Built on

Kingma, D.P., Welling, M.: Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013)
Original
2013
Earlier work this paper cites.
Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., Bengio, Y.: Generative adversarial nets. Advances in neural information processing systems 27
2014
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Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Communications of the ACM 60
2017
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Zhang, R., Isola, P., Efros, A.A., Shechtman, E., Wang, O.: The unreasonable effectiveness of deep features as a perceptual metric. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp. 586–595 (2018)
2018
Earlier work this paper cites.
Song, J., Meng, C., Ermon, S.: Denoising diffusion implicit models. arXiv preprint arXiv:2010.02502 (2020)
Original
2020
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Alaluf, Y., Patashnik, O., Cohen-Or, D.: Restyle: A residual-based StyleGAN encoder via iterative refinement. In: Proc. ICCV (2021)
2021
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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. arXiv preprint arXiv:2108.01073 (2021)
Original
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: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. pp. 2287–2296 (2021)
2021
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Tov, O., Alaluf, Y., Nitzan, Y., Patashnik, O., Cohen-Or, D.: Designing an encoder for stylegan image manipulation. arXiv preprint arXiv:2102.02766 (2021)
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2021
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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. arXiv preprint arXiv:2208.01626 (2022)
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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. pp. 2426–2435 (2022)
2022
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Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al.: Training language models to follow instructions with human feedback. Advances in neural information processing systems 35
2022
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Ramesh, A., Dhariwal, P., Nichol, A., Chu, C., Chen, M.: Hierarchical text-conditional image generation with clip latents. arXiv preprint arXiv:2204.06125 1
Original
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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Wu, C.H., De la Torre, F.: Unifying diffusion models’ latent space, with applications to cyclediffusion and guidance. arXiv preprint arXiv:2210.05559 (2022)
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Then

Podell, D., English, Z., Lacey, K., Blattmann, A., Dockhorn, T., Müller, J., Penna, J., Rombach, R.: Sdxl: Improving latent diffusion models for high-resolution image synthesis. arXiv preprint arXiv:2307.01952 (2023)
Original
2023
Later among the works it cites.
Sauer, A., Lorenz, D., Blattmann, A., Rombach, R.: Adversarial diffusion distillation. arXiv preprint arXiv:2311.17042 (2023)
Original
2023
Later among the works it cites.
Tsaban, L., Passos, A.: Ledits: Real image editing with ddpm inversion and semantic guidance. arXiv preprint arXiv:2307.00522 (2023)
Original
2023
Later among the works it cites.
Wu, Q., Liu, Y., Zhao, H., Kale, A., Bui, T., Yu, T., Lin, Z., Zhang, Y., Chang, S.: Uncovering the disentanglement capability in text-to-image diffusion models. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 1900–1910 (2023)
2023
Later among the works it cites.

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Reading the bibliography…

2024

TurboEdit: Instant text-based image editing

Wu, Zongze, Kolkin, Nicholas, Brandt, Jonathan et al.

Understand

We address the challenges of precise image inversion and disentangled image editing in the context of few-step diffusion models.

We introduce an encoder based iterative inversion technique.
The inversion network is conditioned on the input image and the reconstructed image from the previous step, allowing for correction of the next reconstruction towards the input image.
We demonstrate that disentangled controls can be easily achieved in the few-step diffusion model by conditioning on an (automatically generated) detailed text prompt.

Built on

Kingma, D.P., Welling, M.: Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013)
Original
2013
Earlier work this paper cites.
Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., Bengio, Y.: Generative adversarial nets. Advances in neural information processing systems 27
2014
Earlier work this paper cites.
Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Communications of the ACM 60
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. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp. 586–595 (2018)
2018
Earlier work this paper cites.
Song, J., Meng, C., Ermon, S.: Denoising diffusion implicit models. arXiv preprint arXiv:2010.02502 (2020)
Original
2020
Earlier work this paper cites.
Alaluf, Y., Patashnik, O., Cohen-Or, D.: Restyle: A residual-based StyleGAN encoder via iterative refinement. In: Proc. ICCV (2021)
2021
Earlier work this paper cites.
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)
Original
2021
Earlier work this paper cites.
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: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. pp. 2287–2296 (2021)
2021
Earlier work this paper cites.
Tov, O., Alaluf, Y., Nitzan, Y., Patashnik, O., Cohen-Or, D.: Designing an encoder for stylegan image manipulation. arXiv preprint arXiv:2102.02766 (2021)
Original
2021
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
Earlier work this paper cites.
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. pp. 2426–2435 (2022)
2022
Earlier work this paper cites.
Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al.: Training language models to follow instructions with human feedback. Advances in neural information processing systems 35
2022
Earlier work this paper cites.
Ramesh, A., Dhariwal, P., Nichol, A., Chu, C., Chen, M.: Hierarchical text-conditional image generation with clip latents. arXiv preprint arXiv:2204.06125 1
Original
2022
Earlier work this paper cites.
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
Earlier work this paper cites.

