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We present a new method for making diffusion models faster to sample.
Large sample properties of generalized method of moments estimators
Hansen, L. P · 1982
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Which moments to match?
Gallant, A. R. and Tauchen, G · 1996
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Imagenet: A large-scale hierarchical image database
Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., and Fei-Fei, L · 2009
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Adam: A method for stochastic optimization
Kingma, D. P. and Ba, J · 2014
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Microsoft COCO: common objects in context
Lin, T., Maire, M., Belongie, S. J., Bourdev, L. D., Girshick, R. B., Hays, J., Perona, P., Ramanan, D., Doll’a r, P., and Zitnick, C. L · 2014
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Deep unsupervised learning using nonequilibrium thermodynamics
Sohl-Dickstein, J., Weiss, E. A., Maheswaranathan, N., and Ganguli, S · 2015
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Improved techniques for training gans
Salimans, T., Goodfellow, I., Zaremba, W., Cheung, V., Radford, A., and Chen, X · 2016
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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
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JAX: composable transformations of Python+NumPy programs
Bradbury, J., Frostig, R., Hawkins, P., Johnson, M. J., Leary, C., Maclaurin, D., Necula, G., Paszke, A., VanderPlas, J., Wanderman-Milne, S., and Zhang, Q · 2018
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Generative modeling by estimating gradients of the data distribution
Song, Y. and Ermon, S · 2019
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Generative adversarial networks
Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., and Bengio, Y · 2020
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Denoising diffusion probabilistic models
Ho, J., Jain, A., and Abbeel, P · 2020
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Kingma, D. P., Salimans, T., Poole, B., and Ho, J · 2021
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Learning transferable visual models from natural language supervision
Radford, A., Kim, J. W., Hallacy, C., Ramesh, A., Goh, G., Agarwal, S., Sastry, G., Askell, A., Mishkin, P., Clark, J., et al · 2021
Cited alongside, same era.
Score-based generative modeling through stochastic differential equations
Song, Y., Sohl-Dickstein, J., Kingma, D. P., Kumar, A., Ermon, S., and Poole, B · 2021
Cited alongside, same era.
Tackling the generative learning trilemma with denoising diffusion gans
Xiao, Z., Kreis, K., and Vahdat, A · 2021
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Classifier-free diffusion guidance
Ho, J. and Salimans, T · 2022
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Elucidating the design space of diffusion-based generative models
Karras, T., Aittala, M., Aila, T., and Laine, S · 2022
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Power hungry processing: Watts driving the cost of ai deployment?
Luccioni, A. S., Jernite, Y., and Strubell, E · 2023
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Diff-instruct: A universal approach for transferring knowledge from pre-trained diffusion models
Luo, W., Hu, T., Zhang, S., Sun, J., Li, Z., and Zhang, Z · 2023
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Swiftbrush: One-step text-to-image diffusion model with variational score distillation
Nguyen, T. H. and Tran, A · 2023
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Improved techniques for training consistency models
Song, Y. and Dhariwal, P · 2023
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Consistency models
Song, Y., Dhariwal, P., Chen, M., and Sutskever, I · 2023
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Rombach, R., Blattmann, A., Lorenz, D., Esser, P., and Ommer, B · 2022
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Photorealistic text-to-image diffusion models with deep language understanding
Saharia, C., Chan, W., Saxena, S., Li, L., Whang, J., Denton, E., Ghasemipour, S. K. S., Ayan, B. K., Mahdavi, S. S., Lopes, R. G., Salimans, T., Ho, J., Fleet, D. J., and Norouzi, M · 2022
Cited alongside, same era.
Progressive distillation for fast sampling of diffusion models
Salimans, T. and Ho, J · 2022
Cited alongside, same era.
TRACT: denoising diffusion models with transitive closure time-distillation
Berthelot, D., Autef, A., Lin, J., Yap, D. A., Zhai, S., Hu, S., Zheng, D., Talbott, W., and Gu, E · 2023
Cited alongside, same era.
simple diffusion: End-to-end diffusion for high resolution images
Hoogeboom, E., Heek, J., and Salimans, T · 2023
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Scalable adaptive computation for iterative generation
Jabri, A., Fleet, D. J., and Chen, T · 2023
Cited alongside, same era.
Understanding the diffusion objective as a weighted integral of elbos
Kingma, D. P. and Gao, R · 2023
Cited alongside, same era.
Later among the works it cites.
One-step diffusion with distribution matching distillation
Yin, T., Gharbi, M., Zhang, R., Shechtman, E., Durand, F., Freeman, W. T., and Park, T · 2023
Later among the works it cites.
Heek, J., Hoogeboom, E., and Salimans, T · 2024
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Consistency trajectory models: Learning probability flow ODE trajectory of diffusion
Kim, D., Lai, C.-H., Liao, W.-H., Murata, N., Takida, Y., Uesaka, T., He, Y., Mitsufuji, Y., and Ermon, S · 2024
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Diff-instruct: A universal approach for transferring knowledge from pre-trained diffusion models
Luo, W., Hu, T., Zhang, S., Sun, J., Li, Z., and Zhang, Z · 2024
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Google cloud tpu training
TPUv5e · 2024
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Prolificdreamer: High-fidelity and diverse text-to-3d generation with variational score distillation
Wang, Z., Lu, C., Wang, Y., Bao, F., Li, C., Su, H., and Zhu, J · 2024
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Perflow: Piecewise rectified flow as universal plug-and-play accelerator
Yan, H., Liu, X., Pan, J., Liew, J. H., Liu, Q., and Feng, J · 2024
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