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Noise Calibration: Plug-and-Play Content-Preserving Video Enhancement Using Pre-trained Video Diffusion Models

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Computer Vision – ECCV 2024 (ECCV 2024)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 15094))

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Abstract

In order to improve the quality of synthesized videos, currently, one predominant method involves retraining an expert diffusion model and then implementing a noising-denoising process for refinement. Despite the significant training costs, maintaining consistency of content between the original and enhanced videos remains a major challenge. To tackle this challenge, we propose a novel formulation that considers both visual quality and consistency of content. Consistency of content is ensured by a proposed loss function that maintains the structure of the input, while visual quality is improved by utilizing the denoising process of pretrained diffusion models. To address the formulated optimization problem, we have developed a plug-and-play noise optimization strategy, referred to as Noise Calibration. By refining the initial random noise through a few iterations, the content of original video can be largely preserved, and the enhancement effect demonstrates a notable improvement. Extensive experiments have demonstrated the effectiveness of the proposed method.

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References

  1. Ahn, N., Kwon, P., Back, J., Hong, K., Kim, S.: Interactive cartoonization with controllable perceptual factors. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 16827–16835 (2023)

    Google Scholar 

  2. An, J., et al.: Latent-shift: Latent diffusion with temporal shift for efficient text-to-video generation. arXiv preprint arXiv:2304.08477 (2023)

  3. Avrahami, O., Fried, O., Lischinski, D.: Blended latent diffusion. ACM Trans. Graph. (TOG) 42(4), 1–11 (2023)

    Article  Google Scholar 

  4. Avrahami, O., Lischinski, D., Fried, O.: Blended diffusion for text-driven editing of natural images. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 18208–18218 (2022)

    Google Scholar 

  5. Balaji, Y., Min, M.R., Bai, B., Chellappa, R., Graf, H.P.: Conditional gan with discriminative filter generation for text-to-video synthesis. In: IJCAI, vol. 1, p. 2 (2019)

    Google Scholar 

  6. Bao, F., Li, C., Zhu, J., Zhang, B.: Analytic-dpm: an analytic estimate of the optimal reverse variance in diffusion probabilistic models. arXiv preprint arXiv:2201.06503 (2022)

  7. Brack, M., et al.: Ledits++: Limitless image editing using text-to-image models. arXiv preprint arXiv:2311.16711 (2023)

  8. Brooks, T., Holynski, A., Efros, A.A.: Instructpix2pix: learning to follow image editing instructions. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 18392–18402 (2023)

    Google Scholar 

  9. Chan, K.C.K., Zhou, S., Xu, X., Loy, C.C.: Basicvsr++: Improving video super-resolution with enhanced propagation and alignment (2021)

    Google Scholar 

  10. Chen, C., et al.: Iterative token evaluation and refinement for real-world super-resolution. arXiv preprint arXiv:2312.05616 (2023)

  11. Chen, H., et al.: Videocrafter1: Open diffusion models for high-quality video generation. arXiv preprint arXiv:2310.19512 (2023)

  12. Choi, J., Kim, S., Jeong, Y., Gwon, Y., Yoon, S.: Ilvr: Conditioning method for denoising diffusion probabilistic models. arXiv preprint arXiv:2108.02938 (2021)

  13. Çiçek, Ö., Abdulkadir, A., Lienkamp, S.S., Brox, T., Ronneberger, O.: 3D U-Net: learning dense volumetric segmentation from sparse annotation. In: Ourselin, S., Joskowicz, L., Sabuncu, M.R., Unal, G., Wells, W. (eds.) MICCAI 2016. LNCS, vol. 9901, pp. 424–432. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46723-8_49

    Chapter  Google Scholar 

  14. Creswell, A., White, T., Dumoulin, V., Arulkumaran, K., Sengupta, B., Bharath, A.A.: Generative adversarial networks: an overview. IEEE Signal Process. Mag. 35(1), 53–65 (2018)

    Article  Google Scholar 

  15. Dhariwal, P., Nichol, A.: Diffusion models beat gans on image synthesis. Adv. Neural. Inf. Process. Syst. 34, 8780–8794 (2021)

    Google Scholar 

  16. Dockhorn, T., Vahdat, A., Kreis, K.: Score-based generative modeling with critically-damped langevin diffusion. arXiv preprint arXiv:2112.07068 (2021)

