A collection of resources and papers on Visual Diffusion Models.
| Proceeding | Title | Assets |
|---|---|---|
| NeurIPS 2020 | Denoising Diffusion Probabilistic Models Jonathan Ho, Ajay Jain, Pieter Abbeel |
Paper TensorFlow PyTorch |
| NeurIPS 2021 | Diffusion Models Beat GANs on Image Synthesis Prafulla Dhariwal, Alex Nichol |
Paper PyTorch |
| arXiv 2022 | Video Diffusion Models Jonathan Ho, Tim Salimans, Alexey Gritsenko, William Chan, Mohammad Norouzi, David J. Fleet |
Paper |
| Proceeding | Title | Assets |
|---|---|---|
| ACM MM 2022 | ProDiff: Progressive Fast Diffusion Model For High-Quality Text-to-Speech Rongjie Huang, Zhou Zhao, Huadai Liu, Jinglin Liu, Chenye Cui, Yi Ren |
Paper |
| ECCV 2022 | Accelerating Score-based Generative Models with Preconditioned Diffusion Sampling Hengyuan Ma, Li Zhang, Xiatian Zhu, Jianfeng Feng |
Paper PyTorch |
| MICCAI 2022 | Diffusion Deformable Model for 4D Temporal Medical Image Generation Boah Kim, Jong Chul Ye |
Paper |
| ICML 2022 | Diffusion bridges vector quantized Variational AutoEncoders Max Cohen, Guillaume Quispe, Sylvain Le Corff, Charles Ollion, Eric Moulines |
Paper |
| ICML 2022 | Maximum Likelihood Training for Score-based Diffusion ODEs by High Order Denoising Score Matching Cheng Lu, Kaiwen Zheng, Fan Bao, Chongxuan Li, Jianfei Chen, Jun Zhu |
|
| ICML 2022 | Equivariant Diffusion for Molecule Generation in 3D Emiel Hoogeboom, Victor Garcia Satorras, Clément Vignac, Max Welling |
Paper |
| ICML 2022 | Soft Truncation: A Universal Training Technique of Score-based Diffusion Model for High Precision Score Estimation Dongjun Kim, Seungjae Shin, Kyungwoo Song, Wanmo Kang, Il-Chul Moon |
Paper |
| ICML 2022 | Latent Diffusion Energy-Based Model for Interpretable Text Modeling Peiyu Yu, Sirui Xie, Baoxiong Jia, Bo Pang, Ruiqi Gao, Yixin Zhu, Song-Chun Zhu, Ying Nian Wu |
|
| ICML 2022 | Estimating the Optimal Covariance with Imperfect Mean in Diffusion Probabilistic Models Fan Bao, Chongxuan Li, Jiacheng Sun, Jun Zhu, Bo Zhang |
Paper PyTorch |
| ICML 2022 | Guided-TTS: A Diffusion Model for Text-to-Speech via Classifier Guidance Heeseung Kim, Sungwon Kim, Sungroh Yoon |
Paper |
| ICML 2022 | GLIDE: Towards Photorealistic Image Generation and Editing with Text-Guided Diffusion Models Alex Nichol, Prafulla Dhariwal, Aditya Ramesh, Pranav Shyam, Pamela Mishkin, Bob McGrew, Ilya Sutskever, Mark Chen |
Paper |
| ICML 2022 | Diffusion Models for Adversarial Purification Weili Nie, Brandon Guo, Yujia Huang, Chaowei Xiao, Arash Vahdat, Anima Anandkumar |
Paper Project |
| IJCAI 2022 | FastDiff: A Fast Conditional Diffusion Model for High-Quality Speech Synthesis Rongjie Huang, Max W. Y. Lam, Jun Wang, Dan Su, Dong Yu, Yi Ren, Zhou Zhao |
Paper |
| CVPR 2022 | Diffusion Autoencoders: Toward a Meaningful and Decodable Representation Konpat Preechakul, Nattanat Chatthee, Suttisak Wizadwongsa, Supasorn Suwajanakorn |
Paper Project |
| CVPR 2022 | Blended Diffusion: Text-driven Editing of Natural Images Omri Avrahami, Dani Lischinski, Ohad Fried |
Paper PyTorch Project |
| CVPR 2022 | Vector Quantized Diffusion Model for Text-to-Image Synthesis Shuyang Gu, Dong Chen, Jianmin Bao, Fang Wen, Bo Zhang, Dongdong Chen, Lu Yuan, Baining Guo |
Paper PyTorch |
