README.md

---
license: apache-2.0
tags:
- pytorch
- diffusers
- unconditional-image-generation
---

# Denoising Diffusion Probabilistic Models (DDPM)

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

**Abstract**:

*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.*

## Usage

```python
# !pip install diffusers
from diffusers import DiffusionPipeline
import PIL.Image
import numpy as np

model_id = "google/ddpm-celeba-hq"

# load model and scheduler
ddpm = DiffusionPipeline.from_pretrained(model_id)

# run pipeline in inference (sample random noise and denoise)
image = ddpm()

# process image to PIL
image_processed = image.cpu().permute(0, 2, 3, 1)
image_processed = (image_processed + 1.0) * 127.5
image_processed = image_processed.numpy().astype(np.uint8)
image_pil = PIL.Image.fromarray(image_processed[0])

# save image
image_pil.save("test.png")
```

## Samples

TODO ...