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Diffusion models have demonstrated impressive performance in various image generation, editing, enhancement and translation tasks.
Mittal, A., Soundararajan, R., Bovik, A.C.: Making a “completely blind” image quality analyzer. IEEE Signal Processing Letters 20
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Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., Bengio, Y.: Generative adversarial networks. In: NeurIPS. pp. 2672–2680 (2014)
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Gatys, L.A., Ecker, A.S., Bethge, M.: A neural algorithm of artistic style. In: Arxiv (2015)
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Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Arxiv (2015)
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He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: CVPR. pp. 770––778 (2016)
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Johnson, J., Alahi, A., Li, F.F.: Perceptual losses for real-time style transfer and super-resolution. In: ECCV (2016)
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Redmon, J., Divvala, S., Girshick, R., Farhadi, A.: You only look once: Unified, real-time object detection. In: CVPR (2016)
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Agustsson, E., Timofte, R.: Ntire 2017 challenge on single image super-resolution: Dataset and study. In: CVPRW (2017)
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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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Lai, W.S., Huang, J.B., Ahuja, N., Yang, M.H.: Deep laplacian pyramid networks for fast and accurate super-resolution. In: CVPR (2017)
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Ledig, C., Theis, L., Huszar, F., Caballero, J., Cunningham, A., Acosta, A., Aitken, A., Tejani, A., Totz, J., Wang, Z., Shi, W.: Photo-realistic single image super-resolution using a generative adversarial network. In: CVPR (2017)
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Li, Y., Fang, C., Yang, J., Wang, Z., Lu, X., Yang, M.H.: Universal style transfer via feature transforms. In: NeurIPS (2017)
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Lim, B., Son, S., Kim, H., Nah, S., Lee, K.M.: Enhanced deep residual networks for single image super-resolution. In: CVPRW (2017)
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Timofte, R., Agustsson, E., Gool, L.V., Yang, M.H., Zhang, L.: Ntire 2017 challenge on single image super-resolution: Methods and results. In: CVPRW. pp. 114–125 (2017)
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Zhu, J.Y., Park, T., Isola, P., Efros, A.A.: Unpaired image-to-image translation using cycle-consistent adversarial networks. In: ICCV (2017)
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Chen, Y., Lai, Y.K., Liu, Y.J.: Cartoongan: Generative adversarial networks for photo cartoonization. In: CVPR (2018)
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Wang, X., Yu, K., Dong, C., Loy, C.C.: Recovering realistic texture in image super-resolution by deep spatial feature transform. In: CVPR (2018)
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Wang, X., Yu, K., Wu, S., Gu, J., Liu, Y., Dong, C., Qiao, Y., Loy, C.C.: Esrgan: Enhanced super-resolution generative adversarial networks. In: ECCVW (2018)
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Yu, J., Lin, Z., Yang, J., Shen, X., Lu, X., Huang, T.S.: Generative image inpainting with contextual attention. In: CVPR (2018)
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Zhang, R., Isola, P., Efros, A.A., Shechtman, E., Wang, O.: The unreasonable effectiveness of deep features as a perceptual metric. In: CVPR (2018)
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Anti, J.: jantic/deoldify: A deep learning based project for colorizing and restoring old image (and videos!) (2019), https://github.com/jantic/DeOldify
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Cai, J., Zeng, H., Yong, H., Cao, Z., Zhang, L.: Toward real-world single image super-resolution: A new benchmark and a new model. In: ICCV (2019)
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Karras, T., Laine, S., Aila, T.: A style-based generator architecture for generative adversarial networks. In: CVPR (2019)
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Chen, J., Liu, G., Chen, X.: Animegan: A novel lightweight gan for photo animation. In: CVPR (2020)
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Ho, J., Jain, A., Abbeel, P.: Denoising diffusion probabilistic models. In: NeurIPS. pp. 6840–6851 (2020)
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Wei, P., Xie, Z., Lu, H., Zhan, Z., Ye, Q., Zuo, W., Lin, L.: Component divide-and-conquer for real-world image super-resolution. In: ECCV (2020)
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Ho, J., Salimans, T.: Classifier-free diffusion guidance. In: Arxiv (2021)
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Ke, J., Wang, Q., Wang, Y., Milanfar, P., Yang, F.: Musiq: Multi-scale image quality transformer. In: ICCV (2021)
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Liang, J., Cao, J., Sun, G., Zhang, K., Gool, L.V., Timofte, R.: Swinir: Image restoration using swin transformer. ArXiv (2021)
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Salimans, T., Ho, J.: Progressive distillation for fast sampling of diffusion models. In: ICLR (2022)
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Weng, S., Sun, J., Li, Y., Li, S.: Ct2: Colorization transformer via color tokens. In: ECCV (2022)
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Yang, B., Gu, S., Zhang, B., Zhang, T., Chen, X., Sun, X., Chen, D., Wen, F.: Paint by example: Exemplar-based image editing with diffusion models. In: ArXiv (2022)
