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Negative Prompting (NP) is widely utilized in diffusion models, particularly in text-to-image applications, to prevent the generation of undesired features.
Deep unsupervised learning using nonequilibrium thermodynamics
Jascha Sohl-Dickstein, Eric Weiss, Niru Maheswaranathan, and Surya Ganguli · 2015
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Gans trained by a two time-scale update rule converge to a nash equilibrium
Martin Heusel, Hubert Ramsauer, Thomas Unterthiner, Bernhard Nessler, Günter Klambauer, and Sepp Hochreiter · 2017
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Compositional visual generation with energy based models
Yilun Du, Shuang Li, and Igor Mordatch · 2020
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Denoising diffusion probabilistic models
Jonathan Ho, Ajay Jain, and Pieter Abbeel · 2020
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Visual transformers: Token-based image representation and processing for computer vision
Bichen Wu, Chenfeng Xu, Xiaoliang Dai, Alvin Wan, Peizhao Zhang, Zhicheng Yan, Masayoshi Tomizuka, Joseph Gonzalez, Kurt Keutzer, and Peter Vajda · 2020
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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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Diffwave: A versatile diffusion model for audio synthesis
Zhifeng Kong, Wei Ping, Jiaji Huang, Kexin Zhao, and Bryan Catanzaro · 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 Krueger, and Ilya Sutskever · 2021
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Maximum likelihood training of score-based diffusion models
Yang Song, Conor Durkan, Iain Murray, and Stefano Ermon · 2021
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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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VALSE: A task-independent benchmark for vision and language models centered on linguistic phenomena
Letitia Parcalabescu, Michele Cafagna, Lilitta Muradjan, Anette Frank, Iacer Calixto, and Albert Gatt · 2022
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High-resolution image synthesis with latent diffusion models
R. Rombach, A. Blattmann, D. Lorenz, P. Esser, and B. Ommer · 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 W Gordon, Ross Wightman, Mehdi Cherti, Theo Coombes, Aarush Katta, Clayton Mullis, Mitchell Wortsman, Patrick Schramowski, Srivatsa R Kundurthy, Katherine Crowson, Ludwig Schmidt, Robert Kaczmarczyk, and Jenia Jitsev · 2022
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Mohammadreza Armandpour, Ali Sadeghian, Huangjie Zheng, Amir Sadeghian, and Mingyuan Zhou · 2023
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SEGA: Instructing text-to-image models using semantic guidance
Manuel Brack, Felix Friedrich, Dominik Hintersdorf, Lukas Struppek, Patrick Schramowski, and Kristian Kersting · 2023
Cited alongside, same era.
Adaptive guidance: Training-free acceleration of conditional diffusion models
Angela Castillo, Jonas Kohler, Juan C. Pérez, Juan Pablo Pérez, Albert Pumarola, Bernard Ghanem, Pablo Arbeláez, and Ali Thabet · 2023
Cited alongside, same era.
Reduce, reuse, recycle: Compositional generation with energy-based diffusion models and MCMC
Yilun Du, Conor Durkan, Robin Strudel, Joshua B. Tenenbaum, Sander Dieleman, Rob Fergus, Jascha Sohl-Dickstein, Arnaud Doucet, and Will Sussman Grathwohl · 2023
Cited alongside, same era.
Erasing concepts from diffusion models
Rohit Gandikota, Joanna Materzyńska, Jaden Fiotto-Kaufman, and David Bau · 2023
Cited alongside, same era.
Selective amnesia: A continual learning approach to forgetting in deep generative models
Alvin Heng and Harold Soh · 2023
Cited alongside, same era.
