https://github.com/hojonathanho/diffusion

Denoising Diffusion Probabilistic Models
https://github.com/hojonathanho/diffusion

Last synced: 27 days ago
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Denoising Diffusion Probabilistic Models

• Host: GitHub
• URL: https://github.com/hojonathanho/diffusion
• Owner: hojonathanho
• Created: 2020-06-19T00:47:02.000Z (over 4 years ago)
• Default Branch: master
• Last Pushed: 2023-08-29T16:48:57.000Z (about 1 year ago)
• Last Synced: 2024-10-14T12:02:48.596Z (27 days ago)
• Language: Python
• Size: 100 MB
• Stars: 3,731
• Watchers: 20
• Forks: 366
• Open Issues: 19
• Metadata Files:
  • Readme: README.md

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• awesome-multi-modal - https://github.com/hojonathanho/diffusion
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README

        
# Denoising Diffusion Probabilistic Models

Jonathan Ho, Ajay Jain, Pieter Abbeel

Paper: https://arxiv.org/abs/2006.11239

Website: https://hojonathanho.github.io/diffusion

![Samples generated by our model](resources/samples.png)

Experiments run on Google Cloud TPU v3-8.

Requires TensorFlow 1.15 and Python 3.5, and these dependencies for CPU instances (see `requirements.txt`):

```

pip3 install fire

pip3 install scipy

pip3 install pillow

pip3 install tensorflow-probability==0.8

pip3 install tensorflow-gan==0.0.0.dev0

pip3 install tensorflow-datasets==2.1.0

```

The training and evaluation scripts are in the `scripts/` subdirectory.

The commands to run training and evaluation are in comments at the top of the scripts.

Data is stored in GCS buckets. The scripts are written to assume that the bucket names are of the form `gs://mybucketprefix-us-central1`; i.e. some prefix followed by the region.

The prefix should be passed into the scripts using the `--bucket_name_prefix` flag.

Models and samples can be found at: https://www.dropbox.com/sh/pm6tn31da21yrx4/AABWKZnBzIROmDjGxpB6vn6Ja

## Citation

If you find our work relevant to your research, please cite:

```

@article{ho2020denoising,

    title={Denoising Diffusion Probabilistic Models},

    author={Jonathan Ho and Ajay Jain and Pieter Abbeel},

    year={2020},

    journal={arXiv preprint arxiv:2006.11239}

}

```