diff --git a/examples/textual_inversion/README.md b/examples/textual_inversion/README.md
index 74ebcd5191..b731797ecb 100644
--- a/examples/textual_inversion/README.md
+++ b/examples/textual_inversion/README.md
@@ -3,6 +3,15 @@
 [Textual inversion](https://arxiv.org/abs/2208.01618) is a method to personalize text2image models like stable diffusion on your own images using just 3-5 examples.
 The `textual_inversion.py` script shows how to implement the training procedure and adapt it for stable diffusion.
 
+## Running on Colab 
+
+Colab for training 
+[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_textual_inversion_training.ipynb)
+
+Colab for inference
+[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/stable_conceptualizer_inference.ipynb)
+
+## Running locally 
 ### Installing the dependencies
 
 Before running the scipts, make sure to install the library's training dependencies:
@@ -64,7 +73,6 @@ A full training run takes ~1 hour on one V100 GPU.
 
 Once you have trained a model using above command, the inference can be done simply using the `StableDiffusionPipeline`. Make sure to include the `placeholder_token` in your prompt.
 
-
 ```python
 
 from torch import autocast