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Diffusion models have achieved remarkable success in the image and video generation tasks.
Plug-and-play diffusion features for text-driven image-to-image translation
Tumanyan, N.; Geyer, M.; Bagon, S.; and Dekel, T. 2023 · 1930
Earlier work this paper cites.
Post-training quantization on diffusion models
Shang, Y.; Yuan, Z.; Xie, B.; Wu, B.; and Yan, Y. 2023 · 1981
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Power-law distributions in empirical data
Clauset, A.; Shalizi, C. R.; and Newman, M. E. 2009 · 2009
Earlier work this paper cites.
ImageNet: A large-scale hierarchical image database
Deng, J.; Dong, W.; Socher, R.; Li, L.; Li, K.; and Fei-Fei, L. 2009 · 2009
Earlier work this paper cites.
Learning multiple layers of features from tiny images
Krizhevsky, A.; Hinton, G.; et al. 2009 · 2009
Earlier work this paper cites.
LSUN: Construction of a Large-scale Image Dataset using Deep Learning with Humans in the Loop
Yu, F.; Zhang, Y.; Song, S.; Seff, A.; and Xiao, J. 2015 · 2015
Earlier work this paper cites.
Improved techniques for training gans
Salimans, T.; Goodfellow, I.; Zaremba, W.; Cheung, V.; Radford, A.; and Chen, X. 2016 · 2016
Earlier work this paper cites.
Gans trained by a two time-scale update rule converge to a local nash equilibrium
Heusel, M.; Ramsauer, H.; Unterthiner, T.; Nessler, B.; and Hochreiter, S. 2017 · 2017
Earlier work this paper cites.
Differentiable soft quantization: Bridging full-precision and low-bit neural networks
Gong, R.; Liu, X.; Jiang, S.; Li, T.; Hu, P.; Lin, J.; Yu, F.; and Yan, J. 2019 · 2019
Earlier work this paper cites.
Denoising diffusion probabilistic models
Ho, J.; Jain, A.; and Abbeel, P. 2020 · 2020
Earlier work this paper cites.
Up or down? Adaptive rounding for post-training quantization
Nagel, M.; Amjad, R. A.; Van Baalen, M.; Louizos, C.; and Blankevoort, T. 2020 · 2020
Earlier work this paper cites.
BRECQ: Pushing the limit of post-training quantization by block reconstruction
Li, Y.; Gong, R.; Tan, X.; Yang, Y.; Hu, P.; Zhang, Q.; Yu, F.; Wang, W.; and Gu, S. 2021 · 2021
Earlier work this paper cites.
Knowledge distillation in iterative generative models for improved sampling speed
Luhman, E.; and Luhman, T. 2021 · 2021
Earlier work this paper cites.
Denoising diffusion implicit models
Song, J.; Meng, C.; and Ermon, S. 2021 · 2021
Earlier work this paper cites.
Come-closer-diffuse-faster: Accelerating conditional diffusion models for inverse problems through stochastic contraction
Chung, H.; Sim, B.; and Ye, J. C. 2022 · 2022
Earlier work this paper cites.
Video diffusion models
Ho, J.; Salimans, T.; Gritsenko, A.; Chan, W.; Norouzi, M.; and Fleet, D. J. 2022 · 2022
Cited alongside, same era.
Fp8 quantization: The power of the exponent
Kuzmin, A.; Van Baalen, M.; Ren, Y.; Nagel, M.; Peters, J.; and Blankevoort, T. 2022 · 2022
Cited alongside, same era.
BDDM: Bilateral denoising diffusion models for fast and high-quality speech synthesis
Lam, M. W. Y.; Wang, J.; Su, D.; and Yu, D. 2022 · 2022
Cited alongside, same era.
Srdiff: Single image super-resolution with diffusion probabilistic models
Li, H.; Yang, Y.; Chang, M.; Chen, S.; Feng, H.; Xu, Z.; Li, Q.; and Chen, Y. 2022 · 2022
Cited alongside, same era.
FQ-ViT: Post-training quantization for fully quantized vision transformer
Lin, Y.; Zhang, T.; Sun, P.; Li, Z.; and Zhou, S. 2022 · 2022
Cited alongside, same era.
