[docs] Use other checkpoints with inpaint (#5590)

* tip about inpaint checkpoints

* expand section

* feedback

docs/source/en/using-diffusers/inpaint.md

@@ -184,6 +184,183 @@ make_image_grid([init_image, mask_image, image], rows=1, cols=3)
   </div>
 </div>
 
+## Non-inpaint specific checkpoints
+
+So far, this guide has used inpaint specific checkpoints such as [runwayml/stable-diffusion-inpainting](https://huggingface.co/runwayml/stable-diffusion-inpainting). But you can also use regular checkpoints like [runwayml/stable-diffusion-v1-5](https://huggingface.co/runwayml/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="runwayml/stable-diffusion-v1-5">
+
+```py
+import torch
+from diffusers import AutoPipelineForInpainting
+from diffusers.utils import load_image, make_image_grid
+
+pipeline = AutoPipelineForInpainting.from_pretrained(
+    "runwayml/stable-diffusion-v1-5", torch_dtype=torch.float16, variant="fp16"
+).to("cuda")
+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"
+).to("cuda")
+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">runwayml/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="runwayml/stable-diffusion-v1-5">
+
+```py
+import torch
+from diffusers import AutoPipelineForInpainting
+from diffusers.utils import load_image, make_image_grid
+
+pipeline = AutoPipelineForInpainting.from_pretrained(
+    "runwayml/stable-diffusion-v1-5", torch_dtype=torch.float16, variant="fp16"
+).to("cuda")
+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"
+).to("cuda")
+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">runwayml/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 code below 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")
+
+# 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 + mask_image_arr * repainted_image
+unmasked_unchanged_image = PIL.Image.fromarray(unmasked_unchanged_image_arr.round().astype("uint8"))
+unmasked_unchanged_image.save("force_unmasked_unchanged.png")
+make_image_grid([init_image, mask_image, repainted_image, unmasked_unchanged_image], rows=2, cols=2)
+```
+
 ## Configure pipeline parameters
 
 Image features - like quality and "creativity" - are dependent on pipeline parameters. Knowing what these parameters do is important for getting the results you want. Let's take a look at the most important parameters and see how changing them affects the output.
@@ -309,51 +486,6 @@ make_image_grid([init_image, mask_image, image], rows=1, cols=3)
   </figure>
 </div>
 
-## Preserve unmasked areas
-
-The [`AutoPipelineForInpainting`] (and other inpainting pipelines) generally changes the unmasked parts of an image to create a more natural transition between the masked and unmasked region. If this behavior is undesirable, you can force the unmasked area to remain the same. However, forcing the unmasked portion of the image to remain the same may result in some unusual transitions between the unmasked and masked 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")
-
-# 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 + mask_image_arr * repainted_image
-unmasked_unchanged_image = PIL.Image.fromarray(unmasked_unchanged_image_arr.round().astype("uint8"))
-unmasked_unchanged_image.save("force_unmasked_unchanged.png")
-make_image_grid([init_image, mask_image, repainted_image, unmasked_unchanged_image], rows=2, cols=2)
-```
-
 ## Chained inpainting pipelines
 
 [`AutoPipelineForInpainting`] can be chained with other 🤗 Diffusers pipelines to edit their outputs. This is often useful for improving the output quality from your other diffusion pipelines, and if you're using multiple pipelines, it can be more memory-efficient to chain them together to keep the outputs in latent space and reuse the same pipeline components.