---
title: Concepts Library
---

# :material-library-shelves: The Hugging Face Concepts Library and Importing Textual Inversion files

## Using Textual Inversion Files

Textual inversion (TI) files are small models that customize the output of
Stable Diffusion image generation. They can augment SD with specialized subjects
and artistic styles. They are also known as "embeds" in the machine learning
world.

Each TI file introduces one or more vocabulary terms to the SD model. These are
known in InvokeAI as "triggers." Triggers are often, but not always, denoted
using angle brackets as in "<trigger-phrase>". The two most common type of
TI files that you'll encounter are `.pt` and `.bin` files, which are produced by
different TI training packages. InvokeAI supports both formats, but its
[built-in TI training system](TEXTUAL_INVERSION.md) produces `.pt`.

The [Hugging Face company](https://huggingface.co/sd-concepts-library) has
amassed a large ligrary of >800 community-contributed TI files covering a
broad range of subjects and styles. InvokeAI has built-in support for this
library which downloads and merges TI files automatically upon request. You can
also install your own or others' TI files by placing them in a designated
directory.

### An Example

Here are a few examples to illustrate how it works. All these images were
generated using the command-line client and the Stable Diffusion 1.5 model:

|         Japanese gardener          | Japanese gardener <ghibli-face> | Japanese gardener <hoi4-leaders> | Japanese gardener <cartoona-animals> |
| :--------------------------------: | :-----------------------------------: | :------------------------------------: | :----------------------------------------: |
| ![](../assets/concepts/image1.png) |  ![](../assets/concepts/image2.png)   |   ![](../assets/concepts/image3.png)   |     ![](../assets/concepts/image4.png)     |

You can also combine styles and concepts:

<figure markdown>
  ![](../assets/concepts/image5.png)
  <figcaption>A portrait of &lt;alf&gt; in &lt;cartoona-animal&gt; style</figcaption>
</figure>
## Using a Hugging Face Concept

Hugging Face TI concepts are downloaded and installed automatically as you
require them. This requires your machine to be connected to the Internet. To
find out what each concept is for, you can browse the
[Hugging Face concepts library](https://huggingface.co/sd-concepts-library) and
look at examples of what each concept produces.

When you have an idea of a concept you wish to try, go to the command-line
client (CLI) and type a "&lt;" character and the beginning of the Hugging Face
concept name you wish to load. Press the Tab key, and the CLI will show you all
matching concepts. You can also type "&lt;" and Tab to get a listing of all ~800
concepts, but be prepared to scroll up to see them all! If there is more than
one match you can continue to type and Tab until the concept is completed.

For example if you type "&lt;x" and Tab, you'll be prompted with the
completions:

```
<xatu2>        <xatu>         <xbh>          <xi>           <xidiversity>  <xioboma>      <xuna>         <xyz>
```

Now type "id" and press Tab. It will be autocompleted to "&lt;xidiversity&gt;"
because this is a unique match.

Finish your prompt and generate as usual. You may include multiple concept terms
in the prompt.

If you have never used this concept before, you will see a message that the TI
model is being downloaded and installed. After this, the concept will be saved
locally (in the `models/sd-concepts-library` directory) for future use.

Several steps happen during downloading and installation, including a scan of
the file for malicious code. Should any errors occur, you will be warned and the
concept will fail to load. Generation will then continue treating the trigger
term as a normal string of characters (e.g. as literal "&lt;ghibli-face&gt;").

You can also use <concept-names> in the WebGUI's prompt textbox. There
is no autocompletion at this time.

## Installing your Own TI Files

You may install any number of `.pt` and `.bin` files simply by copying them into
the `embeddings` directory of the InvokeAI runtime directory (usually `invokeai`
in your home directory). You may create subdirectories in order to organize the
files in any way you wish. Be careful not to overwrite one file with another.
For example, TI files generated by the Hugging Face toolkit share the named
`learned_embedding.bin`. You can use subdirectories to keep them distinct.

At startup time, InvokeAI will scan the `embeddings` directory and load any TI
files it finds there. At startup you will see a message similar to this one:

```bash
>> Current embedding manager terms: *, <HOI4-Leader>, <princess-knight>
```

Note the `*` trigger term. This is a placeholder term that many early TI
tutorials taught people to use rather than a more descriptive term.
Unfortunately, if you have multiple TI files that all use this term, only the
first one loaded will be triggered by use of the term.

To avoid this problem, you can use the `merge_embeddings.py` script to merge two
or more TI files together. If it encounters a collision of terms, the script
will prompt you to select new terms that do not collide. See
[Textual Inversion](TEXTUAL_INVERSION.md) for details.

## Further Reading

Please see [the repository](https://github.com/rinongal/textual_inversion) and
associated paper for details and limitations.