patrickvonplaten
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README.md
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---
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tags:
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- ddpm_diffusion
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---
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# Denoising Diffusion Probabilistic Models (DDPM)
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**Paper**: [Denoising Diffusion Probabilistic Models](https://arxiv.org/abs/2006.11239)
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**Abstract**:
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*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.*
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## Usage
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```python
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# !pip install diffusers
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from diffusers import DiffusionPipeline
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import PIL.Image
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import numpy as np
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model_id = "fusing/ddpm-lsun-church-ema"
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# load model and scheduler
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ddpm = DiffusionPipeline.from_pretrained(model_id)
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# run pipeline in inference (sample random noise and denoise)
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image = ddpm()
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# process image to PIL
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image_processed = image.cpu().permute(0, 2, 3, 1)
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image_processed = (image_processed + 1.0) * 127.5
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image_processed = image_processed.numpy().astype(np.uint8)
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image_pil = PIL.Image.fromarray(image_processed[0])
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# save image
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image_pil.save("test.png")
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```
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## Samples
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1. ![sample_1](https://huggingface.co/datasets/patrickvonplaten/images/resolve/main/hf/ddpm-lsun-church-ema/image_0.png)
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2. ![sample_1](https://huggingface.co/datasets/patrickvonplaten/images/resolve/main/hf/ddpm-lsun-church-ema/image_1.png)
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3. ![sample_1](https://huggingface.co/datasets/patrickvonplaten/images/resolve/main/hf/ddpm-lsun-church-ema/image_2.png)
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4. ![sample_1](https://huggingface.co/datasets/patrickvonplaten/images/resolve/main/hf/ddpm-lsun-church-ema/image_3.png)
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