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Update CONCEPTS.md
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Kent Keirsey
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---
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title: Concepts Library
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title: Concepts
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---
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# :material-library-shelves: The Hugging Face Concepts Library and Importing Textual Inversion files
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With the advances in research, many new capabilities are available to customize the knowledge and understanding of novel concepts not originally contained in the base model.
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## Using Textual Inversion Files
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Textual inversion (TI) files are small models that customize the output of
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@ -12,18 +15,16 @@ and artistic styles. They are also known as "embeds" in the machine learning
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world.
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Each TI file introduces one or more vocabulary terms to the SD model. These are
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known in InvokeAI as "triggers." Triggers are often, but not always, denoted
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using angle brackets as in "<trigger-phrase>". The two most common type of
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known in InvokeAI as "triggers." Triggers are denoted using angle brackets
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as in "<trigger-phrase>". The two most common type of
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TI files that you'll encounter are `.pt` and `.bin` files, which are produced by
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different TI training packages. InvokeAI supports both formats, but its
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[built-in TI training system](TEXTUAL_INVERSION.md) produces `.pt`.
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[built-in TI training system](TRAINING.md) produces `.pt`.
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The [Hugging Face company](https://huggingface.co/sd-concepts-library) has
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amassed a large ligrary of >800 community-contributed TI files covering a
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broad range of subjects and styles. InvokeAI has built-in support for this
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library which downloads and merges TI files automatically upon request. You can
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also install your own or others' TI files by placing them in a designated
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directory.
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broad range of subjects and styles. You can also install your own or others' TI files
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by placing them in the designated directory for the compatible model type
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### An Example
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@ -41,66 +42,43 @@ You can also combine styles and concepts:
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| :--------------------------------------------------------: |
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|  |
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</figure>
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## Using a Hugging Face Concept
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!!! warning "Authenticating to HuggingFace"
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Some concepts require valid authentication to HuggingFace. Without it, they will not be downloaded
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and will be silently ignored.
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If you used an installer to install InvokeAI, you may have already set a HuggingFace token.
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If you skipped this step, you can:
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- run the InvokeAI configuration script again (if you used a manual installer): `invokeai-configure`
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- set one of the `HUGGINGFACE_TOKEN` or `HUGGING_FACE_HUB_TOKEN` environment variables to contain your token
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Finally, if you already used any HuggingFace library on your computer, you might already have a token
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in your local cache. Check for a hidden `.huggingface` directory in your home folder. If it
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contains a `token` file, then you are all set.
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Hugging Face TI concepts are downloaded and installed automatically as you
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require them. This requires your machine to be connected to the Internet. To
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find out what each concept is for, you can browse the
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[Hugging Face concepts library](https://huggingface.co/sd-concepts-library) and
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look at examples of what each concept produces.
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To load concepts, you will need to open the Web UI's configuration
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dialogue and activate "Show Textual Inversions from HF Concepts
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Library". This will then add a list of HF Concepts to the dropdown
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"Add Textual Inversion" menu. Select the concept(s) of your choice and
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they will be incorporated into the positive prompt. A few concepts are
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designed for the negative prompt, in which case you can add them to
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the negative prompt box by select the down arrow icon next to the
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textual inversion menu.
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There are nearly 1000 HF concepts, more than will fit into a menu. For
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this reason we only show the most popular concepts (those which have
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received 5 or more likes). If you wish to use a concept that is not on
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the list, you may simply type its name surrounded by brackets. For
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example, to load the concept named "xidiversity", add `<xidiversity>`
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to the positive or negative prompt text.
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## Installing your Own TI Files
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You may install any number of `.pt` and `.bin` files simply by copying them into
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the `embeddings` directory of the InvokeAI runtime directory (usually `invokeai`
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in your home directory). You may create subdirectories in order to organize the
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files in any way you wish. Be careful not to overwrite one file with another.
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the `embedding` directory of the corresponding InvokeAI models directory (usually `invokeai`
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in your home directory). For example, you can simply move a Stable Diffusion 1.5 embedding file to
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the `sd-1/embedding` folder. Be careful not to overwrite one file with another.
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For example, TI files generated by the Hugging Face toolkit share the named
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`learned_embedding.bin`. You can use subdirectories to keep them distinct.
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`learned_embedding.bin`. You can rename these, or use subdirectories to keep them distinct.
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At startup time, InvokeAI will scan the `embeddings` directory and load any TI
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files it finds there. At startup you will see a message similar to this one:
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At startup time, InvokeAI will scan the various `embedding` directories and load any TI
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files it finds there for compatible models. At startup you will see a message similar to this one:
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```bash
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>> Current embedding manager terms: <HOI4-Leader>, <princess-knight>
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```
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To use these when generating, simply type the `<` key in your prompt to open the Textual Inversion WebUI and
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select the embedding you'd like to use. This UI has type-ahead support, so you can easily find supported embeddings.
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The terms you can use will appear in the "Add Textual Inversion"
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dropdown menu above the HF Concepts.
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## Using LoRAs
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## Further Reading
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LoRA files are models that customize the output of Stable Diffusion image generation.
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Larger than embeddings, but much smaller than full models, they augment SD with improved
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understanding of subjects and artistic styles.
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Unlike TI files, LoRAs do not introduce novel vocabulary into the model's known tokens. Instead,
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LoRAs augment the model's weights that are applied to generate imagery. LoRAs may be supplied
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with a "trigger" word that they have been explicitly trained on, or may simply apply their
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effect without being triggered.
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LoRAs are typically stored in .safetensors files, which are the most secure way to store and transmit
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these types of weights. You may install any number of `.safetensors` LoRA files simply by copying them into
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the `lora` directory of the corresponding InvokeAI models directory (usually `invokeai`
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in your home directory). For example, you can simply move a Stable Diffusion 1.5 LoRA file to
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the `sd-1/lora` folder.
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To use these when generating, open the LoRA menu item in the options panel, select the LoRAs you want to apply
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and ensure that they have the appropriate weight recommended by the model provider. Typically, most LoRAs perform best at a weight of .75-1.
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Please see [the repository](https://github.com/rinongal/textual_inversion) and
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associated paper for details and limitations.
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