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Diffusion models have significantly advanced the fields of image, audio, and video generation, but they depend on an iterative sampling process that causes slow generation.
An introduction to numerical analysis
Süli, E. and Mayers, D. F · 2003
Earlier work this paper cites.
Estimation of non-normalized statistical models by score matching
Hyvärinen, A. and Dayan, P · 2005
Earlier work this paper cites.
Imagenet: A large-scale hierarchical image database
Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., and Fei-Fei, L · 2009
Earlier work this paper cites.
Learning multiple layers of features from tiny images
Krizhevsky, A., Hinton, G., et al · 2009
Earlier work this paper cites.
A Connection Between Score Matching and Denoising Autoencoders
Vincent, P · 2011
Earlier work this paper cites.
Playing atari with deep reinforcement learning
Mnih, V., Kavukcuoglu, K., Silver, D., Graves, A., Antonoglou, I., Wierstra, D., and Riedmiller, M · 2013
Earlier work this paper cites.
Generative adversarial nets
Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., and Bengio, Y · 2014
Earlier work this paper cites.
Auto-encoding variational bayes
Kingma, D. P. and Welling, M · 2014
Earlier work this paper cites.
Stochastic backpropagation and approximate inference in deep generative models
Rezende, D. J., Mohamed, S., and Wierstra, D · 2014
Earlier work this paper cites.
NICE: Non-linear independent components estimation
Dinh, L., Krueger, D., and Bengio, Y · 2015
Earlier work this paper cites.
Continuous control with deep reinforcement learning
Lillicrap, T. P., Hunt, J. J., Pritzel, A., Heess, N., Erez, T., Tassa, Y., Silver, D., and Wierstra, D · 2015
Earlier work this paper cites.
Human-level control through deep reinforcement learning
Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A. A., Veness, J., Bellemare, M. G., Graves, A., Riedmiller, M., Fidjeland, A. K., Ostrovski, G., et al · 2015
Earlier work this paper cites.
Deep Unsupervised Learning Using Nonequilibrium Thermodynamics
Sohl-Dickstein, J., Weiss, E., Maheswaranathan, N., and Ganguli, S · 2015
Earlier work this paper cites.
Lsun: Construction of a large-scale image dataset using deep learning with humans in the loop
Yu, F., Seff, A., Zhang, Y., Song, S., Funkhouser, T., and Xiao, J · 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
Earlier work this paper cites.
Density estimation using real NVP
Dinh, L., Sohl-Dickstein, J., and Bengio, S · 2017
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
Earlier work this paper cites.
Neural Ordinary Differential Equations
Chen, R. T., Rubanova, Y., Bettencourt, J., and Duvenaud, D. K · 2018
Earlier work this paper cites.
Progressive growing of GANs for improved quality, stability, and variation
Karras, T., Aila, T., Laine, S., and Lehtinen, J · 2018
Earlier work this paper cites.
Glow: Generative flow with invertible 1x1 convolutions
Kingma, D. P. and Dhariwal, P · 2018
Earlier work this paper cites.
The unreasonable effectiveness of deep features as a perceptual metric
Zhang, R., Isola, P., Efros, A. A., Shechtman, E., and Wang, O · 2018
Earlier work this paper cites.
Large scale GAN training for high fidelity natural image synthesis
Brock, A., Donahue, J., and Simonyan, K · 2019
Earlier work this paper cites.
Residual flows for invertible generative modeling
Chen, R. T., Behrmann, J., Duvenaud, D. K., and Jacobsen, J.-H · 2019
Earlier work this paper cites.
Autogan: Neural architecture search for generative adversarial networks
Gong, X., Chang, S., Jiang, Y., and Wang, Z · 2019
Earlier work this paper cites.
Improved precision and recall metric for assessing generative models
Kynkäänniemi, T., Karras, T., Laine, S., Lehtinen, J., and Aila, T · 2019
Cited alongside, same era.
On the variance of the adaptive learning rate and beyond
Liu, L., Jiang, H., He, P., Chen, W., Liu, X., Gao, J., and Han, J · 2019
Cited alongside, same era.
Generative Modeling by Estimating Gradients of the Data Distribution
Song, Y. and Ermon, S · 2019
Cited alongside, same era.
Sliced score matching: A scalable approach to density and score estimation
Song, Y., Garg, S., Shi, J., and Ermon, S · 2019
Cited alongside, same era.
