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Diffusion models are a powerful class of generative models capable of producing high-quality images from pure noise using a simple text prompt.
Tweedie’s Formula and Selection Bias
Bradley Efron. 2011 · 2011
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A Connection between Score Matching and Denoising Autoencoders
Pascal Vincent. 2011 · 2011
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U-Net: Convolutional Networks for Biomedical Image Segmentation. In
Olaf Ronneberger, Philipp Fischer, and Thomas Brox. 2015 · 2015
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Deep Unsupervised Learning Using Nonequilibrium Thermodynamics. In
Jascha Sohl-Dickstein, Eric Weiss, Niru Maheswaranathan, and Surya Ganguli. 2015 · 2015
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Attention is All You Need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin. 2017 · 2017
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Generative Modeling by Estimating Gradients of the Data Distribution
Yang Song and Stefano Ermon. 2019 · 2019
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YOLOv4: Optimal Speed and Accuracy of Object Detection
Alexey Bochkovskiy, Chien-Yao Wang, and Hong-Yuan Mark Liao. 2020 · 2020
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Denoising Diffusion Probabilistic Models
Jonathan Ho, Ajay Jain, and Pieter Abbeel. 2020 · 2020
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A Survey on Performance Metrics for Object-Detection Algorithms. In
Rafael Padilla, Sergio L Netto, and Eduardo AB Da Silva. 2020 · 2020
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Diffusion Models Beat GANs on Image Synthesis
Prafulla Dhariwal and Alexander Nichol. 2021 · 2021
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Classifier-Free Diffusion Guidance. In
Jonathan Ho and Tim Salimans. 2021 · 2021
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Image Synthesis From Layout with Locality-Aware Mask Adaption. In
Zejian Li, Jingyu Wu, Immanuel Koh, Yongchuan Tang, and Lingyun Sun. 2021 · 2021
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Learning transferable visual models from natural language supervision. In
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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Score-Based Generative Modeling through Stochastic Differential Equations. In
Yang Song, Jascha Sohl-Dickstein, Diederik P Kingma, Abhishek Kumar, Stefano Ermon, and Ben Poole. 2021 · 2021
Cited alongside, same era.
Elucidating the Design Space of Diffusion-Based Generative Models. In
Tero Karras, Miika Aittala, Timo Aila, and Samuli Laine. 2022 · 2022
Cited alongside, same era.
DPM-Solver: A Fast ODE Solver for Diffusion Probabilistic Model Sampling in Around 10 Steps. In
Cheng Lu, Yuhao Zhou, Fan Bao, Jianfei Chen, Chongxuan Li, and Jun Zhu. 2022 · 2022
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SDEdit: Guided Image Synthesis and Editing with Stochastic Differential Equations. In
Chenlin Meng, Yutong He, Yang Song, Jiaming Song, Jiajun Wu, Jun-Yan Zhu, and Stefano Ermon. 2022 · 2022
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Diffusion Self-Guidance for Controllable Image Generation. In
Dave Epstein, Allan Jabri, Ben Poole, Alexei Efros, and Aleksander Holynski. 2023 · 2023
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Prompt-to-Prompt Image Editing with Cross-Attention Control. In
Amir Hertz, Ron Mokady, Jay Tenenbaum, Kfir Aberman, Yael Pritch, and Daniel Cohen-or. 2023 · 2023
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High-Fidelity Guided Image Synthesis with Latent Diffusion Models. In
Jaskirat Singh, Stephen Gould, and Liang Zheng. 2023 · 2023
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Loss-Guided Diffusion Models for Plug-and-Play Controllable Generation. In
Jiaming Song, Qinsheng Zhang, Hongxu Yin, Morteza Mardani, Ming-Yu Liu, Jan Kautz, Yongxin Chen, and Arash Vahdat. 2023 · 2023
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Sketch-Guided Text-to-Image Diffusion Models. In
Andrey Voynov, Kfir Aberman, and Daniel Cohen-Or. 2023 · 2023
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Exploring CLIP for Assessing the Look and Feel of Images. In
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Hierarchical Text-Conditional Image Generation with Clip Latents
Aditya Ramesh, Prafulla Dhariwal, Alex Nichol, Casey Chu, and Mark Chen. 2022 · 2022
Cited alongside, same era.
High-resolution Image Synthesis with Latent Diffusion Models. In
Robin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esser, and Björn Ommer. 2022 · 2022
Cited alongside, same era.
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
Cited alongside, same era.
eDiff-I: Text-to-Image Diffusion Models with an Ensemble of Expert Denoisers
Yogesh Balaji, Seungjun Nah, Xun Huang, Arash Vahdat, Jiaming Song, Qinsheng Zhang, Karsten Kreis, Miika Aittala, Timo Aila, Samuli Laine, Bryan Catanzaro, Tero Karras, and Ming-Yu Liu. 2023 · 2023
Cited alongside, same era.
Multidiffusion: Fusing Diffusion Paths for Controlled Image Generation
Omer Bar-Tal, Lior Yariv, Yaron Lipman, and Tali Dekel. 2023 · 2023
Cited alongside, same era.
Layoutdiffuse: Adapting foundational diffusion models for layout-to-image generation
Jiaxin Cheng, Xiao Liang, Xingjian Shi, Tong He, Tianjun Xiao, and Mu Li. 2023 · 2023
Cited alongside, same era.
Zero-shot Spatial Layout Conditioning for Text-to-Image Diffusion Models. In
Guillaume Couairon, Marlène Careil, Matthieu Cord, Stéphane Lathuilière, and Jakob Verbeek. 2023 · 2023
Cited alongside, same era.
Jianyi Wang, Kelvin CK Chan, and Chen Change Loy. 2023 · 2023
Later among the works it cites.
Boxdiff: Text-to-Image Synthesis with Training-Free Box-Constrained Diffusion. In
Jinheng Xie, Yuexiang Li, Yawen Huang, Haozhe Liu, Wentian Zhang, Yefeng Zheng, and Mike Zheng Shou. 2023 · 2023
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Adding Conditional Control to Text-to-Image Diffusion Models. In
Lvmin Zhang, Anyi Rao, and Maneesh Agrawala. 2023 · 2023
Later among the works it cites.
Layoutdiffusion: Controllable Diffusion Model for Layout-to-Image Generation. In
Guangcong Zheng, Xianpan Zhou, Xuewei Li, Zhongang Qi, Ying Shan, and Xi Li. 2023 · 2023
Later among the works it cites.
Universal Guidance for Diffusion Models. In
Arpit Bansal, Hong-Min Chu, Avi Schwarzschild, Soumyadip Sengupta, Micah Goldblum, Jonas Geiping, and Tom Goldstein. 2024 · 2024
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Training-Free Layout Control with Cross-Attention Guidance. In
Minghao Chen, Iro Laina, and Andrea Vedaldi. 2024 · 2024
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