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* add textual inversion inference to docs * add to toctree --------- Co-authored-by: Sayak Paul <spsayakpaul@gmail.com>
81 lines
3.0 KiB
Plaintext
81 lines
3.0 KiB
Plaintext
# Textual inversion
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[[open-in-colab]]
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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).
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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.
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Login to your Hugging Face account:
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```py
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from huggingface_hub import notebook_login
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notebook_login()
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```
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Import the necessary libraries, and create a helper function to visualize the generated images:
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```py
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import os
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import torch
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import PIL
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from PIL import Image
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from diffusers import StableDiffusionPipeline
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from transformers import CLIPFeatureExtractor, CLIPTextModel, CLIPTokenizer
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def image_grid(imgs, rows, cols):
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assert len(imgs) == rows * cols
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w, h = imgs[0].size
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grid = Image.new("RGB", size=(cols * w, rows * h))
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grid_w, grid_h = grid.size
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for i, img in enumerate(imgs):
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grid.paste(img, box=(i % cols * w, i // cols * h))
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return grid
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```
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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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```py
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pretrained_model_name_or_path = "runwayml/stable-diffusion-v1-5"
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repo_id_embeds = "sd-concepts-library/cat-toy"
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```
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Now you can load a pipeline, and pass the pre-learned concept to it:
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```py
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pipeline = StableDiffusionPipeline.from_pretrained(pretrained_model_name_or_path, torch_dtype=torch.float16).to("cuda")
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pipeline.load_textual_inversion(repo_id_embeds)
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```
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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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```py
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prompt = "a grafitti in a favela wall with a <cat-toy> on it"
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num_samples = 2
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num_rows = 2
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```
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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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```py
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all_images = []
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for _ in range(num_rows):
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images = pipe(prompt, num_images_per_prompt=num_samples, num_inference_steps=50, guidance_scale=7.5).images
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all_images.extend(images)
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grid = image_grid(all_images, num_samples, num_rows)
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grid
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```
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<div class="flex justify-center">
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<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/textual_inversion_inference.png">
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</div>
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