Ground-A-Score: Scaling Up the Score Distillation for Multi-Attribute Editing

Ronneberger, O., Fischer, P., Brox, T.: U-net: Convolutional networks for biomedical image segmentation. Medical Image Computing and Computer-Assisted Intervention–MICCAI 2015: 18th International Conference, Munich, Germany, October 5-9, 2015, Proceedings, Part III 18 pp. 234–241 (2015)

2015

Earlier work this paper cites.

Krishna, R., Zhu, Y., Groth, O., Johnson, J., Hata, K., Kravitz, J., Chen, S., Kalantidis, Y., Li, L.J., Shamma, D.A., et al.: Visual genome: Connecting language and vision using crowdsourced dense image annotations. IJCV 123

2017

Earlier work this paper cites.

Zhang, R., Isola, P., Efros, A.A., Shechtman, E., Wang, O.: The unreasonable effectiveness of deep features as a perceptual metric. CVPR pp. 586–595 (2018)

2018

Earlier work this paper cites.

Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. NeurIPS 33

2020

Earlier work this paper cites.

Ho, J., Jain, A., Abbeel, P.: Denoising diffusion probabilistic models. NeurIPS 33

2020

Earlier work this paper cites.

Kong, Z., Ping, W., Huang, J., Zhao, K., Catanzaro, B.: Diffwave: A versatile diffusion model for audio synthesis. arXiv preprint arXiv:2009.09761 (2020)

Original

2020

Earlier work this paper cites.

Park, T., Efros, A.A., Zhang, R., Zhu, J.Y.: Contrastive learning for unpaired image-to-image translation. ECCV pp. 319–345 (2020)

2020

Earlier work this paper cites.

Austin, J., Johnson, D.D., Ho, J., Tarlow, D., Van Den Berg, R.: Structured denoising diffusion models in discrete state-spaces. NeurIPS 34

2021

Earlier work this paper cites.

Ho, J., Salimans, T.: Classifier-free diffusion guidance. NeurIPS (2021)

2021

Earlier work this paper cites.

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. ICML pp. 8748–8763 (2021)

2021

Earlier work this paper cites.

Song, J., Meng, C., Ermon, S.: Denoising diffusion implicit models. ICLR (2021)

2021

Earlier work this paper cites.

Song, Y., Sohl-Dickstein, J., Kingma, D.P., Kumar, A., Ermon, S., Poole, B.: Score-based generative modeling through stochastic differential equations. ICLR (2021)

2021

Earlier work this paper cites.

Feng, W., He, X., Fu, T.J., Jampani, V., Akula, A., Narayana, P., Basu, S., Wang, X.E., Wang, W.Y.: Training-free structured diffusion guidance for compositional text-to-image synthesis. ICLR (2022)

2022

Earlier work this paper cites.

Gal, R., Alaluf, Y., Atzmon, Y., Patashnik, O., Bermano, A.H., Chechik, G., Cohen-Or, D.: An image is worth one word: Personalizing text-to-image generation using textual inversion. arXiv preprint arXiv:2208.01618 (2022)

Original

2022

Earlier work this paper cites.

Hertz, A., Mokady, R., Tenenbaum, J., Aberman, K., Pritch, Y., Cohen-Or, D.: Prompt-to-prompt image editing with cross attention control. arXiv preprint arXiv:2208.01626 (2022)

Original

2022

Cited alongside, same era.

Hu, M., Zheng, C., Zheng, H., Cham, T.J., Wang, C., Yang, Z., Tao, D., Suganthan, P.N.: Unified discrete diffusion for simultaneous vision-language generation. CVPR (2022)

2022

Cited alongside, same era.

Karras, T., Aittala, M., Aila, T., Laine, S.: Elucidating the design space of diffusion-based generative models. NeurIPS 35

2022

Cited alongside, same era.

Liu, N., Li, S., Du, Y., Torralba, A., Tenenbaum, J.B.: Compositional visual generation with composable diffusion models. ECCV pp. 423–439 (2022)

2022

Cited alongside, same era.

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. ICLR (2022)

Hertz, A., Aberman, K., Cohen-Or, D.: Delta denoising score. ICCV pp. 2328–2337 (2023)

2023

Later among the works it cites.

Kim, J., Park, G.Y., Chung, H., Ye, J.C.: Regularization by texts for latent diffusion inverse solvers. arXiv preprint arXiv:2311.15658 (2023)

Original

2023

Later among the works it cites.

Li, Y., Liu, H., Wu, Q., Mu, F., Yang, J., Gao, J., Li, C., Lee, Y.J.: Gligen: Open-set grounded text-to-image generation. CVPR pp. 22511–22521 (2023)

2023

Later among the works it cites.

Liu, S., Zeng, Z., Ren, T., Li, F., Zhang, H., Yang, J., Li, C., Yang, J., Su, H., Zhu, J., et al.: Grounding dino: Marrying dino with grounded pre-training for open-set object detection. arXiv preprint arXiv:2303.05499 (2023)

Original

2023

Later among the works it cites.

Miyake, D., Iohara, A., Saito, Y., Tanaka, T.: Negative-prompt inversion: Fast image inversion for editing with text-guided diffusion models. arXiv preprint arXiv:2305.16807 (2023)