Update README.md

README.md

@@ -27,12 +27,26 @@ We provide an interactive notebook for sampling new identity-encoding models fro
 **Conditional Sampling.** Additionally, we provide a notebook for sampling models conditioned on labels from *w2w* space in `sampling/conditional_sampling.ipynb`. This allows sampling identities with particular attributes (e.g., only females, only blonde hair, etc.). 
 
 ## Inversion 
-We provide an interactive notebook for inverting a single image into a model in *w2w* space in `inversion/inversion_real.ipynb`. Instructions are provided in the notebook. We provide another notebook that provides an example of inverting an out-of-distribution identity in `inversion/inversion_ood.ipynb`. Assets for these notebooks are provided in `inversion/images/` and you can place your own assets in there. 
+We provide an interactive notebook for inverting a single image into a model in *w2w* space in `inversion/inversion_real.ipynb`. Instructions are provided in the notebook. We provide another notebook that with an example of inverting an out-of-distribution identity in `inversion/inversion_ood.ipynb`. Assets for these notebooks are provided in `inversion/images/` and you can place your own assets in there. 
 
 Additionally, we provide an example script `run_inversion.sh` for running the inversion in `invert.py`.  You can run the command:
 ```
 $ bash inversion/run_inversion.sh
 ```
+The details on the various arguments are provided in `invert.py`.
+
+## Editing 
+We provide an interactive notebook for editing the identity encoded in a model in `editing/identity_editing.ipynb`. Instructions are provided in the notebook. Another notebook is provided which shows how to compose multiple attribute edits together in `editing/multiple_edits.ipynb`.
+
+## Loading and Saving Models
+Various notebooks provide examples on how to save models either as low dimensional *w2w* models (represented by principal component coefficients), or as models compatible with standard LoRA such as with Diffusers [pipelines](https://huggingface.co/docs/diffusers/en/api/pipelines/overview). We provide a notebook in `other/loading.ipynb`that demonstrates how these weights can be loaded into either format. 
+
+## Acknowledgments
+Our code is based on implementations from the following repos: 
+
+>* [PEFT](https://github.com/huggingface/peft)
+>* [Concept Sliders](https://github.com/rohitgandikota/sliders)
+>* [Diffusers](https://github.com/huggingface/diffusers)
 
 
 ## Citation