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We introduce Score identity Distillation (SiD), an innovative data-free method that distills the generative capabilities of pretrained diffusion models into a single-step generator.
An empirical Bayes approach to statistics
Robbins, H. E · 1992
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Exact penalization and necessary optimality conditions for generalized bilevel programming problems
Ye, J., Zhu, D., and Zhu, Q. J · 1997
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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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Learning multiple layers of features from tiny images
Krizhevsky, A. et al · 2009
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Interpretation and generalization of score matching
Lyu, S · 2009
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Tweedie’s formula and selection bias
Efron, B · 2011
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A Connection Between Score Matching and Denoising Autoencoders
Vincent, P · 2011
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Bayesian learning via stochastic gradient Langevin dynamics
Welling, M. and Teh, Y. W · 2011
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Generative adversarial nets
Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., and Bengio, Y · 2014
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Auto-encoding variational Bayes
Kingma, D. P. and Welling, M · 2014
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Stochastic backpropagation and approximate inference in deep generative models
Rezende, D. J., Mohamed, S., and Wierstra, D · 2014
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U-Net: Convolutional networks for biomedical image segmentation
Ronneberger, O., P.Fischer, and Brox, T · 2015
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Deep unsupervised learning using nonequilibrium thermodynamics
Sohl-Dickstein, J., Weiss, E., 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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Assigning a value to a power likelihood in a general Bayesian model
Holmes, C. C. and Walker, S. G · 2017
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Semi-implicit variational inference
Yin, M. and Zhou, M · 2018
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Large scale GAN training for high fidelity natural image synthesis
Brock, A., Donahue, J., and Simonyan, K · 2019
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Semi-implicit graph variational auto-encoders
Hasanzadeh, A., Hajiramezanali, E., Narayanan, K., Duffield, N., Zhou, M., and Qian, X · 2019
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A style-based generator architecture for generative adversarial networks
Karras, T., Laine, S., and Aila, T · 2019
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Improved precision and recall metric for assessing generative models
Kynkäänniemi, T., Karras, T., Laine, S., Lehtinen, J., and Aila, T · 2019
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Energy-inspired models: Learning with sampler-induced distributions
Lawson, J., Tucker, G., Dai, B., and Ranganath, R · 2019
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Doubly semi-implicit variational inference
Molchanov, D., Kharitonov, V., Sobolev, A., and Vetrov, D · 2019
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Importance weighted hierarchical variational inference
Sobolev, A. and Vetrov, D. P · 2019
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Generative Modeling by Estimating Gradients of the Data Distribution
Song, Y. and Ermon, S · 2019
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Unbiased implicit variational inference
Titsias, M. K. and Ruiz, F · 2019
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Variational approximations using Fisher divergence
Yang, Y., Martin, R., and Bondell, H · 2019
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Stargan v2: Diverse image synthesis for multiple domains
Choi, Y., Uh, Y., Yoo, J., and Ha, J.-W · 2020
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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
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Denoising diffusion implicit models
Song, J., Meng, C., and Ermon, S · 2020
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NVAE: A deep hierarchical variational autoencoder
Vahdat, A. and Kautz, J · 2020
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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. L., Ghasemipour, K., Gontijo Lopes, R., Karagol Ayan, B., Salimans, T., Ho, J., Fleet, D. J., and Norouzi, M · 2022
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Progressive distillation for fast sampling of diffusion models
Salimans, T. and Ho, J · 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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Differentiable augmentation for data-efficient gan training
Zhao, S., Liu, Z., Lin, J., Zhu, J.-Y., and Han, S · 2020
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Diffusion models beat gans on image synthesis
Dhariwal, P. and Nichol, A · 2021
Cited alongside, same era.
Kingma, D. P., Salimans, T., Poole, B., and Ho, J · 2021
Cited alongside, same era.
Knowledge distillation in iterative generative models for improved sampling speed
Luhman, E. and Luhman, T · 2021
Cited alongside, same era.
Efficient semi-implicit variational inference
Moens, V., Ren, H., Maraval, A., Tutunov, R., Wang, J., and Ammar, H · 2021
Cited alongside, same era.
Improved denoising diffusion probabilistic models
Nichol, A. Q. and Dhariwal, P · 2021
Cited alongside, same era.
Berthelot, D., Autef, A., Lin, J., Yap, D. A., Zhai, S., Hu, S., Zheng, D., Talbott, W., and Gu, E · 2023
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One-step diffusion distillation via deep equilibrium models
Geng, Z., Pokle, A., and Kolter, J. Z · 2023
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BOOT: Data-free distillation of denoising diffusion models with bootstrapping
Gu, J., Zhai, S., Zhang, Y., Liu, L., and Susskind, J. M · 2023
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A two-timescale stochastic algorithm framework for bilevel optimization: Complexity analysis and application to actor-critic
Hong, M., Wai, H.-T., Wang, Z., and Yang, Z · 2023
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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 · 2023
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On distillation of guided diffusion models
Meng, C., Rombach, R., Gao, R., Kingma, D., Ermon, S., Ho, J., and Salimans, T · 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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Scalable diffusion models with transformers
Peebles, W. and Xie, S · 2023
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Adversarial diffusion distillation
Sauer, A., Lorenz, D., Blattmann, A., and Rombach, R · 2023
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On penalty-based bilevel gradient descent method
Shen, H., Xiao, Q., and Chen, T · 2023
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Improved techniques for training consistency models
Song, Y. and Dhariwal, P · 2023
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Song, Y., Dhariwal, P., Chen, M., and Sutskever, I · 2023
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UFOGen: You forward once large scale text-to-image generation via diffusion GANs
Xu, Y., Zhao, Y., Xiao, Z., and Hou, T · 2023
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SA-Solver: Stochastic Adams solver for fast sampling of diffusion models
Xue, S., Yi, M., Luo, W., Zhang, S., Sun, J., Li, Z., and Ma, Z.-M · 2023
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Score mismatching for generative modeling
Ye, S. and Liu, F · 2023
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One-step diffusion with distribution matching distillation, 2023
Yin, T., Gharbi, M., Zhang, R., Shechtman, E., Durand, F., Freeman, W. T., and Park, T · 2023
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Semi-implicit variational inference via score matching
Yu, L. and Zhang, C · 2023
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Hierarchical semi-implicit variational inference with application to diffusion model acceleration
Yu, L., Xie, T., Zhu, Y., Yang, T., Zhang, X., and Zhang, C · 2023
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Fast sampling of diffusion models with exponential integrator
Zhang, Q. and Chen, Y · 2023
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Physics informed distillation for diffusion models, 2024
Tee, J. T. J., Zhang, K., Kim, C., Gowda, D. N., Yoon, H. S., and Yoo, C. D · 2024
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