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Latent Consistency Model (LCM) extends the Consistency Model to the latent space and leverages the guided consistency distillation technique to achieve impressive performance in accelerating text-to-image synthesis.
Numerical solution of ordinary differential equations , volume 81
Atkinson, K., Han, W., and Stewart, D. E · 2009
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Estimation of non-normalized statistical models
Hyvärinen, A., Hurri, J., Hoyer, P. O., Hyvärinen, A., Hurri, J., and Hoyer, P. O · 2009
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Numerical solution of ordinary differential equations
Süli, E · 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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Deep unsupervised learning using nonequilibrium thermodynamics
Sohl-Dickstein, J., Weiss, E., Maheswaranathan, N., and Ganguli, S · 2015
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Parameter-efficient transfer learning for nlp
Houlsby, N., Giurgiu, A., Jastrzebski, S., Morrone, B., De Laroussilhe, Q., Gesmundo, A., Attariyan, M., and Gelly, S · 2019
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Generative modeling by estimating gradients of the data distribution
Song, Y. and Ermon, S · 2019
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Intrinsic dimensionality explains the effectiveness of language model fine-tuning
Aghajanyan, A., Zettlemoyer, L., and Gupta, S · 2020
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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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Denoising diffusion probabilistic models
Ho, J., Jain, A., and Abbeel, P · 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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Lora: Low-rank adaptation of large language models
Hu, E. J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., and Chen, W · 2021
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Gotta go fast when generating data with score-based models
Jolicoeur-Martineau, A., Li, K., Piché-Taillefer, R., Kachman, T., and Mitliagkas, I · 2021
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Variational diffusion models
Kingma, D., Salimans, T., Poole, B., and Ho, J · 2021
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On fast sampling of diffusion probabilistic models
Kong, Z. and Ping, W · 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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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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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
Cited alongside, same era.
Why are conditional generative models better than unconditional ones?
Bao, F., Li, C., Sun, J., and Zhu, J · 2022
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Sampling is as easy as learning the score: theory for diffusion models with minimal data assumptions
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
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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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Animatediff: Animate your personalized text-to-image diffusion models without specific tuning
Guo, Y., Yang, C., Rao, A., Wang, Y., Qiao, Y., Lin, D., and Dai, B · 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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Pick-a-pic: An open dataset of user preferences for text-to-image generation
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Chen, S., Chewi, S., Li, J., Li, Y., Salim, A., and Zhang, A. R · 2022
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Guidance: a cheat code for diffusion models, 2022
Dieleman, S · 2022
Cited alongside, same era.
Ic9600: A benchmark dataset for automatic image complexity assessment
Feng, T., Zhai, Y., Yang, J., Liang, J., Fan, D.-P., Zhang, J., Shao, L., and Tao, D · 2022
Cited alongside, same era.
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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Glide: Towards photorealistic image generation and editing with text-guided diffusion models
Nichol, A. Q., Dhariwal, P., Ramesh, A., Shyam, P., Mishkin, P., Mcgrew, B., Sutskever, I., and Chen, M · 2022
Cited alongside, same era.
Hierarchical text-conditional image generation with clip latents
Ramesh, A., Dhariwal, P., Nichol, A., Chu, C., and Chen, M · 2022
Cited alongside, same era.
High-resolution image synthesis with latent diffusion models
Rombach, R., Blattmann, A., Lorenz, D., Esser, P., and Ommer, B · 2022
Cited alongside, same era.
Kirstain, Y., Polyak, A., Singer, U., Matiana, S., Penna, J., and Levy, O · 2023
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Do diffusion models suffer error propagation? theoretical analysis and consistency regularization
Li, Y., Qian, Z., and van der Schaar, M · 2023
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Convergence guarantee for consistency models
Lyu, J., Chen, Z., and Feng, 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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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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Adversarial diffusion distillation
Sauer, A., Lorenz, D., Blattmann, A., and Rombach, R · 2023
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Song, Y., Dhariwal, P., Chen, M., and Sutskever, I · 2023
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Tune-a-video: One-shot tuning of image diffusion models for text-to-video generation
Wu, J. Z., Ge, Y., Wang, X., Lei, S. W., Gu, Y., Shi, Y., Hsu, W., Shan, Y., Qie, X., and Shou, M. Z · 2023
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Imagereward: Learning and evaluating human preferences for text-to-image generation
Xu, J., Liu, X., Wu, Y., Tong, Y., Li, Q., Ding, M., Tang, J., and Dong, Y · 2023
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Fast sampling of diffusion models with exponential integrator
Zhang, Q. and Chen, Y · 2023
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Heek, J., Hoogeboom, E., and Salimans, T · 2024
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