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Diffusion inversion is the problem of taking an image and a text prompt that describes it and finding a noise latent that would generate the exact same image.
Fixed-point iteration
J. Douglas Burden, Richard L.; Faires · 1985
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Holistic but customized resources for a course in numerical methods
Autar Kaw, Nathan Collier, Michael Keteltas, Jai Paul, and Glen Besterfield · 2003
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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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Numerical Analysis
R.L. Burden, J.D. Faires, and A.M. Burden · 2015
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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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New versions of newton method: step-size choice, convergence domain and under-determined equations
Boris Polyak and Andrey Tremba · 2020
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Diffusion models beat gans on image synthesis
Prafulla Dhariwal and Alex Nichol · 2021
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Denoising diffusion implicit models
Jiaming Song, Chenlin Meng, and Stefano Ermon · 2021
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eDiff-I: Text-to-image diffusion models with an ensemble of expert denoisers
Yogesh Balaji, Seungjun Nah, Xun Huang, Arash Vahdat, Jiaming Song, Karsten Kreis, Miika Aittala, Timo Aila, Samuli Laine, Bryan Catanzaro, et al · 2022
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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 · 2022
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Elucidating the design space of diffusion-based generative models
Tero Karras, Miika Aittala, Timo Aila, and Samuli Laine · 2022
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DPM-Solver: a fast ode solver for diffusion probabilistic model sampling in around 10 steps
Cheng Lu, Yuhao Zhou, Fan Bao, Jianfei Chen, Chongxuan Li, and Jun Zhu · 2022
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SDEdit: Guided image synthesis and editing with stochastic differential equations
Chenlin Meng, Yutong He, Yang Song, Jiaming Song, Jiajun Wu, Jun-Yan Zhu, and Stefano Ermon · 2022
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Hierarchical text-conditional image generation with clip latents
Aditya Ramesh, Prafulla Dhariwal, Alex Nichol, Casey Chu, and Mark Chen · 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 Denton, Seyed Kamyar Seyed Ghasemipour, Burcu Karagol Ayan, S Sara Mahdavi, Rapha Gontijo Lopes, et al · 2022
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LAION-5B: An open large-scale dataset for training next generation image-text models
Christoph Schuhmann, Romain Beaumont, Richard Vencu, Cade Gordon, Ross Wightman, Mehdi Cherti, Theo Coombes, Aarush Katta, Clayton Mullis, Mitchell Wortsman, et al · 2022
Adversarial diffusion distillation, 2023
Axel Sauer, Dominik Lorenz, Andreas Blattmann, and Robin Rombach · 2023
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EDICT: Exact diffusion inversion via coupled transformations
Bram Wallace, Akash Gokul, and Nikhil Vijay Naik · 2023
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Exact diffusion inversion via bi-directional integration approximation
Guoqiang Zhang, Jonathan P Lewis, and W Bastiaan Kleijn · 2023
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Flux: Diffusion models for layered image generation
Black-Forest · 2024
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Ledits++: Limitless image editing using text-to-image models
Manuel Brack, Felix Friedrich, Katharina Kornmeier, Linoy Tsaban, Patrick Schramowski, Kristian Kersting, and Apolinaros Passos · 2024
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Turboedit: Text-based image editing using few-step diffusion models, 2024
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An edit friendly ddpm noise space: Inversion and manipulations
Inbar Huberman, Vladimir Kulikov, and Tomer Michaeli · 2023
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Direct inversion: Boosting diffusion-based editing with 3 lines of code
Xu Ju, Ailing Zeng, Yuxuan Bian, Shaoteng Liu, and Qiang Xu · 2023
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Latent consistency models: Synthesizing high-resolution images with few-step inference, 2023
Simian Luo, Yiqin Tan, Longbo Huang, Jian Li, and Hang Zhao · 2023
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Negative-prompt inversion: Fast image inversion for editing with text-guided diffusion models
Daiki Miyake, Akihiro Iohara, Yu Saito, and Toshiyuki Tanaka · 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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Effective real image editing with accelerated iterative diffusion inversion
Zhihong Pan, Riccardo Gherardi, Xiufeng Xie, and Stephen Huang · 2023
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Norm-guided latent space exploration for text-to-image generation
Dvir Samuel, Rami Ben-Ari, Nir Darshan, Haggai Maron, and Gal Chechik · 2023
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Gilad Deutch, Rinon Gal, Daniel Garibi, Or Patashnik, and Daniel Cohen-Or · 2024
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Scaling rectified flow transformers for high-resolution image synthesis, 2024
Patrick Esser, Sumith Kulal, Andreas Blattmann, Rahim Entezari, Jonas Müller, Harry Saini, Yam Levi, Dominik Lorenz, Axel Sauer, Frederic Boesel, Dustin Podell, Tim Dockhorn, Zion English, Kyle Lacey, Alex Goodwin, Yannik Marek, and Robin Rombach · 2024
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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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On exact inversion of dpm-solvers
Seongmin Hong, Kyeonghyun Lee, Suh Yoon Jeon, Hyewon Bae, and Se Young Chun · 2024
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Generating images of rare concepts using pre-trained diffusion models
Dvir Samuel, Rami Ben-Ari, Simon Raviv, Nir Darshan, and Gal Chechik · 2024
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Turboedit: Instant text-based image editing
Zongze Wu, Nicholas Kolkin, Jonathan Brandt, Richard Zhang, and Eli Shechtman · 2024
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