diff --git a/docs/source/en/using-diffusers/inpaint.md b/docs/source/en/using-diffusers/inpaint.md
index 7df755e3225b..fe0cb6fb2909 100644
--- a/docs/source/en/using-diffusers/inpaint.md
+++ b/docs/source/en/using-diffusers/inpaint.md
@@ -76,3 +76,49 @@ Check out the Spaces below to try out image inpainting yourself!
 	width="850"
 	height="500"
 ></iframe>
+
+## Preserving the Unmasked Area of the Image
+
+Generally speaking, [`StableDiffusionInpaintPipeline`] (and other inpainting pipelines) will change the unmasked part of the image as well. If this behavior is undesirable, you can force the unmasked area to remain the same as follows:
+
+```python
+import PIL
+import numpy as np
+import torch
+
+from diffusers import StableDiffusionInpaintPipeline
+from diffusers.utils import load_image
+
+device = "cuda"
+pipeline = StableDiffusionInpaintPipeline.from_pretrained(
+    "runwayml/stable-diffusion-inpainting",
+    torch_dtype=torch.float16,
+)
+pipeline = pipeline.to(device)
+
+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 = load_image(img_url).resize((512, 512))
+mask_image = load_image(mask_url).resize((512, 512))
+
+prompt = "Face of a yellow cat, high resolution, sitting on a park bench"
+repainted_image = pipeline(prompt=prompt, image=init_image, mask_image=mask_image).images[0]
+repainted_image.save("repainted_image.png")
+
+# Convert mask to grayscale NumPy array
+mask_image_arr = np.array(mask_image.convert("L"))
+# Add a channel dimension to the end of the grayscale mask
+mask_image_arr = mask_image_arr[:, :, None]
+# Binarize the mask: 1s correspond to the pixels which are repainted
+mask_image_arr = mask_image_arr.astype(np.float32) / 255.0
+mask_image_arr[mask_image_arr < 0.5] = 0
+mask_image_arr[mask_image_arr >= 0.5] = 1
+
+# Take the masked pixels from the repainted image and the unmasked pixels from the initial image
+unmasked_unchanged_image_arr = (1 - mask_image_arr) * init_image_arr + mask_image_arr * repainted_image_arr
+unmasked_unchanged_image = PIL.Image.fromarray(unmasked_unchanged_image_arr.round().astype("uint8"))
+unmasked_unchanged_image.save("force_unmasked_unchanged.png")
+```
+
+Forcing the unmasked portion of the image to remain the same might result in some weird transitions between the unmasked and masked areas, since the model will typically change the masked and unmasked areas to make the transition more natural.