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Image editing has advanced significantly with the development of diffusion models using both inversion-based and instruction-based methods.
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H. Liu, Z. Wan, W. Huang, Y. Song, X. Han, and J. Liao, “Pd-GAN: Probabilistic diverse GAN for image inpainting,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , 2021, pp. 9371–9381
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J. Peng, D. Liu, S. Xu, and H. Li, “Generating diverse structure for image inpainting with hierarchical vq-vae,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , 2021, pp. 10 775–10 784
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A. Radford, J. W. Kim, C. Hallacy, A. Ramesh, G. Goh, S. Agarwal, G. Sastry, A. Askell, P. Mishkin, J. Clark, G. Krueger, and I. Sutskever, “Learning transferable visual models from natural language supervision,” in International Conference on Machine Learning (ICML) . PMLR, 2021, pp. 8748–8763
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R. Rombach, A. Blattmann, D. Lorenz, P. Esser, and B. Ommer, “High-resolution image synthesis with latent diffusion models,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , June 2022, pp. 10 684–10 695
2022
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C. Meng, Y. He, Y. Song, J. Song, J. Wu, J.-Y. Zhu, and S. Ermon, “SDEdit: Guided image synthesis and editing with stochastic differential equations,” in International Conference on Learning Representations (ICLR) , 2022
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O. Avrahami, D. Lischinski, and O. Fried, “Blended diffusion for text-driven editing of natural images,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , 2022, pp. 18 208–18 218
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H. Zheng, Z. Lin, J. Lu, S. Cohen, E. Shechtman, C. Barnes, J. Zhang, N. Xu, S. Amirghodsi, and J. Luo, “Image inpainting with cascaded modulation GAN and object-aware training,” in European Conference on Computer Vision (ECCV) . Springer, 2022, pp. 277–296
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A. Lugmayr, M. Danelljan, A. Romero, F. Yu, R. Timofte, and L. Van Gool, “RePaint: Inpainting using denoising diffusion probabilistic models,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , 2022, pp. 11 461–11 471
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J. Ho and T. Salimans, “Classifier-free diffusion guidance,” arXiv preprint arXiv:2207.12598 , 2022
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G. Kim, T. Kwon, and J. C. Ye, “Diffusionclip: Text-guided diffusion models for robust image manipulation,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , 2022, pp. 2426–2435
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A. Q. Nichol, P. Dhariwal, A. Ramesh, P. Shyam, P. Mishkin, B. Mcgrew, I. Sutskever, and M. Chen, “Glide: Towards photorealistic image generation and editing with text-guided diffusion models,” in International Conference on Machine Learning (ICML) . PMLR, 2022, pp. 16 784–16 804
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A. Hertz, R. Mokady, J. Tenenbaum, K. Aberman, Y. Pritch, and D. Cohen-or, “Prompt-to-prompt image editing with cross-attention control,” in International Conference on Learning Representations (ICLR) , 2023
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G. Couairon, J. Verbeek, H. Schwenk, and M. Cord, “Diffedit: Diffusion-based semantic image editing with mask guidance,” in International Conference on Learning Representations (ICLR) , 2023
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2023
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T. Brooks, A. Holynski, and A. A. Efros, “Instructpix2pix: Learning to follow image editing instructions,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , 2023, pp. 18 392–18 402
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2023
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2023
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2023
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O. Avrahami, O. Fried, and D. Lischinski, “Blended latent diffusion,” ACM transactions on graphics (TOG) , vol. 42, no. 4, pp. 1–11, 2023
2023
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S. Xie, Z. Zhang, Z. Lin, T. Hinz, and K. Zhang, “Smartbrush: Text and shape guided object inpainting with diffusion model,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , 2023, pp. 22 428–22 437
2023
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Z. Zhang, L. Han, A. Ghosh, D. N. Metaxas, and J. Ren, “Sine: Single image editing with text-to-image diffusion models,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , 2023, pp. 6027–6037
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R. Mokady, A. Hertz, K. Aberman, Y. Pritch, and D. Cohen-Or, “Null-text inversion for editing real images using guided diffusion models,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , 2023, pp. 6038–6047
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