| CVPR 2022 | DiffusionCLIP: Text-guided Image Manipulation Using Diffusion Models Gwanghyun Kim, Taesung Kwon, Jong Chul Ye |
Paper PyTorch |
| CVPR 2022 | RePaint: Inpainting using Denoising Diffusion Probabilistic Models Andreas Lugmayr, Martin Danelljan, Andres Romero, Fisher Yu, Radu Timofte, Luc Van Gool |
Paper PyTorch |
| CVPR 2022 | High-Resolution Image Synthesis with Latent Diffusion Models Robin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esser, Björn Ommer |
Paper PyTorch |
| CVPR 2022 | Dynamic Dual-Output Diffusion Models Yaniv Benny, Lior Wolf |
Paper |
| CVPR 2022 | Perception Prioritized Training of Diffusion Models Jooyoung Choi, Jungbeom Lee, Chaehun Shin, Sungwon Kim, Hyunwoo Kim, Sungroh Yoon |
Paper PyTorch |
| CVPR 2022 | Generating High Fidelity Data from Low-density Regions using Diffusion Models Vikash Sehwag, Caner Hazirbas, Albert Gordo, Firat Ozgenel, Cristian Canton Ferrer |
Paper |
| CVPR 2022 | Come-Closer-Diffuse-Faster: Accelerating Conditional Diffusion Models for Inverse Problems through Stochastic Contraction Hyungjin Chung, Byeongsu Sim, Jong Chul Ye |
Paper |
| CVPRW 2022 | On Conditioning the Input Noise for Controlled Image Generation with Diffusion Models Vedant Singh, Surgan Jandial, Ayush Chopra, Siddharth Ramesh, Balaji Krishnamurthy, Vineeth N. Balasubramanian |
Paper |
| ICLR 2022 | Analytic-DPM: an Analytic Estimate of the Optimal Reverse Variance in Diffusion Probabilistic Models Fan Bao, Chongxuan Li, Jun Zhu, Bo Zhang |
Paper PyTorch |
| ICLR 2022 | SDEdit: Image Synthesis and Editing with Stochastic Differential Equations Chenlin Meng, Yang Song, Jiaming Song, Jiajun Wu, Jun-Yan Zhu, Stefano Ermon |
Paper PyTorch Project |
| ICLR 2022 | Denoising Likelihood Score Matching for Conditional Score-based Data Generation Chen-Hao Chao, Wei-Fang Sun, Bo-Wun Cheng, Yi-Chen Lo, Chia-Che Chang, Yu-Lun Liu, Yu-Lin Chang, Chia-Ping Chen, Chun-Yi Lee |
Paper Code |
| ICLR 2022 | Learning Fast Samplers for Diffusion Models by Differentiating Through Sample Quality Daniel Watson, William Chan, Jonathan Ho, Mohammad Norouzi |
Paper |
| ICLR 2022 | Score-Based Generative Modeling with Critically-Damped Langevin Diffusion Tim Dockhorn, Arash Vahdat, Karsten Kreis |
Paper PyTorch |
| ICLR 2022 | Progressive Distillation for Fast Sampling of Diffusion Models Tim Salimans, Jonathan Ho |
Paper TensorFlow |
| ICLR 2022 | Autoregressive Diffusion Models Emiel Hoogeboom, Alexey A. Gritsenko, Jasmijn Bastings, Ben Poole, Rianne van den Berg, Tim Salimans |
Paper |
| ICLR 2022 | Pseudo Numerical Methods for Diffusion Models on Manifolds Luping Liu, Yi Ren, Zhijie Lin, Zhou Zhao |
Paper PyTorch |
| ICLR 2022 | Learning Fast Samplers for Diffusion Models by Differentiating Through Sample Quality Daniel Watson, William Chan, Jonathan Ho, Mohammad Norouzi |
Paper |
| ICLR 2022 | Label-Efficient Semantic Segmentation with Diffusion Models Dmitry Baranchuk, Andrey Voynov, Ivan Rubachev, Valentin Khrulkov, Artem Babenko |
Paper PyTorch |
| ICLR 2022 | Step-unrolled Denoising Autoencoders for Text Generation Nikolay Savinov, Junyoung Chung, Mikolaj Binkowski, Erich Elsen, Aaron van den Oord |
Paper PyTorch |
| ICLR 2022 | Tackling the Generative Learning Trilemma with Denoising Diffusion GANs Zhisheng Xiao, Karsten Kreis, Arash Vahdat |
