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Textual inversion

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.

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 guide.

Login to your Hugging Face account:

    from huggingface_hub import notebook_login

    notebook_login()

Import the necessary libraries, and create a helper function to visualize the generated images:

    import os
    import torch

    import PIL
    from PIL import Image

    from diffusers import StableDiffusionPipeline
    from transformers import CLIPFeatureExtractor, CLIPTextModel, CLIPTokenizer

    def image_grid(imgs, rows, cols):
        assert len(imgs) == rows * cols

        w, h = imgs[0].size
        grid = Image.new("RGB", size=(cols * w, rows * h))
        grid_w, grid_h = grid.size

        for i, img in enumerate(imgs):
            grid.paste(img, box=(i % cols * w, i // cols * h))
        return grid

Pick a Stable Diffusion checkpoint and a pre-learned concept from the Stable Diffusion Conceptualizer:

    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:

    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:

    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:

    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 = image_grid(all_images, num_samples, num_rows)
    grid