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In image editing employing diffusion models, it is crucial to preserve the reconstruction fidelity to the original image while changing its style.
Generative modeling by estimating gradients of the data distribution
Yang Song and Stefano Ermon · 1907
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PyTorch: An imperative style, high-performance deep learning library
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Denoising diffusion probabilistic models
Jonathan Ho, Ajay Jain, and Pieter Abbeel · 2006
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WaveGrad: Estimating gradients for waveform generation
Nanxin Chen, Yu Zhang, Heiga Zen, Ron J. Weiss, Mohammad Norouzi, and William Chan · 2009
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Denoising diffusion implicit models
Jiaming Song, Chenlin Meng, and Stefano Ermon · 2010
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Score-based generative modeling through stochastic differential equations
Yang Song, Jascha Sohl-Dickstein, Diederik P Kingma, Abhishek Kumar, Stefano Ermon, and Ben Poole · 2011
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Microsoft COCO: Common objects in context
Tsung-Yi Lin, Michael Maire, Serge Belongie, James Hays, Pietro Perona, Deva Ramanan, Piotr Dollár, and C. Lawrence Zitnick · 2014
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The unreasonable effectiveness of deep features as a perceptual metric
Richard Zhang, Phillip Isola, Alexei A. Efros, Eli Shechtman, and Oliver Wang · 2018
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WaveGrad 2: Iterative refinement for text-to-speech synthesis
Nanxin Chen, Yu Zhang, Heiga Zen, Ron J. Weiss, Mohammad Norouzi, Najim Dehak, and William Chan · 2021
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Diffusion models beat GANs on image synthesis
Prafulla Dhariwal and Alexander Nichol · 2021
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Classifier-free diffusion guidance
Jonathan Ho and Tim Salimans · 2021
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Learning transferable visual models from natural language supervision
Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, Gretchen Klueger, and Ilya Sutskever · 2021
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Flexible diffusion modeling of long videos
William Harvey, Saeid Naderiparizi, Vaden Masrani, Christian Weilbach, and Frank Wood · 2022
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Jonathan Ho, Tim Salimans, Alexey Gritsenko, William Chan, Mohammad Norouzi, and David J. Fleet · 2022
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Diffusion models for video prediction and infilling
Tobias Höppe, Arash Mehrjou, Stefan Bauer, Didrik Nielsen, and Andrea Dittadi · 2022
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DiffusionCLIP: Text-guided diffusion models for robust image manipulation
Gwanghyun Kim, Taesung Kwon, and Jong Chul Ye · 2022
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SDEdit: Guided image synthesis and editing with stochastic differential equations
Encoder-based domain tuning for fast personalization of text-to-image models
Rinon Gal, Moab Arar, Yuval Atzmon, Amit H. Bermano, Gal Chechik, and Daniel Cohen-Or · 2023
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Prompt-to-Prompt image editing with cross attention control
Amir Hertz, Ron Mokady, Jay Tenenbaum, Kfir Aberman, Yael Pritch, and Daniel Cohen-Or · 2023
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Imagic: Text-based real image editing with diffusion models
Bahjat Kawar, Shiran Zada, Oran Lang, Omer Tov, Huiwen Chang, Tali Dekel, Inbar Mosseri, and Michal Irani · 2023
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Null-text inversion for editing real images using guided diffusion models
Ron Mokady, Amir Hertz, Kfir Aberman, Yael Pritch, and Daniel Cohen-Or · 2023
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Chenlin Meng, Yutong He, Yang Song, Jiaming Song, Jiajun Wu, Jun-Yan Zhu, and Stefano Ermon · 2022
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High-resolution image synthesis with latent diffusion models
Robin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esser, and Björn Ommer · 2022
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Photorealistic text-to-image diffusion models with deep language understanding
Chitwan Saharia, William Chan, Saurabh Saxena, Lala Li, Jay Whang, Emily L. Denton, Kamyar Ghasemipour, Raphael Gontijo Lopes, Burcu Karagol Ayan, Tim Salimans, Jonathan Ho, David J. Fleet, and Mohammad Norouzi · 2022
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Diffusers: State-of-the-art diffusion models
Patrick von Platen, Suraj Patil, Anton Lozhkov, Pedro Cuenca, Nathan Lambert, Kashif Rasul, Mishig Davaadorj, and Thomas Wolf · 2022
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Align your latents: High-resolution video synthesis with latent diffusion models
Andreas Blattmann, Robin Rombach, Huan Ling, Tim Dockhorn, Seung Wook Kim, Sanja Fidler, and Karsten Kreis · 2023
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Noise map guidance: Inversion with spatial context for real image editing
Hansam Cho, Jonghyun Lee, Seoung Bum Kim, Tae-Hyun Oh, and Yonghyun Jeong · 2023
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An image is worth one word: Personalizing text-to-image generation using textual inversion
Rinon Gal, Yuval Alaluf, Yuval Atzmon, Or Patashnik, Amit H. Bermano, Gal Chechik, and Daniel Cohen-Or · 2023
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Nataniel Ruiz, Yuanzhen Li, Varun Jampani, Yael Pritch, Michael Rubinstein, and Kfir Aberman · 2023
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Narek Tumanyan, Michal Geyer, Shai Bagon, and Tali Dekel · 2023
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Dani Valevski, Matan Kalman, Yossi Matias, and Yaniv Leviathan · 2023
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Yuxiang Wei, Yabo Zhang, Zhilong Ji, Jinfeng Bai, Lei Zhang, and Wangmeng Zuo · 2023
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SINE: SINgle image Editing with text-to-image diffusion models
Zhixing Zhang, Ligong Han, Arnab Ghosh, Dimitris Metaxas, and Jian Ren · 2023
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ReNoise: Real image inversion through iterative noising
Daniel Garibi, Or Patashnik, Andrey Voynov, Hadar Averbuch-Elor, and Daniel Cohen-Or · 2024
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