[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"project-9649":3},{"id":4,"name":5,"fullName":6,"owner":7,"repo":5,"description":8,"homepage":9,"htmlUrl":10,"language":11,"languages":10,"totalLinesOfCode":10,"stars":12,"forks":13,"watchers":14,"openIssues":15,"contributorsCount":16,"subscribersCount":16,"size":16,"stars1d":17,"stars7d":18,"stars30d":19,"stars90d":16,"forks30d":16,"starsTrendScore":20,"compositeScore":21,"rankGlobal":10,"rankLanguage":10,"license":22,"archived":23,"fork":23,"defaultBranch":24,"hasWiki":25,"hasPages":25,"topics":26,"createdAt":10,"pushedAt":10,"updatedAt":33,"readmeContent":34,"aiSummary":35,"trendingCount":16,"starSnapshotCount":16,"syncStatus":36,"lastSyncTime":37,"discoverSource":38},9649,"Awesome-Diffusion-Models","diff-usion\u002FAwesome-Diffusion-Models","diff-usion"," A collection of resources and papers on Diffusion Models","https:\u002F\u002Fdiff-usion.github.io\u002FAwesome-Diffusion-Models\u002F",null,"HTML",12347,1017,266,14,0,1,5,31,6,76.62,"MIT License",false,"main",true,[27,28,29,30,31,32],"artificial-intelligence","diffusion-models","generative-model","machine-learning","score-based","score-matching","2026-06-12 04:00:46","[![Awesome](https:\u002F\u002Fcdn.rawgit.com\u002Fsindresorhus\u002Fawesome\u002Fd7305f38d29fed78fa85652e3a63e154dd8e8829\u002Fmedia\u002Fbadge.svg)](https:\u002F\u002Fgithub.com\u002Fhee9joon\u002FAwesome-Diffusion-Models) \n[![License: MIT](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FLicense-MIT-green.svg)](https:\u002F\u002Fopensource.org\u002Flicenses\u002FMIT)\n[![Made With Love](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FMade%20With-Love-red.svg)](https:\u002F\u002Fgithub.com\u002Fchetanraj\u002Fawesome-github-badges)\n\nThis repository contains a collection of resources and papers on ***Diffusion Models***.\n\nPlease refer to [this page](https:\u002F\u002Fdiff-usion.github.io\u002FAwesome-Diffusion-Models\u002F) as this page may not contain all the information due to page constraints.\n\n## Contents\n- [Resources](#resources)\n  - [Introductory Posts](#introductory-posts)\n  - [Introductory Papers](#introductory-papers)\n  - [Introductory Videos](#introductory-videos)\n  - [Introductory Lectures](#introductory-lectures)\n  - [Tutorial and Jupyter Notebook](#tutorial-and-jupyter-notebook)\n- [Papers](#papers)\n  - [Survey](#survey)\n  - [Vision](#vision)\n    - [Generation](#generation)\n    - [Classification](#classification)\n    - [Segmentation](#segmentation)\n    - [Image Translation](#image-translation)\n    - [Inverse Problems](#inverse-problems)\n    - [Medical Imaging](#medical-imaging)\n    - [Multi-modal Learning](#multi-modal-learning)\n    - [3D Vision](#3d-vision)\n    - [Adversarial Attack](#adversarial-attack)\n    - [Miscellany](#miscellany)\n  - [Audio](#audio)\n    - [Generation](#generation-1)\n    - [Conversion](#conversion)\n    - [Enhancement](#enhancement)\n    - [Separation](#separation)\n    - [Text-to-Speech](#text-to-speech)\n    - [Miscellany](#miscellany-1)\n  - [Natural Language](#natural-language)\n  - [Tabular and Time Series](#tabular-and-time-series)\n    - [Generation](#generation-2)\n    - [Forecasting](#forecasting)\n    - [Imputation](#imputation)\n    - [Miscellany](#miscellany-2)\n  - [Graph](#graph)\n    - [Generation](#generation-3)\n    - [Molecular and Material Generation](#molecular-and-material-generation)\n  - [Reinforcement Learning](#reinforcement-learning)\n  - [Theory](#theory)\n  - [Applications](#applications)\n\n\n# Resources\n## Introductory Posts\n\n**:fast_forward: DiffusionFastForward: 01-Diffusion-Theory** \\\n*Mikolaj Czerkawski (@mikonvergence)* \\\n[[Website](https:\u002F\u002Fgithub.com\u002Fmikonvergence\u002FDiffusionFastForward\u002Fblob\u002Fmaster\u002Fnotes\u002F01-Diffusion-Theory.md)] \\\n4 Feb 2023\n\n**How diffusion models work: the math from scratch** \\\n*Sergios Karagiannakos,Nikolas Adaloglou* \\\n[[Website](https:\u002F\u002Ftheaisummer.com\u002Fdiffusion-models\u002F?fbclid=IwAR1BIeNHqa3NtC8SL0sKXHATHklJYphNH-8IGNoO3xZhSKM_GYcvrrQgB0o)] \\\n24 Sep 2022\n\n**A Path to the Variational Diffusion Loss** \\\n*Alex Alemi* \\\n[[Website](https:\u002F\u002Fblog.alexalemi.com\u002Fdiffusion.html)] [[Colab](https:\u002F\u002Fcolab.research.google.com\u002Fgithub\u002Fgoogle-research\u002Fvdm\u002Fblob\u002Fmain\u002Fcolab\u002FSimpleDiffusionColab.ipynb)] \\\n15 Sep 2022\n\n**The Annotated Diffusion Model** \\\n*Niels Rogge, Kashif Rasul* \\\n[[Website](https:\u002F\u002Fhuggingface.co\u002Fblog\u002Fannotated-diffusion)] \\\n06 Jun 2022\n\n**The recent rise of diffusion-based models** \\\n*Maciej Domagała* \\\n[[Website](https:\u002F\u002Fmaciejdomagala.github.io\u002Fgenerative_models\u002F2022\u002F06\u002F06\u002FThe-recent-rise-of-diffusion-based-models.html)] \\\n06 Jun 2022\n\n**Introduction to Diffusion Models for Machine Learning** \\\n*Ryan O'Connor* \\\n[[Website](https:\u002F\u002Fwww.assemblyai.com\u002Fblog\u002Fdiffusion-models-for-machine-learning-introduction\u002F)] \\\n12 May 2022\n\n**Improving Diffusion Models as an Alternative To GANs** \\\n*Arash Vahdat and Karsten Kreis* \\\n[[Website-Part 1](https:\u002F\u002Fdeveloper.nvidia.com\u002Fblog\u002Fimproving-diffusion-models-as-an-alternative-to-gans-part-1\u002F)] [[Website-Part 2](https:\u002F\u002Fdeveloper.nvidia.com\u002Fblog\u002Fimproving-diffusion-models-as-an-alternative-to-gans-part-2\u002F)] \\\n26 Apr 2022\n\n**An introduction to Diffusion Probabilistic Models** \\\n*Ayan Das* \\\n[[Website](https:\u002F\u002Fayandas.me\u002Fblog-tut\u002F2021\u002F12\u002F04\u002Fdiffusion-prob-models.html)] \\\n04 Dec 2021\n\n**Introduction to deep generative modeling: Diffusion-based Deep Generative Models** \\\n*Jakub Tomczak* \\\n[[Website](https:\u002F\u002Fjmtomczak.github.io\u002Fblog\u002F10\u002F10_ddgms_lvm_p2.html)] \\\n30 Aug 2021\n\n**What are Diffusion Models?** \\\n*Lilian Weng* \\\n[[Website](https:\u002F\u002Flilianweng.github.io\u002Flil-log\u002F2021\u002F07\u002F11\u002Fdiffusion-models.html)] \\\n11 Jul 2021\n\n**Diffusion Models as a kind of VAE** \\\n*Angus Turner* \\\n[[Website](https:\u002F\u002Fangusturner.github.io\u002Fgenerative_models\u002F2021\u002F06\u002F29\u002Fdiffusion-probabilistic-models-I.html)] \\\n29 Jun 2021\n\n**Generative Modeling by Estimating Gradients of the Data Distribution** \\\n*Yang Song* \\\n[[Website](https:\u002F\u002Fyang-song.github.io\u002Fblog\u002F2021\u002Fscore\u002F)] \\\n5 May 2021\n\n## Introductory Papers\n\n**Understanding Diffusion Models: A Unified Perspective** \\\n*Calvin Luo* \\\narXiv 2022. [[Paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2208.11970)] \\\n25 Aug 2022\n\n**How to Train Your Energy-Based Models** \\\n*Yang Song, Diederik P. Kingma* \\\narXiv 2022. [[Paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2101.03288)] \\\n9 Jan 2021\n\n## Introductory Videos\n\n**:fast_forward: DiffusionFastForward** \\\n*Mikolaj Czerkawski (@mikonvergence)* \\\n[[Video](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PL5RHjmn-MVHDMcqx-SI53mB7sFOqPK6gN)] \\\n4 Mar 2023\n\n**Diffusion models from scratch in PyTorch** \\\n*DeepFindr* \\\n[[Video](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=a4Yfz2FxXiY)] \\\n18 Jul 2022\n\n**Diffusion Models | Paper Explanation | Math Explained** \\\n*Outlier* \\\n[[Video](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=HoKDTa5jHvg)] \\\n6 Jun 2022\n\n**What are Diffusion Models?