Accelerating diffusion models via early stop of the diffusion process
Fast sampling of diffusion models with exponential integrator
Zhang, Q.; and Chen, Y. 2023 · 2023
Later among the works it cites.
LD-Pruner: Efficient pruning of latent diffusion models using task-agnostic insights
Castells, T.; Song, H.-K.; Kim, B.-K.; and Choi, S. 2024 · 2024
Closest in time.
GenTron: Diffusion transformers for image and video generation
Chen, S.; Xu, M.; Ren, J.; Cong, Y.; He, S.; Xie, Y.; Sinha, A.; Luo, P.; Xiang, T.; and Perez-Rua, J.-M. 2024 · 2024
Closest in time.
Make repvgg greater again: A quantization-qware approach
Chu, X.; Li, L.; and Zhang, B. 2024 · 2024
Closest in time.
Qlora: Efficient finetuning of quantized llms
Dettmers, T.; Pagnoni, A.; Holtzman, A.; and Zettlemoyer, L. 2024 · 2024
Closest in time.
Ptqd: Accurate post-training quantization for diffusion models
He, Y.; Liu, L.; Liu, J.; Wu, W.; Zhou, H.; and Zhuang, B. 2024 · 2024
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Lyu, Z.; Xu, X.; Yang, C.; Lin, D.; and Dai, B. 2022 · 2022
Cited alongside, same era.
Progressive distillation for fast sampling of diffusion models
Salimans, T.; and Ho, J. 2022 · 2022
Cited alongside, same era.
Learning fast samplers for diffusion models by differentiating through sample quality
Watson, D.; Chan, W.; Ho, J.; and Norouzi, M. 2022 · 2022
Cited alongside, same era.
QDrop: Randomly dropping quantization for extremely low-bit post-training quantization
Wei, X.; Gong, R.; Li, Y.; Liu, X.; and Yu, F. 2022 · 2022
Cited alongside, same era.
How much is enough? A study on diffusion times in score-based generative models
Franzese, G.; Rossi, S.; Yang, L.; Finamore, A.; Rossi, D.; Filippone, M.; and Michiardi, P. 2023 · 2023
Cited alongside, same era.
Implicit diffusion models for continuous super-resolution
Gao, S.; Liu, X.; Zeng, B.; Xu, S.; Li, Y.; Luo, X.; Liu, J.; Zhen, X.; and Zhang, B. 2023 · 2023
Cited alongside, same era.
Denoising MCMC for accelerating diffusion-based generative models
Kim, B.; and Ye, J. C. 2023 · 2023
Cited alongside, same era.
Closest in time.
Deepcache: Accelerating diffusion models for free
Ma, X.; Fang, G.; and Wang, X. 2024 · 2024
Closest in time.
Sun, H.; Tang, C.; Wang, Z.; Meng, Y.; Jiang, J.; Ma, X.; and Zhu, W. 2024 · 2024
Closest in time.
Post-training quantization with progressive calibration and activation relaxing for text-to-image diffusion models
Tang, S.; Wang, X.; Chen, H.; Guan, C.; Wu, Z.; Tang, Y.; and Zhu, W. 2024 · 2024
Closest in time.
AdaLog: Post-training quantization for vision transformers with adaptive logarithm quantizer
Wu, Z.; Chen, J.; Zhong, H.; Huang, D.; and Wang, Y. 2024 · 2024
Closest in time.
Timestep-aware correction for quantized diffusion models
Yao, Y.; Tian, F.; Chen, J.; Lin, H.; Dai, G.; Liu, Y.; and Wang, J. 2024 · 2024
Closest in time.
Laptop-diff: Layer pruning and normalized distillation for compressing diffusion models
Zhang, D.; Li, S.; Chen, C.; Xie, Q.; and Lu, H. 2024 · 2024
Closest in time.
Unipc: A unified predictor-corrector framework for fast sampling of diffusion models
Zhao, W.; Bai, L.; Rao, Y.; Zhou, J.; and Lu, J. 2024 · 2024
Closest in time.
Fast ode-based sampling for diffusion models in around 5 steps
Zhou, Z.; Chen, D.; Wang, C.; and Chen, C. 2024 · 2024
Closest in time.