inpaint.ipynb

Kernel: Unknown Kernel

Text-guided image-inpainting

The StableDiffusionInpaintPipeline allows you to edit specific parts of an image by providing a mask and a text prompt. It uses a version of Stable Diffusion, like runwayml/stable-diffusion-inpainting specifically trained for inpainting tasks.

Get started by loading an instance of the StableDiffusionInpaintPipeline:

import PIL
import requests
import torch
from io import BytesIO

from diffusers import StableDiffusionInpaintPipeline

pipeline = StableDiffusionInpaintPipeline.from_pretrained(
    "runwayml/stable-diffusion-inpainting",
    torch_dtype=torch.float16,
)
pipeline = pipeline.to("cuda")

Download an image and a mask of a dog which you'll eventually replace:

def download_image(url):
    response = requests.get(url)
    return PIL.Image.open(BytesIO(response.content)).convert("RGB")


img_url = "https://raw.githubusercontent.com/CompVis/latent-diffusion/main/data/inpainting_examples/overture-creations-5sI6fQgYIuo.png"
mask_url = "https://raw.githubusercontent.com/CompVis/latent-diffusion/main/data/inpainting_examples/overture-creations-5sI6fQgYIuo_mask.png"

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

Now you can create a prompt to replace the mask with something else:

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

image	mask_image	prompt	output
		Face of a yellow cat, high resolution, sitting on a park bench

[removed]

A previous experimental implementation of inpainting 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 inpainting method.

Check out the Spaces below to try out image inpainting yourself!