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try.py
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from diffusers import StableDiffusionControlNetPipeline, ControlNetModel, UniPCMultistepScheduler
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from diffusers.utils import load_image
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import numpy as np
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import torch
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import cv2
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from PIL import Image
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device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
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device
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# load control net and stable diffusion v1-5
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base_model_path = "runwayml/stable-diffusion-v1-5"
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controlnet_path = "LuyangZ/controlnet_Neufert4_64_100"
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controlnet = ControlNetModel.from_pretrained(controlnet_path, use_safetensors=True)
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pipe = StableDiffusionControlNetPipeline.from_pretrained(
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base_model_path, controlnet=controlnet, use_safetensors=True)
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# speed up diffusion process with faster scheduler and memory optimization
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pipe.scheduler = UniPCMultistepScheduler.from_config(pipe.scheduler.config)
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pipe = pipe.to(device)
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# pipe.set_progress_bar_config(disable=True)
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# generate image
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control_image = load_image("C:/Users/luyan/diffusers/examples/controlnet/Test/1030_4465_8e4734b920a2be9f0e7d85b734b7fa7e.png")
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# speed up diffusion process with faster scheduler and memory optimization
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pipe.scheduler = UniPCMultistepScheduler.from_config(pipe.scheduler.config)
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# generate image
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control_image = load_image("C:/Users/luyan/diffusers/examples/controlnet/Test/2179_9871_432b1fbf16d04cd8371cd9ece543cb28.png")
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# pipe = pipe.to(device)
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# generator = torch.manual_seed(0)
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# generator = torch.Generator(device=device).manual_seed(999)
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# generator = None
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# images = []
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# for i in range(5):
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# image = pipe(
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# "floor plan,2 bedrooms", num_inference_steps=100, image=control_image
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# ).images[0]
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# images.append(image)
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generator = torch.Generator(device=device).manual_seed(333)
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images = []
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for i in range(5):
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image = pipe(
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"floor plan,2 bedrooms", num_inference_steps=20, generator=generator, image=control_image
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).images[0]
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images.append(image)
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def make_grid(images, size=512):
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"""Given a list of PIL images, stack them together into a line for easy viewing"""
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output_im = Image.new("RGB", (size * len(images), size))
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for i, im in enumerate(images):
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output_im.paste(im.resize((size, size)), (i * size, 0))
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return output_im
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make_grid(images, size=512)
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