  17. Goodfellow, I., et al.: Generative adversarial networks. Commun. ACM 63(11), 139–144 (2020)

    Article  MathSciNet  Google Scholar 

  18. Hachnochi, R., et al.: Cross-domain compositing with pretrained diffusion models. arXiv preprint arXiv:2302.10167 (2023)

  19. He, Y., Yang, T., Zhang, Y., Shan, Y., Chen, Q.: Latent video diffusion models for high-fidelity video generation with arbitrary lengths. arXiv preprint arXiv:2211.13221 (2022)

  20. Hertz, A., Mokady, R., Tenenbaum, J., Aberman, K., Pritch, Y., Cohen-Or, D.: Prompt-to-prompt image editing with cross attention control (2022). https://arxiv.org/abs/2208.01626 (2022)

  21. Ho, J., et al.: Imagen video: High definition video generation with diffusion models. arXiv preprint arXiv:2210.02303 (2022)

  22. Ho, J., Jain, A., Abbeel, P.: Denoising diffusion probabilistic models. Adv. Neural. Inf. Process. Syst. 33, 6840–6851 (2020)

    Google Scholar 

  23. Ho, J., Salimans, T., Gritsenko, A., Chan, W., Norouzi, M., Fleet, D.J.: Video diffusion models (2022)

    Google Scholar 

  24. Hu, Y., Luo, C., Chen, Z.: Make it move: controllable image-to-video generation with text descriptions. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 18219–18228 (2022)

    Google Scholar 

  25. Jiang, Y., Yang, S., Qiu, H., Wu, W., Loy, C.C., Liu, Z.: Text2human: text-driven controllable human image generation. ACM Trans. Graph. (TOG) 41(4), 1–11 (2022)

    Article  Google Scholar 

  26. Jiménez, Á.B.: Mixture of diffusers for scene composition and high resolution image generation. arXiv preprint arXiv:2302.02412 (2023)

  27. Kawar, B., et al.: Imagic: text-based real image editing with diffusion models. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6007–6017 (2023)

    Google Scholar 

  28. Kim, H., Lee, G., Choi, Y., Kim, J.H., Zhu, J.Y.: 3d-aware blending with generative nerfs. arXiv preprint arXiv:2302.06608 (2023)

  29. Kingma, D.P., Welling, M.: Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013)

  30. Kingma, D.P., Welling, M., et al.: An introduction to variational autoencoders. Foundat. Trends® Mach. Learn. 12(4), 307–392 (2019)

    Google Scholar 

  31. LeCun, Y., Chopra, S., Hadsell, R., Ranzato, M., Huang, F.: A tutorial on energy-based learning. Predicting Structured Data 1(0) (2006)

    Google Scholar 

  32. Li, B., Xue, K., Liu, B., Lai, Y.K.: Vqbb: Image-to-image translation with vector quantized brownian bridge. arXiv preprint arXiv:2205.07680 (2022)

  33. Liu, N., Li, S., Du, Y., Torralba, A., Tenenbaum, J.B.: Compositional visual generation with composable diffusion models. In: European Conference on Computer Vision, pp. 423–439. Springer (2022). https://doi.org/10.1007/978-3-031-19790-1_26

  34. Liu, Y., et al.: Evalcrafter: Benchmarking and evaluating large video generation models. arXiv preprint arXiv:2310.11440 (2023)

  35. Lu, S., Liu, Y., Kong, A.W.K.: Tf-icon: diffusion-based training-free cross-domain image composition. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 2294–2305 (2023)

    Google Scholar 

  36. Luo, F., Xiang, J., Zhang, J., Han, X., Yang, W.: Image super-resolution via latent diffusion: A sampling-space mixture of experts and frequency-augmented decoder approach. arXiv preprint arXiv:2310.12004 (2023)

  37. Ma, Y., et al.: Follow your pose: Pose-guided text-to-video generation using pose-free videos. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 38, pp. 4117–4125 (2024)

    Google Scholar 

  38. Ma, Y., et al.: Follow-your-click: Open-domain regional image animation via short prompts. arXiv preprint arXiv:2403.08268 (2024)

  39. Ma, Y., et al.: Follow-your-emoji: Fine-controllable and expressive freestyle portrait animation. arXiv preprint arXiv:2406.01900 (2024)

  40. Mei, K., Patel, V.: Vidm: video implicit diffusion models. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 37, pp. 9117–9125 (2023)

