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Diffusion models have become a mainstream approach for high-resolution image synthesis.
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Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., et al.: Imagenet large scale visual recognition challenge. IJCV 115
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Heusel, M., Ramsauer, H., Unterthiner, T., Nessler, B., Hochreiter, S.: Gans trained by a two time-scale update rule converge to a local nash equilibrium. In: NeurIPS (2017)
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Song, Y., Ermon, S.: Generative modeling by estimating gradients of the data distribution. In: NeurIPS. pp. 11895–11907 (2019)
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Ho, J., Jain, A., Abbeel, P.: Denoising diffusion probabilistic models. In: NeurIPS. pp. 6840–6851 (2020)
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Dhariwal, P., Nichol, A.: Diffusion models beat gans on image synthesis. vol. 34, pp. 8780–8794 (2021)
2021
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Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., et al.: An image is worth 16x16 words: Transformers for image recognition at scale. In: ICLR (2021)
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Liu, Z., Lin, Y., Cao, Y., Hu, H., Wei, Y., Zhang, Z., Lin, S., Guo, B.: Swin transformer: Hierarchical vision transformer using shifted windows. In: ICCV. pp. 10012–10022 (2021)
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Radford, A., Kim, J.W., Hallacy, C., Ramesh, A., Goh, G., Agarwal, S., Sastry, G., Askell, A., Mishkin, P., Clark, J., et al.: Learning transferable visual models from natural language supervision. In: ICML. pp. 8748–8763. PMLR (2021)
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Song, J., Meng, C., Ermon, S.: Denoising diffusion implicit models. In: ICLR (2021)
2021
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Song, Y., Sohl-Dickstein, J., Kingma, D.P., Kumar, A., Ermon, S., Poole, B.: Score-based generative modeling through stochastic differential equations. In: ICLR (2021)
2021
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Chai, L., Gharbi, M., Shechtman, E., Isola, P., Zhang, R.: Any-resolution training for high-resolution image synthesis. In: ECCV. pp. 170–188. Springer (2022)
2022
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Choi, J., Lee, J., Shin, C., Kim, S., Kim, H., Yoon, S.: Perception prioritized training of diffusion models. In: CVPR. pp. 11472–11481 (2022)
2022
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Lefaudeux, B., Massa, F., Liskovich, D., Xiong, W., Caggiano, V., Naren, S., Xu, M., Hu, J., Tintore, M., Zhang, S., Labatut, P., Haziza, D.: xformers: A modular and hackable transformer modelling library. https://github.com/facebookresearch/xformers (2022)
2022
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Lu, C., Zhou, Y., Bao, F., Chen, J., Li, C., Zhu, J.: Dpm-solver: A fast ode solver for diffusion probabilistic model sampling in around 10 steps. In: NeurIPS. pp. 5775–5787 (2022)
2022
Cited alongside, same era.
2022
Cited alongside, same era.
Lee, Y., Kim, K., Kim, H., Sung, M.: Syncdiffusion: Coherent montage via synchronized joint diffusions. In: NeurIPS (2023)
2023
Closest in time.
Li, L., Li, H., Zheng, X., Wu, J., Xiao, X., Wang, R., Zheng, M., Pan, X., Chao, F., Ji, R.: Autodiffusion: Training-free optimization of time steps and architectures for automated diffusion model acceleration. pp. 7105–7114 (2023)
2023
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Meng, C., Rombach, R., Gao, R., Kingma, D., Ermon, S., Ho, J., Salimans, T.: On distillation of guided diffusion models. In: CVPR. pp. 14297–14306 (2023)
2023
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Pan, X., Ye, T., Xia, Z., Song, S., Huang, G.: Slide-transformer: Hierarchical vision transformer with local self-attention. In: CVPR. pp. 2082–2091 (2023)
2023
Closest in time.
Pan, Z., Gherardi, R., Xie, X., Huang, S.: Effective real image editing with accelerated iterative diffusion inversion. In: ICCV. pp. 15912–15921 (2023)
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Ma, H., Zhang, L., Zhu, X., Feng, J.: Accelerating score-based generative models with preconditioned diffusion sampling. In: ECCV. Springer (2022)
2022
Cited alongside, same era.
Rombach, R., Blattmann, A., Lorenz, D., Esser, P., Ommer, B.: High-resolution image synthesis with latent diffusion models. In: CVPR. pp. 10684–10695 (2022)
2022
Cited alongside, same era.
2022
Cited alongside, same era.
Schuhmann, C., Beaumont, R., Vencu, R., Gordon, C., Wightman, R., Cherti, M., Coombes, T., Katta, A., Mullis, C., Wortsman, M., et al.: Laion-5b: An open large-scale dataset for training next generation image-text models. In: NeurIPS. pp. 25278–25294 (2022)
2022
Cited alongside, same era.
Bar-Tal, O., Yariv, L., Lipman, Y., Dekel, T.: Multidiffusion: Fusing diffusion paths for controlled image generation. In: ICML (2023)
2023
Cited alongside, same era.
Bolya, D., Hoffman, J.: Token merging for fast stable diffusion. In: CVPRW. pp. 4598–4602 (2023)
2023
Cited alongside, same era.
Chen, Y.H., Sarokin, R., Lee, J., Tang, J., Chang, C.L., Kulik, A., Grundmann, M.: Speed is all you need: On-device acceleration of large diffusion models via gpu-aware optimizations. In: CVPR. pp. 4650–4654 (2023)
2023
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2023
Cited alongside, same era.
2023
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2023
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2023
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2023
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2023
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Yang, X., Zhou, D., Feng, J., Wang, X.: Diffusion probabilistic model made slim. In: CVPR. pp. 22552–22562 (2023)
2023
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2023
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Du, R., Chang, D., Hospedales, T., Song, Y.Z., Ma, Z.: Demofusion: Democratising high-resolution image generation with no $$$. In: CVPR (2024)
2024
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He, Y., Yang, S., Chen, H., Cun, X., Xia, M., Zhang, Y., Wang, X., He, R., Chen, Q., Shan, Y.: Scalecrafter: Tuning-free higher-resolution visual generation with diffusion models. In: ICLR (2024)
2024
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Ma, X., Fang, G., Wang, X.: Deepcache: Accelerating diffusion models for free. In: CVPR (2024)
2024
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