Similar

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
Cited alongside, same era.
Wu, C.H., De la Torre, F.: Unifying diffusion models’ latent space, with applications to cyclediffusion and guidance. arXiv preprint arXiv:2210.05559 (2022)
Original
2022
Cited alongside, same era.
Brack, M., Friedrich, F., Kornmeier, K., Tsaban, L., Schramowski, P., Kersting, K., Passos, A.: Ledits++: Limitless image editing using text-to-image models. arXiv preprint arXiv:2311.16711 (2023)
Original
2023
Cited alongside, same era.
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)
2023

Then

Podell, D., English, Z., Lacey, K., Blattmann, A., Dockhorn, T., Müller, J., Penna, J., Rombach, R.: Sdxl: Improving latent diffusion models for high-resolution image synthesis. arXiv preprint arXiv:2307.01952 (2023)
Original
2023
Later among the works it cites.
Sauer, A., Lorenz, D., Blattmann, A., Rombach, R.: Adversarial diffusion distillation. arXiv preprint arXiv:2311.17042 (2023)
Original
2023
Later among the works it cites.
Tsaban, L., Passos, A.: Ledits: Real image editing with ddpm inversion and semantic guidance. arXiv preprint arXiv:2307.00522 (2023)
Original
2023
Later among the works it cites.
Wu, Q., Liu, Y., Zhao, H., Kale, A., Bui, T., Yu, T., Lin, Z., Zhang, Y., Chang, S.: Uncovering the disentanglement capability in text-to-image diffusion models. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 1900–1910 (2023)
2023
Later among the works it cites.

Beyond the bibliography

alphaXiv searches the wider corpus for related work and actual follow-ups.

Open on alphaXiv

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Cited alongside, same era.

Cao, M., Wang, X., Qi, Z., Shan, Y., Qie, X., Zheng, Y.: Masactrl: Tuning-free mutual self-attention control for consistent image synthesis and editing. arXiv preprint arXiv:2304.08465 (2023)

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Cited alongside, same era.

Chiang, W.L., Li, Z., Lin, Z., Sheng, Y., Wu, Z., Zhang, H., Zheng, L., Zhuang, S., Zhuang, Y., Gonzalez, J.E., Stoica, I., Xing, E.P.: Vicuna: An open-source chatbot impressing gpt-4 with 90%* chatgpt quality (March 2023), https://lmsys.org/blog/2023-03-30-vicuna/

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Cited alongside, same era.

Huberman-Spiegelglas, I., Kulikov, V., Michaeli, T.: An edit friendly ddpm noise space: Inversion and manipulations. arXiv preprint arXiv:2304.06140 (2023)

Original

2023

Cited alongside, same era.

Ju, X., Zeng, A., Bian, Y., Liu, S., Xu, Q.: Direct inversion: Boosting diffusion-based editing with 3 lines of code. arXiv preprint arXiv:2310.01506 (2023)

Original

2023

Cited alongside, same era.

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: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 6007–6017 (2023)

2023

Cited alongside, same era.

Luo, S., Tan, Y., Huang, L., Li, J., Zhao, H.: Latent consistency models: Synthesizing high-resolution images with few-step inference. arXiv preprint arXiv:2310.04378 (2023)

Original

2023

Cited alongside, same era.

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)

2023

Cited alongside, same era.

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)

2023

Cited alongside, same era.

Xu, S., Huang, Y., Pan, J., Ma, Z., Chai, J.: Inversion-free image editing with natural language. arXiv preprint arXiv:2312.04965 (2023)

Original

2023

Later among the works it cites.

Yin, T., Gharbi, M., Zhang, R., Shechtman, E., Durand, F., Freeman, W.T., Park, T.: One-step diffusion with distribution matching distillation. arXiv preprint arXiv:2311.18828 (2023)

Original

2023

Later among the works it cites.

Zhang, S., Yang, X., Feng, Y., Qin, C., Chen, C.C., Yu, N., Chen, Z., Wang, H., Savarese, S., Ermon, S., et al.: Hive: Harnessing human feedback for instructional visual editing. arXiv preprint arXiv:2303.09618 (2023)

Original

2023

Later among the works it cites.

Garibi, D., Patashnik, O., Voynov, A., Averbuch-Elor, H., Cohen-Or, D.: Renoise: Real image inversion through iterative noising. arXiv preprint arXiv:2403.14602 (2024)

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2024

Closest in time.

Hui, M., Yang, S., Zhao, B., Shi, Y., Wang, H., Wang, P., Zhou, Y., Xie, C.: Hq-edit: A high-quality dataset for instruction-based image editing. arXiv preprint arXiv:2404.09990 (2024)

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Lin, S., Wang, A., Yang, X.: Sdxl-lightning: Progressive adversarial diffusion distillation. arXiv preprint arXiv:2402.13929 (2024)

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Liu, H., Li, C., Wu, Q., Lee, Y.J.: Visual instruction tuning. Advances in neural information processing systems 36

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Yu, H., Luo, H., Wang, F., Zhao, F.: Uncovering the text embedding in text-to-image diffusion models. arXiv preprint arXiv:2404.01154 (2024)

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Zhang, K., Mo, L., Chen, W., Sun, H., Su, Y.: Magicbrush: A manually annotated dataset for instruction-guided image editing. Advances in Neural Information Processing Systems 36

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