** \\\n*Ari Seff* \\\n[[Video](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=fbLgFrlTnGU&list=LL&index=2)] \\\n20 Apr 2022\n\n**Diffusion models explained** \\\n*AI Coffee Break with Letitia* \\\n[[Video](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=344w5h24-h8&ab_channel=AICoffeeBreakwithLetitia)] \\\n23 Mar 2022\n\n## Introductory Lectures\n\n**Denoising Diffusion-based Generative Modeling: Foundations and Applications** \\\n*Karsten Kreis, Ruiqi Gao, Arash Vahdat* \\\n[[Page](https:\u002F\u002Fcvpr2022-tutorial-diffusion-models.github.io\u002F)] \\\n19 Jun 2022\n\n**Diffusion Probabilistic Models** \\\n*Jascha Sohl-Dickstein, MIT 6.S192 - Lecture 22* \\\n[[Video](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=XCUlnHP1TNM)] \\\n19 Apr 2022\n\n## Tutorial and Jupyter Notebook\n\n**:fast_forward: DiffusionFastForward: train from scratch in colab** \\\n*Mikolaj Czerkawski (@mikonvergence)* \\\n[[Github](https:\u002F\u002Fgithub.com\u002Fmikonvergence\u002FDiffusionFastForward)]\n[[notebook](https:\u002F\u002Fgithub.com\u002Fmikonvergence\u002FDiffusionFastForward#computer-code)]\n\n**diffusion-for-beginners** \\\n*ozanciga* \\\n[[Github](https:\u002F\u002Fgithub.com\u002Fozanciga\u002Fdiffusion-for-beginners)]\n\n**Beyond Diffusion: What is Personalized Image Generation and How Can You Customize Image Synthesis?** \\\n*J. Rafid Siddiqui* \\\n[[Github](https:\u002F\u002Fgithub.com\u002Fazad-academy\u002Fpersonalized-diffusion)] [[Medium](https:\u002F\u002Fmedium.com\u002Fmlearning-ai\u002Fbeyond-diffusion-what-is-personalized-image-generation-and-how-can-you-customize-image-synthesis-26a89d5b335)]\n\n**Diffusion_models_tutorial** \\\n*FilippoMB* \\\n[[Github](https:\u002F\u002Fgithub.com\u002FFilippoMB\u002FDiffusion_models_tutorial)]\n\n**ScoreDiffusionModel** \\\n*JeongJiHeon* \\\n[[Github](https:\u002F\u002Fgithub.com\u002FJeongJiHeon\u002FScoreDiffusionModel)]\n\n**Minimal implementation of diffusion models** \\\n*VSehwag* \\\n[[Github](https:\u002F\u002Fgithub.com\u002FVSehwag\u002Fminimal-diffusion)]\n\n**diffusion_tutorial** \\\n*sunlin-ai* \\\n[[Github](https:\u002F\u002Fgithub.com\u002Fsunlin-ai\u002Fdiffusion_tutorial)] \n\n**Denoising diffusion probabilistic models** \\\n*acids-ircam* \\\n[[Github](https:\u002F\u002Fgithub.com\u002Facids-ircam\u002Fdiffusion_models)] \n\n\n**Centipede Diffusion** \\\n*Zalring* \\\n[[Notebook](https:\u002F\u002Fcolab.research.google.com\u002Fgithub\u002FZalring\u002FCentipede_Diffusion\u002Fblob\u002Fmain\u002FCentipede_Diffusion.ipynb)]\n\n**Deforum Stable Diffusion** \\\n*deforum* \\\n[[Notebook](https:\u002F\u002Fcolab.research.google.com\u002Fgithub\u002Fdeforum\u002Fstable-diffusion\u002Fblob\u002Fmain\u002FDeforum_Stable_Diffusion.ipynb)]\n\n**Stable Diffusion Interpolation** \\\n*None* \\\n[[Notebook](https:\u002F\u002Fcolab.research.google.com\u002Fdrive\u002F1EHZtFjQoRr-bns1It5mTcOVyZzZD9bBc?usp=sharing)]\n\n**Keras Stable Diffusion: GPU starter example** \\\n*None* \\\n[[Notebook](https:\u002F\u002Fcolab.research.google.com\u002Fdrive\u002F1zVTa4mLeM_w44WaFwl7utTaa6JcaH1zK)]\n\n**Huemin Jax Diffusion** \\\n*huemin-art* \\\n[[Notebook](https:\u002F\u002Fcolab.research.google.com\u002Fgithub\u002Fhuemin-art\u002Fjax-guided-diffusion\u002Fblob\u002Fv2.7\u002FHuemin_Jax_Diffusion_2_7.ipynb)]\n\n**Disco Diffusion** \\\n*alembics* \\\n[[Notebook](https:\u002F\u002Fcolab.research.google.com\u002Fgithub\u002Falembics\u002Fdisco-diffusion\u002Fblob\u002Fmain\u002FDisco_Diffusion.ipynb)]\n\n**Simplified Disco Diffusion** \\\n*entmike* \\\n[[Notebook](https:\u002F\u002Fcolab.research.google.com\u002Fgithub\u002Fentmike\u002Fdisco-diffusion-1\u002Fblob\u002Fmain\u002FSimplified_Disco_Diffusion.ipynb)]\n\n**WAS's Disco Diffusion - Portrait Generator Playground** \\\n*WASasquatch* \\\n[[Notebook](https:\u002F\u002Fcolab.research.google.com\u002Fgithub\u002FWASasquatch\u002Fdisco-diffusion-portrait-playground\u002Fblob\u002Fmain\u002FWAS's_Disco_Diffusion_v5_6_9_%5BPortrait_Generator_Playground%5D.ipynb)]\n\n**Diffusers - Hugging Face** \\\n*huggingface* \\\n[[Notebook](https:\u002F\u002Fcolab.research.google.com\u002Fgithub\u002Fhuggingface\u002Fnotebooks\u002Fblob\u002Fmain\u002Fdiffusers\u002Fdiffusers_intro.ipynb)] \n\n\n# Papers\n\n## Survey\n\n**A Survey on Video Diffusion Models** \\\n*Zhen Xing, Qijun Feng, Haoran Chen, Qi Dai, Han Hu, Hang Xu, Zuxuan Wu and Yu-Gang Jiang*\narXiv 2023. [[Paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2310.10647.pdf)] \\\n16 Oct 2023\n\n**State of the Art on Diffusion Models for Visual Computing** \\\n*Ryan Po, Wang Yifan, Vladislav Golyanik, Kfir Aberman, Jonathan T. Barron, Amit H. Bermano, Eric Ryan Chan, Tali Dekel, Aleksander Holynski, Angjoo Kanazawa, C. Karen Liu, Lingjie Liu, Ben Mildenhall, Matthias Nießner, Björn Ommer, Christian Theobalt, Peter Wonka, Gordon Wetzstein* \\\narXiv 2023. [[Paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2310.07204)] \\\n11 Oct 2023\n\n**Memory in Plain Sight: A Survey of the Uncanny Resemblances between Diffusion Models and Associative Memories** \\\n*Benjamin Hoover, Hendrik Strobelt, Dmitry Krotov, Judy Hoffman, Zsolt Kira, Duen Horng Chau* \\\narXiv 2023. [[Paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2309.16750)] \\\n28 Sep 2023\n\n**A Survey of Diffusion Based Image Generation Models: Issues and Their Solutions** \\\n*Tianyi Zhang, Zheng Wang, Jing Huang, Mohiuddin Muhammad Tasnim, Wei Shi* \\\narXiv 2023. [[Paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2308.13142)] \\\n25 Aug 2023\n\n**Diffusion Models for Image Restoration and Enhancement -- A Comprehensive Survey** \\\n*Xin Li, Yulin Ren, Xin Jin, Cuiling Lan, Xingrui Wang, Wenjun Zeng, Xinchao Wang, Zhibo Chen* \\\narXiv 2023. [[Paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2308.09388)] \\\n18 Aug 2023\n\n**A Comprehensive Survey on Generative Diffusion Models for Structured Data** \\\n*Heejoon Koo, To Eun Kim* \\\narXiv 2023. [[Paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2306.04139)] \\\n7 Jun 2023\n\n**On the Design Fundamentals of Diffusion Models: A Survey** \\\n*Ziyi Chang, George A. Koulieris, Hubert P. H. Shum* \\\narXiv 2023. [[Paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2306.04542)] \\\n7 Jun 2023\n\n**Diffusion Models in NLP: A Survey** \\\n*Hao Zou, Zae Myung Kim, Dongyeop Kang* \\\narXiv 2023. [[Paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2305.14671)] \\\n24 May 2023\n\n**Diffusion Models for Time Series Applications: A Survey** \\\n*Lequan Lin, Zhengkun Li, Ruikun Li, Xuliang Li, Junbin Gao* \\\narXiv 2023. [[Paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2305.00624)] \\\n1 May 2023\n\n**A Comprehensive Survey on Knowledge Distillation of Diffusion Models** \\\n*Weijian Luo* \\\narXiv 2023. [[Paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2304.04262)] \\\n9 Apr 2023\n\n**A Survey on Graph Diffusion Models: Generative AI in Science for Molecule, Protein and Material** \\\n*Mengchun Zhang, Maryam Qamar, Taegoo Kang, Yuna Jung, Chenshuang Zhang, Sung-Ho Bae, Chaoning Zhang* \\\narXiv 2023. [[Paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2304.01565)] \\\n4 Apr 2023\n\n**Audio Diffusion Model for Speech Synthesis: A Survey on Text To Speech and Speech Enhancement in Generative AI** \\\n*Chenshuang Zhang, Chaoning Zhang, Sheng Zheng, Mengchun Zhang, Maryam Qamar, Sung-Ho Bae, In So Kweon* \\\narXiv 2023. [[Paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2303.13336)] \\\n23 Mar 2023\n\n**Diffusion Models in NLP: A Survey** \\\n*Yuansong Zhu, Yu Zhao* \\\narXiv 2023. [[Paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2303.07576)] \\\n14 Mar 2023\n\n**Text-to-image Diffusion Model in Generative AI: A Survey** \\\n*Chenshuang Zhang, Chaoning Zhang, Mengchun Zhang, In So Kweon* \\\narXiv 2023. [[Paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2303.07909)] \\\n14 