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Diffusion models have recently dominated image synthesis tasks.
An omnibus test of normality for moderate and large sample sizes
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Imagenet: A large-scale hierarchical image database
J. Deng, W. Dong, R. Socher, L.-J. Li, K. Li, and L. Fei-Fei · 2009
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Auto-encoding variational bayes
D. P. Kingma and M. Welling · 2014
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Lsun: Construction of a large-scale image dataset using deep learning with humans in the loop
F. Yu, A. Seff, Y. Zhang, S. Song, T. Funkhouser, and J. Xiao · 2015
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Improved techniques for training gans
T. Salimans, I. Goodfellow, W. Zaremba, V. Cheung, A. Radford, and X. Chen · 2016
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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 · 2017
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Cutlass: Fast linear algebra in cuda c++
A. Kerr, D. Merrill, J. Demouth, and J. Tran · 2017
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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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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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Towards effective low-bitwidth convolutional neural networks
B. Zhuang, C. Shen, M. Tan, L. Liu, and I. Reid · 2018
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Hawq: Hessian aware quantization of neural networks with mixed-precision
Z. Dong, Z. Yao, A. Gholami, M. W. Mahoney, and K. Keutzer · 2019
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Differentiable soft quantization: Bridging full-precision and low-bit neural networks
R. Gong, X. Liu, S. Jiang, T. Li, P. Hu, J. Lin, F. Yu, and J. Yan · 2019
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Relaxed quantization for discretized neural networks
C. Louizos, M. Reisser, T. Blankevoort, E. Gavves, and M. Welling · 2019
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Data-free quantization through weight equalization and bias correction
M. Nagel, M. van Baalen, T. Blankevoort, and M. Welling · 2019
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Zeroq: A novel zero shot quantization framework
Y. Cai, Z. Yao, Z. Dong, A. Gholami, M. W. Mahoney, and K. Keutzer · 2020
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Generative adversarial networks
I. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, and Y. Bengio · 2020
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Denoising diffusion probabilistic models
J. Ho, A. Jain, and P. Abbeel · 2020
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Improving post training neural quantization: Layer-wise calibration and integer programming
I. Hubara, Y. Nahshan, Y. Hanani, R. Banner, and D. Soudry · 2020
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Up or down? adaptive rounding for post-training quantization
M. Nagel, R. A. Amjad, M. van Baalen, C. Louizos, and T. Blankevoort · 2020
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Scipy 1.0: fundamental algorithms for scientific computing in python
P. Virtanen, R. Gommers, T. E. Oliphant, M. Haberland, T. Reddy, D. Cournapeau, E. Burovski, P. Peterson, W. Weckesser, J. Bright, et al · 2020
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Towards accurate post-training network quantization via bit-split and stitching
P. Wang, Q. Chen, X. He, and J. Cheng · 2020
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Differentiable joint pruning and quantization for hardware efficiency
Y. Wang, Y. Lu, and T. Blankevoort · 2020
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Wavegrad: Estimating gradients for waveform generation
N. Chen, Y. Zhang, H. Zen, R. J. Weiss, M. Norouzi, and W. Chan · 2021
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Towards mixed-precision quantization of neural networks via constrained optimization
W. Chen, P. Wang, and J. Cheng · 2021
Cited alongside, same era.
Diffusion models beat gans on image synthesis
P. Dhariwal and A. Q. Nichol · 2021
Cited alongside, same era.
Gotta go fast when generating data with score-based models
A. Jolicoeur-Martineau, K. Li, R. Piché-Taillefer, T. Kachman, and I. Mitliagkas · 2021
Cited alongside, same era.
Variational diffusion models
D. Kingma, T. Salimans, B. Poole, and J. Ho · 2021
Cited alongside, same era.
BRECQ: pushing the limit of post-training quantization by block reconstruction
Y. Li, R. Gong, X. Tan, Y. Yang, P. Hu, Q. Zhang, F. Yu, W. Wang, and S. Gu · 2021
Cited alongside, same era.
