From f09defd3f595cae1c6b36f8db8431c1f4cd5e06b Mon Sep 17 00:00:00 2001
From: Patrick von Platen <patrick.v.platen@gmail.com>
Date: Thu, 2 Jun 2022 14:17:54 +0200
Subject: [PATCH] add readme ddpm

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 models/vision/ddpm/README.md | 28 ++++++++++++++++++++++++++++
 1 file changed, 28 insertions(+)

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+<!--Copyright 2022 The HuggingFace Team. All rights reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
+the License. You may obtain a copy of the License at
+
+http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
+an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
+specific language governing permissions and limitations under the License.
+-->
+
+# Denoising Diffusion Probabilistic Models (DDPM)
+
+## Overview
+
+DDPM was proposed in [Denoising Diffusion Probabilistic Models](https://arxiv.org/abs/2006.11239) by *Jonathan Ho, Ajay Jain, Pieter Abbeel*.
+
+The abstract from 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. Our implementation is available at this https URL*
+
+Tips:
+
+- ...
+- ...
+
+This model was contributed by [???](https://huggingface.co/???). The original code can be found [here](https://github.com/hojonathanho/diffusion).