Mar 2023\n\n**Diffusion Models for Non-autoregressive Text Generation: A Survey** \\\n*Yifan Li, Kun Zhou, Wayne Xin Zhao, Ji-Rong Wen* \\\narXiv 2023. [[Paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2303.06574)] \\\n12 Mar 2023\n\n**Diffusion Models in Bioinformatics: A New Wave of Deep Learning Revolution in Action** \\\n*Zhiye Guo, Jian Liu, Yanli Wang, Mengrui Chen, Duolin Wang, Dong Xu, Jianlin Cheng* \\\narXiv 2023. [[Paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2302.10907)] \\\n13 Feb 2023\n\n**Generative Diffusion Models on Graphs: Methods and Applications** \\\n*Wenqi Fan, Chengyi Liu, Yunqing Liu, Jiatong Li, Hang Li, Hui Liu, Jiliang Tang, Qing Li* \\\narXiv 2023. [[Paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2302.02591)] \\\n6 Feb 2023\n\n**Diffusion Models for Medical Image Analysis: A Comprehensive Survey** \\\n*Amirhossein Kazerouni, Ehsan Khodapanah Aghdam, Moein Heidari, Reza Azad, Mohsen Fayyaz, Ilker Hacihaliloglu, Dorit Merhof* \\\narXiv 2022. [[Paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2211.07804)] [[Github](https:\u002F\u002Fgithub.com\u002Famirhossein-kz\u002FAwesome-Diffusion-Models-in-Medical-Imaging)] \\\n14 Nov 2022\n\n**Efficient Diffusion Models for Vision: A Survey** \\\n*Anwaar Ulhaq, Naveed Akhtar, Ganna Pogrebna* \\\narXiv 2022. [[Paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2210.09292)] \\\n7 Oct 2022\n\n**Diffusion Models in Vision: A Survey** \\\n*Florinel-Alin Croitoru, Vlad Hondru, Radu Tudor Ionescu, Mubarak Shah* \\\narXiv 2022. [[Paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2209.04747)] \\\n10 Sep 2022\n\n**A Survey on Generative Diffusion Model** \\\n*Hanqun Cao, Cheng Tan, Zhangyang Gao, Guangyong Chen, Pheng-Ann Heng, Stan Z. Li* \\\narXiv 2022. [[Paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2209.02646)] \\\n6 Sep 2022\n\n**Diffusion Models: A Comprehensive Survey of Methods and Applications** \\\n*Ling Yang, Zhilong Zhang, Shenda Hong, Wentao Zhang* \\\narXiv 2022. [[Paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2209.00796)] \\\n2 Sep 2022\n\n## Vision\n### Generation\n\n**DiffEnc: Variational Diffusion with a Learned Encoder** \\\n*Beatrix M. G. Nielsen, Anders Christensen, Andrea Dittadi, Ole Winther* \\\narXiv 2023. [[Paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2310.19789)] \\\n30 Oct 2023\n\n**Upgrading VAE Training With Unlimited Data Plans Provided by Diffusion Models** \\\n*Tim Z. Xiao, Johannes Zenn, Robert Bamler* \\\narXiv 2023. [[Paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2310.19653)] \\\n30 Oct 2023\n\n**Successfully Applying Lottery Ticket Hypothesis to Diffusion Model** \\\n*Chao Jiang, Bo Hui, Bohan Liu, Da Yan* \\\narXiv 2023. [[Paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2310.18823)] \\\n28 Oct 2023\n\n**Noise-Free Score Distillation** \\\n*Oren Katzir, Or Patashnik, Daniel Cohen-Or, Dani Lischinski* \\\narXiv 2023. [[Paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2310.17590)] \\\n26 Oct 2023\n\n**The statistical thermodynamics of generative diffusion models** \\\n*Luca Ambrogioni* \\\narXiv 2023. [[Paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2310.17467)] \\\n26 Oct 2023\n\n**Improving Denoising Diffusion Models via Simultaneous Estimation of Image and Noise** \\\n*Zhenkai Zhang, Krista A. Ehinger, Tom Drummond* \\\narXiv 2023. [[Paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2310.17167)] \\\n26 Oct 2023\n\n**Hierarchical Semi-Implicit Variational Inference with Application to Diffusion Model Acceleration** \\\n*Longlin Yu, Tianyu Xie, Yu Zhu, Tong Yang, Xiangyu Zhang, Cheng Zhang* \\\narXiv 2023. [[Paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2310.17153)] [[Github](https:\u002F\u002Fgithub.com\u002Flonginyu\u002Fhsivi)] \\\n26 Oct 2023\n\n**RePoseDM: Recurrent Pose Alignment and Gradient Guidance for Pose Guided Image Synthesis** \\\n*Anant Khandelwal* \\\narXiv 2023. [[Paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2310.16074)] \\\n24 Oct 2023\n\n**Improved Techniques for Training Consistency Models** \\\n*Yang Song, Prafulla Dhariwal* \\\narXiv 2023. [[Paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2310.14189)] \\\n22 Oct 2023\n\n**ScaleLong: Towards More Stable Training of Diffusion Model via Scaling Network Long Skip Connection** \\\n*Zhongzhan Huang, Pan Zhou, Shuicheng Yan, Liang Lin* \\\nNeurIPS 2023. [[Paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2310.13545)] [[Github](https:\u002F\u002Fgithub.com\u002Fsail-sg\u002FScaleLong)] \\\n20 Oct 2023\n\n\n**Particle Guidance: non-I.I.D. Diverse Sampling with Diffusion Models** \\\n*Gabriele Corso, Yilun Xu, Valentin de Bortoli, Regina Barzilay, Tommi Jaakkola* \\\narXiv 2023. [[Paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2310.13102)] [[Github](https:\u002F\u002Fgithub.com\u002Fgcorso\u002Fparticle-guidance)] \\\n19 Oct 2023\n\n**Closed-Form Diffusion Models** \\\n*Christopher Scarvelis, Haitz Sáez de Ocáriz Borde, Justin Solomon* \\\narXiv 2023. [[Paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2310.12395)] \\\n19 Oct 2023\n\n**Elucidating The Design Space of Classifier-Guided Diffusion Generation** \\\n*Jiajun Ma, Tianyang Hu, Wenjia Wang, Jiacheng Sun* \\\narXiv 2023. [[Paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2310.11311)] [[Github](https:\u002F\u002Fgithub.com\u002Falexmaols\u002Felucd)] \\\n17 Oct 2023\n\n\n**BayesDiff: Estimating Pixel-wise Uncertainty in Diffusion via Bayesian Inference** \\\n*Siqi Kou, Lei Gan, Dequan Wang, Chongxuan Li, Zhijie Deng* \\\narXiv 2023. 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[[Paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2310.08442)] \\\n12 Oct 2023\n\n**Neural Diffusion Models** \\\n*Grigory Bartosh, Dmitry Vetrov, Christian A. Naesseth* \\\narXiv 2023. [[Paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2310.08337)] \\\n12 Oct 2023\n\n**Efficient Integrators for Diffusion Generative Models** \\\n*Kushagra Pandey, Maja Rudolph, Stephan Mandt* \\\narXiv 2023. [[Paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2310.07894)] \\\n11 Oct 2023\n\n\n**Learning Stackable and Skippable LEGO Bricks for Efficient, Reconfigurable, and Variable-Resolution Diffusion Modeling** \\\n*Huangjie Zheng, Zhendong Wang, Jianbo Yuan, Guanghan Ning, Pengcheng He, Quanzeng You, Hongxia Yang, Mingyuan Zhou* \\\narXiv 2023. [[Paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2310.06389)] \\\n10 Oct 2023\n\n**Language Model Beats Diffusion -- Tokenizer is Key to Visual Generation** \\\n*Lijun Yu, José Lezama, Nitesh B. 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[[Paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2310.04041)] \\\n6 Oct 2023\n\n**Latent Consistency Models: Synthesizing High-Resolution Images with Few-Step Inference** \\\n*Simian Luo, Yiqin Tan, Longbo Huang, Jian Li, Hang Zhao* \\\narXiv 2023. [[Paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2310.04378)] \\\n6 Oct 2023\n\n**Denoising Diffusion Step-aware Models** \\\n*Shuai Yang, Yukang Chen, Luozhou Wang, Shu Liu, Yingcong Chen* \\\narXiv 2023. [[Paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2310.03337)] \\\n5 Oct 2023\n\n\n**EfficientDM: Efficient Quantization-Aware Fine-Tuning of Low-Bit Diffusion Models** \\\n*Yefei He, Jing Liu, Weijia Wu, Hong Zhou, Bohan Zhuang* \\\narXiv 2023. [[Paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2310.03270)] \\\n5 Oct 2023\n\n**Learning Energy-Based Prior Model with Diffusion-Amortized MCMC** \\\n*Peiyu Yu, Yaxuan Zhu, Sirui Xie, Xiaojian Ma, Ruiqi Gao, Song-Chun Zhu, Ying Nian Wu* \\\nNeurIPS 2023. [[Paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2310.03218)] [[Github](https:\u002F\u002Fgithub.com\u002FyuPeiyu98\u002FDiffusion-Amortized-MCMC)] \\\n5 Oct 2023\n\n**On Memorization in Diffusion Models** \\\n*Xiangming Gu, Chao Du, Tianyu Pang, Chongxuan Li, Min Lin, Ye Wang* \\\narXiv 2023. [[Paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2310.02664)] [[Github](https:\u002F\u002Fgithub.com\u002Fsail-sg\u002FDiffMemorize)] \\\n4 Oct 2023\n\n\n**Sequential Data Generation with Groupwise Diffusion Process** \\\n*Sangyun Lee, Gayoung Lee, Hyunsu Kim, Junho Kim, Youngjung Uh* \\\narXiv 2023. [[Paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2310.01400)] \\\n2 Oct 2023\n\n**Consistency Trajectory Models: Learning Probability Flow ODE Trajectory of Diffusion** \\\n*Dongjun Kim, Chieh-Hsin Lai, Wei-Hsiang Liao, Naoki Murata, Yuhta Takida, Toshimitsu Uesaka, Yutong He, Yuki Mitsufuji, Stefano Ermon* \\\narXiv 2023. 