[[open-in-colab]]  
The [`StableDiffusionPipeline`] supports textual inversion, a technique that enables a model like Stable Diffusion to learn a new concept from just a few sample images. This gives you more control over the generated images and allows you to tailor the model towards specific concepts. You can get started quickly with a collection of community created concepts in the [Stable Diffusion Conceptualizer](https://huggingface.co/spaces/sd-concepts-library/stable-diffusion-conceptualizer).  
This guide will show you how to run inference with textual inversion using a pre-learned concept from the Stable Diffusion Conceptualizer. If you're interested in teaching a model new concepts with textual inversion, take a look at the [Textual Inversion](../training/text_inversion) training guide.  
Import the necessary libraries:  
```py
import torch
from diffusers import StableDiffusionPipeline
from diffusers.utils import make_image_grid
```
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/using-diffusers/textual_inversion_inference.md