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| # !pip install diffusers transformers | |
| import PIL | |
| import requests | |
| import torch | |
| from io import BytesIO | |
| from diffusers import DiffusionPipeline | |
| """ | |
| Step 1: Download demo images | |
| """ | |
| def download_image(url): | |
| response = requests.get(url) | |
| return PIL.Image.open(BytesIO(response.content)).convert("RGB") | |
| img_url = "https://github.com/IDEA-Research/detrex-storage/blob/main/assets/grounded_sam/paint_by_example/input_image.png?raw=true" | |
| mask_url = "https://github.com/IDEA-Research/detrex-storage/blob/main/assets/grounded_sam/paint_by_example/mask.png?raw=true" | |
| example_url = "https://github.com/IDEA-Research/detrex-storage/blob/main/assets/grounded_sam/paint_by_example/pomeranian_example.jpg?raw=True" | |
| # example_url = "https://raw.githubusercontent.com/Fantasy-Studio/Paint-by-Example/main/examples/reference/example_1.jpg" | |
| init_image = download_image(img_url).resize((512, 512)) | |
| mask_image = download_image(mask_url).resize((512, 512)) | |
| example_image = download_image(example_url).resize((512, 512)) | |
| """ | |
| Step 2: Download pretrained weights and initialize model | |
| """ | |
| # set cache dir to store the weights | |
| cache_dir = "/comp_robot/rentianhe/weights/diffusers/" | |
| pipe = DiffusionPipeline.from_pretrained( | |
| "Fantasy-Studio/Paint-by-Example", | |
| torch_dtype=torch.float16, | |
| cache_dir=cache_dir, | |
| ) | |
| # set to device | |
| pipe = pipe.to("cuda:1") | |
| """ | |
| Step 3: Run PaintByExample pipeline and save image | |
| """ | |
| image = pipe( | |
| image=init_image, | |
| mask_image=mask_image, | |
| example_image=example_image, | |
| num_inference_steps=200, | |
| ).images[0] | |
| image.save("./paint_by_example_demo.jpg") | |