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Yang, T., Ren, P., Xie, X., Hua, X., Zhang, L.: Beyond a video frame interpolator: A space decoupled learning approach to continuous image transition. In: ECCVW (2022)
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Zhou, S., Chan, K.C., Li, C., Loy, C.C.: Towards robust blind face restoration with codebook lookup transformer. In: NeurIPS (2022)
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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., Krueger, G., Sutskever, I.: Learning transferable visual models from natural language supervision. In: ICML (2021)
2021
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Rombach, R., Blattmann, A., Lorenz, D., Esser, P., Ommer, B.: High-resolution image synthesis with latent diffusion models. In: CVPR (2021)
2021
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Song, J., Meng, C., Ermon, S.: Denoising diffusion implicit models. In: ICLR (2021)
2021
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Wan, Z., Zhang, B., Chen, D., Zhang, P., Chen, D., Liao, J., Wen, F.: Bringing old photos back to life. In: CVPR (2021)
2021
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Wang, X., Li, Y., Zhang, H., Shan, Y.: Towards real-world blind face restoration with generative facial prior. In: CVPR (2021)
2021
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Wang, X., Xie, L., Dong, C., Shan, Y.: Real-esrgan: Training real-world blind super-resolution with pure synthetic data. In: ICCVW (2021)
2021
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Yang, T., Ren, P., Xie, X., , Zhang, L.: Gan prior embedded network for blind face restoration in the wild. In: CVPR (2021)
2021
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Brooks, T., Holynski, A., Efros, A.A.: Instructpix2pix: Learning to follow image editing instructions. In: CVPR (2023)
2023
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Girdhar, R., Singh, M., Brown, A., Duval, Q., Azadi, S., Rambhatla, S.S., Shah, A., Yin, X., Parikh, D., Misra, I.: Emu video: Factorizing text-to-video generation by explicit image conditioning. In: ArXiv (2023)
2023
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Guo, Y., Yang, C., Rao, A., Wang, Y., Qiao, Y., Lin, D., Dai, B.: Animatediff: Animate your personalized text-to-image diffusion models without specific tuning. In: ArXiv (2023)
2023
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Kang, X., Yang, T., Ouyang, W., Ren, P., Li, L., Xie, X.: Ddcolor: Towards photo-realistic and semantic-aware image colorization via dual decoders. In: ICCV (2023)
2023
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Kawar, B., Zada, S., Lang, O., Tov, O., Chang, H., Dekel, T., Mosseri, I., Irani, M.: Imagic: Text-based real image editing with diffusion models. In: CVPR (2023)
2023
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Kumari, N., Zhang, B., Zhang, R., Shechtman, E., Zhu, J.Y.: Multi-concept customization of text-to-image diffusion. In: CVPR (2023)
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Li, J., Li, D., Savarese, S., Hoi, S.: Blip-2: Bootstrapping language-image pre-training with frozen image encoders and large language models. In: ICML (2023)
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Lin, S., Liu, B., Li, J., Yang, X.: Common diffusion noise schedules and sample steps are flawed. In: ArXiv (2023)
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Lin, X., He, J., Chen, Z., Lyu, Z., Fei, B., Dai, B., Ouyang, W., Qiao, Y., Dong, C.: Diffbir: Towards blind image restoration with generative diffusion prior. In: ArXiv (2023)
2023
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Liu, H., Xing, J., Xie, M., Li, C., Wong, T.T.: Improved diffusion-based image colorization via piggybacked models. In: ArXiv (2023)
2023
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Meng, C., Rombach, R., Gao, R., Kingma, D.P., Ermon, S., Ho, J., Salimans, T.: On distillation of guided diffusion models. In: CVPR (2023)
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Qin, C., Zhang, S., Yu, N., Feng, Y., Yang, X., Zhou, Y., Wang, H., Niebles, J.C., Xiong, C., Savarese, S., Ermon, S., Fu, Y., Xu, R.: Unicontrol: A unified diffusion model for controllable visual generation in the wild. In: ArXiv (2023)
2023
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Ruiz, N., Li, Y., Jampani, V., Pritch, Y., Rubinstein, M., Aberman, K.: Dreambooth: Fine tuning text-to-image diffusion models for subject-driven generation. In: CVPR (2023)
2023
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Wang, J., Yue, Z., Zhou, S., Chan, K.C., Loy, C.C.: Exploiting diffusion prior for real-world image super-resolution. In: Arxiv (2023)
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Wu, H., Zhang, Z., Zhang, W., Chen, C., Li, C., Liao, L., Wang, A., Zhang, E., Sun, W., Yan, Q., Min, X., Zhai, G., Lin, W.: Q-align: Teaching lmms for visual scoring via discrete text-defined levels. In: ArXiv (2023)
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Wu, R., Yang, T., Sun, L., Zhang, Z., Li, S., Zhang, L.: Seesr: Towards semantics-aware real-world image super-resolution. In: CVPR (2023)
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Yang, T., Ren, P., Xie, X., Zhang, L.: Synthesizing realistic image restoration training pairs: A diffusion approach. In: Arxiv (2023)
2023
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Yue, Z., Wang, J., Loy, C.C.: Resshift: Efficient diffusion model for image super-resolution by residual shifting. In: NeurIPS (2023)
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Zhang, L., Agrawala, M.: Adding conditional control to text-to-image diffusion models. In: NeurIPS (2023)
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Zhang, Y., Huang, N., Tang, F., Huang, H., Ma, C., Dong, W., Xu, C.: Inversion-based style transfer with diffusion models. In: CVPR (2023)
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