    Google Scholar 

  41. Meng, C., He, Y., Song, Y., Song, J., Wu, J., Zhu, J.Y., Ermon, S.: Sdedit: Guided image synthesis and editing with stochastic differential equations. arXiv preprint arXiv:2108.01073 (2021)

  42. Miech, A., Zhukov, D., Alayrac, J.B., Tapaswi, M., Laptev, I., Sivic, J.: Howto100m: learning a text-video embedding by watching hundred million narrated video clips. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 2630–2640 (2019)

    Google Scholar 

  43. Mishra, S., Saenko, K., Saligrama, V.: Syncdr: Training cross domain retrieval models with synthetic data. arXiv preprint arXiv:2401.00420 (2023)

  44. Mokady, R., Hertz, A., Aberman, K., Pritch, Y., Cohen-Or, D.: Null-text inversion for editing real images using guided diffusion models. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6038–6047 (2023)

    Google Scholar 

  45. Ngiam, J., Chen, Z., Koh, P.W., Ng, A.Y.: Learning deep energy models. In: Proceedings of the 28th International Conference on Machine Learning (ICML 2011), pp. 1105–1112 (2011)

    Google Scholar 

  46. Nichol, A., et al.: Glide: Towards photorealistic image generation and editing with text-guided diffusion models. arXiv preprint arXiv:2112.10741 (2021)

  47. Nichol, A.Q., Dhariwal, P.: Improved denoising diffusion probabilistic models. In: International Conference on Machine Learning, pp. 8162–8171. PMLR (2021)

    Google Scholar 

  48. Oussidi, A., Elhassouny, A.: Deep generative models: Survey. In: 2018 International Conference on Intelligent Systems and Computer Vision (ISCV), pp. 1–8. IEEE (2018)

    Google Scholar 

  49. Pandey, K., Mukherjee, A., Rai, P., Kumar, A.: Vaes meet diffusion models: efficient and high-fidelity generation. In: NeurIPS 2021 Workshop on Deep Generative Models and Downstream Applications (2021)

    Google Scholar 

  50. Parmar, G., Kumar Singh, K., Zhang, R., Li, Y., Lu, J., Zhu, J.Y.: Zero-shot image-to-image translation. In: ACM SIGGRAPH 2023 Conference Proceedings, pp. 1–11 (2023)

    Google Scholar 

  51. Peng, D., Hu, P., Ke, Q., Liu, J.: Diffusion-based image translation with label guidance for domain adaptive semantic segmentation. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 808–820 (2023)

    Google Scholar 

  52. Podell, D.et al.: Sdxl: Improving latent diffusion models for high-resolution image synthesis. arXiv preprint arXiv:2307.01952 (2023)

  53. Rezende, D., Mohamed, S.: Variational inference with normalizing flows. In: International Conference on Machine Learning, pp. 1530–1538. PMLR (2015)

    Google Scholar 

  54. Rombach, R., Blattmann, A., Lorenz, D., Esser, P., Ommer, B.: High-resolution image synthesis with latent diffusion models. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 10684–10695 (2022)

    Google Scholar 

  55. Saharia, C., et al.: Palette: image-to-image diffusion models. In: ACM SIGGRAPH 2022 Conference Proceedings, pp. 1–10 (2022)

    Google Scholar 

  56. Saharia, C., et al.: Photorealistic text-to-image diffusion models with deep language understanding. Adv. Neural. Inf. Process. Syst. 35, 36479–36494 (2022)

    Google Scholar 

  57. Saharia, C., Ho, J., Chan, W., Salimans, T., Fleet, D.J., Norouzi, M.: Image super-resolution via iterative refinement. IEEE Trans. Pattern Anal. Mach. Intell. 45(4), 4713–4726 (2022)

    Google Scholar 

  58. Si, C., Huang, Z., Jiang, Y., Liu, Z.: Freeu: Free lunch in diffusion u-net. arXiv preprint arXiv:2309.11497 (2023)

  59. Singh, J., Gould, S., Zheng, L.: High-fidelity guided image synthesis with latent diffusion models. In: 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 5997–6006. IEEE (2023)

    Google Scholar 

  60. Sinha, A., Song, J., Meng, C., Ermon, S.: D2c: diffusion-decoding models for few-shot conditional generation. Adv. Neural. Inf. Process. Syst. 34, 12533–12548 (2021)

