readme: ---
  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)
  
  **Authors**: Jonathan Ho, Ajay Jain, Pieter Abbeel
  
  **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.*
  
  ## Inference
  
  **DDPM** models can use *discrete noise schedulers* such as:
  
  - [scheduling_ddpm](https://github.com/huggingface/diffusers/blob/main/src/diffusers/schedulers/scheduling_ddpm.py)
  - [scheduling_ddim](https://github.com/huggingface/diffusers/blob/main/src/diffusers/schedulers/scheduling_ddim.py)
  - [scheduling_pndm](https://github.com/huggingface/diffusers/blob/main/src/diffusers/schedulers/scheduling_pndm.py)
  
  for inference. Note that while the *ddpm* scheduler yields the highest quality, it also takes the longest.
  For a good trade-off between quality and inference speed you might want to consider the *ddim* or *pndm* schedulers instead.
  
  See the following code:
  
  ```python
  # !pip install diffusers
  from diffusers import DDPMPipeline, DDIMPipeline, PNDMPipeline
  
  model_id = "google/ddpm-church-256"
  
  # 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")
  ```
  
  For more in-detail information, please have a look at the [official inference example](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/diffusers_intro.ipynb)
  
  ## Training
  
  If you want to train your own model, please have a look at the [official training example](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/training_example.ipynb)
  
  ## Samples
  1. ![sample_1](https://huggingface.co/google/ddpm-church-256/resolve/main/images/generated_image_0.png)
  2. ![sample_2](https://huggingface.co/google/ddpm-church-256/resolve/main/images/generated_image_1.png)
  3. ![sample_3](https://huggingface.co/google/ddpm-church-256/resolve/main/images/generated_image_2.png)
  4. ![sample_4](https://huggingface.co/google/ddpm-church-256/resolve/main/images/generated_image_3.png)