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[[Paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2307.08698)] [[Project](https:\u002F\u002Fvinairesearch.github.io\u002FLFM\u002F)] \\\n17 Jul 2023\n\n**Manifold-Guided Sampling in Diffusion Models for Unbiased Image Generation** \\\n*Xingzhe Su, Wenwen Qiang, Zeen Song, Hang Gao, Fengge Wu, Changwen Zheng* \\\narXiv 2023. [[Paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2307.08199)] \\\n17 Jul 2023\n\n**Complexity Matters: Rethinking the Latent Space for Generative Modeling** \\\n*Tianyang Hu, Fei Chen, Haonan Wang, Jiawei Li, Wenjia Wang, Jiacheng Sun, Zhenguo Li* \\\narXiv 2023. [[Paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2307.08283)] \\\n17 Jul 2023\n\n**Collaborative Score Distillation for Consistent Visual Synthesis** \\\n*Subin Kim, Kyungmin Lee, June Suk Choi, Jongheon Jeong, Kihyuk Sohn, Jinwoo Shin* \\\narXiv 2023. 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[[Paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2306.14153)] \\\n25 Jun 2023\n\n**Decoupled Diffusion Models with Explicit Transition Probability** \\\n*Yuhang Huang, Zheng Qin, Xinwang Liu, Kai Xu* \\\narXiv 2023. [[Paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2306.13720)] \\\n23 Jun 2023\n\n**Continuous Layout Editing of Single Images with Diffusion Models** \\\n*Zhiyuan Zhang, Zhitong Huang, Jing Liao* \\\narXiv 2023. [[Paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2306.13078)] \\\n22 Jun 2023\n\n**Semi-Implicit Denoising Diffusion Models (SIDDMs)** \\\n*Yanwu Xu, Mingming Gong, Shaoan Xie, Wei Wei, Matthias Grundmann, kayhan Batmanghelich, Tingbo Hou* \\\narXiv 2023. [[Paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2306.12511)] \\\n21 Jun 2023\n\n**Eliminating Lipschitz Singularities in Diffusion Models** \\\n*Zhantao Yang, Ruili Feng, Han Zhang, Yujun Shen, Kai Zhu, Lianghua Huang, Yifei Zhang, Yu Liu, Deli Zhao, Jingren Zhou, Fan Cheng* \\\narXiv 2023. 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[[Paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2306.05423)] \\\n8 Jun 2023\n\n**Multi-Architecture Multi-Expert Diffusion Models** \\\n*Yunsung Lee, Jin-Young Kim, Hyojun Go, Myeongho Jeong, Shinhyeok Oh, Seungtaek Choi* \\\narXiv 2023. [[Paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2306.04990)] \\\n8 Jun 2023\n\n**Interpreting and Improving Diffusion Models Using the Euclidean Distance Function** \\\n*Frank Permenter, Chenyang Yuan* \\\narXiv 2023. [[Paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2306.04848)] \\\n8 Jun 2023\n\n**Video Diffusion Models with Local-Global Context Guidance** \\\n*Siyuan Yang, Lu Zhang, Yu Liu, Zhizhuo Jiang, You He* \\\nIJCAI 2023. [[Paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2306.02562)] [[Github](https:\u002F\u002Fgithub.com\u002Fexisas\u002FLGC-VD)] \\\n5 Jun 2023\n\n**Brain Diffusion for Visual Exploration: Cortical Discovery using Large Scale Generative Models** \\\n*Andrew F. Luo, Margaret M. Henderson, Leila Wehbe, Michael J. Tarr* \\\narXiv 2023. [[Paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2306.03089)] \\\n5 Jun 2023\n\n**Faster Training of Diffusion Models and Improved Density Estimation via Parallel Score Matching** \\\n*Etrit Haxholli, Marco Lorenzi* \\\narXiv 2023. [[Paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2306.02658)] \\\n5 Jun 2023\n\n**Temporal Dynamic Quantization for Diffusion Models** \\\n*Junhyuk So, Jungwon Lee, Daehyun Ahn, Hyungjun Kim, Eunhyeok Park* \\\narXiv 2023. [[Paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2306.02316)] \\\n4 Jun 2023\n\n**Conditional Generation from Unconditional Diffusion Models using Denoiser Representations** \\\n*Alexandros Graikos, Srikar Yellapragada, Dimitris Samaras* \\\nBMVC 2023. 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[[Paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2306.00354)] \\\n1 Jun 2023\n\n**A Geometric Perspective on Diffusion Models** \\\n*Defang Chen, Zhenyu Zhou, Jian-Ping Mei, Chunhua Shen, Chun Chen, Can Wang* \\\narXiv 2023. [[Paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2305.19947)] \\\n31 May 2023\n\n\n\n**Spontaneous symmetry breaking in generative diffusion models** \\\n*Gabriel Raya, Luca Ambrogioni* \\\narXiv 2023. [[Paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2305.19693)] \\\n31 May 2023\n\n**Perturbation-Assisted Sample Synthesis: A Novel Approach for Uncertainty Quantification** \\\n*Yifei Liu, Rex Shen, Xiaotong Shen* \\\narXiv 2023. [[Paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2305.18671)] \\\n30 May 2023\n\n**One-Line-of-Code Data Mollification Improves Optimization of Likelihood-based Generative Models** \\\n*Ba-Hien Tran, Giulio Franzese, Pietro Michiardi, Maurizio Filippone* \\\narXiv 2023. [[Paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2305.18900)] \\\n30 May 2023\n\n**Ambient Diffusion: Learning Clean Distributions from Corrupted Data** \\\n*Giannis Daras, Kulin Shah, Yuval Dagan, Aravind Gollakota, Alexandros G. Dimakis, Adam Klivans* \\\narXiv 2023. [[Paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2305.19256)] \\\n30 May 2023\n\n**Towards Accurate Data-free Quantization for Diffusion Models** \\\n*Changyuan Wang, Ziwei Wang, Xiuwei Xu, Yansong Tang, Jie Zhou, Jiwen Lu* \\\narXiv 2023. [[Paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2305.18723)] \\\n30 May 2023\n\n**BRIGHT: Bi-level Feature Representation of Image Collections using Groups of Hash Tables** \\\n*Dingdong Yang, Yizhi Wang, Ali Mahdavi-Amiri, Hao Zhang* \\\narXiv 2023. [[Paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2305.18601)] [[Project](https:\u002F\u002Fbright-project01.github.io\u002F)] \\\n29 May 2023\n\n**Diff-Instruct: A Universal Approach for Transferring Knowledge From Pre-trained Diffusion Models** \\\n*Weijian Luo, Tianyang Hu, Shifeng Zhang, Jiacheng Sun, Zhenguo Li, Zhihua Zhang* \\\narXiv 2023. [[Paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2305.18455)] \\\n29 May 2023\n\n**Learning to Jump: Thinning and Thickening Latent Counts for Generative Modeling** \\\n*Tianqi Chen, Mingyuan Zhou* \\\nICML 2023. [[Paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2305.18375)] [[Github](https:\u002F\u002Fgithub.com\u002Ftqch\u002Fpoisson-jump)] \\\n28 May 2023\n\n**Reconstructing the Mind's Eye: fMRI-to-Image with Contrastive Learning and Diffusion Priors** \\\n*Paul S. Scotti, Atmadeep Banerjee, Jimmie Goode, Stepan Shabalin, Alex Nguyen, Ethan Cohen, Aidan J. Dempster, Nathalie Verlinde, Elad Yundler, David Weisberg, Kenneth A. Norman, Tanishq Mathew Abraham* \\\narXiv 2023. [[Paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2305.18274)] [[Github](https:\u002F\u002Fmedarc-ai.github.io\u002Fmindeye\u002F)] \\\n29 