Forgot the README.

Signed-off-by: Virginia <[email protected]>

Author: Virginia Fernandez

generation/README.md

@@ -70,8 +70,14 @@ Example shows the use cases of applying a spatial VAE to a 2D synthesis example.
 ## [Spatial variational autoencoder for 3D modelling and synthesis](./3d_autoencoderkl)
 Example shows the use cases of applying a spatial VAE to a 3D synthesis example. To obtain realistic results, the model is trained on the original VAE losses, as well as perceptual and adversarial ones.
 
-## Performing anomaly detection with diffusion models: [implicit guidance](./anomaly_detection/2d_classifierfree_guidance_anomalydetection_tutorial.ipynb), [using transformers](./anomaly_detection/anomaly_detection_with_transformers.ipynb) and [classifier free guidance](./anomaly_detection/anomalydetection_tutorial_classifier_guidance.ipynb)
+## [Performing anomaly detection with diffusion models] [implicit guidance](./anomaly_detection/2d_classifierfree_guidance_anomalydetection_tutorial.ipynb), [using transformers](./anomaly_detection/anomaly_detection_with_transformers.ipynb) and [classifier free guidance](./anomaly_detection/anomalydetection_tutorial_classifier_guidance.ipynb)
 Examples show how to perform anomaly detection in 2D, using implicit guidance [2D-classifier free guiance](./anomaly_detection/2d_classifierfree_guidance_anomalydetection_tutorial.ipynb), transformers [using transformers](./anomaly_detection/anomaly_detection_with_transformers.ipynb) and [classifier free guidance](./anomalydetection_tutorial_classifier_guidance).
 
-## 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).
+## [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.
+
+## [Classifier-free guidance on DDPMs] (./classifier_free_guidance/2d_ddpm_classifier_free_guidance_tutorial.ipynb)
+Example shows how to perform classifier-free guidance inference on a DDPM trained on MedNIST Head and Hand data.
+
+## [Guidance of the diffusion process using an image encoder](./2d_diffusion_autoencoder/2d_diffusion_autoencoder_tutorial.ipynb)
+Example shows how to train an encoder and DDPM simultaenously to guide the diffusion process using the latents of the encoder.