Exploiting Diffusion Prior for Real-World Image Super-Resolution
We present a novel approach to leverage prior knowledge encapsulated in pre-trained text-to-image diffusion models for blind super-resolution. Specifically, by employing our time-aware encoder, we can achieve promising restoration results without altering the pre-trained synthesis model, thereby pre...
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| Vydáno v: | International journal of computer vision Ročník 132; číslo 12; s. 5929 - 5949 |
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| Hlavní autoři: | , , , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
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New York
Springer US
01.12.2024
Springer Springer Nature B.V |
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| ISSN: | 0920-5691, 1573-1405 |
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| Abstract | We present a novel approach to leverage prior knowledge encapsulated in pre-trained text-to-image diffusion models for blind super-resolution. Specifically, by employing our time-aware encoder, we can achieve promising restoration results without altering the pre-trained synthesis model, thereby preserving the generative prior and minimizing training cost. To remedy the loss of fidelity caused by the inherent stochasticity of diffusion models, we employ a controllable feature wrapping module that allows users to balance quality and fidelity by simply adjusting a scalar value during the inference process. Moreover, we develop a progressive aggregation sampling strategy to overcome the fixed-size constraints of pre-trained diffusion models, enabling adaptation to resolutions of any size. A comprehensive evaluation of our method using both synthetic and real-world benchmarks demonstrates its superiority over current state-of-the-art approaches. Code and models are available at
https://github.com/IceClear/StableSR
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| AbstractList | We present a novel approach to leverage prior knowledge encapsulated in pre-trained text-to-image diffusion models for blind super-resolution. Specifically, by employing our time-aware encoder, we can achieve promising restoration results without altering the pre-trained synthesis model, thereby preserving the generative prior and minimizing training cost. To remedy the loss of fidelity caused by the inherent stochasticity of diffusion models, we employ a controllable feature wrapping module that allows users to balance quality and fidelity by simply adjusting a scalar value during the inference process. Moreover, we develop a progressive aggregation sampling strategy to overcome the fixed-size constraints of pre-trained diffusion models, enabling adaptation to resolutions of any size. A comprehensive evaluation of our method using both synthetic and real-world benchmarks demonstrates its superiority over current state-of-the-art approaches. Code and models are available at https://github.com/IceClear/StableSR. We present a novel approach to leverage prior knowledge encapsulated in pre-trained text-to-image diffusion models for blind super-resolution. Specifically, by employing our time-aware encoder, we can achieve promising restoration results without altering the pre-trained synthesis model, thereby preserving the generative prior and minimizing training cost. To remedy the loss of fidelity caused by the inherent stochasticity of diffusion models, we employ a controllable feature wrapping module that allows users to balance quality and fidelity by simply adjusting a scalar value during the inference process. Moreover, we develop a progressive aggregation sampling strategy to overcome the fixed-size constraints of pre-trained diffusion models, enabling adaptation to resolutions of any size. A comprehensive evaluation of our method using both synthetic and real-world benchmarks demonstrates its superiority over current state-of-the-art approaches. Code and models are available at We present a novel approach to leverage prior knowledge encapsulated in pre-trained text-to-image diffusion models for blind super-resolution. Specifically, by employing our time-aware encoder, we can achieve promising restoration results without altering the pre-trained synthesis model, thereby preserving the generative prior and minimizing training cost. To remedy the loss of fidelity caused by the inherent stochasticity of diffusion models, we employ a controllable feature wrapping module that allows users to balance quality and fidelity by simply adjusting a scalar value during the inference process. Moreover, we develop a progressive aggregation sampling strategy to overcome the fixed-size constraints of pre-trained diffusion models, enabling adaptation to resolutions of any size. A comprehensive evaluation of our method using both synthetic and real-world benchmarks demonstrates its superiority over current state-of-the-art approaches. Code and models are available at https://github.com/IceClear/StableSR . |
| Audience | Academic |
| Author | Yue, Zongsheng Zhou, Shangchen Loy, Chen Change Chan, Kelvin C. K. Wang, Jianyi |
