Modification of README.md file to incorporate 2d_diffusion_autoencode…

…r info. (Project-MONAI#1876)

In the previous (closed) PR
(Project-MONAI#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
<!--- Put an `x` in all the boxes that apply, and remove the not
applicable items -->
- [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 <path to .ipynb file>`

---------

Signed-off-by: Virginia <[email protected]>
Co-authored-by: Virginia <[email protected]>
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>

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.