May 2023\n\n**Contrast, Attend and Diffuse to Decode High-Resolution Images from Brain Activities** \\\n*Jingyuan Sun, Mingxiao Li, Zijiao Chen, Yunhao Zhang, Shaonan Wang, Marie-Francine Moens* \\\narXiv 2023. [[Paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2305.17214)] \\\n26 May 2023\n\n**Parallel Sampling of Diffusion Models** \\\n*Andy Shih, Suneel Belkhale, Stefano Ermon, Dorsa Sadigh, Nima Anari* \\\narXiv 2023. [[Paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2305.16317)] [[Github](https:\u002F\u002Fgithub.com\u002FAndyShih12\u002Fparadigms)] \\\n25 May 2023\n\n**Trans-Dimensional Generative Modeling via Jump Diffusion Models** \\\n*Andrew Campbell, William Harvey, Christian Weilbach, Valentin De Bortoli, Tom Rainforth, Arnaud Doucet* \\\narXiv 2023. [[Paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2305.16261)] \\\n25 May 2023\n\n**UDPM: Upsampling Diffusion Probabilistic Models** \\\n*Shady Abu-Hussein, Raja Giryes* \\\narXiv 2023. [[Paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2305.16269)] \\\n25 May 2023\n\n\n**Unifying GANs and Score-Based Diffusion as Generative Particle Models** \\\n*Jean-Yves Franceschi, Mike Gartrell, Ludovic Dos Santos, Thibaut Issenhuth, Emmanuel de Bézenac, Mickaël Chen, Alain Rakotomamonjy* \\\narXiv 2023. [[Paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2305.16150)] \\\n25 May 2023\n\n**DuDGAN: Improving Class-Conditional GANs via Dual-Diffusion** \\\n*Taesun Yeom, Minhyeok Lee* \\\narXiv 2023. [[Paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2305.14849)] \\\n24 May 2023\n\n**Alleviating Exposure Bias in Diffusion Models through Sampling with Shifted Time Steps** \\\n*Mingxiao Li, Tingyu Qu, Wei Sun, Marie-Francine Moens* \\\narXiv 2023. [[Paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2305.15583)] \\\n24 May 2023\n\n\n**Robust Classification via a Single Diffusion Model** \\\n*Huanran Chen, Yinpeng Dong, Zhengyi Wang, Xiao Yang, Chengqi Duan, Hang Su, Jun Zhu* \\\narXiv 2023. [[Paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2305.15241)] \\\n24 May 2023\n\n**On the Generalization of Diffusion Model** \\\n*Mingyang Yi, Jiacheng Sun, Zhenguo Li* \\\narXiv 2023. [[Paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2305.14712)] \\\n24 May 2023\n\n**VDT: An Empirical Study on Video Diffusion with Transformers** \\\n*Haoyu Lu, Guoxing Yang, Nanyi Fei, Yuqi Huo, Zhiwu Lu, Ping Luo, Mingyu Ding* \\\narXiv 2023. [[Paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2305.13311)] [[Github](https:\u002F\u002Fgithub.com\u002FRERV\u002FVDT)] \\\n22 May 2023\n\n**Cinematic Mindscapes: High-quality Video Reconstruction from Brain Activity** \\\n*Zijiao Chen, Jiaxin Qing, Juan Helen Zhou* \\\narXiv 2023. [[Paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2305.11675)] [[Project](https:\u002F\u002Fmind-video.com\u002F)] \\\n19 May 2023\n\n**PTQD: Accurate Post-Training Quantization for Diffusion Models** \\\n*Yefei He, Luping Liu, Jing Liu, Weijia Wu, Hong Zhou, Bohan Zhuang* \\\narXiv 2023. [[Paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2305.10657)] \\\n18 May 2023\n\n**Blackout Diffusion: Generative Diffusion Models in Discrete-State Spaces** \\\n*Javier E Santos, Zachary R. Fox, Nicholas Lubbers, Yen Ting Lin* \\\narXiv 2023. [[Paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2305.11089)] \\\n18 May 2023\n\n**Structural Pruning for Diffusion Models** \\\n*Gongfan Fang, Xinyin Ma, Xinchao Wang* \\\narXiv 2023. [[Paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2305.10924)] [[Github](https:\u002F\u002Fgithub.com\u002FVainF\u002FDiff-Pruning)] \\\n18 May 2023\n\n\n**Catch-Up Distillation: You Only Need to Train Once for Accelerating Sampling** \\\n*Shitong Shao, Xu Dai, Shouyi Yin, Lujun Li, Huanran Chen, Yang Hu* \\\narXiv 2023. [[Paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2305.10769)] \\\n18 May 2023\n\n**Controllable Mind Visual Diffusion Model** \\\n*Bohan Zeng, Shanglin Li, Xuhui Liu, Sicheng Gao, Xiaolong Jiang, Xu Tang, Yao Hu, Jianzhuang Liu, Baochang Zhang* \\\narXiv 2023. [[Paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2305.10135)] \\\n17 May 2023\n\n**Analyzing Bias in Diffusion-based Face Generation Models** \\\n*Malsha V. Perera, Vishal M. Patel* \\\narXiv 2023. [[Paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2305.06402)] \\\n10 May 2023\n\n\n**Improved Techniques for Maximum Likelihood Estimation for Diffusion ODEs** \\\n*Kaiwen Zheng, Cheng Lu, Jianfei Chen, Jun Zhu* \\\nICML 2023. [[Paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2305.03935)] \\\n6 May 2023\n\n**LEO: Generative Latent Image Animator for Human Video Synthesis** \\\n*Yaohui Wang, Xin Ma, Xinyuan Chen, Antitza Dantcheva, Bo Dai, Yu Qiao* \\\narXiv 2023. [[Paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2305.03989)] [[Project](https:\u002F\u002Fwyhsirius.github.io\u002FLEO-project\u002F)] [[Github](https:\u002F\u002Fgithub.com\u002Fwyhsirius\u002FLEO)] \\\n6 May 2023\n\n**Iterative α-(de)Blending: a Minimalist Deterministic Diffusion Model** \\\n*Eric Heitz, Laurent Belcour, Thomas Chambon* \\\nSIGGRAPH 2023. 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[[Paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2304.13224)] \\\n26 Apr 2023\n\n**Exploring Compositional Visual Generation with Latent Classifier Guidance** \\\n*Changhao Shi, Haomiao Ni, Kai Li, Shaobo Han, Mingfu Liang, Martin Renqiang Min* \\\nCVPR Workshop 2023. [[Paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2304.12536)] \\\n25 Apr 2023\n\n**Patch Diffusion: Faster and More Data-Efficient Training of Diffusion Models** \\\n*Zhendong Wang, Yifan Jiang, Huangjie Zheng, Peihao Wang, Pengcheng He, Zhangyang Wang, Weizhu Chen, Mingyuan Zhou* \\\narXiv 2023. [[Paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2304.12526)] \\\n25 Apr 2023\n\n\n**Variational Diffusion Auto-encoder: Deep Latent Variable Model with Unconditional Diffusion Prior** \\\n*Georgios Batzolis, Jan Stanczuk, Carola-Bibiane Schönlieb* \\\narXiv 2023. [[Paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2304.12141)] \\\n24 Apr 2023\n\n\n**LaMD: Latent Motion Diffusion for Video Generation** \\\n*Yaosi Hu, Zhenzhong Chen, Chong Luo* \\\narXiv 2023. [[Paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2304.11603)] \\\n23 Apr 2023\n\n\n**Lookahead Diffusion Probabilistic Models for Refining Mean Estimation** \\\n*Guoqiang Zhang, Niwa Kenta, W. Bastiaan Kleijn* \\\nCVPR 2023. [[Paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2304.11312)] [[Github](https:\u002F\u002Fgithub.com\u002Fguoqiang-zhang-x\u002FLA-DPM)] \\\n22 Apr 2023\n\n**NeuralField-LDM: Scene Generation with Hierarchical Latent Diffusion Models** \\\n*Seung Wook Kim, Bradley Brown, Kangxue Yin, Karsten Kreis, Katja Schwarz, Daiqing Li, Robin Rombach, Antonio Torralba, Sanja Fidler* \\\nCVPR 2023. [[Paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2304.09787)] \\\n19 Apr 2023\n\n**Attributing Image Generative Models using Latent Fingerprints** \\\n*Guangyu Nie, Changhoon Kim, Yezhou Yang, Yi Ren* \\\narXiv 2023. [[Paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2304.09752)] \\\n17 Apr 2023\n\n\n**Identity Encoder for Personalized Diffusion** \\\n*Yu-Chuan Su, Kelvin C.K. Chan, Yandong Li, Yang Zhao, Han Zhang, Boqing Gong, Huisheng Wang, Xuhui Jia* \\\narXiv 2023. [[Paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2304.07429)] \\\n14 Apr 2023\n\n**Memory Efficient Diffusion Probabilistic Models via Patch-based Generation** \\\n*Shinei Arakawa, Hideki Tsunashima, Daichi Horita, Keitaro Tanaka, Shigeo Morishima* \\\narXiv 2023. [[Paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2304.07087)] \\\n14 