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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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**Authors**: Jonathan Ho, Ajay Jain, Pieter Abbeel |
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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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See the following code: |
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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 = "google/ddpm-cifar10" |
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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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