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from diffusers import ControlNetModel, StableDiffusionXLControlNetPipeline, AutoencoderKL |
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from diffusers.utils import load_image |
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from PIL import Image |
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import torch |
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import numpy as np |
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import cv2 |
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prompt = "aerial view, a futuristic research complex in a bright foggy jungle, hard lighting" |
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negative_prompt = 'low quality, bad quality, sketches' |
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image = load_image("https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/hf-logo.png") |
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controlnet_conditioning_scale = 0.5 |
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controlnet = ControlNetModel.from_pretrained( |
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"diffusers/controlnet-canny-sdxl-1.0", |
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torch_dtype=torch.float16 |
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) |
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vae = AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16) |
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pipe = StableDiffusionXLControlNetPipeline.from_pretrained( |
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"stabilityai/stable-diffusion-xl-base-1.0", |
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controlnet=controlnet, |
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vae=vae, |
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torch_dtype=torch.float16, |
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) |
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pipe.enable_model_cpu_offload() |
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image = np.array(image) |
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image = cv2.Canny(image, 100, 200) |
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image = image[:, :, None] |
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image = np.concatenate([image, image, image], axis=2) |
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image = Image.fromarray(image) |
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