lucidrains/denoising-diffusion-pytorch
Implementation of Denoising Diffusion Probabilistic Model in Pytorch
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README & documentation
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Implementation of Denoising Diffusion Probabilistic Model in Pytorch. It is a new approach to generative modeling that may have the potential to rival GANs. It uses denoising score matching to estimate the gradient of the data distribution, followed by Langevin sampling to sample from the true distribution. This implementation was inspired by the official Tensorflow version here Youtube AI Educators - Yannic Kilcher | AI Coffeebreak with Letitia | Outlier Flax implementation from YiYi Xu Annotated code by Research Scientists / Engineers from 🤗 Huggingface Update: Turns out none of the technicalities really matters at all | "Cold Diffusion" paper | Muse [](https://badge.fury.io/py/denoising-diffusion-pytorch) Or, if you simply want to pass in a folder name and the desired image dimensions, you can use the Trainer class to easily train a model. Samples and model checkpoints will be logged to ./results periodically To use Explorative Modeling (Forward XM) for multi-candidate…
Read the full README ↗ · Preview checked 2026-10-28T05:32:58.774Z
Inside the original README — Document outline
- Denoising Diffusion Probabilistic Model, in Pytorch
- Install
- Usage
- Explorative Modeling (Forward XM)
- Multi-GPU Training
- Miscellaneous
- 1D Sequence
- Citations
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What this repository does
Implementation of Denoising Diffusion Probabilistic Model in Pytorch
Repository facts
- Owner
- lucidrains
- Primary language
- Python
- Stars
- 10,722
- Forks
- 1,284
- Open issues + pull requests
- 172
- License
- MIT
- Archived
- No
- Default branch
- main
- Created
- 2020-08-26T02:22:10.000Z
- Last push
- 2026-09-29T14:13:08.000Z
Topics and intended use
Owner-supplied topics: artificial-intelligence, deep-learning, generative-model, score-matching
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