Create try.py

try.py

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