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Recent advancements in text-to-image diffusion models have demonstrated their remarkable capability to generate high-quality images from textual prompts.
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Lee, K.H., Chen, X., Hua, G., Hu, H., He, X.: Stacked cross attention for image-text matching. In: Proceedings of the European conference on computer vision (ECCV). pp. 201–216 (2018)
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Ho, J., Jain, A., Abbeel, P.: Denoising diffusion probabilistic models. Advances in neural information processing systems 33
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Wei, X., Zhang, T., Li, Y., Zhang, Y., Wu, F.: Multi-modality cross attention network for image and sentence matching. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. pp. 10941–10950 (2020)
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Carlini, N., Tramer, F., Wallace, E., Jagielski, M., Herbert-Voss, A., Lee, K., Roberts, A., Brown, T., Song, D., Erlingsson, U., et al.: Extracting training data from large language models. In: 30th USENIX Security Symposium (USENIX Security 21). pp. 2633–2650 (2021)
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Koh, J.Y., Baldridge, J., Lee, H., Yang, Y.: Text-to-image generation grounded by fine-grained user attention. In: Proceedings of the IEEE/CVF winter conference on applications of computer vision. pp. 237–246 (2021)
2021
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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., et al.: Learning transferable visual models from natural language supervision. In: International conference on machine learning. pp. 8748–8763. PMLR (2021)
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Carlini, N., Jagielski, M., Zhang, C., Papernot, N., Terzis, A., Tramer, F.: The privacy onion effect: Memorization is relative. Advances in Neural Information Processing Systems 35
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: The Eleventh International Conference on Learning Representations (2022)
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2022
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Pizzi, E., Roy, S.D., Ravindra, S.N., Goyal, P., Douze, M.: A self-supervised descriptor for image copy detection. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 14532–14542 (2022)
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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)
2022
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Saharia, C., Chan, W., Saxena, S., Li, L., Whang, J., Denton, E.L., Ghasemipour, K., Gontijo Lopes, R., Karagol Ayan, B., Salimans, T., et al.: Photorealistic text-to-image diffusion models with deep language understanding. Advances in Neural Information Processing Systems 35
2022
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Schuhmann, C., Beaumont, R., Vencu, R., Gordon, C., Wightman, R., Cherti, M., Coombes, T., Katta, A., Mullis, C., Wortsman, M., et al.: Laion-5b: An open large-scale dataset for training next generation image-text models. Advances in Neural Information Processing Systems 35
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: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 22500–22510 (2023)
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Somepalli, G., Singla, V., Goldblum, M., Geiping, J., Goldstein, T.: Diffusion art or digital forgery? investigating data replication in diffusion models. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 6048–6058 (2023)
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2022
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2022
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2023
Cited alongside, same era.
Carlini, N., Hayes, J., Nasr, M., Jagielski, M., Sehwag, V., Tramer, F., Balle, B., Ippolito, D., Wallace, E.: Extracting training data from diffusion models. In: 32nd USENIX Security Symposium (USENIX Security 23). pp. 5253–5270 (2023)
2023
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Chefer, H., Alaluf, Y., Vinker, Y., Wolf, L., Cohen-Or, D.: Attend-and-excite: Attention-based semantic guidance for text-to-image diffusion models. ACM Transactions on Graphics (TOG) 42
2023
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2023
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Jiang, H.H., Brown, L., Cheng, J., Khan, M., Gupta, A., Workman, D., Hanna, A., Flowers, J., Gebru, T.: Ai art and its impact on artists. In: Proceedings of the 2023 AAAI/ACM Conference on AI, Ethics, and Society. pp. 363–374 (2023)
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2023
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2023
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2023
Later among the works it cites.
2023
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Zhai, S., Likhomanenko, T., Littwin, E., Busbridge, D., Ramapuram, J., Zhang, Y., Gu, J., Susskind, J.M.: Stabilizing transformer training by preventing attention entropy collapse. In: Krause, A., Brunskill, E., Cho, K., Engelhardt, B., Sabato, S., Scarlett, J. (eds.) Proceedings of the 40th International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 202, pp. 40770–40803. PMLR (23–29 Jul 2023), https://proceedings.mlr.press/v202/zhai23a.html
2023
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Daras, G., Shah, K., Dagan, Y., Gollakota, A., Dimakis, A., Klivans, A.: Ambient diffusion: Learning clean distributions from corrupted data. Advances in Neural Information Processing Systems 36
2024
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Somepalli, G., Singla, V., Goldblum, M., Geiping, J., Goldstein, T.: Understanding and mitigating copying in diffusion models. Advances in Neural Information Processing Systems 36
2024
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Wallace, B., Dang, M., Rafailov, R., Zhou, L., Lou, A., Purushwalkam, S., Ermon, S., Xiong, C., Joty, S., Naik, N.: Diffusion model alignment using direct preference optimization. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 8228–8238 (2024)
2024
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Wen, Y., Liu, Y., Chen, C., Lyu, L.: Detecting, explaining, and mitigating memorization in diffusion models. In: The Twelfth International Conference on Learning Representations (2024)
2024
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Zhang, C., Ippolito, D., Lee, K., Jagielski, M., Tramèr, F., Carlini, N.: Counterfactual memorization in neural language models. Advances in Neural Information Processing Systems 36
2024
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