#
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.

    import PIL
    import requests
    import torch
    from io import BytesIO

    from diffusers import StableDiffusionInpaintPipeline

    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))

    pipe = StableDiffusionInpaintPipeline.from_pretrained(
        "runwayml/stable-diffusion-inpainting",
        torch_dtype=torch.float16,
    )
    pipe = pipe.to("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
--- | --- | --- | ---
|  | Face of a yellow cat, high resolution, sitting on a park bench |

You can also run this example on 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.