| Author_xml | – sequence: 1 givenname: Jianyi orcidid: 0000-0001-7025-3626 surname: Wang fullname: Wang, Jianyi organization: S-Lab, Nanyang Technological University – sequence: 2 givenname: Zongsheng surname: Yue fullname: Yue, Zongsheng organization: S-Lab, Nanyang Technological University – sequence: 3 givenname: Shangchen surname: Zhou fullname: Zhou, Shangchen organization: S-Lab, Nanyang Technological University – sequence: 4 givenname: Kelvin C. K. surname: Chan fullname: Chan, Kelvin C. K. organization: S-Lab, Nanyang Technological University – sequence: 5 givenname: Chen Change surname: Loy fullname: Loy, Chen Change email: ccloy@ntu.edu.sg organization: S-Lab, Nanyang Technological University |
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| Cites_doi | 10.1109/ICCV.2019.00140 10.1109/CVPR.2018.00068 10.1109/CVPR46437.2021.00073 10.1109/ICCVW54120.2021.00217 10.1007/978-3-030-11021-5_5 10.1145/3610548.3618173 10.1109/CVPRW.2017.150 10.1109/CVPR52688.2022.01043 10.1109/CVPR52688.2022.01118 10.1109/ICCV48922.2021.00475 10.1109/CVPR52733.2024.02425 10.1109/CVPR42600.2020.00308 10.1109/CVPR46437.2021.01402 10.1007/978-3-031-73016-0_6 10.1609/aaai.v38i5.28226 10.1109/ICCVW.2019.00445 10.1109/ICCV.2017.244 10.1109/ICCV.2017.36 10.1109/CVPR52688.2022.00750 10.1145/3503161.3547833 10.1109/TPAMI.2015.2439281 10.1109/CVPR.2019.00453 10.1109/CVPR42600.2020.00583 10.1109/ICCV.2017.355 10.1109/CVPR46437.2021.00214 10.1007/978-3-319-24574-4_28 10.1109/CVPR42600.2020.00251 10.1007/978-3-030-01234-2_18 10.1109/CVPR42600.2020.00282 10.1109/CVPR52688.2022.01042 10.1109/ICCV51070.2023.00355 10.1007/978-3-031-19797-0_33 10.1109/CVPR.2018.00652 10.1109/ICCV.2019.00318 10.1109/CVPR46437.2021.01318 10.1109/TPAMI.2022.3186715 10.1007/978-3-030-58598-3_7 10.1109/CVPR46437.2021.01212 10.1109/CVPR.2019.00182 10.1109/CVPR.2019.00183 10.1016/j.neucom.2022.02.082 10.1109/TPAMI.2022.3204461 10.1145/3528233.3530757 10.1007/978-3-030-01231-1_6 10.1109/ICCV.2017.481 10.1109/ICCV48922.2021.01410 10.1109/CVPR46437.2021.00905 10.1109/CVPR.2019.01132 10.1109/ICCV48922.2021.00510 10.1007/978-3-319-10593-2_13 10.1007/978-3-319-46475-6_25 10.1109/ICCVW54120.2021.00210 10.1109/ICCV51070.2023.00701 10.1109/ICCVW.2019.00435 10.1109/CVPR.2019.00817 10.1109/CVPR.2017.19 10.1109/CVPR52688.2022.01767 10.1109/CVPRW50498.2020.00241 10.1109/CVPR46437.2021.01044 10.1109/CVPR.2018.00070 10.1609/aaai.v37i2.25353 10.1109/CVPR42600.2020.00037 10.1109/TPAMI.2021.3115428 10.1109/CVPR.2017.195 10.1109/ICCV48922.2021.00986 10.1109/ICCV51070.2023.01460 10.1109/CVPR.2018.00259 10.1109/TPAMI.2024.3461721 |
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| Keywords | Generative prior Diffusion models Super-resolution Image restoration |
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| References | Xu, X., Ma, Y., & Sun, W. (2019). Towards real scene super-resolution with raw images. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition (CVPR). Ji, X., Cao, Y., Tai, Y., Wang, C., Li, J., & Huang, F. (2020). Real-world super-resolution via kernel estimation and noise injection. In Proceedings of the IEEE/CVF international conference on computer vision workshops (CVPR-W). Ke, J., Wang, Q., Wang, Y., Milanfar, P., & Yang, F. (2021). Musiq: Multi-scale image quality transformer. In Proceedings of the IEEE/CVF international conference on computer vision (ICCV). Balaji, Y., Nah, S., Huang, X., Vahdat, A., Song, J., Kreis, K., Aittala, M., Aila, T., Laine, S., Catanzaro, B., Karras, T., & Liu, M. Y. (2022). ediff-i: Text-to-image diffusion models with ensemble of expert denoisers. arXiv preprint arXiv:2211.01324 Liang, J., Zeng, H., & Zhang, L. (2022). Efficient and degradation-adaptive network for real-world image super-resolution. In Proceedings of the European conference on computer vision (ECCV). Howard, A., Sandler, M., Chu, G., Chen, L. C., Chen, B., Tan, M., Wang, W., Zhu, Y., Pang, R., Vasudevan, V., & Le, Q. V. (2019). Searching for mobilenetv3. In Proceedings of the IEEE/CVF international conference on computer vision (ICCV). Kingma, D. P., & Ba, J. (2014). Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 Sohl-Dickstein, J., Weiss, E., Maheswaranathan, N., & Ganguli, S. (2015). Deep unsupervised learning using nonequilibrium thermodynamics. In Proceedings of international conference on machine learning (ICML). Wei, Y., Gu, S., Li, Y., Timofte, R., & Jin, L., Song, H. (2021). Unsupervised real-world image super resolution via domain-distance aware training. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition (CVPR). Choi, J., Lee, J., Shin, C., Kim, S., Kim, H., & Yoon, S. (2022). Perception prioritized training of diffusion models. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition (CVPR). Agustsson, E., & Timofte, R. (2017). Ntire 2017 challenge on single image super-resolution: Dataset and study. In Proceedings of the IEEE/CVF international conference on computer vision workshops (CVPR-W). Chung, H., Sim, B., Ryu, D., & Ye, J. C. (2022). Improving diffusion models for inverse problems using manifold constraints. In Proceedings of advances in neural information processing systems (NeurIPS). Podell, D., English, Z., Lacey, K., Blattmann, A., Dockhorn, T., Müller, J., Penna, J., & Rombach, R. (2023). Sdxl: Improving latent diffusion models for high-resolution image synthesis. In Proceedings of international conference on learning representations (ICLR). Wang, J., Chan, K. C., & Loy, C. C. (2023). Exploring clip for assessing the look and feel of images. In Proceedings of the AAAI conference on artificial intelligence. Dai, T., Cai, J., Zhang, Y., Xia, S. T., & Zhang, L. (2019). Second-order attention network for single image super-resolution. