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Efficient differential equation solvers have significantly reduced the sampling time of diffusion models (DMs) while retaining high sampling quality.
Denoising diffusion probabilistic models
Ho, J., Jain, A., and Abbeel, P. (2020) · 2006
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Improved techniques for training Score-Based generative models
Song, Y. and Ermon, S. (2020) · 2006
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Numerical recipes 3rd edition: The art of scientific computing
Press, W. H., Teukolsky, S. A., Vetterling, W. T., and Flannery, B. P. (2007) · 2007
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Diffwave: A versatile diffusion model for audio synthesis
Kong, Z., Ping, W., Huang, J., Zhao, K., and Catanzaro, B. (2020) · 2009
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Exponential integrators
Hochbruck, M. and Ostermann, A. (2010) · 2010
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Tweedie’s formula and selection bias
Efron, B. (2011) · 2011
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A connection between score matching and denoising autoencoders
Vincent, P. (2011) · 2011
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Microsoft coco: Common objects in context
Lin, T.-Y., Maire, M., Belongie, S., Hays, J., Perona, P., Ramanan, D., Dollár, P., and Zitnick, C. L. (2014) · 2014
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ImageNet large scale visual recognition challenge
Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., Berg, A. C., and Fei-Fei, L. (2015) · 2015
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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) · 2017
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pytorch-fid: FID Score for PyTorch
Seitzer, M. (2020) · 2020
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Diffusion schrödinger bridge with applications to Score-Based generative modeling
De Bortoli, V., Thornton, J., Heng, J., and Doucet, A. (2021) · 2021
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Diffusion models beat GANs on image synthesis
Dhariwal, P. and Nichol, A. (2021) · 2021
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Score-Based generative modeling with Critically-Damped langevin diffusion
Dockhorn, T., Vahdat, A., and Kreis, K. (2021) · 2021
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A variational perspective on diffusion-based generative models and score matching
Huang, C.-W., Lim, J. H., and Courville, A. C. (2021) · 2021
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Openclip
Ilharco, G., Wortsman, M., Wightman, R., Gordon, C., Carlini, N., Taori, R., Dave, A., Shankar, V., Namkoong, H., Miller, J., Hajishirzi, H., Farhadi, A., and Schmidt, L. (2021) · 2021
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Variational diffusion models
Kingma, D., Salimans, T., Poole, B., and Ho, J. (2021) · 2021
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On fast sampling of diffusion probabilistic models
Kong, Z. and Ping, W. (2021) · 2021
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Knowledge distillation in iterative generative models for improved sampling speed
Luhman, E. and Luhman, T. (2021) · 2021
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Csdi: Conditional score-based diffusion models for probabilistic time series imputation
Tashiro, Y., Song, J., Song, Y., and Ermon, S. (2021) · 2021
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Score-based generative modeling in latent space
Vahdat, A., Kreis, K., and Kautz, J. (2021) · 2021
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Solving Schrödinger bridges via maximum likelihood
Vargas, F., Thodoroff, P., Lamacraft, A., and Lawrence, N. (2021) · 2021
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Deep generative learning via schrödinger bridge
Wang, G., Jiao, Y., Xu, Q., Wang, Y., and Yang, C. (2021) · 2021
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On distillation of guided diffusion models
Meng, C., Gao, R., Kingma, D. P., Ermon, S., Ho, J., and Salimans, T. (2022) · 2022
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Dreamfusion: Text-to-3d using 2d diffusion
Poole, B., Jain, A., Barron, J. T., and Mildenhall, B. (2022) · 2022
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Dynamic-backbone protein-ligand structure prediction with multiscale generative diffusion models
Qiao, Z., Nie, W., Vahdat, A., Miller III, T. F., and Anandkumar, A. (2022) · 2022
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Generative modelling with inverse heat dissipation
Rissanen, S., Heinonen, M., and Solin, A. (2022) · 2022
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High-resolution image synthesis with latent diffusion models
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Watson, D., Ho, J., Norouzi, M., and Chan, W. (2021) · 2021
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Zhang, Q. and Chen, Y. (2021) · 2021
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ediffi: Text-to-image diffusion models with an ensemble of expert denoisers
Balaji, Y., Nah, S., Huang, X., Vahdat, A., Song, J., Kreis, K., Aittala, M., Aila, T., Laine, S., Catanzaro, B., et al. (2022) · 2022
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Analytic-DPM: An Analytic Estimate of the Optimal Reverse Variance in Diffusion Probabilistic Models
Bao, F., Li, C., Zhu, J., and Zhang, B. (2022) · 2022
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Gaudi: A neural architect for immersive 3d scene generation
Bautista, M. A., Guo, P., Abnar, S., Talbott, W., Toshev, A., Chen, Z., Dinh, L., Zhai, S., Goh, H., Ulbricht, D., et al. (2022) · 2022
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Modeling temporal data as continuous functions with process diffusion
Biloš, M., Rasul, K., Schneider, A., Nevmyvaka, Y., and Günnemann, S. (2022) · 2022
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Classifier-free diffusion guidance
Ho, J. and Salimans, T. (2022) · 2022
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Rombach, R., Blattmann, A., Lorenz, D., Esser, P., and Ommer, B. (2022) · 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., et al. (2022) · 2022
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Progressive distillation for fast sampling of diffusion models
Salimans, T. and Ho, J. (2022) · 2022
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3d neural field generation using triplane diffusion
Shue, J. R., Chan, E. R., Po, R., Ankner, Z., Wu, J., and Wetzstein, G. (2022) · 2022
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Diffusion-gan: Training gans with diffusion
Wang, Z., Zheng, H., He, P., Chen, W., and Zhou, M. (2022) · 2022
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Protein structure generation via folding diffusion
Wu, K. E., Yang, K. K., Berg, R. v. d., Zou, J. Y., Lu, A. X., and Amini, A. P. (2022) · 2022
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Tackling the generative learning trilemma with denoising diffusion GANs
Xiao, Z., Kreis, K., and Vahdat, A. (2022) · 2022
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Geodiff: A geometric diffusion model for molecular conformation generation
Xu, M., Yu, L., Song, Y., Shi, C., Ermon, S., and Tang, J. (2022) · 2022
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Fast sampling of diffusion models with exponential integrator
Zhang, Q. and Chen, Y. (2022) · 2022
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gddim: Generalized denoising diffusion implicit models
Zhang, Q., Tao, M., and Chen, Y. (2022) · 2022
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Era-solver: Error-robust adams solver for fast sampling of diffusion probabilistic models
Li, S., Liu, L., Chai, Z., Li, R., and Tan, X. (2023) · 2023
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Song, Y., Dhariwal, P., Chen, M., and Sutskever, I. (2023) · 2023
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Unipc: A unified predictor-corrector framework for fast sampling of diffusion models
Zhao, W., Bai, L., Rao, Y., Zhou, J., and Lu, J. (2023) · 2023
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