---
datasets:
- luethan2025/AFHQ-64x64
---


## Quick Start

```console
git clone https://github.com/bareform/ddpm.git
cd ddpm
```

You can download the pre-trained models [here](https://huggingface.co/luethan2025/ddpm) and use the provided Jupyter Notebook `inference.ipynb` to generate some samples.

## Method

[Denoising Diffusion Probabilistic Models](https://arxiv.org/abs/2006.11239)

Jonathan Ho, Ajay Jain, Pieter Abbeel

UC Berkeley

> We present high quality image synthesis results using diffusion probabilistic models, a class of latent variable models inspired by considerations from nonequilibrium thermodynamics. Our best results are obtained by training on a weighted variational bound designed according to a novel connection between diffusion probabilistic models and denoising score matching with Langevin dynamics, and our models naturally admit a progressive lossy decompression scheme that can be interpreted as a generalization of autoregressive decoding. On the unconditional CIFAR10 dataset, we obtain an Inception score of 9.46 and a state-of-the-art FID score of 3.17. On 256x256 LSUN, we obtain sample quality similar to ProgressiveGAN.

## Citation

The original paper can be found at:
```
@misc{ho2020ddpm,
    title={Denoising Diffusion Probabilistic Models},
    author={Jonathan Ho and Ajay Jain and Pieter Abbeel},
    year={2020},
    eprint={2006.11239},
    archivePrefix={arXiv},
    primaryClass={cs.LG}
}
```