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Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with |
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the License. You may obtain a copy of the License at |
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http: |
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Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on |
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an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the |
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# Denoising Diffusion Probabilistic Models (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 paper can be found [here](https://arxiv.org/abs/2010.02502). |
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## DDPMScheduler |
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[[autodoc]] DDPMScheduler |
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