    Google Scholar 

  61. Skorokhodov, I., Tulyakov, S., Elhoseiny, M.: Stylegan-v: A continuous video generator with the price, image quality and perks of stylegan2. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 3626–3636 (2022)

    Google Scholar 

  62. Sohl-Dickstein, J., Weiss, E., Maheswaranathan, N., Ganguli, S.: Deep unsupervised learning using nonequilibrium thermodynamics. In: International Conference on Machine Learning, pp. 2256–2265. PMLR (2015)

    Google Scholar 

  63. Song, J., Meng, C., Ermon, S.: Denoising diffusion implicit models. arXiv preprint arXiv:2010.02502 (2020)

  64. Song, J., Meng, C., Ermon, S.: Denoising diffusion implicit models. In: International Conference on Learning Representations (2021)

    Google Scholar 

  65. Song, Y., Durkan, C., Murray, I., Ermon, S.: Maximum likelihood training of score-based diffusion models. Adv. Neural. Inf. Process. Syst. 34, 1415–1428 (2021)

    Google Scholar 

  66. Song, Y., Ermon, S.: Generative modeling by estimating gradients of the data distribution. Adv. Neural Inform. Process. Syst. 32 (2019)

    Google Scholar 

  67. Song, Y., Ermon, S.: Improved techniques for training score-based generative models. Adv. Neural. Inf. Process. Syst. 33, 12438–12448 (2020)

    Google Scholar 

  68. Song, Y., Sohl-Dickstein, J., Kingma, D.P., Kumar, A., Ermon, S., Poole, B.: Score-based generative modeling through stochastic differential equations. arXiv preprint arXiv:2011.13456 (2020)

  69. Tulyakov, S., Liu, M.Y., Yang, X., Kautz, J.: Mocogan: Decomposing motion and content for video generation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1526–1535 (2018)

    Google Scholar 

  70. Tumanyan, N., Geyer, M., Bagon, S., Dekel, T.: Plug-and-play diffusion features for text-driven image-to-image translation. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 1921–1930 (2023)

    Google Scholar 

  71. Vahdat, A., Kreis, K., Kautz, J.: Score-based generative modeling in latent space. Adv. Neural. Inf. Process. Syst. 34, 11287–11302 (2021)

    Google Scholar 

  72. Wang, J., Chan, K.C., Loy, C.C.: Exploring clip for assessing the look and feel of images. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 37, pp. 2555–2563 (2023)

    Google Scholar 

  73. Wang, J., Yue, Z., Zhou, S., Chan, K.C., Loy, C.C.: Exploiting diffusion prior for real-world image super-resolution. arXiv preprint arXiv:2305.07015 (2023)

  74. Wang, J., Yuan, H., Chen, D., Zhang, Y., Wang, X., Zhang, S.: Modelscope text-to-video technical report. arXiv preprint arXiv:2308.06571 (2023)

  75. Wang, T., et al.: Pretraining is all you need for image-to-image translation. arXiv preprint arXiv:2205.12952 (2022)

  76. Wang, W., et al.: Videofactory: Swap attention in spatiotemporal diffusions for text-to-video generation. arXiv preprint arXiv:2305.10874 (2023)

  77. Wang, Y., Bilinski, P., Bremond, F., Dantcheva, A.: G3an: disentangling appearance and motion for video generation. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5264–5273 (2020)

    Google Scholar 

  78. Wang, Y., Bilinski, P., Bremond, F., Dantcheva, A.: Imaginator: Conditional spatio-temporal gan for video generation. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 1160–1169 (2020)

    Google Scholar 

  79. Wang, Y., et al.: Lavie: High-quality video generation with cascaded latent diffusion models. arXiv preprint arXiv:2309.15103 (2023)

  80. Wang, Y., Jiang, L., Loy, C.C.: Styleinv: a temporal style modulated inversion network for unconditional video generation. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 22851–22861 (2023)

    Google Scholar 

  81. Wang, Z., Bovik, A.C., Sheikh, H.R., Simoncelli, E.P.: Image quality assessment: from error visibility to structural similarity. IEEE Trans. Image Process. 13(4), 600–612 (2004)

    Article  Google Scholar 

  82. Watson, D., Chan, W., Ho, J., Norouzi, M.: Learning fast samplers for diffusion models by differentiating through sample quality. arXiv preprint arXiv:2202.05830 (2022)

