tasks, but their high computational costs and large memory requirements hinder their use in real-world applications. To address this issue, we propose DilateQuant, a novel quantization framework for diffusion models that balances accuracy and efficiency. The key innovation is Weight Dilation (WD), which exploits unsaturated in-channel weights to reduce the range of activations without additional computation cost. This enables easy activation quantization and efficient model convergence during training. To further enhance performance while preserving efficiency, we introduce Temporal Parallel Quantizer (TPQ) for parallel quantization across time steps, and Block-wise Knowledge Distillation (BKD) to align quantized models with full-precision models at a block level. These techniques significantly improve the performance and reduce the time cost of the quantization process. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Diffusion models are really good at creating images, but they use a lot of computer power and memory. This makes it hard to use them in real-life situations. Our solution is called DilateQuant. It’s a new way to make diffusion models smaller and faster without losing their ability to create accurate images. We do this by finding special weights that don’t need to be changed very much, so we can use less computer power and memory. This makes it easier for the model to learn and creates more accurate images. To make things even better, we also developed a way to speed up the process of quantizing the model, which is called Temporal Parallel Quantizer. And finally, we showed that our method works well by comparing it to other methods. |
Keywords
» Artificial intelligence » Diffusion » Image generation » Knowledge distillation » Precision » Quantization