Apr 2023\n\n**DCFace: Synthetic Face Generation with Dual Condition Diffusion Model** \\\n*Minchul Kim, Feng Liu, Anil Jain, Xiaoming Liu* \\\narXiv 2023. [[Paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2304.07060)] [[Github](https:\u002F\u002Fgithub.com\u002Fmk-minchul\u002Fdcface)] \\\n14 Apr 2023\n\n**DiffFit: Unlocking Transferability of Large Diffusion Models via Simple Parameter-Efficient Fine-Tuning** \\\n*Enze Xie, Lewei Yao, Han Shi, Zhili Liu, Daquan Zhou, Zhaoqiang Liu, Jiawei Li, Zhenguo Li* \\\narXiv 2023. [[Paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2304.06648)] \\\n13 Apr 2023\n\n**RAFT: Reward rAnked FineTuning for Generative Foundation Model Alignment** \\\n*Hanze Dong, Wei Xiong, Deepanshu Goyal, Rui Pan, Shizhe Diao, Jipeng Zhang, Kashun Shum, Tong Zhang* \\\narXiv 2023. [[Paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2304.06767)] \\\n13 Apr 2023\n\n**DreamPose: Fashion Image-to-Video Synthesis via Stable Diffusion** \\\n*Johanna Karras, Aleksander Holynski, Ting-Chun Wang, Ira Kemelmacher-Shlizerman* \\\narXiv 2023. [[Paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2304.06025)] [[Project](https:\u002F\u002Fgrail.cs.washington.edu\u002Fprojects\u002Fdreampose\u002F)] [[Github](https:\u002F\u002Fgithub.com\u002Fjohannakarras\u002FDreamPose)] \\\n12 Apr 2023\n\n**Reflected Diffusion Models** \\\n*Aaron Lou, Stefano Ermon* \\\nICML 2023. [[Paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2304.04740)] [[Project](https:\u002F\u002Faaronlou.com\u002Fblog\u002F2023\u002Freflected-diffusion\u002F)] [[Github](https:\u002F\u002Fgithub.com\u002Flouaaron\u002FReflected-Diffusion)] \\\n10 Apr 2023\n\n**Binary Latent Diffusion** \\\n*Ze Wang, Jiang Wang, Zicheng Liu, Qiang Qiu* \\\narXiv 2023. [[Paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2304.04820)] \\\n10 Apr 2023\n\n\n**Diffusion Models as Masked Autoencoders** \\\n*Chen Wei, Karttikeya Mangalam, Po-Yao Huang, Yanghao Li, Haoqi Fan, Hu Xu, Huiyu Wang, Cihang Xie, Alan Yuille, Christoph Feichtenhofer* \\\narXiv 2023. [[Paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2304.03283)] [[Project](https:\u002F\u002Fweichen582.github.io\u002Fdiffmae.html)] \\\n6 Apr 2023\n\n**Few-shot Semantic Image Synthesis with Class Affinity Transfer** \\\n*Marlène Careil, Jakob Verbeek, Stéphane Lathuilière* \\\nCVPR 2023. [[Paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2304.02321)] \\\n5 Apr 2023\n\n\n**EGC: Image Generation and Classification via a Diffusion Energy-Based Model** \\\n*Qiushan Guo, Chuofan Ma, Yi Jiang, Zehuan Yuan, Yizhou Yu, Ping Luo* \\\narxiv 2023. [[Paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2304.02012)] [[Project](https:\u002F\u002Fguoqiushan.github.io\u002Fegc.github.io\u002F)] \\\n4 Apr 2023\n\n\n\n**Token Merging for Fast Stable Diffusion** \\\n*Daniel Bolya, Judy Hoffman* \\\narXiv 2023. [[Paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2303.17604)] [[Github](https:\u002F\u002Fgithub.com\u002Fdbolya\u002Ftomesd)] \\\n30 Mar 2023\n\n**A Closer Look at Parameter-Efficient Tuning in Diffusion Models** \\\n*Chendong Xiang, Fan Bao, Chongxuan Li, Hang Su, Jun Zhu* \\\narXiv 2023. [[Paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2303.18181)] \\\n31 Mar 2023\n\n**-Diff: Infinite Resolution Diffusion with Subsampled Mollified States** \\\n*Sam Bond-Taylor, Chris G. Willcocks* \\\narXiv 2023. [[Paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2303.18242)] \\\n31 Mar 2023\n\n**3D-aware Image Generation using 2D Diffusion Models** \\\n*Jianfeng Xiang, Jiaolong Yang, Binbin Huang, Xin Tong* \\\narXiv 2023. [[Paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2303.17905)] [[Project](https:\u002F\u002Fjeffreyxiang.github.io\u002Fivid\u002F)] \\\n31 Mar 2023\n\n**Consistent View Synthesis with Pose-Guided Diffusion Models** \\\n*Hung-Yu Tseng, Qinbo Li, Changil Kim, Suhib Alsisan, Jia-Bin Huang, Johannes Kopf* \\\nCVPR 2023. [[Paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2303.17598)] \\\n30 Mar 2023\n\n\n**DiffCollage: Parallel Generation of Large Content with Diffusion Models** \\\n*Qinsheng Zhang, Jiaming Song, Xun Huang, Yongxin Chen, Ming-Yu Liu* \\\nCVPR 2023. [[Paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2303.17076)] [[Project](https:\u002F\u002Fresearch.nvidia.com\u002Flabs\u002Fdir\u002Fdiffcollage\u002F)] \\\n30 Mar 2023\n\n**Masked Diffusion Transformer is a Strong Image Synthesizer** \\\n*Shanghua Gao, Pan Zhou, Ming-Ming Cheng, Shuicheng Yan* \\\narXiv 2023.  [[Paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2303.14389)] [[Github](https:\u002F\u002Fgithub.com\u002Fsail-sg\u002FMDT)] \\\n25 Mar 2023\n\n**Conditional Image-to-Video Generation with Latent Flow Diffusion Models** \\\n*Haomiao Ni, Changhao Shi, Kai Li, Sharon X. Huang, Martin Renqiang Min* \\\nCVPR 2023. [[Paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2303.13744)] [[Github](https:\u002F\u002Fgithub.com\u002Fnihaomiao\u002FCVPR23_LFDM)] \\\n24 Mar 2023\n\n**NUWA-XL: Diffusion over Diffusion for eXtremely Long Video Generation** \\\n*Shengming Yin, Chenfei Wu, Huan Yang, Jianfeng Wang, Xiaodong Wang, Minheng Ni, Zhengyuan Yang, Linjie Li, Shuguang Liu, Fan Yang, Jianlong Fu, Gong Ming, Lijuan Wang, Zicheng Liu, Houqiang Li, Nan Duan* \\\narXiv 2023. [[Paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2303.12346)] [[Project](https:\u002F\u002Fmsra-nuwa.azurewebsites.net\u002F#\u002F)] \\\n22 Mar 2023\n\n**Object-Centric Slot Diffusion** \\\n*Jindong Jiang, Fei Deng, Gautam Singh, Sungjin Ahn* \\\narXiv 2023. [[Paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2303.10834)] \\\n20 Mar 2023\n\n\n**LDMVFI: Video Frame Interpolation with Latent Diffusion Models** \\\n*Duolikun Danier, Fan Zhang, David Bull* \\\narXiv 2023. [[Paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2303.09508)] \\\n16 Mar 2023\n\n**Efficient Diffusion Training via Min-SNR Weighting Strategy** \\\n*Tiankai Hang, Shuyang Gu, Chen Li, Jianmin Bao, Dong Chen, Han Hu, Xin Geng, Baining Guo* \\\narXiv 2023. [[Paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2303.09556)] \\\n16 Mar 2023\n\n**VideoFusion: Decomposed Diffusion Models for High-Quality Video Generation** \\\nCVPR 2023. [[Paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2303.08320)] \\\n15 Mar 2023\n\n**Interpretable ODE-style Generative Diffusion Model via Force Field Construction** \\\n*Weiyang Jin, Yongpei Zhu, Yuxi Peng* \\\narXiv 2023. [[Paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2303.08063)] \\\n14 Mar 2023\n\n**Regularized Vector Quantization for Tokenized Image Synthesis** \\\n*Jiahui Zhang, Fangneng Zhan, Christian Theobalt, Shijian Lu* \\\narXiv 2023. [[Paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2303.06424)] \\\n11 Mar 2023\n\n\n**PARASOL: Parametric Style Control for Diffusion Image Synthesis** \\\n*Gemma Canet Tarrés, Dan Ruta, Tu Bui, John Collomosse* \\\narXiv 2023. [[Paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2303.06464)] \\\n11 Mar 2023\n\n**Brain-Diffuser: Natural scene reconstruction from fMRI signals using generative latent diffusion** \\\n*Furkan Ozcelik, Rufin VanRullen* \\\narXiv 2023. [[Paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2303.05334)] \\\n9 Mar 2023\n\n**Multilevel Diffusion: Infinite Dimensional Score-Based Diffusion Models for Image Generation** \\\n*Paul Hagemann, Lars Ruthotto, Gabriele Steidl, Nicole Tianjiao Yang* \\\narXiv 2023. [[Paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2303.04772)] \\\n8 Mar 2023\n\n\n**TRACT: Denoising Diffusion Models with Transitive Closure Time-Distillation** \\\n*David Berthelot, Arnaud Autef, Jierui