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+# MONAI Generative Models Tutorials
+This directory hosts the MONAI Generative Models tutorials.
+
+## Requirements
+To run the tutorials, you will need to install the Generative Models package.
+Besides that, most of the examples and tutorials require
+[matplotlib](https://matplotlib.org/) and [Jupyter Notebook](https://jupyter.org/).
+
+These can be installed with the following:
+
+```bash
+python -m pip install -U pip
+python -m pip install -U matplotlib
+python -m pip install -U notebook
+```
+
+Some of the examples may require optional dependencies. In case of any optional import errors,
+please install the relevant packages according to MONAI's [installation guide](https://docs.monai.io/en/latest/installation.html).
+Or install all optional requirements with the following:
+
+```bash
+pip install -r requirements-dev.txt
+```
+
+## List of notebooks and examples
+
+### Table of Contents
+1. [Diffusion Models](#1-diffusion-models)
+2. [Latent Diffusion Models](#2-latent-diffusion-models)
+3. [VQ-VAE + Transformers](#3-vq-vae--transformers)
+
+
+### 1. Diffusion Models
+
+#### Image synthesis with Diffusion Models
+
+* [Trainning a 3D Denoising Diffusion Probabilistic Model](./generative/3d_ddpm/3d_ddpm_tutorial.ipynb): This tutorial shows how to easily
+train a DDPM on 3D medical data. In this example, we use a downsampled version of the BraTS dataset. We will show how to
+make use of the UNet model and the Noise Scheduler necessary to train a diffusion model. Besides that, we show how to
+use the DiffusionInferer class to simplify the training and sampling processes. Finally, after training the model, we
+show how to use a Noise Scheduler with fewer timesteps to sample synthetic images.
+
+* [Trainning a 2D Denoising Diffusion Probabilistic Model](./generative/2d_ddpm/2d_ddpm_tutorial.ipynb): This tutorial shows how to easily
+train a DDPM on medical data. In this example, we use the MedNIST dataset, which is very suitable for beginners as a tutorial.
+
+* [Comparing different noise schedulers](./generative/2d_ddpm/2d_ddpm_compare_schedulers.ipynb): In this tutorial, we compare the
+performance of different noise schedulers. We will show how to sample a diffusion model using the DDPM, DDIM, and PNDM
+schedulers and how different numbers of timesteps affect the quality of the samples.
+
+* [Trainning a 2D Denoising Diffusion Probabilistic Model with different parameterisation](./generative/2d_ddpm/2d_ddpm_tutorial_v_prediction.ipynb):
+In MONAI Generative Models, we support different parameterizations for the diffusion model (epsilon, sample, and
+v-prediction). In this tutorial, we show how to train a DDPM using the v-prediction parameterization, which improves the
+stability and convergence of the model.
+
+* [Training a 2D DDPM using Pytorch Ignite](./generative/2d_ddpm/2d_ddpm_compare_schedulers.ipynb): Here, we show how to train a DDPM
+on medical data using Pytorch Ignite. We will show how to use the DiffusionPrepareBatch to prepare the model inputs and MONAI's SupervisedTrainer and SupervisedEvaluator to train DDPMs.
+
+* [Using a 2D DDPM to inpaint images](./generative/2d_ddpm/2d_ddpm_inpainting.ipynb): In this tutorial, we show how to use a DDPM to
+inpaint of 2D images from the MedNIST dataset using the RePaint method.
+
+* [Generating conditional samples with a 2D DDPM using classifier-free guidance](./generative/classifier_free_guidance/2d_ddpm_classifier_free_guidance_tutorial.ipynb):
+This tutorial shows how easily we can train a Diffusion Model and generate conditional samples using classifier-free guidance in
+the MONAI's framework.
+
+* [Training Diffusion models with Distributed Data Parallel](./generative/distributed_training/ddpm_training_ddp.py): This example shows how to execute distributed training and evaluation based on PyTorch native DistributedDataParallel
+module with torch.distributed.launch.
+
+#### Anomaly Detection with Diffusion Models
+
+* [Weakly Supervised Anomaly Detection with Classifier-free Guidance](./generative/anomaly_detection/2d_classifierfree_guidance_anomalydetection_tutorial.ipynb):
+This tutorial shows how to use a DDPM to perform weakly supervised anomaly detection using classifier-free guidance based on the
+method proposed by Sanchez et al. [What is Healthy? Generative Counterfactual Diffusion for Lesion Localization](https://arxiv.org/abs/2207.12268). DGM 4 MICCAI 2022
+
+
+### 2. Latent Diffusion Models
+
+#### Image synthesis with Latent Diffusion Models
+
+* [Training a 3D Latent Diffusion Model](./generative/3d_ldm/3d_ldm_tutorial.ipynb): This tutorial shows how to train a LDM on 3D medical
+data. In this example, we use the BraTS dataset. We show how to train an AutoencoderKL and connect it to an LDM. We also
+comment on the importance of the scaling factor in the LDM used to scale the latent representation of the AEKL to a suitable
+range for the diffusion model. Finally, we show how to use the LatentDiffusionInferer class to simplify the training and sampling.
+
+* [Training a 2D Latent Diffusion Model](./generative/2d_ldm/2d_ldm_tutorial.ipynb): This tutorial shows how to train an LDM on medical
+on the MedNIST dataset. We show how to train an AutoencoderKL and connect it to an LDM.
+
+* Training Autoencoder with KL-regularization: In this section, we focus on training an AutoencoderKL on [2D](./generative/2d_autoencoderkl/2d_autoencoderkl_tutorial.ipynb) and [3D](./generative/3d_autoencoderkl/3d_autoencoderkl_tutorial.ipynb) medical data,
+that can be used as the compression model used in a Latent Diffusion Model.
+
+#### Super-resolution with Latent Diffusion Models
+
+* [Super-resolution using Stable Diffusion Upscalers method](./generative/2d_super_resolution/2d_stable_diffusion_v2_super_resolution.ipynb):
+In this tutorial, we show how to perform super-resolution on 2D images from the MedNIST dataset using the Stable
+Diffusion Upscalers method. In this example, we will show how to condition a latent diffusion model on a low-resolution image
+as well as how to use the DiffusionModelUNet's class_labels conditioning to condition the model on the level of noise added to the image
+(aka "noise conditioning augmentation")
+
+
+### 3. VQ-VAE + Transformers
+
+#### Image synthesis with VQ-VAE + Transformers
+
+* [Training a 2D VQ-VAE + Autoregressive Transformers](./generative/2d_vqvae_transformer/2d_vqvae_transformer_tutorial.ipynb): This tutorial shows how to train
+a Vector-Quantized Variation Autoencoder + Transformers on the MedNIST dataset.
+
+* Training VQ-VAEs and VQ-GANs: In this section, we show how to train Vector Quantized Variation Autoencoder (on [2D](./generative/2d_vqvae/2d_vqvae_tutorial.ipynb) and [3D](./generative/3d_autoencoderkl/3d_autoencoderkl_tutorial.ipynb) data) and
+show how to use the PatchDiscriminator class to train a [VQ-GAN](./generative/2d_vqgan/2d_vqgan_tutorial.ipynb) and improve the quality of the generated images.
+
+#### Anomaly Detection with VQ-VAE + Transformers
+
+* [Anomaly Detection with 2D VQ-VAE + Autoregressive Transformers](./generative/anomaly_detection/anomaly_detection_with_transformers.ipynb): This tutorial shows how to
+ train a Vector-Quantized Variation Autoencoder + Transformers on the MedNIST dataset and use it to extract the likelihood of
+testing images to be part of the in-distribution class (used during training).