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* [Outputs] Improve syntax * improve more * fix docstring return * correct all * uP Co-authored-by: Mishig Davaadorj <dmishig@gmail.com>
25 lines
1.5 KiB
Plaintext
25 lines
1.5 KiB
Plaintext
# DDPM
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## Overview
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[Denoising Diffusion Probabilistic Models](https://arxiv.org/abs/2006.11239)
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(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.
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The abstract of the paper is the following:
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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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The original codebase of this paper can be found [here](https://github.com/hojonathanho/diffusion).
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## Available Pipelines:
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| Pipeline | Tasks | Colab
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| [pipeline_ddpm.py](https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/ddpm/pipeline_ddpm.py) | *Unconditional Image Generation* | - |
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# DDPMPipeline
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[[autodoc]] DDPMPipeline
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- __call__
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