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import torch |
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import os |
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from huggingface_hub import HfApi |
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from pathlib import Path |
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from diffusers.utils import load_image |
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from transformers import AutoImageProcessor, UperNetForSemanticSegmentation |
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from PIL import Image |
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import numpy as np |
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from diffusers import ( |
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ControlNetModel, |
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StableDiffusionControlNetPipeline, |
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UniPCMultistepScheduler, |
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) |
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import sys |
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image_processor = AutoImageProcessor.from_pretrained("openmmlab/upernet-convnext-small") |
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image_segmentor = UperNetForSemanticSegmentation.from_pretrained("openmmlab/upernet-convnext-small") |
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checkpoint = sys.argv[1] |
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ada_palette = np.asarray([ |
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[0, 0, 0], |
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[120, 120, 120], |
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[180, 120, 120], |
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[6, 230, 230], |
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[80, 50, 50], |
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[4, 200, 3], |
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[120, 120, 80], |
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[140, 140, 140], |
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[204, 5, 255], |
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[230, 230, 230], |
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[4, 250, 7], |
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[224, 5, 255], |
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[235, 255, 7], |
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[150, 5, 61], |
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[120, 120, 70], |
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[8, 255, 51], |
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[255, 6, 82], |
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[143, 255, 140], |
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[204, 255, 4], |
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[255, 51, 7], |
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[204, 70, 3], |
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[0, 102, 200], |
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[61, 230, 250], |
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[255, 6, 51], |
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[11, 102, 255], |
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[255, 7, 71], |
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[255, 9, 224], |
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[9, 7, 230], |
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[220, 220, 220], |
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[255, 9, 92], |
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[112, 9, 255], |
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[8, 255, 214], |
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[7, 255, 224], |
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[255, 184, 6], |
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[10, 255, 71], |
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[255, 41, 10], |
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[7, 255, 255], |
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[224, 255, 8], |
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[102, 8, 255], |
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[255, 61, 6], |
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[255, 194, 7], |
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[255, 122, 8], |
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[0, 255, 20], |
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[255, 8, 41], |
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[255, 5, 153], |
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[6, 51, 255], |
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[235, 12, 255], |
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[160, 150, 20], |
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[0, 163, 255], |
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[140, 140, 140], |
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[250, 10, 15], |
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[20, 255, 0], |
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[31, 255, 0], |
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[255, 31, 0], |
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[255, 224, 0], |
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[153, 255, 0], |
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[0, 0, 255], |
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[255, 71, 0], |
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[0, 235, 255], |
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[0, 173, 255], |
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[31, 0, 255], |
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[11, 200, 200], |
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[255, 82, 0], |
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[0, 255, 245], |
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[0, 61, 255], |
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[0, 255, 112], |
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[0, 255, 133], |
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[255, 0, 0], |
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[255, 163, 0], |
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[255, 102, 0], |
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[194, 255, 0], |
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[0, 143, 255], |
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[51, 255, 0], |
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[0, 82, 255], |
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[0, 255, 41], |
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[0, 255, 173], |
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[10, 0, 255], |
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[173, 255, 0], |
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[0, 255, 153], |
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[255, 92, 0], |
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[255, 0, 255], |
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[255, 0, 245], |
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[255, 0, 102], |
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[255, 173, 0], |
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[255, 0, 20], |
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[255, 184, 184], |
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[0, 31, 255], |
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[0, 255, 61], |
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[0, 71, 255], |
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[255, 0, 204], |
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[0, 255, 194], |
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[0, 255, 82], |
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[0, 10, 255], |
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[0, 112, 255], |
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[51, 0, 255], |
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[0, 194, 255], |
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[0, 122, 255], |
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[0, 255, 163], |
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[255, 153, 0], |
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[0, 255, 10], |
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[255, 112, 0], |
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[143, 255, 0], |
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[82, 0, 255], |
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[163, 255, 0], |
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[255, 235, 0], |
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[8, 184, 170], |
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[133, 0, 255], |
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[0, 255, 92], |
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[184, 0, 255], |
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[255, 0, 31], |
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[0, 184, 255], |
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[0, 214, 255], |
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[255, 0, 112], |
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[92, 255, 0], |
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[0, 224, 255], |
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[112, 224, 255], |
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[70, 184, 160], |
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[163, 0, 255], |
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[153, 0, 255], |
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[71, 255, 0], |
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[255, 0, 163], |
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[255, 204, 0], |
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[255, 0, 143], |
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[0, 255, 235], |
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[133, 255, 0], |
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[255, 0, 235], |
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[245, 0, 255], |
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[255, 0, 122], |
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[255, 245, 0], |
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[10, 190, 212], |
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[214, 255, 0], |
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[0, 204, 255], |
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[20, 0, 255], |
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[255, 255, 0], |
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[0, 153, 255], |
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[0, 41, 255], |
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[0, 255, 204], |
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[41, 0, 255], |
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[41, 255, 0], |
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[173, 0, 255], |
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[0, 245, 255], |
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[71, 0, 255], |
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[122, 0, 255], |
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[0, 255, 184], |
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[0, 92, 255], |
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[184, 255, 0], |
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[0, 133, 255], |
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[255, 214, 0], |
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[25, 194, 194], |
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[102, 255, 0], |
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[92, 0, 255], |
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]) |
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image = load_image("https://huggingface.co/lllyasviel/sd-controlnet-seg/resolve/main/images/house.png").convert('RGB') |
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prompt = "old house in stormy weather with rain and wind" |
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pixel_values = image_processor(image, return_tensors="pt").pixel_values |
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with torch.no_grad(): |
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outputs = image_segmentor(pixel_values) |
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seg = image_processor.post_process_semantic_segmentation(outputs, target_sizes=[image.size[::-1]])[0] |
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color_seg = np.zeros((seg.shape[0], seg.shape[1], 3), dtype=np.uint8) |
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for label, color in enumerate(ada_palette): |
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color_seg[seg == label, :] = color |
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color_seg = color_seg.astype(np.uint8) |
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image = Image.fromarray(color_seg) |
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controlnet = ControlNetModel.from_pretrained(checkpoint, torch_dtype=torch.float16) |
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pipe = StableDiffusionControlNetPipeline.from_pretrained( |
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"runwayml/stable-diffusion-v1-5", controlnet=controlnet, torch_dtype=torch.float16 |
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) |
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pipe.scheduler = UniPCMultistepScheduler.from_config(pipe.scheduler.config) |
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pipe.enable_model_cpu_offload() |
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generator = torch.manual_seed(0) |
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out_image = pipe(prompt, num_inference_steps=30, generator=generator, image=image).images[0] |
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path = os.path.join(Path.home(), "images", "aa.png") |
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out_image.save(path) |
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api = HfApi() |
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api.upload_file( |
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path_or_fileobj=path, |
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path_in_repo=path.split("/")[-1], |
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repo_id="patrickvonplaten/images", |
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repo_type="dataset", |
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) |
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print("https://huggingface.co/datasets/patrickvonplaten/images/blob/main/aa.png") |
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