Lin, Dian Ang Yap, Shuangfei Zhai, Siyuan Hu, Daniel Zheng, Walter Talbott, Eric Gu* \\\narXiv 2023. [[Paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2303.04248)] \\\n7 Mar 2023\n\n**Generative Diffusions in Augmented Spaces: A Complete Recipe** \\\n*Kushagra Pandey, Stephan Mandt* \\\narXiv 2023. [[Paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2303.01748)] \\\n3 Mar 2023\n\n**Consistency Models** \\\n*Yang Song, Prafulla Dhariwal, Mark Chen, Ilya Sutskever* \\\narXiv 2023. [[Paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2303.01469)] \\\n2 Mar 2023\n\n**Diffusion Probabilistic Fields** \\\n*Peiye Zhuang, Samira Abnar, Jiatao Gu, Alex Schwing, Joshua M. Susskind, Miguel Ángel Bautista* \\\nICLR 2023. [[Paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2303.00165)] \\\n1 Mar 2023\n\n**Unsupervised Discovery of Semantic Latent Directions in Diffusion Models** \\\n*Yong-Hyun Park, Mingi Kwon, Junghyo Jo, Youngjung Uh* \\\narXiv 2023. [[Paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2302.12469)] \\\n24 Feb 2023\n\n**Reduce, Reuse, Recycle: Compositional Generation with Energy-Based Diffusion Models and MCMC** \\\n*Yilun Du, Conor Durkan, Robin Strudel, Joshua B. Tenenbaum, Sander Dieleman, Rob Fergus, Jascha Sohl-Dickstein, Arnaud Doucet, Will Grathwohl* \\\narXiv 2023. [[Paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2302.11552)] [[Project](https:\u002F\u002Fenergy-based-model.github.io\u002Freduce-reuse-recycle\u002F)] \\\n22 Feb 2023\n\n**Learning 3D Photography Videos via Self-supervised Diffusion on Single Images** \\\n*Xiaodong Wang, Chenfei Wu, Shengming Yin, Minheng Ni, Jianfeng Wang, Linjie Li, Zhengyuan Yang, Fan Yang, Lijuan Wang, Zicheng Liu, Yuejian Fang, Nan Duan* \\\narXiv 2023. [[Paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2302.10781)] \\\n21 Feb 2023\n\n**On Calibrating Diffusion Probabilistic Models** \\\n*Tianyu Pang, Cheng Lu, Chao Du, Min Lin, Shuicheng Yan, Zhijie Deng* \\\narXiv 2023. [[Paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2302.10688)] [[Github](https:\u002F\u002Fgithub.com\u002Fthudzj\u002FCalibrated-DPMs)] \\\n21 Feb 2023\n\n**Diffusion Models and Semi-Supervised Learners Benefit Mutually with Few Labels** \\\n*Zebin You, Yong Zhong, Fan Bao, Jiacheng Sun, Chongxuan Li, Jun Zhu* \\\narXiv 2023. [[Paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2302.10586)] \\\n21 Feb 2023\n\n**Cross-domain Compositing with Pretrained Diffusion Models** \\\n*Roy Hachnochi, Mingrui Zhao, Nadav Orzech, Rinon Gal, Ali Mahdavi-Amiri, Daniel Cohen-Or, Amit Haim Bermano* \\\narXiv 2023. [[Paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2302.10167)] [[Github](https:\u002F\u002Fgithub.com\u002Fcross-domain-compositing\u002Fcross-domain-compositing)] \\\n20 Feb 2023\n\n\n\n**Restoration based Generative Models** \\\n*Jaemoo Choi, Yesom Park, Myungjoo Kang* \\\narXiv 2023. [[Paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2303.05456)] \\\n20 Feb 2023\n\n\n\n**Consistent Diffusion Models: Mitigating Sampling Drift by Learning to be Consistent** \\\n*Giannis Daras, Yuval Dagan, Alexandros G. Dimakis, Constantinos Daskalakis* \\\narXiv 2023. [[Paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2302.09057)] [[Github](https:\u002F\u002Fgithub.com\u002Fgiannisdaras\u002Fcdm)] \\\n17 Feb 2023\n\n**LayoutDiffuse: Adapting Foundational Diffusion Models for Layout-to-Image Generation** \\\n*Jiaxin Cheng, Xiao Liang, Xingjian Shi, Tong He, Tianjun Xiao, Mu Li* \\\narXiv 2023. [[Paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2302.08908)] \\\n16 Feb 2023\n\n**Video Probabilistic Diffusion Models in Projected Latent Space** \\\n*Sihyun Yu, Kihyuk Sohn, Subin Kim, Jinwoo Shin* \\\narXiv 2023. [[Paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2302.07685)] [[Github](https:\u002F\u002Fsihyun.me\u002FPVDM\u002F)] \\\n15 Feb 2023\n\n**DiffFaceSketch: High-Fidelity Face Image Synthesis with Sketch-Guided Latent Diffusion Model** \\\n*Yichen Peng, Chunqi Zhao, Haoran Xie, Tsukasa Fukusato, Kazunori Miyata* \\\narXiv 2023. [[Paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2302.06908)] \\\n14 Feb 2023\n\n**Where to Diffuse, How to Diffuse, and How to Get Back: Automated Learning for Multivariate Diffusions** \\\n*Raghav Singhal, Mark Goldstein, Rajesh Ranganath* \\\narXiv 2023. [[Paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2302.07261)] \\\n14 Feb 2023\n\n\n**Preconditioned Score-based Generative Models** \\\n*Li Zhang, Hengyuan Ma, Xiatian Zhu, Jianfeng Feng* \\\narXiv 2023. [[Paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2302.06504)] [Github](https:\u002F\u002Fgithub.com\u002Ffudan-zvg\u002FPDS)] \\\n13 Feb 2023\n\n**Star-Shaped Denoising Diffusion Probabilistic Models** \\\n*Andrey Okhotin, Dmitry Molchanov, Vladimir Arkhipkin, Grigory Bartosh, Aibek Alanov, Dmitry Vetrov* \\\narXiv 2023. [[Paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2302.05259)] \\\n10 Feb 2023 \n\n\n**UniPC: A Unified Predictor-Corrector Framework for Fast Sampling of Diffusion Models** \\\n*Wenliang Zhao, Lujia Bai, Yongming Rao, Jie Zhou, Jiwen Lu* \\\narXiv 2023. [[Paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2302.04867)] [[Project](https:\u002F\u002Funipc.ivg-research.xyz)] [[Github](https:\u002F\u002Fgithub.com\u002Fwl-zhao\u002FUniPC)] \\\n9 Feb 2023\n\n**Geometry of Score Based Generative Models** \\\n*Sandesh Ghimire, Jinyang Liu, Armand Comas, Davin Hill, Aria Masoomi, Octavia Camps, Jennifer Dy* \\\narXiv 2023. [[Paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2302.04411)] \\\n9 Feb 2023\n\n**Q-Diffusion: Quantizing Diffusion Models** \\\n*Xiuyu Li, Long Lian, Yijiang Liu, Huanrui Yang, Zhen Dong, Daniel Kang, Shanghang Zhang, Kurt Keutzer* \\\narXiv 2023. [[Paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2302.04304)] \\\n8 Feb 2023\n\n**PFGM++: Unlocking the Potential of Physics-Inspired Generative Models** \\\n*Yilun Xu, Ziming Liu, Yonglong Tian, Shangyuan Tong, Max Tegmark, Tommi Jaakkola* \\\narXiv 2023. [[Paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2302.04265)] [[Github](https:\u002F\u002Fgithub.com\u002FNewbeeer\u002Fpfgmpp)] \\\n8 Feb 2023\n\n**Long Horizon Temperature Scaling** \\\n*Andy Shih, Dorsa Sadigh, Stefano Ermon* \\\narXiv 2023. [[Paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2302.03686)] \\\n7 Feb 2023\n\n**Spatial Functa: Scaling Functa to ImageNet Classification and Generation** \\\n*Matthias Bauer, Emilien Dupont, Andy Brock, Dan Rosenbaum, Jonathan Schwarz, Hyunjik Kim* \\\narXiv 2023. [[Paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2302.03130)] \\\n6 Feb 2023\n\n**ShiftDDPMs: Exploring Conditional Diffusion Models by Shifting Diffusion Trajectories** \\\n*Zijian Zhang, Zhou Zhao, Jun Yu, Qi Tian* \\\nAAAI 2023. [[Paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2302.02373)] \\\n5 Feb 2023\n\n**Divide and Compose with Score Based Generative Models** \\\n*Sandesh Ghimire, Armand Comas, Davin Hill, Aria Masoomi, Octavia Camps, Jennifer Dy* \\\narXiv 2023. [[Paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2302.02272)] [[Github](https:\u002F\u002Fgithub.com\u002Fsandeshgh\u002FScore-based-disentanglement)] \\\n5 Feb 2023\n\n\n**Stable Target Field for Reduced Variance Score Estimation in Diffusion Models** \\\n*Yilun Xu, Shangyuan Tong, Tommi Jaakkola* \\\nICLR 2023. [[Paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2302.00670)] [[Github](https:\u002F\u002Fgithub.com\u002FNewbeeer\u002Fstf)] \\\n1 Feb 2023\n\n**DisDiff: Unsupervised Disentanglement of Diffusion Probabilistic Models** \\\n*Tao Yang, Yuwang Wang, Yan Lv, Nanning Zheng* \\\nNeurIPS 