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Text-conditioned image editing has emerged as a powerful tool for editing images.
2015
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Zhu, M., Pan, P., Chen, W., Yang, Y.: Dm-gan: Dynamic memory generative adversarial networks for text-to-image synthesis. In: arXiv (2019)
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Ho, J., Jain, A., Abbeel, P.: Denoising diffusion probabilistic models. In: arXiv (2020)
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Dhariwal, P., Nichol, A.: Diffusion models beat gans on image synthesis. In: Advances in Neural Information Processing Systems. vol. 34, pp. 8780–8794. Curran Associates, Inc. (2021),
2021
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Esser, P., Rombach, R., Ommer, B.: Taming transformers for high-resolution image synthesis. In: arXiv (2021)
2021
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Gal, R., Patashnik, O., Maron, H., Chechik, G., Cohen-Or, D.: Stylegan-nada: Clip-guided domain adaptation of image generators. In: arXiv (2021)
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Patashnik, O., Wu, Z., Shechtman, E., Cohen-Or, D., Lischinski, D.: Styleclip: Text-driven manipulation of stylegan imagery. In: Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV). pp. 2085–2094 (October 2021)
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2021
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Richardson, E., Alaluf, Y., Patashnik, O., Nitzan, Y., Azar, Y., Shapiro, S., Cohen-Or, D.: Encoding in style: a stylegan encoder for image-to-image translation. In: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (June 2021)
2021
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Roich, D., Mokady, R., Bermano, A.H., Cohen-Or, D.: Pivotal tuning for latent-based editing of real images. In: arXiv (2021)
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Rombach, R., Blattmann, A., Lorenz, D., Esser, P., Ommer, B.: High-resolution image synthesis with latent diffusion models. In: arXiv (2021)
2021
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2021
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Alaluf, Y., Patashnik, O., Wu, Z., Zamir, A., Shechtman, E., Lischinski, D., Cohen-Or, D.: Third time’s the charm? image and video editing with stylegan3. In: arXiv (2022)
2022
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2022
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2022
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Saharia, C., Chan, W., Chang, H., Lee, C.A., Ho, J., Salimans, T., Fleet, D.J., Norouzi, M.: Palette: Image-to-image diffusion models. In: arXiv (2022)
2022
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Witteveen, S., Andrews, M.: Investigating prompt engineering in diffusion models. In: arXiv (2022)
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Dinh, T.M., Tran, A.T., Nguyen, R., Hua, B.S.: Hyperinverter: Improving stylegan inversion via hypernetwork. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2022)
2022
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Gafni, O., Polyak, A., Ashual, O., Sheynin, S., Parikh, D., Taigman, Y.: Make-a-scene: Scene-based text-to-image generation with human priors. In: arXiv (2022)
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2022
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Hao, Y., Chi, Z., Dong, L., Wei, F.: Optimizing prompts for text-to-image generation. In: arXiv (2022)
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Hertz, A., Mokady, R., Tenenbaum, J., Aberman, K., Pritch, Y., Cohen-Or, D.: Prompt-to-prompt image editing with cross attention control. In: arXiv (2022)
2022
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Kim, G., Kwon, T., Ye, J.C.: Diffusionclip: Text-guided diffusion models for robust image manipulation. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). pp. 2426–2435 (June 2022)
2022
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Liu, Y., Gal, R., Bermano, A.H., Chen, B., Cohen-Or, D.: Self-conditioned generative adversarial networks for image editing. In: arXiv (2022)
2022
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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. In: arXiv (2022)
2022
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Yang, Z., Wang, J., Gan, Z., Li, L., Lin, K., Wu, C., Duan, N., Liu, Z., Liu, C., Zeng, M., Wang, L.: Reco: Region-controlled text-to-image generation. In: arXiv (2022)
2022
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Yu, J., Xu, Y., Koh, J.Y., Luong, T., Baid, G., Wang, Z., Vasudevan, V., Ku, A., Yang, Y., Ayan, B.K., Hutchinson, B., Han, W., Parekh, Z., Li, X., Zhang, H., Baldridge, J., Wu, Y.: Scaling autoregressive models for content-rich text-to-image generation. In: arXiv (2022)
2022
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Brooks, T., Holynski, A., Efros, A.A.: Instructpix2pix: Learning to follow image editing instructions. In: arXiv (2023)
2023
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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: Conference on Computer Vision and Pattern Recognition 2023 (2023)
2023
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Parmar, G., Singh, K.K., Zhang, R., Li, Y., Lu, J., Zhu, J.Y.: Zero-shot image-to-image translation. In: arXiv (2023)
2023
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Wen, Y., Jain, N., Kirchenbauer, J., Goldblum, M., Geiping, J., Goldstein, T.: Hard prompts made easy: Gradient-based discrete optimization for prompt tuning and discovery. In: arXiv (2023)
2023
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Zhang, L., Agrawala, M.: Adding conditional control to text-to-image diffusion models. In: arXiv (2023)
2023
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Zhang, Y., Zhou, K., Liu, Z.: What makes good examples for visual in-context learning? In: arXiv (2023)
2023
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