Effective Quantization for Diffusion Models on CPUs

Microsoft coco: Common objects in context

T.-Y. Lin, M. Maire, S. J. Belongie, J. Hays, P. Perona, D. Ramanan, P. Dollár, and C. L. Zitnick · 2014

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Quantization and training of neural networks for efficient integer-arithmetic-only inference

B. Jacob, S. Kligys, B. Chen, M. Zhu, M. Tang, A. Howard, H. Adam, and D. Kalenichenko · 2018

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Gptq: Accurate post-training quantization for generative pre-trained transformers

Original

E. Frantar, S. Ashkboos, T. Hoefler, and D. Alistarh · 2022

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High-resolution image synthesis with latent diffusion models

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Photorealistic text-to-image diffusion models with deep language understanding

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Optimize weight rounding via signed gradient descent for the quantization of llms

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W. Cheng, W. Zhang, H. Shen, Y. Cai, X. He, and K. Lv · 2023

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Gans trained by a two time-scale update rule converge to a local nash equilibrium

M. Heusel, H. Ramsauer, T. Unterthiner, B. Nessler, and S. Hochreiter

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Gans trained by a two time-scale update rule converge to a nash equilibrium

Original

M. Heusel, H. Ramsauer, T. Unterthiner, B. Nessler, G. Klambauer, and S. Hochreiter

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Efficient spatially sparse inference for conditional gans and diffusion models

M. Li, J. Lin, C. Meng, S. Ermon, S. Han, and J.-Y. Zhu

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Q-diffusion: Quantizing diffusion models

Original

X. Li, L. Lian, Y. Liu, H. Yang, Z. Dong, D. Kang, S. Zhang, and K. Keutzer

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Intel® extension for transformers, 2023

Intel · 2023

Closest in time.

Post-training quantization on diffusion models

Y. Shang, Z. Yuan, B. Xie, B. Wu, and Y. Yan · 2023

Closest in time.