Paper PyTorch Project |
| ICLR 2022 | PriorGrad: Improving Conditional Denoising Diffusion Models with Data-Dependent Adaptive Prior Sang-gil Lee, Heeseung Kim, Chaehun Shin, Xu Tan, Chang Liu, Qi Meng, Tao Qin, Wei Chen, Sungroh Yoon, Tie-Yan Liu |
Paper |
| ICLR 2022 | A Conditional Point Diffusion-Refinement Paradigm for 3D Point Cloud Completion Zhaoyang Lyu, Zhifeng Kong, Xudong Xu, Liang Pan, Dahua Lin |
Paper PyTorch |
| ICLR 2022 | BDDM: Bilateral Denoising Diffusion Models for Fast and High-Quality Speech Synthesis Max W. Y. Lam, Jun Wang, Dan Su, Dong Yu |
Paper PyTorch |
| ICLR 2022 Workshop | Denoising Diffusion Restoration Models Bahjat Kawar, Michael Elad, Stefano Ermon, Jiaming Song |
Paper PyTorch Project |
| MIDL 2022 | Diffusion Models for Implicit Image Segmentation Ensembles Julia Wolleb, Robin Sandkuehler, Florentin Bieder, Philippe Valmaggia, Philippe C. Cattin |
Paper PyTorch |
| NeurIPS 2021 | Argmax Flows and Multinomial Diffusion: Learning Categorical Distributions Emiel Hoogeboom, Didrik Nielsen, Priyank Jaini, Patrick Forré, Max Welling |
Paper PyTorch |
| NeurIPS 2021 | Diffusion Normalizing Flow Qinsheng Zhang, Yongxin Chen |
Paper Code |
| NeurIPS 2021 | ImageBART: Bidirectional Context with Multinomial Diffusion for Autoregressive Image Synthesis Patrick Esser, Robin Rombach, Andreas Blattmann, Bjorn Ommer |
Paper PyTorch Project |
| NeurIPS 2021 | Maximum Likelihood Training of Score-Based Diffusion Models Yang Song, Conor Durkan, Iain Murray, Stefano Ermon |
Paper TensorFlow |
| NeurIPS 2021 | Variational Diffusion Models Diederik P. Kingma, Tim Salimans, Ben Poole, Jonathan Ho |
Paper TensorFlow |
| NeurIPS 2021 | D2C: Diffusion-Decoding Models for Few-Shot Conditional Generation Abhishek Sinha, Jiaming Song, Chenlin Meng, Stefano Ermon |
Paper PyTorch |
| NeurIPS 2021 | Structured Denoising Diffusion Models in Discrete State-Spaces Jacob Austin, Daniel D. Johnson, Jonathan Ho, Daniel Tarlow, Rianne van den Berg |
Paper |
| NeurIPS 2021 | CSDI: Conditional Score-based Diffusion Models for Probabilistic Time Series Imputation Yusuke Tashiro, Jiaming Song, Yang Song, Stefano Ermon |
Paper PyTorch |
| ICCV 2021 | ILVR: Conditioning Method for Denoising Diffusion Probabilistic Models Jooyoung Choi, Sungwon Kim, Yonghyun Jeong, Youngjune Gwon, Sungroh Yoon |
Paper PyTorch |
| ICML 2021 | Improved Denoising Diffusion Probabilistic Models Alex Nichol, Prafulla Dhariwal |
Paper PyTorch |
| CVPR 2021 | Diffusion Probabilistic Models for 3D Point Cloud Generation Shitong Luo, Wei Hu |
Paper PyTorch |
| ICLR 2021 | Denoising Diffusion Implicit Models Jiaming Song, Chenlin Meng, Stefano Ermon |
Paper PyTorch |
| Journal | Title | Assets |
|---|---|---|
| JMLR 2022 | Cascaded Diffusion Models for High Fidelity Image Generation Jonathan Ho, Chitwan Saharia, William Chan, David J. Fleet, Mohammad Norouzi, Tim Salimans |
Paper Project |
| Neurocomputing 2022 | SRDiff: Single Image Super-Resolution with Diffusion Probabilistic Models Haoying Li, Yifan Yang, Meng Chang, Huajun Feng, Zhihai Xu, Qi Li, Yueting Chen |
Paper |
| Preprints | Title | Assets |
|---|---|---|
