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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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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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