Sliced wasserstein generative models
Wu, J., Huang, Z., Acharya, D., Li, W., Thoma, J., Paudel, D. P., and Gool, L. V · 2019
Cited alongside, same era.
Dual contradistinctive generative autoencoder
Parmar, G., Li, D., Lee, K., and Tu, Z · 2021
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Grad-TTS: A diffusion probabilistic model for text-to-speech
Popov, V., Vovk, I., Gogoryan, V., Sadekova, T., and Kudinov, M · 2021
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Score-based generative modeling through stochastic differential equations
Song, Y., Sohl-Dickstein, J., Kingma, D. P., Kumar, A., Ermon, S., and Poole, B · 2021
Later among the works it cites.
Score-based generative modeling in latent space
Vahdat, A., Kreis, K., and Kautz, J · 2021
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ediff-i: Text-to-image diffusion models with ensemble of expert denoisers
Balaji, Y., Nah, S., Huang, X., Vahdat, A., Song, J., Kreis, K., Aittala, M., Aila, T., Laine, S., Catanzaro, B., Karras, T., and Liu, M.-Y · 2022
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Xiao, Z., Yan, Q., and Amit, Y · 2019
Cited alongside, same era.
Bootstrap your own latent-a new approach to self-supervised learning
Grill, J.-B., Strub, F., Altché, F., Tallec, C., Richemond, P., Buchatskaya, E., Doersch, C., Avila Pires, B., Guo, Z., Gheshlaghi Azar, M., et al · 2020
Cited alongside, same era.
Momentum contrast for unsupervised visual representation learning
He, K., Fan, H., Wu, Y., Xie, S., and Girshick, R · 2020
Cited alongside, same era.
Denoising Diffusion Probabilistic Models
Ho, J., Jain, A., and Abbeel, P · 2020
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Analyzing and improving the image quality of stylegan
Karras, T., Laine, S., Aittala, M., Hellsten, J., Lehtinen, J., and Aila, T · 2020
Cited alongside, same era.
DiffWave: A Versatile Diffusion Model for Audio Synthesis
Kong, Z., Ping, W., Huang, J., Zhao, K., and Catanzaro, B · 2020
Cited alongside, same era.
Denoising diffusion implicit models
Song, J., Meng, C., and Ermon, S · 2020
Cited alongside, same era.
Dockhorn, T., Vahdat, A., and Kreis, K · 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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Denoising diffusion restoration models
Kawar, B., Elad, M., Ermon, S., and Song, J · 2022
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Flow straight and fast: Learning to generate and transfer data with rectified flow
Liu, X., Gong, C., and Liu, Q · 2022
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Dpm-solver: A fast ode solver for diffusion probabilistic model sampling in around 10 steps
Lu, C., Zhou, Y., Bao, F., Chen, J., Li, C., and Zhu, J · 2022
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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
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Hierarchical text-conditional image generation with clip latents
Ramesh, A., Dhariwal, P., Nichol, A., Chu, C., and Chen, M · 2022
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High-resolution image synthesis with latent diffusion models
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., et al · 2022
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Progressive distillation for fast sampling of diffusion models
Salimans, T. and Ho, J · 2022
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Stylegan-xl: Scaling stylegan to large diverse datasets
Sauer, A., Schwarz, K., and Geiger, A · 2022
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Solving inverse problems in medical imaging with score-based generative models
Song, Y., Shen, L., Xing, L., and Ermon, S · 2022
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Tackling the generative learning trilemma with denoising diffusion GANs
Xiao, Z., Kreis, K., and Vahdat, A · 2022
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Poisson flow generative models
Xu, Y., Liu, Z., Tegmark, M., and Jaakkola, T. S · 2022
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Fast sampling of diffusion models with exponential integrator
Zhang, Q. and Chen, Y · 2022
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Fast sampling of diffusion models via operator learning
Zheng, H., Nie, W., Vahdat, A., Azizzadenesheli, K., and Anandkumar, A · 2022
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Diffusion posterior sampling for general noisy inverse problems
Chung, H., Kim, J., Mccann, M. T., Klasky, M. L., and Ye, J. C · 2023
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Pseudoinverse-guided diffusion models for inverse problems
Song, J., Vahdat, A., Mardani, M., and Kautz, J · 2023
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Truncated diffusion probabilistic models and diffusion-based adversarial auto-encoders
Zheng, H., He, P., Chen, W., and Zhou, M · 2023
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