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition (CVPR). Pan, X., Zhan, X., Dai, B., Lin, D., Loy, C. C., & Luo, P. (2021). Exploiting deep generative prior for versatile image restoration and manipulation. In IEEE transactions on pattern analysis and machine intelligence (TPAMI). Sauer, A., Lorenz, D., Blattmann, A., & Rombach, R. (2023). Adversarial diffusion distillation. arXiv preprint arXiv:2311.17042 Gu, S., Lugmayr, A., Danelljan, M., Fritsche, M., Lamour, J., & Timofte, R. (2019). Div8k: Diverse 8k resolution image dataset. In Proceedings of the IEEE/CVF international conference on computer vision workshops (ICCV-W). Ledig, C., Theis, L., Huszár, F., Caballero, J., Cunningham, A., Acosta, A., Aitken, A., Tejani, A., Totz, J., Wang, Z., & Shi, W. (2017). Photo-realistic single image super-resolution using a generative adversarial network. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition (CVPR). Ramesh, A., Dhariwal, P., Nichol, A., Chu, C., & Chen, M. (2022). Hierarchical text-conditional image generation with clip latents. arXiv preprint arXiv:2204.06125 Chollet, F. (2017). Xception: Deep learning with depthwise separable convolutions. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition (CVPR). Deep-floyd. (2023). If. https://github.com/deep-floyd/IF Timofte, R., Agustsson, E., Van Gool, L., Yang, M. H., & Zhang, L. (2017). Ntire 2017 challenge on single image super-resolution: Methods and results. In Proceedings of the IEEE/CVF international conference on computer vision workshops (CVPR-W). Wang, X., Yu, K., Dong, C., & Loy, C. C. (2018a). Recovering realistic texture in image super-resolution by deep spatial feature transform. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition (CVPR). Hertz, A., Mokady, R., Tenenbaum, J., Aberman, K., Pritch, Y., & Cohen-Or, D. (2022). Prompt-to-prompt image editing with cross attention control. arXiv preprint arXiv:2208.01626 Saharia, C., Ho, J., Chan, W., Salimans, T., Fleet, D. J., & Norouzi, M. (2022b). Image super-resolution via iterative refinement. In IEEE transactions on pattern analysis and machine intelligence (TPAMI). Heusel, M., Ramsauer, H., Unterthiner, T., Nessler, B., & Hochreiter, S. (2017). Gans trained by a two time-scale update rule converge to a local nash equilibrium. In Proceedings of advances in neural information processing systems (NeurIPS). Qi, C., Cun, X., Zhang, Y., Lei, C., Wang, X., Shan, Y., & Chen, Q. (2023). Fatezero: Fusing attentions for zero-shot text-based video editing. arXiv preprint arXiv:2303.09535 Chan, K. C., Wang, X., Xu, X., Gu, J., & Loy, C. C. (2021). GLEAN: Generative latent bank for large-factor image super-resolution. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition (CVPR). Nichol, A. Q., Dhariwal, P., Ramesh, A., Shyam, P., Mishkin, P., Mcgrew, B., Sutskever, I., & Chen, M. (2022). Glide: Towards photorealistic image generation and editing with text-guided diffusion models. In Proceedings of international conference on machine learning (ICML). Sahak, H., Watson, D., Saharia, C., & Fleet, D. (2023). Denoising diffusion probabilistic models for robust image super-resolution in the wild. arXiv preprint arXiv:2302.07864 Feng, W., He, X., Fu, T. J., Jampani, V., Akula, A., Narayana, P., Basu, S., Wang, X. E., & Wang, W. Y. (2023). Training-free structured diffusion guidance for compositional text-to-image synthesis. In Proceedings of international conference on learning representations (ICLR). Zhang, Y., Li, K., Li, K., Wang, L., Zhong, B., & Fu, Y. (2018b). Image super-resolution using very deep residual channel attention networks. In Proceedings of the European conference on computer vision (ECCV). Jiang, Y., Chan, K. C., Wang, X., Loy, C. C., & Liu, Z. (2021). Robust reference-based super-resolution via c2-matching. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition (CVPR). Yang, S., Sohl-Dickstein, J., Kingma, D. P., Kumar, A., Ermon, S., & Poole, B. (2021a). Score-based generative modeling through stochastic differential equations. In Proceedings of international conference on learning representations (ICLR). Zhang, K., Liang, J., Van Gool, L., & Timofte, R. (2021b). Designing a practical degradation model for deep blind image super-resolution. In Proceedings of the IEEE/CVF international conference on computer vision (ICCV). He, X., Mo, Z., Wang, P., Liu, Y., Yang, M., & Cheng, J. (2019). Ode-inspired network design for single image super-resolution. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition (CVPR). Choi, J., Kim, S., Jeong, Y., Gwon, Y., & Yoon, S. (2021). Ilvr: Conditioning method for denoising diffusion probabilistic models. In Proceedings of the IEEE/CVF international conference on computer vision (ICCV). LiHYangYChangMChenSFengHXuZLiQChenYSRDiff: Single image super-resolution with diffusion probabilistic modelsNeurocomputing202266610.1016/j.neucom.2022.02.082 Ramesh, A., Pavlov, M., Goh, G., Gray, S., Voss, C., Radford, A., Chen, M., & Sutskever, I. (2021). Zero-shot text-to-image generation. In Proceedings of international conference on machine learning (ICML). Hu, E. J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., & Chen, W. (2022). Lora: Low-rank adaptation of large language models. In Proceedings of international conference on learning representations (ICLR). Dong, C., Loy, C. C., He, K., & Tang, X. (2014). Learning a deep convolutional network for image super-resolution. In Proceedings of the European conference on computer vision (ECCV). Dong, C., Loy, C. C., He, K., & Tang, X. (2015). Image super-resolution