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Classifier-free guidance (CFG) is a fundamental tool in modern diffusion models for text-guided generation.
Tweedie’s formula and selection bias
Bradley Efron · 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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A style-based generator architecture for generative adversarial networks
Tero Karras, Samuli Laine, and Timo Aila · 2019
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Denoising diffusion probabilistic models
Jonathan Ho, Ajay Jain, and Pieter Abbeel · 2020
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Diffusion models beat GANs on image synthesis
Prafulla Dhariwal and Alexander Quinn Nichol · 2021
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Classifier-free diffusion guidance
Jonathan Ho and Tim Salimans · 2021
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Stochastic solutions for linear inverse problems using the prior implicit in a denoiser
Zahra Kadkhodaie and Eero P Simoncelli · 2021
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Improving diffusion models for inverse problems using manifold constraints
Hyungjin Chung, Byeongsu Sim, Dohoon Ryu, and Jong Chul Ye · 2022
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Adjusting guidance weight as a function of time
Jeremy Howard and Rekil Prashanth · 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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Dreamfusion: Text-to-3d using 2d diffusion
Ben Poole, Ajay Jain, Jonathan T. Barron, and Ben Mildenhall · 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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Palette: Image-to-image diffusion models
Chitwan Saharia, William Chan, Huiwen Chang, Chris Lee, Jonathan Ho, Tim Salimans, David Fleet, and Mohammad Norouzi · 2022
Cited alongside, same era.
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
Cited alongside, same era.
Turning off classifier-free guidance at low noise levels
Alex Birch · 2023
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Textdiffuser: Diffusion models as text painters
Jingye Chen, Yupan Huang, Tengchao Lv, Lei Cui, Qifeng Chen, and Furu Wei · 2023
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Regularization by texts for latent diffusion inverse solvers
Jeongsol Kim, Geon Yeong Park, Hyungjin Chung, and Jong Chul Ye · 2023
Cited alongside, same era.
Classifier-free guidance is a predictor-corrector
Arwen Bradley and Preetum Nakkiran · 2024
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PixArt-a: Fast training of diffusion transformer for photorealistic text-to-image synthesis
Junsong Chen, Jincheng YU, Chongjian GE, Lewei Yao, Enze Xie, Zhongdao Wang, James Kwok, Ping Luo, Huchuan Lu, and Zhenguo Li · 2024
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Decomposed diffusion sampler for accelerating large-scale inverse problems
Hyungjin Chung, Suhyeon Lee, and Jong Chul Ye · 2024
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Dreamsampler: Unifying diffusion sampling and score distillation for image manipulation
Jeongsol Kim, Geon Yeong Park, and Jong Chul Ye · 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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Yixun Liang, Xin Yang, Jiantao Lin, Haodong Li, Xiaogang Xu, and Yingcong Chen · 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
Cited alongside, same era.
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 · 2023
Cited alongside, same era.
Adversarial diffusion distillation
Axel Sauer, Dominik Lorenz, Andreas Blattmann, and Robin Rombach · 2023
Cited alongside, same era.
Pseudoinverse-guided diffusion models for inverse problems
Jiaming Song, Arash Vahdat, Morteza Mardani, and Jan Kautz · 2023
Cited alongside, same era.
EDICT: Exact diffusion inversion via coupled transformations
Bram Wallace, Akash Gokul, and Nikhil Naik · 2023
Cited alongside, same era.
Diffusion posterior sampling for general noisy inverse problems
Hyungjin Chung, Jeongsol Kim, Michael Thompson Mccann, Marc Louis Klasky, and Jong Chul Ye
Cited in the paper.
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Sdxl-lightning: Progressive adversarial diffusion distillation
Shanchuan Lin, Anran Wang, and Xiao Yang · 2024
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Energy-based cross attention for bayesian context update in text-to-image diffusion models
Geon Yeong Park, Jeongsol Kim, Beomsu Kim, Sang Wan Lee, and Jong Chul Ye · 2024
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Anton Pelykh, Ozge Mercanoglu Sincan, and Richard Bowden · 2024
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Solving linear inverse problems provably via posterior sampling with latent diffusion models
Litu Rout, Negin Raoof, Giannis Daras, Constantine Caramanis, Alex Dimakis, and Sanjay Shakkottai · 2024
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Solving inverse problems with latent diffusion models via hard data consistency
Bowen Song, Soo Min Kwon, Zecheng Zhang, Xinyu Hu, Qing Qu, and Liyue Shen · 2024
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Prolificdreamer: High-fidelity and diverse text-to-3d generation with variational score distillation
Zhengyi Wang, Cheng Lu, Yikai Wang, Fan Bao, Chongxuan Li, Hang Su, and Jun Zhu · 2024
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