| --- |
| 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} |
| } |
| ``` |
|
|