Dynamical regimes of diffusion models
Giulio Biroli, Tony Bonnaire, Valentin de Bortoli, and Marc Mézard · 2024
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Towards memorization-free diffusion models
Chen Chen, Daochang Liu, and Chang Xu · 2024
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Applying guidance in a limited interval improves sample and distribution quality in diffusion models
Tuomas Kynkäänniemi, Miika Aittala, Tero Karras, Samuli Laine, Timo Aila, and Jaakko Lehtinen · 2024
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Critical windows: non-asymptotic theory for feature emergence in diffusion models
Marvin Li and Sitan Chen · 2024
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Get what you want, not what you don’t: Image content suppression for text-to-image diffusion models
Senmao Li, Joost van de Weijer, taihang Hu, Fahad Khan, Qibin Hou, Yaxing Wang, and jian Yang · 2024
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Benjamin Hoover, Hendrik Strobelt, Dmitry Krotov, Judy Hoffman, Zsolt Kira, and Duen Horng Chau · 2023
Cited alongside, same era.
Ablating concepts in text-to-image diffusion models
Nupur Kumari, Bingliang Zhang, Sheng-Yu Wang, Eli Shechtman, Richard Zhang, and Jun-Yan Zhu · 2023
Cited alongside, same era.
Your diffusion model is secretly a zero-shot classifier
Alexander Cong Li, Mihir Prabhudesai, Shivam Duggal, Ellis Langham Brown, and Deepak Pathak · 2023
Cited alongside, same era.
Safe latent diffusion: Mitigating inappropriate degeneration in diffusion models
Patrick Schramowski, Manuel Brack, Björn Deiseroth, and Kristian Kersting · 2023
Cited alongside, same era.
De novo design of protein structure and function with rfdiffusion
Joseph L. Watson, David Juergens, Nathaniel R. Bennett, Brian L. Trippe, Jason Yim, Helen E. Eisenach, Woody Ahern, Andrew J. Borst, Robert J. Ragotte, Lukas F. Milles, Basile I. M. Wicky, Nikita Hanikel, Samuel J. Pellock, Alexis Courbet, William Sheffler, Jue Wang, Preetham Venkatesh, Isaac Sappington, Susana Vázquez Torres, Anna Lauko, Valentin De Bortoli, Emile Mathieu, Sergey Ovchinnikov, Regina Barzilay, Tommi S. Jaakkola, Frank DiMaio, Minkyung Baek, and David Baker · 2023
Cited alongside, same era.
Freedom: Training-free energy-guided conditional diffusion model
Jiwen Yu, Yinhuai Wang, Chen Zhao, Bernard Ghanem, and Jian Zhang · 2023
Cited alongside, same era.
How to use negative prompts
Andrew · 2024
Cited alongside, same era.
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Circumventing concept erasure methods for text-to-image generative models
Minh Pham, Kelly O. Marshall, Niv Cohen, Govind Mittal, and Chinmay Hegde · 2024
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SDXL: Improving latent diffusion models for high-resolution image synthesis
Dustin Podell, Zion English, Kyle Lacey, Andreas Blattmann, Tim Dockhorn, Jonas Müller, Joe Penna, and Robin Rombach · 2024
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How and where does clip process negation?
Vincent Quantmeyer, Pablo Mosteiro, and Albert Gatt · 2024
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Spontaneous symmetry breaking in generative diffusion models
Gabriel Raya and Luca Ambrogioni · 2024
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A phase transition in diffusion models reveals the hierarchical nature of data
Antonio Sclocchi, Alessandro Favero, and Matthieu Wyart · 2024
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Understanding and improving training-free loss-based diffusion guidance
Yifei Shen, Xinyang Jiang, Yezhen Wang, Yifan Yang, Dongqi Han, and Dongsheng Li · 2024
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Learn ”no” to say ”yes” better: Improving vision-language models via negations
Jaisidh Singh, Ishaan Shrivastava, Mayank Vatsa, Richa Singh, and Aparna Bharati · 2024
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Analysis of classifier-free guidance weight schedulers
Xi Wang, Nicolas Dufour, Nefeli Andreou, Marie-Paule Cani, Victoria Fernandez Abrevaya, David Picard, and Vicky Kalogeiton · 2024
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Erasediff: Erasing data influence in diffusion models
Jing Wu, Trung Le, Munawar Hayat, and Mehrtash Harandi · 2024
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Forget-me-not: Learning to forget in text-to-image diffusion models
Gong Zhang, Kai Wang, Xingqian Xu, Zhangyang Wang, and Humphrey Shi · 2024
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