From 162b3a11881dd1a165bb9fa9d738869c45ccfddd Mon Sep 17 00:00:00 2001 From: Walter Hugo Lopez Pinaya Date: Mon, 20 Mar 2023 18:06:36 +0000 Subject: [PATCH] Update README.md --- README.md | 30 ++++++++++++++++++++---------- 1 file changed, 20 insertions(+), 10 deletions(-) diff --git a/README.md b/README.md index 183f1e50..0381e583 100644 --- a/README.md +++ b/README.md @@ -3,27 +3,28 @@

# MONAI Generative Models -Prototyping repository for generative models to be integrated into MONAI core. +Prototyping repository for generative models to be integrated into MONAI core, MONAI tutorials, and MONAI model zoo. ## Features -* Network architectures: Diffusion Model, Autoencoder-KL, VQ-VAE, (Multi-scale) Patch-GAN discriminator. -* Diffusion Model Schedulers: DDPM, DDIM, and PNDM. +* Network architectures: Diffusion Model, Autoencoder-KL, VQ-VAE, Autoregressive transformers, (Multi-scale) Patch-GAN discriminator. +* Diffusion Model Noise Schedulers: DDPM, DDIM, and PNDM. * Losses: Adversarial losses, Spectral losses, and Perceptual losses (for 2D and 3D data using LPIPS, RadImageNet, and 3DMedicalNet pre-trained models). -* Metrics: Multi-Scale Structural Similarity Index Measure (MS-SSIM) and Maximum Mean Discrepancy (MMD). -* Diffusion Models and Latent Diffusion Models Inferers classes (compatible with MONAI style) containing methods to train, sample synthetic images, and obtain the likelihood of inputted data. +* Metrics: Multi-Scale Structural Similarity Index Measure (MS-SSIM) and Fréchet inception distance (FID). +* Diffusion Models, Latent Diffusion Models, and VQ-VAE + Transformer Inferers classes (compatible with MONAI style) containing methods to train, sample synthetic images, and obtain the likelihood of inputted data. * MONAI-compatible trainer engine (based on Ignite) to train models with reconstruction and adversarial components. * Tutorials including: - * How to train VQ-VAEs, VQ-GANs, AutoencoderKLs, Diffusion Models and Latent Diffusion Models on 2D and 3D data. + * How to train VQ-VAEs, VQ-GANs, VQ-VAE + Transformers, AutoencoderKLs, Diffusion Models, and Latent Diffusion Models on 2D and 3D data. * Train diffusion model to perform conditional image generation with classifier-free guidance. * Comparison of different diffusion model schedulers. - * Diffusion models with different parameterisation (e.g. v prediction and epsilon parameterisation). + * Diffusion models with different parameterizations (e.g., v-prediction and epsilon parameterization). + * Anomaly Detection using VQ-VAE + Transformers and Diffusion Models. * Inpainting with diffusion model (using Repaint method) * Super-resolution with Latent Diffusion Models (using Noise Conditioning Augmentation) ## Roadmap Our short-term goals are available in the [Milestones](https://github.com/Project-MONAI/GenerativeModels/milestones) -section of the repository and this [document](https://docs.google.com/document/d/1vEjrr6dSWUnzmP-Nfc7Y6NpnWdT6fUBK/edit?usp=sharing&ouid=118224691516664207451&rtpof=true&sd=true). +section of the repository. -In the longer term, we aim to integrate the generative models into the MONAI core library (supporting tasks such as, +In the longer term, we aim to integrate the generative models into the MONAI core repository (supporting tasks such as, image synthesis, anomaly detection, MRI reconstruction, domain transfer) ## Installation @@ -31,7 +32,7 @@ To install MONAI Generative Models, it is recommended to clone the codebase dire ``` git clone https://github.com/Project-MONAI/GenerativeModels.git ``` -This command will create a GenerativeModels/ folder in your current directory. You can install it by running: +This command will create a GenerativeModels/ folder in your current directory. You can install it by running the following: ``` cd GenerativeModels/ python setup.py install @@ -39,3 +40,12 @@ python setup.py install ## Contributing For guidance on making a contribution to MONAI, see the [contributing guidelines](https://github.com/Project-MONAI/GenerativeModels/blob/main/CONTRIBUTING.md). + +## Community +Join the conversation on Twitter [@ProjectMONAI](https://twitter.com/ProjectMONAI) or join our [Slack channel](https://forms.gle/QTxJq3hFictp31UM9). + +## Links +- Website: https://monai.io/ +- Code: https://github.com/Project-MONAI/GenerativeModels +- Project tracker: https://github.com/Project-MONAI/GenerativeModels/projects +- Issue tracker: https://github.com/Project-MONAI/GenerativeModels/issues