using deep convolutional networks. In IEEE transactions on pattern analysis and machine intelligence (TPAMI). Ignatov, A., Kobyshev, N., Timofte, R., Vanhoey, K., & Van Gool, L. (2017). Dslr-quality photos on mobile devices with deep convolutional networks. In Proceedings of the IEEE/CVF international conference on computer vision (ICCV) Wei, P., Xie, Z., Lu, H., Zhan, Z., Ye, Q., Zuo, W., & Lin, L. (2020). Component divide-and-conquer for real-world image super-resolution. In Proceedings of the European conference on computer vision (ECCV). Song, J., Meng, C., & Ermon, S. (2020). Denoising diffusion implicit models. In Proceedings of international conference on learning representations (ICLR). Wu, J. Z., Ge, Y., Wang, X., Lei, S. W., Gu, Y., Hsu, W., Shan, Y., Qie, X., & Shou, M. Z. (2022). Tune-A-Video: One-shot tuning of image diffusion models for text-to-video generation. arXiv preprint arXiv:2212.11565 Avrahami, O., Lischinski, D., & Fried, O. (2022). Blended diffusion for text-driven editing of natural images. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition (CVPR). Chen, H., Wang, Y., Guo, T., Xu, C., Deng, Y., Liu, Z., Ma, S., Xu, C., Xu, C., & Gao, W. (2021). 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| References_xml | – reference: Ho, J., & Salimans, T. (2021). Classifier-free diffusion guidance. In Proceedings of advances in neural information processing systems (NeurIPS). – reference: Dong, C., Loy, C. C., He, K., & Tang, X. (2015). Image super-resolution using deep convolutional networks. In IEEE transactions on pattern analysis and machine intelligence (TPAMI). – reference: Saharia, C., Chan, W., Saxena, S., Li, L., Whang, J., Denton, E. L., Ghasemipour, K., Gontijo Lopes, R., Karagol Ayan, B., Salimans, T., & Ho, J. (2022a). Photorealistic text-to-image diffusion models with deep language understanding. In Proceedings of advances in neural information processing systems (NeurIPS). – reference: Wang, X., Yu, K., Wu, S., Gu, J., Liu, Y., Dong, C., Qiao, Y., & Loy, C. C. (2018b). Esrgan: Enhanced super-resolution generative adversarial networks. In Proceedings of the European conference on computer vision workshops (ECCV-W). – reference: Zhou, S., Chan, K. C., Li, C., & Loy, C. C. (2022). Towards robust blind face restoration with codebook lookup transformer. In Proceedings of advances in neural information processing systems (NeurIPS). – reference: Zhu, J. Y., Park, T., Isola, P., & Efros, A. A. (2017). Unpaired image-to-image translation using cycle-consistent adversarial networks. In Proceedings of the IEEE/CVF international conference on computer vision (ICCV). – reference: Ho, J., Jain, A., & Abbeel, P. (2020). Denoising diffusion probabilistic models. In Proceedings of advances in neural information processing systems (NeurIPS) (vol. 33). – reference: Lu, C., Zhou, Y., Bao, F., Chen, J., Li, C., & Zhu, J. (2022). Dpm-solver: A fast ode solver for diffusion probabilistic model sampling in around 10 steps. In Proceedings of advances in neural information processing systems (NeurIPS). – reference: Chan, K. C., Wang, X., Xu, X., Gu, J., & Loy, C. C. (2022). GLEAN: Generative latent bank for large-factor image super-resolution and beyond. In IEEE transactions on pattern analysis and machine intelligence (TPAMI). – reference: Ji, X., Cao, Y., Tai, Y., Wang, C., Li, J., & Huang, F. (2020). Real-world super-resolution via kernel estimation and noise injection. In Proceedings of the IEEE/CVF international conference on computer vision workshops (CVPR-W). – reference: Lin, X., He, J., Chen, Z., Lyu, Z., Fei, B., Dai, B., Ouyang, W., Qiao, Y., & Dong, C. (2023). Diffbir: Towards blind image restoration with generative diffusion prior. arXiv preprint arXiv:2308.15070 – reference: Yu, K., Dong, C., Lin, L., & Loy, C. C. (2018). Crafting a toolchain for image restoration by deep reinforcement learning. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition (CVPR). – reference: Wei, P., Xie, Z., Lu, H., Zhan, Z., Ye, Q., Zuo, W., & Lin, L. (2020). Component divide-and-conquer for real-world image super-resolution. In Proceedings of the European conference on computer vision (ECCV). – reference: Wang, J., Chan, K. C., & Loy, C. C. (2023). Exploring clip for assessing the look and feel of images. In Proceedings of the AAAI conference on artificial intelligence. – reference: Zhang, Z., Wang, Z., Lin, Z., & Qi, H. (2019). Image super-resolution by neural texture transfer. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition (CVPR). – reference: Xu, X., Ma, Y., & Sun, W. (2019). Towards real scene super-resolution with raw images. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition (CVPR). – reference: Avrahami, O., Lischinski, D., & Fried, O. (2022). Blended diffusion for text-driven editing of natural images. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition (CVPR). – reference: Gu, S., Lugmayr, A., Danelljan, M., Fritsche, M., Lamour, J., & Timofte, R. (2019). Div8k: Diverse 8k resolution image dataset. In Proceedings of the IEEE/CVF international conference on computer vision workshops (ICCV-W). – reference: Kingma, D. P., & Ba, J. (2014). Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 – reference: Jiang, Y., Chan, K. C., Wang, X., Loy, C. C., & Liu, Z. (2021). Robust reference-based super-resolution via c2-matching. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition (CVPR). – reference: Balaji, Y., Nah, S., Huang, X., Vahdat, A., Song, J., Kreis, K., Aittala, M., Aila, T., Laine, S., Catanzaro, B., Karras, T., & Liu, M. Y. (2022). ediff-i: Text-to-image diffusion models with ensemble of expert denoisers. arXiv preprint arXiv:2211.01324 – reference: Karras, T., Laine, S., & Aila, T. (2019). A style-based generator architecture for generative adversarial networks. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition (CVPR). – reference: Timofte, R., Agustsson, E., Van Gool, L., Yang, M. H., & Zhang, L. (2017). Ntire 2017 challenge on single image super-resolution: Methods and results. In Proceedings of the IEEE/CVF international conference on computer vision workshops (CVPR-W). – reference: Podell, D., English, Z., Lacey, K., Blattmann, A., Dockhorn, T., Müller, J., Penna, J., & Rombach, R. (2023). Sdxl: Improving latent diffusion models for high-resolution image synthesis. In Proceedings of international conference on learning representations (ICLR). – reference: Ronneberger, O., Fischer, P., & Brox, T. (2015). U-net: Convolutional networks for biomedical image segmentation. In Medical image computing and computer-assisted intervention (MICCAI) (pp. 234–241). Springer. – reference: Chollet, F. (2017). Xception: Deep learning with depthwise separable convolutions. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition (CVPR). – reference: Ignatov, A., Kobyshev, N., Timofte, R., Vanhoey, K., & Van Gool, L. (2017). Dslr-quality photos on mobile devices with deep convolutional networks. In Proceedings of the IEEE/CVF international conference on computer vision (ICCV) – reference: Ke, J., Wang, Q., Wang, Y., Milanfar, P., & Yang, F. (2021). Musiq: Multi-scale image quality transformer. In Proceedings of the IEEE/CVF international conference on computer vision (ICCV). – reference: Chen, H., Wang, Y., Guo, T., Xu, C., Deng, Y., Liu, Z., Ma, S., Xu, C., Xu, C., & Gao, W. (2021). Pre-trained image processing transformer. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition (CVPR). – reference: Oord, Avd., Li, Y., & Vinyals, O. (2018). Representation learning with contrastive predictive coding. arXiv preprint arXiv:1807.03748 – reference: Fang, G., Ma, X., & Wang, X. (2023). Structural pruning for diffusion models. In Proceedings of advances in neural information processing systems (NeurIPS). – reference: Wan, Z., Zhang, B., Chen, D., Zhang, P., Chen, D., Liao, J., & Wen, F. (2020). Bringing old photos back to life. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition (CVPR). – reference: Blau, Y., & Michaeli, T. (2018). The perception-distortion tradeoff. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition (CVPR). – reference: Sauer, A., Lorenz, D., Blattmann, A., & Rombach, R. (2023). Adversarial diffusion distillation. arXiv preprint arXiv:2311.17042 – reference: LiHYangYChangMChenSFengHXuZLiQChenYSRDiff: Single image super-resolution with diffusion probabilistic modelsNeurocomputing202266610.1016/j.neucom.2022.02.082 – reference: Song, J., Meng, C., & Ermon, S. (2020). Denoising diffusion implicit models. In Proceedings of international conference on learning representations (ICLR). – reference: Cai, J., Zeng, H., Yong, H., Cao, Z., & Zhang, L. (2019). Toward real-world single image super-resolution: A new benchmark and a new model. In Proceedings of the IEEE/CVF international conference on computer vision (ICCV). – reference: Wang, X., Li, Y., Zhang, H., & Shan, Y. (2021b). Towards real-world blind face restoration with generative facial prior. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition (CVPR). – reference: Salimans, T., & Ho, J. (2021). Progressive distillation for fast sampling of diffusion models. In Proceedings of international conference on learning representations (ICLR). – reference: Deep-floyd. (2023). If. https://github.com/deep-floyd/IF – reference: Yu, F., Gu, J., Li, Z., Hu, J., Kong, X., Wang, X., He, J., Qiao, Y., & Dong, C. (2024). Scaling up to excellence: Practicing model scaling for photo-realistic image restoration in the wild. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition (CVPR). – reference: Yue, Z., Wang, J., & Loy, C. C. (2023). Resshift: Efficient diffusion model for image super-resolution by residual shifting. In Proceedings of advances in neural information processing systems (NeurIPS). – reference: Choi, J., Lee, J., Shin, C., Kim, S., Kim, H., & Yoon, S. (2022). Perception prioritized training of diffusion models. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition (CVPR). – reference: Zhao, Y., Su, Y. C., Chu, C. T., Li, Y., Renn, M., Zhu, Y., Chen, C., & Jia, X. (2022). Rethinking deep face restoration. In CVPR. – reference: Ramesh, A., Dhariwal, P., Nichol, A., Chu, C., & Chen, M. (2022). Hierarchical text-conditional image generation with clip latents. arXiv preprint arXiv:2204.06125 – reference: Yue, Z., & Loy, C. C. (2022). Difface: Blind face restoration with diffused error contraction. arXiv preprint arXiv:2212.06512 – reference: Liang, J., Cao, J., Sun, G., Zhang, K., Van Gool, L., & Timofte, R. (2021). SwinIR: Image restoration using swin transformer. In Proceedings of the IEEE/CVF international conference on computer vision workshops (ICCV-W). – reference: Gu, S., Chen, D., Bao, J., Wen, F., Zhang, B., Chen, D., Yuan, L., & Guo, B. (2022). Vector quantized diffusion model for text-to-image synthesis. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition (CVPR). – reference: Mou, C., Wang, X., Xie, L., Wu, Y., Zhang, J., Qi, Z., & Shan, Y. (2024). T2i-adapter: Learning adapters to dig out more controllable ability for text-to-image diffusion models. In Proceedings of the AAAI conference on artificial intelligence. – reference: ThorndikeELA constant error in psychological ratingsJournal of Applied Psychology1920666 – reference: Song, Y., Dhariwal, P., Chen, M., & Sutskever, I. (2023b). Consistency models. arXiv preprint arXiv:2303.01469 – reference: Howard, A., Sandler, M., Chu, G., Chen, L. C., Chen, B., Tan, M., Wang, W., Zhu, Y., Pang, R., Vasudevan, V., & Le, Q. V. (2019). Searching for mobilenetv3. In Proceedings of the IEEE/CVF international conference on computer vision (ICCV). – reference: Hertz, A., Mokady, R., Tenenbaum, J., Aberman, K., Pritch, Y., & Cohen-Or, D. (2022). Prompt-to-prompt image editing with cross attention control. arXiv preprint arXiv:2208.01626 – reference: Wang, X., Xie, L., Dong, C., & Shan, Y. (2021c). Real-esrgan: Training real-world blind super-resolution with pure synthetic data. In Proceedings of the IEEE/CVF international conference on computer vision workshops (ICCV-W). – reference: Yang, F., Yang, H., Fu, J., Lu, H., & Guo, B. (2020). Learning texture transformer network for image super-resolution. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition (CVPR). – reference: Heusel, M., Ramsauer, H., Unterthiner, T., Nessler, B., & Hochreiter, S. (2017). Gans trained by a two time-scale update rule converge to a local nash equilibrium. In Proceedings of advances in neural information processing systems (NeurIPS). – reference: Menon, S., Damian, A., Hu, S., Ravi, N., & Rudin, C. (2020). Pulse: Self-supervised photo upsampling via latent space exploration of generative models. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition (CVPR). – reference: Chen, C., Shi, X., Qin, Y., Li, X., Han, X., Yang, T., & Guo, S. (2022). Real-world blind super-resolution via feature matching with implicit high-resolution priors. In Proceedings of the ACM international conference on multimedia (ACM MM). – reference: Pan, X., Zhan, X., Dai, B., Lin, D., Loy, C. C., & Luo, P. (2021). Exploiting deep generative prior for versatile image restoration and manipulation. In IEEE transactions on pattern analysis and machine intelligence (TPAMI). – reference: Luo, S., Tan, Y., Huang, L., Li, J., & Zhao, H. (2023). Latent consistency models: Synthesizing high-resolution images with few-step inference. arXiv preprint arXiv:2310.04378 – reference: Choi, J., Kim, S., Jeong, Y., Gwon, Y., & Yoon, S. (2021). Ilvr: Conditioning method for denoising diffusion probabilistic models. In Proceedings of the IEEE/CVF international conference on computer vision (ICCV). – reference: Karras, T., Aittala, M., Aila, T., & Laine, S. (2022). Elucidating the design space of diffusion-based generative models. In Proceedings of advances in neural information processing systems (NeurIPS). – reference: Yang, T., Ren, P., Xie, X., & Zhang, L. (2021b). Gan prior embedded network for blind face restoration in the wild. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition (CVPR). – reference: Gal, R., Arar, M., Atzmon, Y., Bermano, A. H., Chechik, G., & Cohen-Or, D. (2023). Designing an encoder for fast personalization of text-to-image models. arXiv preprint arXiv:2302.12228 – reference: Jiménez, Á. B. (2023). Mixture of diffusers for scene composition and high resolution image generation. arXiv preprint arXiv:2302.02412 – reference: Zhang, L., Rao, A., & Agrawala, M. (2023). Adding conditional control to text-to-image diffusion models. In Proceedings of the IEEE/CVF international conference on computer vision (ICCV). – reference: Hu, E. J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., & Chen, W. (2022). Lora: Low-rank adaptation of large language models. In Proceedings of international conference on learning representations (ICLR). – reference: Liang, J., Zeng, H., & Zhang, L. (2022). Efficient and degradation-adaptive network for real-world image super-resolution. In Proceedings of the European conference on computer vision (ECCV). – reference: Molad, E., Horwitz, E., Valevski, D., Acha, A. R., Matias, Y., Pritch, Y., Leviathan, Y., & Hoshen, Y. (2023). Dreamix: Video diffusion models are general video editors. arXiv preprint arXiv:2302.01329 – reference: Sahak, H., Watson, D., Saharia, C., & Fleet, D. (2023). Denoising diffusion probabilistic models for robust image super-resolution in the wild. arXiv preprint arXiv:2302.07864 – reference: Wang, X., Yu, K., Dong, C., & Loy, C. C. (2018a). Recovering realistic texture in image super-resolution by deep spatial feature transform. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition (CVPR). – reference: Maeda, S. (2020). Unpaired image super-resolution using pseudo-supervision. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition (CVPR). – reference: Qi, C., Cun, X., Zhang, Y., Lei, C., Wang, X., Shan, Y., & Chen, Q. (2023). Fatezero: Fusing attentions for zero-shot text-based video editing. arXiv preprint arXiv:2303.09535 – reference: Ledig, C., Theis, L., Huszár, F., Caballero, J., Cunningham, A., Acosta, A., Aitken, A., Tejani, A., Totz, J., Wang, Z., & Shi, W. (2017). Photo-realistic single image super-resolution using a generative adversarial network. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition (CVPR). – reference: Saharia, C., Ho, J., Chan, W., Salimans, T., Fleet, D. J., & Norouzi, M. (2022b). Image super-resolution via iterative refinement. In IEEE transactions on pattern analysis and machine intelligence (TPAMI). – reference: Zhang, Y., Li, K., Li, K., Wang, L., Zhong, B., & Fu, Y. (2018b). Image super-resolution using very deep residual channel attention networks. In Proceedings of the European conference on computer vision (ECCV). – reference: Chan, K. C., Wang, X., Xu, X., Gu, J., & Loy, C. C. (2021). GLEAN: Generative latent bank for large-factor image super-resolution. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition (CVPR). – reference: Zhang, J., Lu, S., Zhan, F., & Yu, Y. (2021a). Blind image super-resolution via contrastive representation learning. arXiv preprint arXiv:2107.00708 – reference: Song, J., Vahdat, A., Mardani, M., & Kautz, J. (2023a). Pseudoinverse-guided diffusion models for inverse problems. In Proceedings of international conference on learning representations (ICLR). – reference: Zhang, K., Liang, J., Van Gool, L., & Timofte, R. (2021b). Designing a practical degradation model for deep blind image super-resolution. In Proceedings of the IEEE/CVF international conference on computer vision (ICCV). – reference: Nichol, A. Q., Dhariwal, P., Ramesh, A., Shyam, P., Mishkin, P., Mcgrew, B., Sutskever, I., & Chen, M. (2022). Glide: Towards photorealistic image generation and editing with text-guided diffusion models. In Proceedings of international conference on machine learning (ICML). – reference: Wu, J. Z., Ge, Y., Wang, X., Lei, S. W., Gu, Y., Hsu, W., Shan, Y., Qie, X., & Shou, M. Z. (2022). Tune-A-Video: One-shot tuning of image diffusion models for text-to-video generation. arXiv preprint arXiv:2212.11565 – reference: Xu, X., Sun, D., Pan, J., Zhang, Y., Pfister, H., & Yang, M. H. (2017). Learning to super-resolve blurry face and text images. In Proceedings of the IEEE/CVF international conference on computer vision (ICCV). – reference: Zheng, H., Ji, M., Wang, H., Liu, Y., & Fang, L. (2018). Crossnet: An end-to-end reference-based super resolution network using cross-scale warping. In Proceedings of the European conference on computer vision (ECCV). – reference: Sajjadi, M. S., Scholkopf, B., & Hirsch, M. (2017). Enhancenet: Single image super-resolution through automated texture synthesis. In Proceedings of the IEEE/CVF international conference on computer vision (ICCV). – reference: Gu, J., Shen, Y., & Zhou, B. (2020). Image processing using multi-code gan prior. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition (CVPR). – reference: Sohl-Dickstein, J., Weiss, E., Maheswaranathan, N., & Ganguli, S. (2015). Deep unsupervised learning using nonequilibrium thermodynamics. In Proceedings of international conference on machine learning (ICML). – reference: Dai, T., Cai, J., Zhang, Y., Xia, S. T., & Zhang, L. (2019). Second-order attention network for single image super-resolution. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition (CVPR). – reference: He, X., Mo, Z., Wang, P., Liu, Y., Yang, M., & Cheng, J. (2019). Ode-inspired network design for single image super-resolution. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition (CVPR). – reference: Liu, Z., Lin, Y., Cao, Y., Hu, H., Wei, Y., Zhang, Z., Lin, S., & Guo, B. (2021). Swin transformer: Hierarchical vision transformer using shifted windows. In Proceedings of the IEEE/CVF international conference on computer vision (ICCV). – reference: Feng, W., He, X., Fu, T. J., Jampani, V., Akula, A., Narayana, P., Basu, S., Wang, X. E., & Wang, W. Y. (2023). Training-free structured diffusion guidance for compositional text-to-image synthesis. In Proceedings of international conference on learning representations (ICLR). – reference: Wang, Y., Yu, J., & Zhang, J. (2022). Zero-shot image restoration using denoising diffusion null-space model. In Proceedings of international conference on learning representations (ICLR). – reference: Yang, S., Sohl-Dickstein, J., Kingma, D. P., Kumar, A., Ermon, S., & Poole, B. (2021a). Score-based generative modeling through stochastic differential equations. In Proceedings of international conference on learning representations (ICLR). – reference: Ramesh, A., Pavlov, M., Goh, G., Gray, S., Voss, C., Radford, A., Chen, M., & Sutskever, I. (2021). Zero-shot text-to-image generation. In Proceedings of international conference on machine learning (ICML). – reference: Dong, C., Loy, C. C., & Tang, X. (2016). Accelerating the super-resolution convolutional neural network. In Proceedings of the European conference on computer vision (ECCV) – reference: Zhou, S., Zhang, J., Zuo, W., & Loy, C. C. (2020). Cross-scale internal graph neural network for image super-resolution. In Proceedings of advances in neural information processing systems (NeurIPS). – reference: Agustsson, E., & Timofte, R. (2017). Ntire 2017 challenge on single image super-resolution: Dataset and study. In Proceedings of the IEEE/CVF international conference on computer vision workshops (CVPR-W). – reference: Fritsche, M., Gu, S., & Timofte, R. (2019). Frequency separation for real-world super-resolution. In Proceedings of the IEEE/CVF international conference on computer vision workshops (ICCV-W). – reference: Dong, C., Loy, C. C., He, K., & Tang, X. (2014). Learning a deep convolutional network for image super-resolution. In Proceedings of the European conference on computer vision (ECCV). – reference: Zhang, R., Isola, P., Efros, A. A., Shechtman, E., & Wang, O. (2018a). The unreasonable effectiveness of deep features as a perceptual metric. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition (CVPR). – reference: Chung, H., Sim, B., Ryu, D., & Ye, J. C. (2022). Improving diffusion models for inverse problems using manifold constraints. In Proceedings of advances in neural information processing systems (NeurIPS). – reference: Wei, Y., Gu, S., Li, Y., Timofte, R., & Jin, L., Song, H. (2021). Unsupervised real-world image super resolution via domain-distance aware training. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition (CVPR). – reference: Wang, L., Wang, Y., Dong, X., Xu, Q., Yang, J., An, W., & Guo, Y. (2021a). Unsupervised degradation representation learning for blind super-resolution. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition (CVPR). – reference: Meng, X., & Kabashima, Y. (2022). Diffusion model based posterior sampling for noisy linear inverse problems. arXiv preprint arXiv:2211.12343 – reference: Rombach, R., Blattmann, A., Lorenz, D., Esser, P., & Ommer, B. (2022). High-resolution image synthesis with latent diffusion models. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition (CVPR). – ident: 2168_CR31 doi: 10.1109/ICCV.2019.00140 – ident: 2168_CR98 doi: 10.1109/CVPR.2018.00068 – ident: 2168_CR90 doi: 10.1109/CVPR46437.2021.00073 – ident: 2168_CR79 doi: 10.1109/ICCVW54120.2021.00217 – ident: 2168_CR81 doi: 10.1007/978-3-030-11021-5_5 – ident: 2168_CR22 doi: 10.1145/3610548.3618173 – ident: 2168_CR74 doi: 10.1109/CVPRW.2017.150 – ident: 2168_CR24 doi: 10.1109/CVPR52688.2022.01043 – ident: 2168_CR27 – ident: 2168_CR11 doi: 10.1109/CVPR52688.2022.01118 – ident: 2168_CR96 doi: 10.1109/ICCV48922.2021.00475 – ident: 2168_CR71 – ident: 2168_CR91 doi: 10.1109/CVPR52733.2024.02425 – ident: 2168_CR23 doi: 10.1109/CVPR42600.2020.00308 – ident: 2168_CR6 doi: 10.1109/CVPR46437.2021.01402 – ident: 2168_CR47 – ident: 2168_CR68 doi: 10.1007/978-3-031-73016-0_6 – ident: 2168_CR60 – ident: 2168_CR53 doi: 10.1609/aaai.v38i5.28226 – ident: 2168_CR15 – ident: 2168_CR21 doi: 10.1109/ICCVW.2019.00445 – ident: 2168_CR105 doi: 10.1109/ICCV.2017.244 – ident: 2168_CR1 doi: 10.1109/CVPRW.2017.150 – ident: 2168_CR87 doi: 10.1109/ICCV.2017.36 – ident: 2168_CR101 doi: 10.1109/CVPR52688.2022.00750 – ident: 2168_CR8 doi: 10.1145/3503161.3547833 – volume: 6 start-page: 66 year: 1920 ident: 2168_CR73 publication-title: Journal of Applied Psychology – ident: 2168_CR36 – ident: 2168_CR17 doi: 10.1109/TPAMI.2015.2439281 – ident: 2168_CR67 – ident: 2168_CR38 doi: 10.1109/CVPR.2019.00453 – ident: 2168_CR40 – ident: 2168_CR88 doi: 10.1109/CVPR42600.2020.00583 – ident: 2168_CR48 – ident: 2168_CR3 – ident: 2168_CR33 doi: 10.1109/ICCV.2017.355 – ident: 2168_CR103 – ident: 2168_CR35 doi: 10.1109/CVPR46437.2021.00214 – ident: 2168_CR54 – ident: 2168_CR62 doi: 10.1007/978-3-319-24574-4_28 – ident: 2168_CR45 – ident: 2168_CR51 doi: 10.1109/CVPR42600.2020.00251 – ident: 2168_CR20 – ident: 2168_CR93 – ident: 2168_CR99 doi: 10.1007/978-3-030-01234-2_18 – ident: 2168_CR75 doi: 10.1109/CVPR42600.2020.00282 – ident: 2168_CR28 – ident: 2168_CR82 – ident: 2168_CR59 – ident: 2168_CR61 doi: 10.1109/CVPR52688.2022.01042 – ident: 2168_CR97 doi: 10.1109/ICCV51070.2023.00355 – ident: 2168_CR44 doi: 10.1007/978-3-031-19797-0_33 – ident: 2168_CR4 doi: 10.1109/CVPR.2018.00652 – ident: 2168_CR69 – ident: 2168_CR5 doi: 10.1109/ICCV.2019.00318 – ident: 2168_CR84 doi: 10.1109/CVPR46437.2021.01318 – ident: 2168_CR7 doi: 10.1109/TPAMI.2022.3186715 – ident: 2168_CR83 doi: 10.1007/978-3-030-58598-3_7 – ident: 2168_CR9 doi: 10.1109/CVPR46437.2021.01212 – ident: 2168_CR86 doi: 10.1109/CVPR.2019.00182 – ident: 2168_CR26 doi: 10.1109/CVPR.2019.00183 – volume: 6 start-page: 66 year: 2022 ident: 2168_CR42 publication-title: Neurocomputing doi: 10.1016/j.neucom.2022.02.082 – ident: 2168_CR65 doi: 10.1109/TPAMI.2022.3204461 – ident: 2168_CR37 – ident: 2168_CR52 – ident: 2168_CR64 doi: 10.1145/3528233.3530757 – ident: 2168_CR70 – ident: 2168_CR102 doi: 10.1007/978-3-030-01231-1_6 – ident: 2168_CR66 doi: 10.1109/ICCV.2017.481 – ident: 2168_CR95 – ident: 2168_CR89 – ident: 2168_CR10 doi: 10.1109/ICCV48922.2021.01410 – ident: 2168_CR57 – ident: 2168_CR32 – ident: 2168_CR78 doi: 10.1109/CVPR46437.2021.00905 – ident: 2168_CR14 doi: 10.1109/CVPR.2019.01132 – ident: 2168_CR39 doi: 10.1109/ICCV48922.2021.00510 – ident: 2168_CR16 doi: 10.1007/978-3-319-10593-2_13 – ident: 2168_CR19 – ident: 2168_CR18 doi: 10.1007/978-3-319-46475-6_25 – ident: 2168_CR43 doi: 10.1109/ICCVW54120.2021.00210 – ident: 2168_CR85 doi: 10.1109/ICCV51070.2023.00701 – ident: 2168_CR25 doi: 10.1109/ICCVW.2019.00435 – ident: 2168_CR63 – ident: 2168_CR100 doi: 10.1109/CVPR.2019.00817 – ident: 2168_CR29 – ident: 2168_CR41 doi: 10.1109/CVPR.2017.19 – ident: 2168_CR2 doi: 10.1109/CVPR52688.2022.01767 – ident: 2168_CR34 doi: 10.1109/CVPRW50498.2020.00241 – ident: 2168_CR50 – ident: 2168_CR77 doi: 10.1109/CVPR46437.2021.01044 – ident: 2168_CR80 doi: 10.1109/CVPR.2018.00070 – ident: 2168_CR76 doi: 10.1609/aaai.v37i2.25353 – ident: 2168_CR49 doi: 10.1109/CVPR42600.2020.00037 – ident: 2168_CR56 doi: 10.1109/TPAMI.2021.3115428 – ident: 2168_CR104 – ident: 2168_CR12 doi: 10.1109/CVPR.2017.195 – ident: 2168_CR46 doi: 10.1109/ICCV48922.2021.00986 – ident: 2168_CR58 doi: 10.1109/ICCV51070.2023.01460 – ident: 2168_CR13 – ident: 2168_CR92 doi: 10.1109/CVPR.2018.00259 – ident: 2168_CR30 – ident: 2168_CR55 – ident: 2168_CR72 – ident: 2168_CR94 doi: 10.1109/TPAMI.2024.3461721 |
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