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Despite the remarkable progress in text-to-image generative models, they are prone to adversarial attacks and inadvertently generate unsafe, unethical content.
Regression shrinkage and selection via the lasso
Robert Tibshirani · 1996
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Sparse autoencoder
Andrew Ng et al · 2011
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Adam: A method for stochastic optimization
Diederik P Kingma · 2014
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Tsung-Yi Lin, Michael Maire, Serge Belongie, James Hays, Pietro Perona, Deva Ramanan, Piotr Dollár, and C Lawrence Zitnick · 2014
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Gans trained by a two time-scale update rule converge to a local nash equilibrium
Martin Heusel, Hubert Ramsauer, Thomas Unterthiner, Bernhard Nessler, and Sepp Hochreiter · 2017
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Marian Mazzone and Ahmed Elgammal · 2019
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Nudenet: Neural nets for nudity classification, detection and selective censoring
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Pytorch: An imperative style, high-performance deep learning library
A Paszke · 2019
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Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, and Peter J Liu · 2020
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nostalgebraist · 2020
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Lora: Low-rank adaptation of large language models
Edward J Hu, Yelong Shen, Phillip Wallis, Zeyuan Allen-Zhu, Yuanzhi Li, Shean Wang, Lu Wang, and Weizhu Chen · 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, et al · 2021
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Clipscore: A reference-free evaluation metric for image captioning
Jack Hessel, Ari Holtzman, Maxwell Forbes, Ronan Le Bras, and Yejin Choi · 2021
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Red-teaming the stable diffusion safety filter
Javier Rando, Daniel Paleka, David Lindner, Lennart Heim, and Florian Tramèr · 2022
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The stable artist: Steering semantics in diffusion latent space
Manuel Brack, Patrick Schramowski, Felix Friedrich, Dominik Hintersdorf, and Kristian Kersting · 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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Hierarchical text-conditional image generation with clip latents
Aditya Ramesh, Prafulla Dhariwal, Alex Nichol, Casey Chu, and Mark Chen · 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, et al · 2022
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Can machines help us answering question 16 in datasheets, and in turn reflecting on inappropriate content?
Patrick Schramowski, Christopher Tauchmann, and Kristian Kersting · 2022
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Instructpix2pix: Learning to follow image editing instructions
Tim Brooks, Aleksander Holynski, and Alexei A Efros · 2023
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Diffusion models in medical imaging: A comprehensive survey
Amirhossein Kazerouni, Ehsan Khodapanah Aghdam, Moein Heidari, Reza Azad, Mohsen Fayyaz, Ilker Hacihaliloglu, and Dorit Merhof · 2023
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Safe latent diffusion: Mitigating inappropriate degeneration in diffusion models
Patrick Schramowski, Manuel Brack, Björn Deiseroth, and Kristian Kersting · 2023
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Erasing concepts from diffusion models
Rohit Gandikota, Joanna Materzynska, Jaden Fiotto-Kaufman, and David Bau · 2023
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Selective amnesia: A continual learning approach to forgetting in deep generative models
Alvin Heng and Harold Soh · 2023
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Towards monosemanticity: Decomposing language models with dictionary learning
Trenton Bricken, Adly Templeton, Joshua Batson, Brian Chen, Adam Jermyn, Tom Conerly, Nick Turner, Cem Anil, Carson Denison, Amanda Askell, Robert Lasenby, Yifan Wu, Shauna Kravec, Nicholas Schiefer, Tim Maxwell, Nicholas Joseph, Zac Hatfield-Dodds, Alex Tamkin, Karina Nguyen, Brayden McLean, Josiah E Burke, Tristan Hume, Shan Carter, Tom Henighan, and Christopher Olah · 2023
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Sparse autoencoders find highly interpretable features in language models
Hoagy Cunningham, Aidan Ewart, Logan Riggs, Robert Huben, and Lee Sharkey · 2023
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Mitigating stereotypical biases in text to image generative systems
Piero Esposito, Parmida Atighehchian, Anastasis Germanidis, and Deepti Ghadiyaram · 2023
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Human preference score: Better aligning text-to-image models with human preference
Xiaoshi Wu, Keqiang Sun, Feng Zhu, Rui Zhao, and Hongsheng Li · 2023
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Universal guidance for diffusion models
Arpit Bansal, Hong-Min Chu, Avi Schwarzschild, Soumyadip Sengupta, Micah Goldblum, Jonas Geiping, and Tom Goldstein · 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
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One-dimensional adapter to rule them all: Concepts diffusion models and erasing applications
