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Add link to diffusers library #14

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Add link to `diffusers` library
patrickvonplaten Nov 8, 2022
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Update README.md
patrickvonplaten Nov 8, 2022
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Expand Up @@ -26,6 +26,36 @@ The prefix should be passed into the scripts using the `--bucket_name_prefix` fl

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

## **Integration with 🤗 Diffusers library in PyTorch**

If you are looking for a PyTorch version for DDPM, you can check out the [Diffusers library](https://huggingface.co/docs/diffusers/api/pipelines/ddpm).
You can install diffusers as follows:

```
pip install diffusers torch accelerate
```

And then try out the model with just a couple lines of code:

```python
from diffusers import DDPMPipeline
model_id = "google/ddpm-cifar10-32"

# load model and scheduler
ddpm = DDPMPipeline.from_pretrained(model_id) # you can replace DDPMPipeline with DDIMPipeline or PNDMPipeline for faster inference

# run pipeline in inference (sample random noise and denoise)
image = ddpm().images[0]

# save image
image.save("ddpm_generated_image.png")
```

More DDPM models can be found directly [on the Hub](https://huggingface.co/models?library=diffusers&sort=downloads&search=ddpm)

To better understand DDPM, you can check out [this introductionary google colab](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/diffusers_intro.ipynb)
To train your own DDPM from scratch, feel free to have a look at [unconditional_image_generation](https://github.com/huggingface/diffusers/tree/main/examples/unconditional_image_generation)

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