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SegQuant: A Semantics-Aware and Generalizable Quantization Framework for Diffusion Models

Created by
  • Haebom

Author

Jiaji Zhang, Ruichao Sun, Hailiang Zhao, Jiaju Wu, Peng Chen, Hao Li, Yuying Liu, Kingsum Chow, Gang Xiong, Shuiguang Deng

Outline

This paper proposes SegQuant, a novel quantization framework for reducing the computational cost of diffusion models. Addressing the challenges of existing post-training quantization (PTQ) methods, which struggle with generalization due to their specificity in model structure, SegQuant combines the SegLinear strategy, which captures structural semantics and spatial heterogeneity, with the DualScale technique, which preserves polar asymmetric activation, to achieve high performance and applicability to a wide range of models. It is applicable to a wide range of models, including Transformer-based diffusion models, and ensures compatibility with major deployment tools.

Takeaways, Limitations

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Takeaways:
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We present SegQuant, a novel quantization framework that effectively reduces the computational cost of diffusion models.
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Provides a generalizable quantization technique that does not depend on model structure.
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Solving the model specificity and deployment difficulties of the existing PTQ method, Limitations.
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Ensure seamless compatibility with major deployment tools.
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Applicable to various diffusion models beyond Transformer-based models.
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Limitations:
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Additional experimental results are needed to determine how well SegQuant performs compared to other state-of-the-art quantization techniques.
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Extensive evaluation of real-world performance and stability across a variety of models and deployment environments is required.
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Further research is needed to determine whether optimizations are possible for specific hardware platforms.
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