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Diffusion models and Flow Matching generate high-quality samples but are slow at inference, and distilling them into few-step models often leads to instability and extensive tuning.
Integral probability metrics and their generating classes of functions
Müller, A · 1997
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Support vector machines
Steinwart, I. and Christmann, A · 2008
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Conditionally positive definite kernels: theoretical contribution, application to interpolation and approximation
Auffray, Y. and Barbillon, P · 2009
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Denoising diffusion implicit models
Song, J., Meng, C., and Ermon, S · 2010
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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 · 2011
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A kernel two-sample test
Gretton, A., Borgwardt, K. M., Rasch, M. J., Schölkopf, B., and Smola, A · 2012
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Generative moment matching networks
Li, Y., Swersky, K., and Zemel, R · 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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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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Mmd gan: Towards deeper understanding of moment matching network
Li, C.-L., Chang, W.-C., Cheng, Y., Yang, Y., and Póczos, B · 2017
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Large scale gan training for high fidelity natural image synthesis
Brock, A · 2018
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The unreasonable effectiveness of deep features as a perceptual metric
Zhang, R., Isola, P., Efros, A. A., Shechtman, E., and Wang, O · 2018
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Wavegrad: Estimating gradients for waveform generation
Chen, N., Zhang, Y., Zen, H., Weiss, R. J., Norouzi, M., and Chan, W · 2020
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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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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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Diffwave: A versatile diffusion model for audio synthesis
Kong, Z., Ping, W., Huang, J., Zhao, K., and Catanzaro, B · 2020
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Diffusion models beat gans on image synthesis
Dhariwal, P. and Nichol, A · 2021
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Taming transformers for high-resolution image synthesis
Esser, P., Rombach, R., and Ommer, B · 2021
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Variational diffusion models
Kingma, D., Salimans, T., Poole, B., and Ho, J · 2021
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Knowledge distillation in iterative generative models for improved sampling speed
Luhman, E. and Luhman, T · 2021
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Improved denoising diffusion probabilistic models
Nichol, A. Q. and Dhariwal, P · 2021
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Tackling the generative learning trilemma with denoising diffusion gans
Xiao, Z., Kreis, K., and Vahdat, A · 2021
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Building normalizing flows with stochastic interpolants
Albergo, M. S. and Vanden-Eijnden, E · 2022
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Maskgit: Masked generative image transformer
Chang, H., Zhang, H., Jiang, L., Liu, C., and Freeman, W. T · 2022
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Classifier-free diffusion guidance
Ho, J. and Salimans, T · 2022
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Scalable diffusion models with transformers
Peebles, W. and Xie, S · 2023
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Sdxl: Improving latent diffusion models for high-resolution image synthesis
Podell, D., English, Z., Lacey, K., Blattmann, A., Dockhorn, T., Müller, J., Penna, J., and Rombach, R · 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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Restart sampling for improving generative processes
Xu, Y., Deng, M., Cheng, X., Tian, Y., Liu, Z., and Jaakkola, T · 2023
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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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Flow matching for generative modeling
Lipman, Y., Chen, R. T., Ben-Hamu, H., Nickel, M., and Le, M · 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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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., 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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Fast sampling of diffusion models via operator learning
Zheng, H., Nie, W., Vahdat, A., Azizzadenesheli, K., and Anandkumar, A · 2023
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Scaling rectified flow transformers for high-resolution image synthesis
Esser, P., Kulal, S., Blattmann, A., Entezari, R., Müller, J., Saini, H., Levi, Y., Lorenz, D., Sauer, A., Boesel, F., et al · 2024
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One step diffusion via shortcut models
Frans, K., Hafner, D., Levine, S., and Abbeel, P · 2024
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Geng, Z., Pokle, A., Luo, W., Lin, J., and Kolter, J. Z · 2024
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Heek, J., Hoogeboom, E., and Salimans, T · 2024
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Analyzing and improving the training dynamics of diffusion models
Karras, T., Aittala, M., Lehtinen, J., Hellsten, J., Aila, T., and Laine, S · 2024
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Understanding diffusion objectives as the elbo with simple data augmentation
Kingma, D. and Gao, R · 2024
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Autoregressive image generation without vector quantization
Li, T., Tian, Y., Li, H., Deng, M., and He, K · 2024
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Simplifying, stabilizing and scaling continuous-time consistency models
Lu, C. and Song, Y · 2024
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Sit: Exploring flow and diffusion-based generative models with scalable interpolant transformers
Ma, N., Goldstein, M., Albergo, M. S., Boffi, N. M., Vanden-Eijnden, E., and Xie, S · 2024
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Video generation models as world simulators
OpenAI · 2024
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Multistep distillation of diffusion models via moment matching
Salimans, T., Mensink, T., Heek, J., and Hoogeboom, E · 2024
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Physics informed distillation for diffusion models
Tee, J. T. J., Zhang, K., Yoon, H. S., Gowda, D. N., Kim, C., and Yoo, C. D · 2024
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One-step diffusion with distribution matching distillation
Yin, T., Gharbi, M., Zhang, R., Shechtman, E., Durand, F., Freeman, W. T., and Park, T · 2024
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Score identity distillation: Exponentially fast distillation of pretrained diffusion models for one-step generation
Zhou, M., Zheng, H., Wang, Z., Yin, M., and Huang, H · 2024
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Adversarial diffusion distillation
Sauer, A., Lorenz, D., Blattmann, A., and Rombach, R · 2025
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