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In this paper, we propose SegQuant, a novel method to quantize pre-trained models without retraining to reduce the computational cost of diffusion models. SegQuant provides a unified quantization framework applicable to various models by combining SegLinear, which captures the semantics and spatial heterogeneity of the model structure, and DualScale, which preserves polar asymmetric activations that are important for maintaining the visual fidelity of the generated results. We aim to address the generalization of existing PTQ methods and the integration with industrial deployment pipelines.
Takeaways, Limitations
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Takeaways :
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SegQuant, a model-structure-independent integrated quantization framework that overcomes the limitations of existing PTQ methods
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Proof of applicability to various models as well as transformer-based diffusion models
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Ensure seamless compatibility with major deployment tools
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Maintain visual fidelity of generated results
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Limitations :
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Lack of specific experimental results on how SegQuant performs compared to other state-of-the-art PTQ methods (estimated)
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Lack of scope and detail in evaluating generalization performance across models (estimated)
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Lack of application and performance evaluation in real industrial deployment environments (estimated)