| arXiv 2022 | Riemannian Diffusion Models Chin-Wei Huang, Milad Aghajohari, Avishek Joey Bose, Prakash Panangaden, Aaron Courville |
Paper |
| arXiv 2022 | High-Frequency Space Diffusion Models for Accelerated MRI Chentao Cao, Zhuo-Xu Cui, Shaonan Liu, Dong Liang, Yanjie Zhu |
Paper |
| arXiv 2022 | Analog Bits: Generating Discrete Data using Diffusion Models with Self-Conditioning Ting Chen, Ruixiang Zhang, Geoffrey Hinton |
Paper |
| arXiv 2022 | Pyramidal Denoising Diffusion Probabilistic Models Dohoon Ryu, Jong Chul Ye |
Paper |
| arXiv 2022 | DeScoD-ECG: Deep Score-Based Diffusion Model for ECG Baseline Wander and Noise Removal Huayu Li, Gregory Ditzler, Janet Roveda, Ao Li |
Paper |
| arXiv 2022 | Restoring Vision in Adverse Weather Conditions with Patch-Based Denoising Diffusion Models Ozan Özdenizci, Robert Legenstein |
Paper |
| arXiv 2022 | Text-Guided Synthesis of Artistic Images with Retrieval-Augmented Diffusion Models Robin Rombach, Andreas Blattmann, Björn Ommer |
Paper |
| arXiv 2022 | Progressive Deblurring of Diffusion Models for Coarse-to-Fine Image Synthesis Sangyun Lee, Hyungjin Chung, Jaehyeon Kim, Jong Chul Ye |
Paper |
| arXiv 2022 | Deep Diffusion Models for Seismic Processing Ricard Durall, Ammar Ghanim, Mario Fernandez, Norman Ettrich, Janis Keuper |
Paper |
| arXiv 2022 | Diffsound: Discrete Diffusion Model for Text-to-sound Generation Dongchao Yang, Jianwei Yu, Helin Wang, Wen Wang, Chao Weng, Yuexian Zou, Dong Yu |
Paper |
| arXiv 2022 | Non-Uniform Diffusion Models Georgios Batzolis, Jan Stanczuk, Carola-Bibiane Schönlieb, Christian Etmann |
Paper |
| arXiv 2022 | Diffsound: Discrete Diffusion Model for Text-to-sound Generation Dongchao Yang, Jianwei Yu, Helin Wang, Wen Wang, Chao Weng, Yuexian Zou, Dong Yu |
Paper |
| arXiv 2022 | Adaptive Diffusion Priors for Accelerated MRI Reconstruction Salman UH Dar, Şaban Öztürk, Yilmaz Korkmaz, Gokberk Elmas, Muzaffer Özbey, Alper Güngör, Tolga Çukur |
Paper |
| arXiv 2022 | Unsupervised Medical Image Translation with Adversarial Diffusion Models Muzaffer Özbey, Salman UH Dar, Hasan A Bedel, Onat Dalmaz, Şaban Özturk, Alper Güngör, Tolga Çukur |
Paper |
| arXiv 2022 | Threat Model-Agnostic Adversarial Defense using Diffusion Models Tsachi Blau, Roy Ganz, Bahjat Kawar, Alex Bronstein, Michael Elad |
Paper |
| arXiv 2022 | Improving Diffusion Model Efficiency Through Patching Troy Luhman, Eric Luhman |
Paper |
| arXiv 2022 | Semantic Image Synthesis via Diffusion Models Weilun Wang, Jianmin Bao, Wengang Zhou, Dongdong Chen, Dong Chen, Lu Yuan, Houqiang Li |
Paper |
| arXiv 2022 | DDPM-CD: Remote Sensing Change Detection using Denoising Diffusion Probabilistic Models Wele Gedara Chaminda Bandara, Nithin Gopalakrishnan Nair, Vishal M. Patel |
Paper PyTorch |
| arXiv 2022 | Guided Diffusion Model for Adversarial Purification from Random Noise Quanlin Wu, Hang Ye, Yuntian Gu |
Paper |
| arXiv 2022 | A Flexible Diffusion Model Weitao Du, Tao Yang, He Zhang, Yuanqi Du |
Paper |
| arXiv 2022 | Diffusion models as plug-and-play priors Alexandros Graikos, Nikolay Malkin, Nebojsa Jojic, Dimitris Samaras |
Paper PyTorch |
| arXiv 2022 | CARD: Classification and Regression Diffusion Models Xizewen Han, Huangjie Zheng, Mingyuan Zhou |
Paper |