Knowledge distillation in iterative generative models for improved sampling speed
BDDM: bilateral denoising diffusion models for fast and high-quality speech synthesis
M. W. Y. Lam, J. Wang, D. Su, and D. Yu · 2022
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Zero-shot voice conditioning for denoising diffusion TTS models
A. Levkovitch, E. Nachmani, and L. Wolf · 2022
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Fq-vit: Post-training quantization for fully quantized vision transformer
Y. Lin, T. Zhang, P. Sun, Z. Li, and S. Zhou · 2022
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Pseudo numerical methods for diffusion models on manifolds
L. Liu, Y. Ren, Z. Lin, and Z. Zhao · 2022
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Dpm-solver: A fast ODE solver for diffusion probabilistic model sampling in around 10 steps
C. Lu, Y. Zhou, F. Bao, J. Chen, C. Li, and J. Zhu · 2022
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Dpm-solver++: Fast solver for guided sampling of diffusion probabilistic models
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E. Luhman and T. Luhman · 2021
Cited alongside, same era.
Diffusion probabilistic models for 3d point cloud generation
S. Luo and W. Hu · 2021
Cited alongside, same era.
Generating images with sparse representations
C. Nash, J. Menick, S. Dieleman, and P. W. Battaglia · 2021
Cited alongside, same era.
Improved denoising diffusion probabilistic models
A. Q. Nichol and P. Dhariwal · 2021
Cited alongside, same era.
Learning gradient fields for molecular conformation generation
C. Shi, S. Luo, M. Xu, and J. Tang · 2021
Cited alongside, same era.
Denoising diffusion implicit models
J. Song, C. Meng, and S. Ermon · 2021
Cited alongside, same era.
Score-based generative modeling through stochastic differential equations
Y. Song, J. Sohl-Dickstein, D. P. Kingma, A. Kumar, S. Ermon, and B. Poole · 2021
Cited alongside, same era.
C. Lu, Y. Zhou, F. Bao, J. Chen, C. Li, and J. Zhu · 2022
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Accelerating diffusion models via early stop of the diffusion process
Z. Lyu, X. Xu, C. Yang, D. Lin, and B. Dai · 2022
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High-resolution image synthesis with latent diffusion models
R. Rombach, A. Blattmann, D. Lorenz, P. Esser, and B. Ommer · 2022
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Progressive distillation for fast sampling of diffusion models
T. Salimans and J. Ho · 2022
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Post-training quantization on diffusion models
Y. Shang, Z. Yuan, B. Xie, B. Wu, and Y. Yan · 2022
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MCVD - masked conditional video diffusion for prediction, generation, and interpolation
V. Voleti, A. Jolicoeur-Martineau, and C. Pal · 2022
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Learning fast samplers for diffusion models by differentiating through sample quality
D. Watson, W. Chan, J. Ho, and M. Norouzi · 2022
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Qdrop: Randomly dropping quantization for extremely low-bit post-training quantization
X. Wei, R. Gong, Y. Li, X. Liu, and F. Yu · 2022
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Fast sampling of diffusion models with exponential integrator
Q. Zhang and Y. Chen · 2022
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Truncated diffusion probabilistic models
H. Zheng, P. He, W. Chen, and M. Zhou · 2022
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Int4 for ai inference
N. D. Blog · 2023
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Denoising mcmc for accelerating diffusion-based generative models
B. Kim and J. C. Ye · 2023
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On fast sampling of diffusion probabilistic models
Z. Kong and W. Ping · 2023
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Q-diffusion: Quantizing diffusion models
X. Li, Y. Liu, L. Lian, H. Yang, Z. Dong, D. Kang, S. Zhang, and K. Keutzer · 2023
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TensorRT
NVIDIA Corporation · 2023
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W. Wu, Y. Zhao, M. Z. Shou, H. Zhou, and C. Shen · 2023
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Root quantization: a self-adaptive supplement ste
L. Zhang, Y. He, Z. Lou, X. Ye, Y. Wang, and H. Zhou · 2023
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gddim: Generalized denoising diffusion implicit models
Q. Zhang, M. Tao, and Y. Chen · 2023
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