# Textual inversion

## Textual inversion

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The [StableDiffusionPipeline](https://huggingface.co/docs/diffusers/v0.21.0/en/api/pipelines/stable_diffusion/text2img#diffusers.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](https://huggingface.co/docs/diffusers/using-diffusers/training/text_inversion) training guide.

Login to your Hugging Face account:

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```
from boincai_hub import notebook_login

notebook_login()
```

Import the necessary libraries:

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```
import os
import torch

import PIL
from PIL import Image

from diffusers import StableDiffusionPipeline
from diffusers.utils import make_image_grid
from transformers import CLIPFeatureExtractor, CLIPTextModel, CLIPTokenizer
```

Pick a Stable Diffusion checkpoint and a pre-learned concept from the [Stable Diffusion Conceptualizer](https://huggingface.co/spaces/sd-concepts-library/stable-diffusion-conceptualizer):

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```
pretrained_model_name_or_path = "runwayml/stable-diffusion-v1-5"
repo_id_embeds = "sd-concepts-library/cat-toy"
```

Now you can load a pipeline, and pass the pre-learned concept to it:

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```
pipeline = StableDiffusionPipeline.from_pretrained(
    pretrained_model_name_or_path, torch_dtype=torch.float16, use_safetensors=True
).to("cuda")

pipeline.load_textual_inversion(repo_id_embeds)
```

Create a prompt with the pre-learned concept by using the special placeholder token `<cat-toy>`, and choose the number of samples and rows of images you’d like to generate:

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```
prompt = "a grafitti in a favela wall with a <cat-toy> on it"

num_samples = 2
num_rows = 2
```

Then run the pipeline (feel free to adjust the parameters like `num_inference_steps` and `guidance_scale` to see how they affect image quality), save the generated images and visualize them with the helper function you created at the beginning:

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```
all_images = []
for _ in range(num_rows):
    images = pipe(prompt, num_images_per_prompt=num_samples, num_inference_steps=50, guidance_scale=7.5).images
    all_images.extend(images)

grid = make_image_grid(all_images, num_samples, num_rows)
grid
```

<figure><img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/textual_inversion_inference.png" alt=""><figcaption></figcaption></figure>