| arXiv 2022 | Diffusion Models for Video Prediction and Infilling Tobias Höppe, Arash Mehrjou, Stefan Bauer, Didrik Nielsen, Andrea Dittadi |
Paper |
| arXiv 2022 | gDDIM: Generalized denoising diffusion implicit models Qinsheng Zhang, Molei Tao, Yongxin Chen |
Paper Code |
| arXiv 2022 | How Much is Enough? A Study on Diffusion Times in Score-based Generative Models Giulio Franzese, Simone Rossi, Lixuan Yang, Alessandro Finamore, Dario Rossi, Maurizio Filippone, Pietro Michiardi |
Paper |
| arXiv 2022 | Image Generation with Multimodal Priors using Denoising Diffusion Probabilistic Models Nithin Gopalakrishnan Nair, Wele Gedara Chaminda Bandara, Vishal M Patel |
Paper |
| arXiv 2022 | SAR Despeckling using a Denoising Diffusion Probabilistic Model Malsha V. Perera, Nithin Gopalakrishnan Nair, Wele Gedara Chaminda Bandara, Vishal M. Patel |
Paper |
| arXiv 2022 | Diffusion probabilistic modeling of protein backbones in 3D for the motif-scaffolding problem Brian L. Trippe, Jason Yim, Doug Tischer, Tamara Broderick, David Baker, Regina Barzilay, Tommi Jaakkola |
Paper |
| arXiv 2022 | Fast Unsupervised Brain Anomaly Detection and Segmentation with Diffusion Models Walter H. L. Pinaya, Mark S. Graham, Robert Gray, Pedro F Da Costa, Petru-Daniel Tudosiu, Paul Wright, Yee H. Mah, Andrew D. MacKinnon, James T. Teo, Rolf Jager, David Werring, Geraint Rees, Parashkev Nachev, Sebastien Ourselin, M. Jorge Cardoso |
Paper |
| arXiv 2022 | Blended Latent Diffusion Omri Avrahami, Ohad Fried, Dani Lischinski |
Paper PyTorch Project |
| arXiv 2022 | Diffusion-GAN: Training GANs with Diffusion Zhendong Wang, Huangjie Zheng, Pengcheng He, Weizhu Chen, Mingyuan Zhou |
Paper |
| arXiv 2022 | Compositional Visual Generation with Composable Diffusion Models Nan Liu, Shuang Li, Yilun Du, Antonio Torralba, Joshua B. Tenenbaum |
Paper |
| arXiv 2022 | Improving Diffusion Models for Inverse Problems using Manifold Constraints Hyungjin Chung, Byeongsu Sim, Dohoon Ryu, Jong Chul Ye |
Paper |
| arXiv 2022 | 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, Jun Zhu |
Paper |
| arXiv 2022 | On Analyzing Generative and Denoising Capabilities of Diffusion-based Deep Generative Models Kamil Deja, Anna Kuzina, Tomasz Trzciński, Jakub M. Tomczak |
Paper |
| arXiv 2022 | Elucidating the Design Space of Diffusion-Based Generative Models Tero Karras, Miika Aittala, Timo Aila, Samuli Laine |
Paper |
| arXiv 2022 | Improved Vector Quantized Diffusion Models Zhicong Tang, Shuyang Gu, Jianmin Bao, Dong Chen, Fang Wen |
Paper PyTorch |
| arXiv 2022 | Few-Shot Diffusion Models Giorgio Giannone, Didrik Nielsen, Ole Winther |
Paper PyTorch |
| arXiv 2022 | Protein Structure and Sequence Generation with Equivariant Denoising Diffusion Probabilistic Models Namrata Anand, Tudor Achim |
Paper Project |
| arXiv 2022 | Guided Diffusion Model for Adversarial Purification Jinyi Wang, Zhaoyang Lyu, Dahua Lin, Bo Dai, Hongfei Fu |
Paper |
| arXiv 2022 | Pretraining is All You Need for Image-to-Image Translation Tengfei Wang, Ting Zhang, Bo Zhang, Hao Ouyang, Dong Chen, Qifeng Chen, Fang Wen |
Paper PyTorch Project |
| arXiv 2022 | Maximum Likelihood Training of Implicit Nonlinear Diffusion Models Dongjun Kim, Byeonghu Na, Se Jung Kwon, Dongsoo Lee, Wanmo Kang, Il-Chul Moon |
Paper |
