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<! |
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Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with |
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the License. You may obtain a copy of the License at |
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http://www.apache.org/licenses/LICENSE-2.0 |
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Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on |
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an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the |
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specific language governing permissions and limitations under the License. |
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# Text-guided image-inpainting |
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[[open-in-colab]] |
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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`](https://huggingface.co/runwayml/stable-diffusion-inpainting) specifically trained for inpainting tasks. |
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Get started by loading an instance of the [`StableDiffusionInpaintPipeline`]: |
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```python |
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import PIL |
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import requests |
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import torch |
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from io import BytesIO |
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from diffusers import StableDiffusionInpaintPipeline |
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pipeline = StableDiffusionInpaintPipeline.from_pretrained( |
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"runwayml/stable-diffusion-inpainting", |
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torch_dtype=torch.float16, |
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) |
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pipeline = pipeline.to("cuda") |
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``` |
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Download an image and a mask of a dog which you'll eventually replace: |
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```python |
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def download_image(url): |
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response = requests.get(url) |
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return PIL.Image.open(BytesIO(response.content)).convert("RGB") |
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img_url = "https://raw.githubusercontent.com/CompVis/latent-diffusion/main/data/inpainting_examples/overture-creations-5sI6fQgYIuo.png" |
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mask_url = "https://raw.githubusercontent.com/CompVis/latent-diffusion/main/data/inpainting_examples/overture-creations-5sI6fQgYIuo_mask.png" |
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init_image = download_image(img_url).resize((512, 512)) |
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mask_image = download_image(mask_url).resize((512, 512)) |
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``` |
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Now you can create a prompt to replace the mask with something else: |
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```python |
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prompt = "Face of a yellow cat, high resolution, sitting on a park bench" |
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image = pipe(prompt=prompt, image=init_image, mask_image=mask_image).images[0] |
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``` |
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`image` | `mask_image` | `prompt` | output | |
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:-------------------------:|:-------------------------:|:-------------------------:|-------------------------:| |
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<img src="https://raw.githubusercontent.com/CompVis/latent-diffusion/main/data/inpainting_examples/overture-creations-5sI6fQgYIuo.png" alt="drawing" width="250"/> | <img src="https://raw.githubusercontent.com/CompVis/latent-diffusion/main/data/inpainting_examples/overture-creations-5sI6fQgYIuo_mask.png" alt="drawing" width="250"/> | ***Face of a yellow cat, high resolution, sitting on a park bench*** | <img src="https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/in_paint/yellow_cat_sitting_on_a_park_bench.png" alt="drawing" width="250"/> | |
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<Tip warning={true}> |
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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. |
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</Tip> |
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Check out the Spaces below to try out image inpainting yourself! |
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<iframe |
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src="https://runwayml-stable-diffusion-inpainting.hf.space" |
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frameborder="0" |
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width="850" |
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height="500" |
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></iframe> |
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