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Text-Guided Image-Inpainting

The StableDiffusionInpaintPipeline lets you edit specific parts of an image by providing a mask and a text prompt. It uses a version of Stable Diffusion specifically trained for in-painting tasks.

Note that this model is distributed separately from the regular Stable Diffusion model, so you have to accept its license even if you accepted the Stable Diffusion one in the past.

Please, visit the model card, read the license carefully and tick the checkbox if you agree. You have to be a registered user in 🤗 Hugging Face Hub, and you’ll also need to use an access token for the code to work. For more information on access tokens, please refer to this section of the documentation.

import PIL
import requests
import torch
from io import BytesIO

from diffusers import StableDiffusionInpaintPipeline

def download_image(url):
    response = requests.get(url)

img_url = ""
mask_url = ""

init_image = download_image(img_url).resize((512, 512))
mask_image = download_image(mask_url).resize((512, 512))

pipe = StableDiffusionInpaintPipeline.from_pretrained(
pipe ="cuda")

prompt = "Face of a yellow cat, high resolution, sitting on a park bench"
image = pipe(prompt=prompt, image=init_image, mask_image=mask_image).images[0]
image mask_image prompt Output
drawing drawing Face of a yellow cat, high resolution, sitting on a park bench drawing

You can also run this example on colab Open In Colab

A previous experimental implementation of in-painting used a different, lower-quality process. To ensure backwards compatibility, loading a pretrained pipeline that doesn't contain the new model will still apply the old in-painting method.