From 2db8c620ae7e9e8f28cb2c7abb9e6cc75e3bc5c1 Mon Sep 17 00:00:00 2001 From: Virginia Fernandez <61539159+virginiafdez@users.noreply.github.com> Date: Mon, 11 Nov 2024 17:46:58 +0000 Subject: [PATCH] Modification of README.md file to incorporate 2d_diffusion_autoencoder info. (#1876) In the previous (closed) PR (https://github.com/Project-MONAI/tutorials/pull/1871) we had to do a hard reset and the README.md modifications were not incorporated at the end. This is just an addition of the notebook info to the generation README file. ### Checks - [x] Avoid including large-size files in the PR. - [x] Clean up long text outputs from code cells in the notebook. - [x] For security purposes, please check the contents and remove any sensitive info such as user names and private key. - [x] Ensure (1) hyperlinks and markdown anchors are working (2) use relative paths for tutorial repo files (3) put figure and graphs in the `./figure` folder - [x] Notebook runs automatically `./runner.sh -t ` --------- Signed-off-by: Virginia Co-authored-by: Virginia Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com> --- generation/README.md | 3 +++ 1 file changed, 3 insertions(+) diff --git a/generation/README.md b/generation/README.md index f2932f6b8..df50ed899 100644 --- a/generation/README.md +++ b/generation/README.md @@ -78,3 +78,6 @@ Examples show how to perform anomaly detection in 2D, using implicit guidance [2 ## 2D super-resolution using diffusion models: [using torch](./2d_super_resolution/2d_sd_super_resolution.ipynb) and [using torch lightning](./2d_super_resolution/2d_sd_super_resolution_lightning.ipynb). Examples show how to perform super-resolution in 2D, using PyTorch and PyTorch Lightning. + +## [Guiding the synthetic process using a semantic encoder](./2d_diffusion_autoencoder/2d_diffusion_autoencoder.ipynb) +Example shows how to train a DDPM and an encoder simultaneously, resulting in the latents of the encoder guiding the inference process of the DDPM.