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This repository categorizes the papers about diffusion models applied in computer vision according to their target task. The classifcation is based on our survey: https://arxiv.org/abs/2209.04747v1
| Date | Stars |
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| 2026-07-31 | 406 |
| 2026-08-02 | 406 |
| 2026-08-06 | 406 |
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# Diffusion Models in Vision: A Survey (accepted at IEEE TPAMI 2023) Denoising diffusion models represent a recent emerging topic in computer vision, demonstrating remarkable results in the area of generative modeling. A diffusion model is a deep generative model that is based on two stages, a forward diffusion stage and a reverse diffusion stage. In the forward diffusion stage, the input data is gradually perturbed over several steps by adding Gaussian noise. In the reverse stage, a model is tasked at recovering the original input data by learning to gradually reverse the diffusion process, step by step. Diffusion models are widely appreciated for the quality and diversity of the generated samples, despite their known computational burdens, i.e. low speeds due to the high number of steps involved during sampling. This repository categorizes the papers about diffusion models, applied in computer vision, according to their target task. The classifcation is based on our survey [Diffusion Models in Vision: A Survey](https://arxiv.org/abs/2209.04747v2), which was accepted for publication in IEEE TPAMI. ## Summary 1. [Unconditional Generation](#1) 2. [Conditional Generation](#2) 3. [Text-to-Image generation ](#3) 4. [Super-Resolution ](#4) 5. [Image Editing](#5) 6. [Region Image Editing](#6) 7. [Inpainting](#7) 8. [Image-to-Image Translation](#8) 9. [Image Segmentation](#9) 10. [Multi-Task](#10) 11. [Medical Image-to-Image Translation](#11) 12. [Medical Image Generation](#12) 13. [Medical Image Segmentation](#13) 14. [Medical Image Anomaly Detection](#14) 15. [Video Generation](#15) 16. [Few-Shot Image Generation ](#16) 17. [Counterfactual Explanations and Estimations](#17) 18. [Image Restoration](#18) 19. [Image Registration](#19) 20. [Adversarial Purification](#20) 21. [Semantic Image Generation](#21) 22. [Shape Generation and Completion](#22) 23. [Classification](#23) 24. [Point Cloud Generation](#24) 25. [Theoretical](#25) 26. [Graphs](#26) 27. [Deblurring](#27) 28. [Face Morphing Attack Detection](#28) 29. [Trajectory/Motion Prediction](#29) 30. [Attacks](#30) 31. [Study on data memorization](#31) 32. [Out-of-Distribution Detection](#32) 33. [Image-to-Text Generation](#33) 34. [Quantization](#34) 35. [Image/Video anomaly detection](#35) 36. [Video-to-Speech](#36) 37. [Pose estimation](#37) 38. [Graphic layout generation](#38) 39. [Image watermarking](#39) 40. [Video Editing](#40) 41. [Information retrieval from video](#41) 42. [Object detection](#42) ## Content ### Unconditional Generation <a name="1"></a> 1. [Deep unsupervised learning using non-equilibrium thermodynamics](https://arxiv.org/pdf/1503.03585.pdf) 2. [Denoising diffusion probabilistic models](https://arxiv.org/pdf/2006.11239.pdf) 3. [Improved techniques for training score-based generative models](https://arxiv.org/pdf/2006.09011.pdf) 4. [Adversarial score matching and improved sampling for image generation](https://arxiv.org/pdf/2009.05475.pdf) 5. [Maximum likelihood training of score-based diffusion models](https://proceedings.neurips.cc/paper/2021/file/0a9fdbb17feb6ccb7ec405cfb85222c4-Paper.pdf) 6. [D2C: Diffusion-Decoding Models for Few-Shot Conditional Generation](https://proceedings.neurips.cc/paper/2021/file/682e0e796084e163c5ca053dd8573b0c-Paper.pdf) 7. [Diffusion Normalizing Flow](https://proceedings.neurips.cc/paper/2021/file/876f1f9954de0aa402d91bb988d12cd4-Paper.pdf) 8. [Diffusion Schrodinger bridge with applications to score-based generative modeling](https://proceedings.neurips.cc/paper/2021/file/940392f5f32a7ade1cc201767cf83e31-Paper.pdf) 9. [Structured denoising diffusion models in discrete state-spaces](https://proceedings.neurips.cc/paper/2021/file/958c530554f78bcd8e97125b70e6973d-Paper.pdf) 10. [Score-based generative modeling in latent space](https://papers.nips.cc/paper/2021/file/5dca4c6b9e244d24a30b4c45601d9720-Paper.pdf) 11. [Improved denoising diffusion probabilistic models](https://arxiv.org/pdf/2102.09672.pdf) 12
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Read on GitHubWould you bet a product on this? Bounded 0–100 and slow moving.
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