added README.md

README.md

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+---
+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}
+}
+```