So far, this guide has used inpaint specific checkpoints such as [stable-diffusion-v1-5/stable-diffusion-inpainting](https://huggingface.co/stable-diffusion-v1-5/stable-diffusion-inpainting). But you can also use regular checkpoints like [stable-diffusion-v1-5/stable-diffusion-v1-5](https://huggingface.co/stable-diffusion-v1-5/stable-diffusion-v1-5). Let's compare the results of the two checkpoints.  
The image on the left is generated from a regular checkpoint, and the image on the right is from an inpaint checkpoint. You'll immediately notice the image on the left is not as clean, and you can still see the outline of the area the model is supposed to inpaint. The image on the right is much cleaner and the inpainted area appears more natural.  
<hfoptions id="regular-specific">
<hfoption id="stable-diffusion-v1-5/stable-diffusion-v1-5">  
```py
import torch
from diffusers import AutoPipelineForInpainting
from diffusers.utils import load_image, make_image_grid

pipeline = AutoPipelineForInpainting.from_pretrained(
"stable-diffusion-v1-5/stable-diffusion-v1-5", torch_dtype=torch.float16, variant="fp16"
)
pipeline.enable_model_cpu_offload()
# remove following line if xFormers is not installed or you have PyTorch 2.0 or higher installed
pipeline.enable_xformers_memory_efficient_attention()

# load base and mask image
init_image = load_image("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/inpaint.png")
mask_image = load_image("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/inpaint_mask.png")

generator = torch.Generator("cuda").manual_seed(92)
prompt = "concept art digital painting of an elven castle, inspired by lord of the rings, highly detailed, 8k"
image = pipeline(prompt=prompt, image=init_image, mask_image=mask_image, generator=generator).images[0]
make_image_grid([init_image, image], rows=1, cols=2)
```  
</hfoption>
<hfoption id="runwayml/stable-diffusion-inpainting">  
```py
import torch
from diffusers import AutoPipelineForInpainting
from diffusers.utils import load_image, make_image_grid

pipeline = AutoPipelineForInpainting.from_pretrained(
"runwayml/stable-diffusion-inpainting", torch_dtype=torch.float16, variant="fp16"
)
pipeline.enable_model_cpu_offload()
# remove following line if xFormers is not installed or you have PyTorch 2.0 or higher installed
pipeline.enable_xformers_memory_efficient_attention()

# load base and mask image
init_image = load_image("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/inpaint.png")
mask_image = load_image("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/inpaint_mask.png")

generator = torch.Generator("cuda").manual_seed(92)
prompt = "concept art digital painting of an elven castle, inspired by lord of the rings, highly detailed, 8k"
image = pipeline(prompt=prompt, image=init_image, mask_image=mask_image, generator=generator).images[0]
make_image_grid([init_image, image], rows=1, cols=2)
```  
</hfoption>
</hfoptions>  
<div class="flex gap-4">
<div>
<img class="rounded-xl" src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/non-inpaint-specific.png"/>
<figcaption class="mt-2 text-center text-sm text-gray-500">stable-diffusion-v1-5/stable-diffusion-v1-5</figcaption>
</div>
<div>
<img class="rounded-xl" src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/inpaint-specific.png"/>
<figcaption class="mt-2 text-center text-sm text-gray-500">runwayml/stable-diffusion-inpainting</figcaption>
</div>
</div>  
However, for more basic tasks like erasing an object from an image (like the rocks in the road for example), a regular checkpoint yields pretty good results. There isn't as noticeable of difference between the regular and inpaint checkpoint.  
<hfoptions id="inpaint">
<hfoption id="stable-diffusion-v1-5/stable-diffusion-v1-5">  
```py
import torch
from diffusers import AutoPipelineForInpainting
from diffusers.utils import load_image, make_image_grid

pipeline = AutoPipelineForInpainting.from_pretrained(
"stable-diffusion-v1-5/stable-diffusion-v1-5", torch_dtype=torch.float16, variant="fp16"
)
pipeline.enable_model_cpu_offload()
# remove following line if xFormers is not installed or you have PyTorch 2.0 or higher installed
pipeline.enable_xformers_memory_efficient_attention()

# load base and mask image
init_image = load_image("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/inpaint.png")
mask_image = load_image("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/road-mask.png")

image = pipeline(prompt="road", image=init_image, mask_image=mask_image).images[0]
make_image_grid([init_image, image], rows=1, cols=2)
```  
</hfoption>
<hfoption id="runwayml/stable-diffusion-inpaint">  
```py
import torch
from diffusers import AutoPipelineForInpainting
from diffusers.utils import load_image, make_image_grid

pipeline = AutoPipelineForInpainting.from_pretrained(
"runwayml/stable-diffusion-inpainting", torch_dtype=torch.float16, variant="fp16"
)
pipeline.enable_model_cpu_offload()
# remove following line if xFormers is not installed or you have PyTorch 2.0 or higher installed
pipeline.enable_xformers_memory_efficient_attention()

# load base and mask image
init_image = load_image("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/inpaint.png")
mask_image = load_image("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/road-mask.png")

image = pipeline(prompt="road", image=init_image, mask_image=mask_image).images[0]
make_image_grid([init_image, image], rows=1, cols=2)
```  
</hfoption>
</hfoptions>  
<div class="flex gap-4">
<div>
<img class="rounded-xl" src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/regular-inpaint-basic.png"/>
<figcaption class="mt-2 text-center text-sm text-gray-500">stable-diffusion-v1-5/stable-diffusion-v1-5</figcaption>
</div>
<div>
<img class="rounded-xl" src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/specific-inpaint-basic.png"/>
<figcaption class="mt-2 text-center text-sm text-gray-500">runwayml/stable-diffusion-inpainting</figcaption>
</div>
</div>  
The trade-off of using a non-inpaint specific checkpoint is the overall image quality may be lower, but it generally tends to preserve the mask area (that is why you can see the mask outline). The inpaint specific checkpoints are intentionally trained to generate higher quality inpainted images, and that includes creating a more natural transition between the masked and unmasked areas. As a result, these checkpoints are more likely to change your unmasked area.  
If preserving the unmasked area is important for your task, you can use the [`VaeImageProcessor.apply_overlay`] method to force the unmasked area of an image to remain the same at the expense of some more unnatural transitions between the masked and unmasked areas.  
```py
import PIL
import numpy as np
import torch

from diffusers import AutoPipelineForInpainting
from diffusers.utils import load_image, make_image_grid

device = "cuda"
pipeline = AutoPipelineForInpainting.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")

unmasked_unchanged_image = pipeline.image_processor.apply_overlay(mask_image, init_image, repainted_image)
unmasked_unchanged_image.save("force_unmasked_unchanged.png")
make_image_grid([init_image, mask_image, repainted_image, unmasked_unchanged_image], rows=2, cols=2)
```
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/using-diffusers/inpaint.md