  83. Wolleb, J., Sandkühler, R., Bieder, F., Cattin, P.C.: The swiss army knife for image-to-image translation: Multi-task diffusion models. arXiv preprint arXiv:2204.02641 (2022)

  84. Wu, C.H., De la Torre, F.: A latent space of stochastic diffusion models for zero-shot image editing and guidance. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 7378–7387 (2023)

    Google Scholar 

  85. Wu, H., et al.: Exploring video quality assessment on user generated contents from aesthetic and technical perspectives. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 20144–20154 (2023)

    Google Scholar 

  86. Xia, B., et al.: Diffir: Efficient diffusion model for image restoration. arXiv preprint arXiv:2303.09472 (2023)

  87. Yang, B., et al.: Paint by example: Exemplar-based image editing with diffusion models. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 18381–18391 (2023)

    Google Scholar 

  88. Yang, Z., Chu, T., Lin, X., Gao, E., Liu, D., Yang, J., Wang, C.: Eliminating contextual prior bias for semantic image editing via dual-cycle diffusion. IEEE Trans. Circ. Syst. Video Technol. (2023)

    Google Scholar 

  89. Ye, Y., et al.: Affordance diffusion: synthesizing hand-object interactions. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 22479–22489 (2023)

    Google Scholar 

  90. Yu, S., Sohn, K., Kim, S., Shin, J.: Video probabilistic diffusion models in projected latent space. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 18456–18466 (2023)

    Google Scholar 

  91. Yue, Z., Wang, J., Loy, C.C.: Resshift: Efficient diffusion model for image super-resolution by residual shifting. arXiv preprint arXiv:2307.12348 (2023)

  92. Zhang, D.J., et al.: Show-1: Marrying pixel and latent diffusion models for text-to-video generation. arXiv preprint arXiv:2309.15818 (2023)

  93. Zhang, S., et al.: I2vgen-xl: High-quality image-to-video synthesis via cascaded diffusion models. arXiv preprint arXiv:2311.04145 (2023)

  94. Zhang, S., Xiao, S., Huang, W.: Forgedit: Text guided image editing via learning and forgetting. arXiv preprint arXiv:2309.10556 (2023)

  95. Zhao, M., Bao, F., Li, C., Zhu, J.: Egsde: unpaired image-to-image translation via energy-guided stochastic differential equations. Adv. Neural. Inf. Process. Syst. 35, 3609–3623 (2022)

    Google Scholar 

  96. Zhou, D., Wang, W., Yan, H., Lv, W., Zhu, Y., Feng, J.: Magicvideo: Efficient video generation with latent diffusion models. arXiv preprint arXiv:2211.11018 (2022)

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Acknowledgement

This research is supported by National Key R&D Program of China (No. 2018AAA0100300).

Author information

Authors and Affiliations

  1. Dalian University of Technology, Dalian, China

    Qinyu Yang & Zhixun Su

  2. Tencent AI Lab, Shenzhen, China

    Haoxin Chen, Yong Zhang, Menghan Xia, Xiaodong Cun & Ying Shan

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  1. Qinyu Yang
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  2. Haoxin Chen
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  3. Yong Zhang
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  4. Menghan Xia
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  5. Xiaodong Cun
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Corresponding author

Correspondence to Yong Zhang .

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Editors and Affiliations

  1. University of Birmingham, Birmingham, UK

    Aleš Leonardis

  2. University of Trento, Trento, Italy

    Elisa Ricci

  3. Technical University of Darmstadt, Darmstadt, Germany

    Stefan Roth

  4. Princeton University, Princeton, NJ, USA

    Olga Russakovsky

  5. Czech Technical University in Prague, Prague, Czech Republic

    Torsten Sattler

  6. École des Ponts ParisTech, Marne-la-Vallée, France

    Gül Varol

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Limitation

Like SDEdit, the enhancement effectiveness of our method is also limited by the performance of the base model.

Societal Impact

As our method is for improving video quality, it does not introduce additional ethical concerns.

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Yang, Q. et al. (2025). Noise Calibration: Plug-and-Play Content-Preserving Video Enhancement Using Pre-trained Video Diffusion Models. In: Leonardis, A., Ricci, E., Roth, S., Russakovsky, O., Sattler, T., Varol, G. (eds) Computer Vision – ECCV 2024. ECCV 2024. Lecture Notes in Computer Science, vol 15094. Springer, Cham. https://doi.org/10.1007/978-3-031-72764-1_18

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