2023. [[Paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2301.13721)] \\\n31 Jan 2023\n\n\n**Optimizing DDPM Sampling with Shortcut Fine-Tuning** \\\n*Ying Fan, Kangwook Lee* \\\narXiv 2023. [[Paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2301.13362)] \\\n31 Jan 2023\n\n**Learning Data Representations with Joint Diffusion Models** \\\n*Kamil Deja, Tomasz Trzcinski, Jakub M. Tomczak* \\\narXiv 2023. [[Paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2301.13622)] \\\n31 Jan 2023\n\n**ERA-Solver: Error-Robust Adams Solver for Fast Sampling of Diffusion Probabilistic Models** \\\n*Shengmeng Li, Luping Liu, Zenghao Chai, Runnan Li, Xu Tan* \\\narXiv 2023. [[Paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2301.12935)] \\\n30 Jan 2023\n\n**Don't Play Favorites: Minority Guidance for Diffusion Models** \\\n*Soobin Um, Jong Chul Ye* \\\narXiv 2023. [[Paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2301.12334)] [[Github](https:\u002F\u002Fgithub.com\u002Fsangyun884\u002Ffast-ode)] \\\n29 Jan 2023\n\n**Accelerating Guided Diffusion Sampling with Splitting Numerical Methods** \\\n*Suttisak Wizadwongsa, Supasorn Suwajanakorn* \\\nICLR 2023. [[Paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2301.11558)] \\\n27 Jan 2023\n\n**Input Perturbation Reduces Exposure Bias in Diffusion Models** \\\n*Mang Ning, Enver Sangineto, Angelo Porrello, Simone Calderara, Rita Cucchiara* \\\narXiv 2023. [[Paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2301.11706)] [[Github](https:\u002F\u002Fgithub.com\u002Fforever208\u002FDDPM-IP)] \\\n27 Jan 2023\n\n**Minimizing Trajectory Curvature of ODE-based Generative Models** \\\n*Sangyun Lee, Beomsu Kim, Jong Chul Ye* \\\narXiv 2023. [[Paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2301.12003)] \\\n27 Jan 2023\n\n\n**On the Importance of Noise Scheduling for Diffusion Models** \\\n*Ting Chen* \\\narXiv 2023. [[Paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2301.10972)] \\\n26 Jan 2023\n\n**simple diffusion: End-to-end diffusion for high resolution images** \\\n*Emiel Hoogeboom, Jonathan Heek, Tim Salimans* \\\narXiv 2023. [[Paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2301.11093)] \\\n26 Jan 2023\n\n**Fast Inference in Denoising Diffusion Models via MMD Finetuning** \\\n*Emanuele Aiello, Diego Valsesia, Enrico Magli* \\\narXiv 2023. [[Paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2301.07969)] [[Github](https:\u002F\u002Fgithub.com\u002Fdiegovalsesia\u002FMMD-DDM)] \\\n19 Jan 2023\n\n**Exploring Transformer Backbones for Image Diffusion Models** \\\n*Princy Chahal* \\\narXiv 2022. [[Paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2212.14678)] \\\n27 Dec 2022\n\n**Unsupervised Representation Learning from Pre-trained Diffusion Probabilistic Models** \\\n*Zijian Zhang, Zhou Zhao, Zhijie Lin* \\\narXiv 2022. [[Paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2212.12990)] \\\n26 Dec 2022\n\n\n**Scalable Adaptive Computation for Iterative Generation** \\\n*Allan Jabri, David Fleet, Ting Chen* \\\narXiv 2022. [[Paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2212.11972)] \\\n22 Dec 2022\n\n**Hierarchically branched diffusion models for efficient and interpretable multi-class conditional generation** \\\n*Alex M. Tseng, Tommaso Biancalani, Max Shen, Gabriele Scalia* \\\narXiv 2022. [[Paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2212.10777)] \\\n21 Dec 2022\n\n\n**MM-Diffusion: Learning Multi-Modal Diffusion Models for Joint Audio and Video Generation** \\\n*Ludan Ruan, Yiyang Ma, Huan Yang, Huiguo He, Bei Liu, Jianlong Fu, Nicholas Jing Yuan, Qin Jin, Baining Guo* \\\narXiv 2022. [[Paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2212.09478)] [[Github](https:\u002F\u002Fgithub.com\u002Fresearchmm\u002FMM-Diffusion)] \\\n19 Dec 2022\n\n\n**Scalable Diffusion Models with Transformers** \\\n*William Peebles, Saining Xie* \\\narXiv 2022. [[Paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2212.09748)] [[Project](https:\u002F\u002Fwww.wpeebles.com\u002FDiT)] [[Github](https:\u002F\u002Fgithub.com\u002Ffacebookresearch\u002FDiT)] \\\n19 Dec 2022\n\n\n**DAG: Depth-Aware Guidance with Denoising Diffusion Probabilistic Models** \\\n*Gyeongnyeon Kim, Wooseok Jang, Gyuseong Lee, Susung Hong, Junyoung Seo, Seungryong Kim* \\\narXiv 2022. [[Paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2212.08861)] [[Project](https:\u002F\u002Fku-cvlab.github.io\u002FDAG\u002F)] \\\n17 Dec 2022\n\n\n**Towards Practical Plug-and-Play Diffusion Models** \\\n*Hyojun Go, Yunsung Lee, Jin-Young Kim, Seunghyun Lee, Myeongho Jeong, Hyun Seung Lee, Seungtaek Choi* \\\narXiv 2022. [[Paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2212.05973)] \\\n12 Dec 2022\n\n**Semantic Brain Decoding: from fMRI to conceptually similar image reconstruction of visual stimuli** \\\n*Matteo Ferrante, Tommaso Boccato, Nicola Toschi* \\\narXiv 2022. [[Paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2212.06726)] \\\n13 Dec 2022\n\n**MAGVIT: Masked Generative Video Transformer** \\\n*Lijun Yu, Yong Cheng, Kihyuk Sohn, José Lezama, Han Zhang, Huiwen Chang, Alexander G. Hauptmann, Ming-Hsuan Yang, Yuan Hao, Irfan Essa, Lu Jiang* \\\narXiv 2022. [[Paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2212.05199)] [Project](https:\u002F\u002Fmagvit.cs.cmu.edu\u002F)] \\\n10 Dec 2022\n\n**Diffusion Video Autoencoders: Toward Temporally Consistent Face Video Editing via Disentangled Video Encoding** \\\n*Gyeongman Kim, Hajin Shim, Hyunsu Kim, Yunjey Choi, Junho Kim, Eunho Yang* \\\narXiv 2022. [[Paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2212.02802)] \\\n6 Dec 2022\n\n**Fine-grained Image Editing by Pixel-wise Guidance Using Diffusion Models** \\\n*Naoki Matsunaga, Masato Ishii, Akio Hayakawa, Kenji Suzuki, Takuya Narihira* \\\narXiv 2022. [[Paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2212.02024)] \\\n5 Dec 2022\n\n\n**VIDM: Video Implicit Diffusion Models** \\\n*Kangfu Mei, Vishal M. Patel* \\\narXiv 2022. [[Paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2212.00235)] [[Project](https:\u002F\u002Fkfmei.page\u002Fvidm\u002F)] [[Github](https:\u002F\u002Fgithub.com\u002FMKFMIKU\u002FVIDM)] \\\n1 Dec 2022\n\n**Why Are Conditional Generative Models Better Than Unconditional Ones?** \\\n*Fan Bao, Chongxuan Li, Jiacheng Sun, Jun Zhu* \\\narXiv 2022. [[Paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2212.00362)] \\\n1 Dec 2022\n\n\n**High-Fidelity Guided Image Synthesis with Latent Diffusion Models** \\\n*Jaskirat Singh, Stephen Gould, Liang Zheng* \\\narXiv 2022. [[Paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2211.17084)] [[Project](https:\u002F\u002F1jsingh.github.io\u002Fgradop)] \\\n30 Nov 2022\n\n\n**Score-based Continuous-time Discrete Diffusion Models** \\\n*Haoran Sun, Lijun Yu, Bo Dai, Dale Schuurmans, Hanjun Dai* \\\narXiv 2022. [[Paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2211.16750)] \\\n30 Nov 2022\n\n**Wavelet Diffusion Models are fast and scalable Image Generators** \\\n*Hao Phung, Quan Dao, Anh Tran* \\\narXiv 2022. [[Paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2211.16152)] \\\n29 Nov 2022\n\n\n**Dimensionality-Varying Diffusion Process** \\\n*Han Zhang, Ruili Feng, Zhantao Yang, Lianghua Huang, Yu Liu, Yifei Zhang, Yujun Shen, Deli Zhao, Jingren Zhou, Fan Cheng* \\\narXiv 2022. [[Paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2211.16032)] \\\n29 Nov 202","Awesome-Diffusion-Models 是一个汇集了关于扩散模型的资源和论文的集合。该项目主要功能是提供一系列介绍性文章、学术论文、教程视频以及Jupyter笔记本，帮助研究者和开发者深入了解和应用扩散模型这一生成模型领域的重要技术。它涵盖了从基础理论到实际应用（如图像生成、音频处理、自然语言处理等）的广泛内容。适用于对机器学习特别是生成模型感兴趣的学者、学生及工程师，在研究开发基于扩散模型的新算法或改进现有方法时作为参考指南。",2,"2026-06-11 03:24:00","top_topic"]