| arXiv 2022 | Accelerating Diffusion Models via Early Stop of the Diffusion Process Zhaoyang Lyu, Xudong XU, Ceyuan Yang, Dahua Lin, Bo Dai |
Paper |
| arXiv 2022 | Masked Conditional Video Diffusion for Prediction, Generation, and Interpolation Vikram Voleti, Alexia Jolicoeur-Martineau, Christopher Pal |
Paper Project |
| arXiv 2022 | Flexible Diffusion Modeling of Long Videos William Harvey, Saeid Naderiparizi, Vaden Masrani, Christian Weilbach, Frank Wood |
Paper |
| arXiv 2022 | 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, Tim Salimans, Jonathan Ho, David J Fleet, Mohammad Norouzi |
Paper |
| arXiv 2022 | Subspace Diffusion Generative Models Bowen Jing, Gabriele Corso, Renato Berlinghieri, Tommi Jaakkola |
Paper PyTorch |
| arXiv 2022 | Fast Sampling of Diffusion Models with Exponential Integrator Qinsheng Zhang, Yongxin Chen |
Paper |
| arXiv 2022 | Retrieval-Augmented Diffusion Models Andreas Blattmann, Robin Rombach, Kaan Oktay, Björn Ommer |
Paper |
| arXiv 2022 | Hierarchical Text-Conditional Image Generation with CLIP Latents Aditya Ramesh, Prafulla Dhariwal, Alex Nichol, Casey Chu, Mark Chen |
Paper |
| arXiv 2022 | Truncated Diffusion Probabilistic Models Huangjie Zheng, Pengcheng He, Weizhu Chen, Mingyuan Zhou |
Paper |
| arXiv 2022 | The Swiss Army Knife for Image-to-Image Translation: Multi-Task Diffusion Models Julia Wolleb, Robin Sandkühler, Florentin Bieder, Philippe C. Cattin |
Paper |
| arXiv 2022 | Dual Diffusion Implicit Bridges for Image-to-Image Translation Xuan Su, Jiaming Song, Chenlin Meng, Stefano Ermon |
Paper |
| arXiv 2022 | DiffuseVAE: Efficient, Controllable and High-Fidelity Generation from Low-Dimensional Latents Kushagra Pandey, Avideep Mukherjee, Piyush Rai, Abhishek Kumar |
Paper PyTorch |
| arXiv 2022 | Diffusion Probabilistic Modeling for Video Generation Ruihan Yang, Prakhar Srivastava, Stephan Mandt |
Paper |
| arXiv 2021 | More Control for Free! Image Synthesis with Semantic Diffusion Guidance Xihui Liu, Dong Huk Park, Samaneh Azadi, Gong Zhang, Arman Chopikyan, Yuxiao Hu, Humphrey Shi, Anna Rohrbach, Trevor Darrell |
Paper Project |
| arXiv 2021 | DiffuseMorph: Unsupervised Deformable Image Registration Along Continuous Trajectory Using Diffusion Models Boah Kim, Inhwa Han, Jong Chul Ye |
Paper |
| arXiv 2021 | Global Context with Discrete Diffusion in Vector Quantised Modelling for Image Generation Minghui Hu, Yujie Wang, Tat-Jen Cham, Jianfei Yang, P.N.Suganthan |
Paper |
| arXiv 2021 | Conditional Image Generation with Score-Based Diffusion Models Georgios Batzolis, Jan Stanczuk, Carola-Bibiane Schönlieb, Christian Etmann |
Paper |
| arXiv 2021 | SegDiff: Image Segmentation with Diffusion Probabilistic Models Tomer Amit, Eliya Nachmani, Tal Shaharbany, Lior Wolf |
Paper |
| arXiv 2021 | Palette: Image-to-Image Diffusion Models Chitwan Saharia, William Chan, Huiwen Chang, Chris A. Lee, Jonathan Ho, Tim Salimans, David J. Fleet, Mohammad Norouzi |
Paper Project |
| arXiv 2021 | UNIT-DDPM: UNpaired Image Translation with Denoising Diffusion Probabilistic Models Hiroshi Sasaki, Chris G. Willcocks, Toby P. Breckon |
Paper |
- Denoising Diffusion-based Generative Modeling: Foundations and Applications, CVPR 2022. Website
- What are Diffusion Models? Lilian Weng. Website