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--- |
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license: apache-2.0 |
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tags: |
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- pytorch |
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- diffusers |
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- unconditional-image-generation |
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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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## Inference |
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**DDPM** models can use *discrete noise schedulers* such as: |
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- [scheduling_ddpm](https://github.com/huggingface/diffusers/blob/main/src/diffusers/schedulers/scheduling_ddpm.py) |
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- [scheduling_ddim](https://github.com/huggingface/diffusers/blob/main/src/diffusers/schedulers/scheduling_ddim.py) |
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- [scheduling_pndm](https://github.com/huggingface/diffusers/blob/main/src/diffusers/schedulers/scheduling_pndm.py) |
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for inference. Note that while the *ddpm* scheduler yields the highest quality, it also takes the longest. |
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For a good trade-off between quality and inference speed you might want to consider the *ddim* or *pndm* schedulers instead. |
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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 DDPMPipeline, DDIMPipeline, PNDMPipeline |
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model_id = "google/ddpm-cifar10-32" |
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# load model and scheduler |
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ddpm = DDPMPipeline.from_pretrained(model_id) # you can replace DDPMPipeline with DDIMPipeline or PNDMPipeline for faster inference |
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# run pipeline in inference (sample random noise and denoise) |
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image = ddpm()["sample"] |
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# save image |
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image[0].save("ddpm_generated_image.png") |
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``` |
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For more in-detail information, please have a look at the [official inference example](_) # <- TODO(PVP) add link |
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## Training |
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If you want to train your own model, please have a look at the [official training example]( ) # <- TODO(PVP) add link |
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## Samples |
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