wehub-resource-sync
54 lines
1.6 KiB
Python
54 lines
1.6 KiB
Python
# !pip install diffusers transformers
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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 DiffusionPipeline
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"""
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Step 1: Download demo images
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"""
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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://github.com/IDEA-Research/detrex-storage/blob/main/assets/grounded_sam/paint_by_example/input_image.png?raw=true"
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mask_url = "https://github.com/IDEA-Research/detrex-storage/blob/main/assets/grounded_sam/paint_by_example/mask.png?raw=true"
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example_url = "https://github.com/IDEA-Research/detrex-storage/blob/main/assets/grounded_sam/paint_by_example/pomeranian_example.jpg?raw=True"
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# example_url = "https://raw.githubusercontent.com/Fantasy-Studio/Paint-by-Example/main/examples/reference/example_1.jpg"
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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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example_image = download_image(example_url).resize((512, 512))
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"""
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Step 2: Download pretrained weights and initialize model
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"""
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# set cache dir to store the weights
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cache_dir = "/comp_robot/rentianhe/weights/diffusers/"
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pipe = DiffusionPipeline.from_pretrained(
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"Fantasy-Studio/Paint-by-Example",
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torch_dtype=torch.float16,
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cache_dir=cache_dir,
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)
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# set to device
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pipe = pipe.to("cuda:1")
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"""
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Step 3: Run PaintByExample pipeline and save image
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"""
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image = pipe(
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image=init_image,
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mask_image=mask_image,
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example_image=example_image,
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num_inference_steps=200,
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).images[0]
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image.save("./paint_by_example_demo.jpg")
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