Mengyao Lyu, Yuhong Yang, Haiwen Hong, Hui Chen, Xuan Jin, Yuan He, Hui Xue, Jungong Han, and Guiguang Ding · 2024
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Receler: Reliable concept erasing of text-to-image diffusion models via lightweight erasers
Chi-Pin Huang, Kai-Po Chang, Chung-Ting Tsai, Yung-Hsuan Lai, Fu-En Yang, and Yu-Chiang Frank Wang · 2024
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Safeguard text-to-image diffusion models with human feedback inversion
Sanghyun Kim, Seohyeon Jung, Balhae Kim, Moonseok Choi, Jinwoo Shin, and Juho Lee · 2024
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Score forgetting distillation: A swift, data-free method for machine unlearning in diffusion models
Tianqi Chen, Shujian Zhang, and Mingyuan Zhou · 2024
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Conceptprune: Concept editing in diffusion models via skilled neuron pruning
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Ablating concepts in text-to-image diffusion models
Nupur Kumari, Bingliang Zhang, Sheng-Yu Wang, Eli Shechtman, Richard Zhang, and Jun-Yan Zhu · 2023
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Chongyu Fan, Jiancheng Liu, Yihua Zhang, Eric Wong, Dennis Wei, and Sijia Liu · 2023
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Taking features out of superposition with sparse autoencoders
Lee Sharkey, Dan Braun, and Beren Millidge · 2023
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Reproducible scaling laws for contrastive language-image learning
Mehdi Cherti, Romain Beaumont, Ross Wightman, Mitchell Wortsman, Gabriel Ilharco, Cade Gordon, Christoph Schuhmann, Ludwig Schmidt, and Jenia Jitsev · 2023
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Ring-a-bell! how reliable are concept removal methods for diffusion models?
Yu-Lin Tsai, Chia-Yi Hsu, Chulin Xie, Chih-Hsun Lin, Jia-You Chen, Bo Li, Pin-Yu Chen, Chia-Mu Yu, and Chun-Ying Huang · 2023
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Scalable diffusion models with transformers
William Peebles and Saining Xie · 2023
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Prompting4debugging: Red-teaming text-to-image diffusion models by finding problematic prompts
Zhi-Yi Chin, Chieh-Ming Jiang, Ching-Chun Huang, Pin-Yu Chen, and Wei-Chen Chiu · 2023
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Ruchika Chavhan, Da Li, and Timothy Hospedales · 2024
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Unified concept editing in diffusion models
Rohit Gandikota, Hadas Orgad, Yonatan Belinkov, Joanna Materzyńska, and David Bau · 2024
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Self-discovering interpretable diffusion latent directions for responsible text-to-image generation
Hang Li, Chengzhi Shen, Philip Torr, Volker Tresp, and Jindong Gu · 2024
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Towards multimodal interpretability: Learning sparse interpretable features in vision transformers
Hugo Fry · 2024
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Interpreting and steering features in images
Gytis Daujotas · 2024
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Revelio: Interpreting and leveraging semantic information in diffusion models
Dahye Kim, Xavier Thomas, and Deepti Ghadiyaram · 2024
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Unpacking sdxl turbo: Interpreting text-to-image models with sparse autoencoders
Viacheslav Surkov, Chris Wendler, Mikhail Terekhov, Justin Deschenaux, Robert West, and Caglar Gulcehre · 2024
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Scaling and evaluating sparse autoencoders
Leo Gao, Tom Dupré la Tour, Henk Tillman, Gabriel Goh, Rajan Troll, Alec Radford, Ilya Sutskever, Jan Leike, and Jeffrey Wu · 2024
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Mma-diffusion: Multimodal attack on diffusion models
Yijun Yang, Ruiyuan Gao, Xiaosen Wang, Tsung-Yi Ho, Nan Xu, and Qiang Xu · 2024
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Diffusion lens: Interpreting text encoders in text-to-image pipelines
Michael Toker, Hadas Orgad, Mor Ventura, Dana Arad, and Yonatan Belinkov · 2024
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Introducing SDXL Turbo: A Real-Time Text-to-Image Generation Model, 2024
StabilityAI · 2024
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Concept sliders: Lora adaptors for precise control in diffusion models
Rohit Gandikota, Joanna Materzyńska, Tingrui Zhou, Antonio Torralba, and David Bau · 2025
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A general framework for inference-time scaling and steering of diffusion models
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Reliable and efficient concept erasure of text-to-image diffusion models
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Sparse autoencoders learn monosemantic features in vision-language models
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The multi-faceted monosemanticity in multimodal representations
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Adversarial diffusion distillation
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To generate or not? safety-driven unlearned diffusion models are still easy to generate unsafe images… for now
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