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| # DDPM | |
| ## Overview | |
| [Denoising Diffusion Probabilistic Models](https://arxiv.org/abs/2006.11239) | |
| (DDPM) by Jonathan Ho, Ajay Jain and Pieter Abbeel proposes the diffusion based model of the same name, but in the context of the 🤗 Diffusers library, DDPM refers to the discrete denoising scheduler from the paper as well as the pipeline. | |
| The abstract of the paper is the following: | |
| 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. | |
| The original codebase of this paper can be found [here](https://github.com/hojonathanho/diffusion). | |
| ## Available Pipelines: | |
| | Pipeline | Tasks | Colab | |
| |---|---|:---:| | |
| | [pipeline_ddpm.py](https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/ddpm/pipeline_ddpm.py) | *Unconditional Image Generation* | - | | |
| # DDPMPipeline | |
| [[autodoc]] DDPMPipeline | |
| - all | |
| - __call__ | |