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The iterative sampling procedure employed by diffusion models (DMs) often leads to significant inference latency.
Score-based generative modeling through stochastic differential equations
Song, Y.; Sohl-Dickstein, J.; Kingma, D. P.; Kumar, A.; Ermon, S.; and Poole, B. 2020 · 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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Deep unsupervised learning using nonequilibrium thermodynamics
Sohl-Dickstein, J.; Weiss, E.; Maheswaranathan, N.; and Ganguli, S. 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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The unreasonable effectiveness of deep features as a perceptual metric
Zhang, R.; Isola, P.; Efros, A. A.; Shechtman, E.; and Wang, O. 2018 · 2018
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Generative modeling by estimating gradients of the data distribution
Song, Y.; and Ermon, S. 2019 · 2019
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Denoising diffusion probabilistic models
Ho, J.; Jain, A.; and Abbeel, P. 2020 · 2020
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Reliable fidelity and diversity metrics for generative models
Naeem, M. F.; Oh, S. J.; Uh, Y.; Choi, Y.; and Yoo, J. 2020 · 2020
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Denoising Diffusion Implicit Models
Song, J.; Meng, C.; and Ermon, S. 2020 · 2020
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Improved techniques for training score-based generative models
Song, Y.; and Ermon, S. 2020 · 2020
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Diffusion models beat gans on image synthesis
Dhariwal, P.; and Nichol, A. 2021 · 2021
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CLIPScore: A Reference-free Evaluation Metric for Image Captioning
Hessel, J.; Holtzman, A.; Forbes, M.; Bras, R. L.; and Choi, Y. 2021 · 2021
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LoRA: Low-Rank Adaptation of Large Language Models
Hu, E. J.; Wallis, P.; Allen-Zhu, Z.; Li, Y.; Wang, S.; Wang, L.; Chen, W.; et al. 2021 · 2021
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SDEdit: Guided Image Synthesis and Editing with Stochastic Differential Equations
Meng, C.; He, Y.; Song, Y.; Song, J.; Wu, J.; Zhu, J.-Y.; and Ermon, S. 2021 · 2021
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Learning transferable visual models from natural language supervision
Radford, A.; Kim, J. W.; Hallacy, C.; Ramesh, A.; Goh, G.; Agarwal, S.; Sastry, G.; Askell, A.; Mishkin, P.; Clark, J.; et al. 2021 · 2021
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Maximum likelihood training of score-based diffusion models
Song, Y.; Durkan, C.; Murray, I.; and Ermon, S. 2021 · 2021
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A study on the evaluation of generative models
Betzalel, E.; Penso, C.; Navon, A.; and Fetaya, E. 2022 · 2022
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An image is worth one word: Personalizing text-to-image generation using textual inversion
Gal, R.; Alaluf, Y.; Atzmon, Y.; Patashnik, O.; Bermano, A. H.; Chechik, G.; and Cohen-Or, D. 2022 · 2022
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Imagen video: High definition video generation with diffusion models
Ho, J.; Chan, W.; Saharia, C.; Whang, J.; Gao, R.; Gritsenko, A.; Kingma, D. P.; Poole, B.; Norouzi, M.; Fleet, D. J.; et al. 2022 · 2022
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Align your latents: High-resolution video synthesis with latent diffusion models
Blattmann, A.; Rombach, R.; Ling, H.; Dockhorn, T.; Kim, S. W.; Fidler, S.; and Kreis, K. 2023 · 2023
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Elucidating the solution space of extended reverse-time SDE for diffusion models
Cui, Q.; Zhang, X.; Lu, Z.; and Liao, Q. 2023 · 2023
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SEEDS: Exponential SDE Solvers for Fast High-Quality Sampling from Diffusion Models
Gonzalez, M.; Fernandez, N.; Tran, T.; Gherbi, E.; Hajri, H.; and Masmoudi, N. 2023 · 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 · 2023
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Instaflow: One step is enough for high-quality diffusion-based text-to-image generation
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Ho, J.; and Salimans, T. 2022 · 2022
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Elucidating the design space of diffusion-based generative models
Karras, T.; Aittala, M.; Aila, T.; and Laine, S. 2022 · 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 · 2022
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Repaint: Inpainting using denoising diffusion probabilistic models
Lugmayr, A.; Danelljan, M.; Romero, A.; Yu, F.; Timofte, R.; and Van Gool, L. 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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Hierarchical text-conditional image generation with clip latents
Ramesh, A.; Dhariwal, P.; Nichol, A.; Chu, C.; and Chen, M. 2022 · 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 · 2022
Cited alongside, same era.
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 · 2022
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Liu, X.; Zhang, X.; Ma, J.; Peng, J.; and Liu, Q. 2023 · 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 · 2023
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Mou, C.; Wang, X.; Xie, L.; Zhang, J.; Qi, Z.; Shan, Y.; and Qie, X. 2023 · 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 · 2023
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Improved Techniques for Training Consistency Models
Song, Y.; and Dhariwal, P. 2023 · 2023
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Song, Y.; Dhariwal, P.; Chen, M.; and Sutskever, I. 2023 · 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 · 2023
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Swiftbrush: One-step text-to-image diffusion model with variational score distillation
Nguyen, T. H.; and Tran, A. 2024 · 2024
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Prolificdreamer: High-fidelity and diverse text-to-3d generation with variational score distillation
Wang, Z.; Lu, C.; Wang, Y.; Bao, F.; Li, C.; Su, H.; and Zhu, J. 2024 · 2024
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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. 2024 · 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 · 2024
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