# Textual inversion [[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 ``` ## Stable Diffusion 1 and 2 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): ```py 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: ```py 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 ``, and choose the number of samples and rows of images you'd like to generate: ```py prompt = "a grafitti in a favela wall with a on it" num_samples_per_row = 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: ```py all_images = [] for _ in range(num_rows): images = pipeline(prompt, num_images_per_prompt=num_samples_per_row, num_inference_steps=50, guidance_scale=7.5).images all_images.extend(images) grid = make_image_grid(all_images, num_rows, num_samples_per_row) grid ```

## Stable Diffusion XL Stable Diffusion XL (SDXL) can also use textual inversion vectors for inference. In contrast to Stable Diffusion 1 and 2, SDXL has two text encoders so you'll need two textual inversion embeddings - one for each text encoder model. Let's download the SDXL textual inversion embeddings and have a closer look at it's structure: ```py from huggingface_hub import hf_hub_download from safetensors.torch import load_file file = hf_hub_download("dn118/unaestheticXL", filename="unaestheticXLv31.safetensors") state_dict = load_file(file) state_dict ``` ``` {'clip_g': tensor([[ 0.0077, -0.0112, 0.0065, ..., 0.0195, 0.0159, 0.0275], ..., [-0.0170, 0.0213, 0.0143, ..., -0.0302, -0.0240, -0.0362]], 'clip_l': tensor([[ 0.0023, 0.0192, 0.0213, ..., -0.0385, 0.0048, -0.0011], ..., [ 0.0475, -0.0508, -0.0145, ..., 0.0070, -0.0089, -0.0163]], ``` There are two tensors, `"clip_g"` and `"clip_l"`. `"clip_g"` corresponds to the bigger text encoder in SDXL and refers to `pipe.text_encoder_2` and `"clip_l"` refers to `pipe.text_encoder`. Now you can load each tensor separately by passing them along with the correct text encoder and tokenizer to [`~loaders.TextualInversionLoaderMixin.load_textual_inversion`]: ```py from diffusers import AutoPipelineForText2Image import torch pipe = AutoPipelineForText2Image.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0", variant="fp16", torch_dtype=torch.float16) pipe.to("cuda") pipe.load_textual_inversion(state_dict["clip_g"], token="unaestheticXLv31", text_encoder=pipe.text_encoder_2, tokenizer=pipe.tokenizer_2) pipe.load_textual_inversion(state_dict["clip_l"], token="unaestheticXLv31", text_encoder=pipe.text_encoder, tokenizer=pipe.tokenizer) # the embedding should be used as a negative embedding, so we pass it as a negative prompt generator = torch.Generator().manual_seed(33) image = pipe("a woman standing in front of a mountain", negative_prompt="unaestheticXLv31", generator=generator).images[0] image ```