Update README.md

README.md

@@ -28,4 +28,46 @@ prompt: Cinematic, neoclassical table in the living room, cinematic, contour, li
 ![images_3)](./out_room.png)
 
 prompt: a tornado hitting grass field, 1980's film grain. overcast, muted colors.
-![images_0)](./out_tornado.png)
+![images_0)](./out_tornado.png)
+
+## Usage
+
+```python
+from diffusers import ControlNetModel, StableDiffusionXLControlNetPipeline, AutoencoderKL
+from diffusers.utils import load_image
+from PIL import Image
+import torch
+import numpy as np
+import cv2
+
+prompt = "aerial view, a futuristic research complex in a bright foggy jungle, hard lighting"
+negative_prompt = 'low quality, bad quality, sketches'
+
+image = load_image("https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/hf-logo.png")
+
+controlnet_conditioning_scale = 0.5  # recommended for good generalization
+
+controlnet = ControlNetModel.from_pretrained(
+    "diffusers/controlnet-sdxl-1.0", subfolder="checkpoint-3000/controlnet", torch_dtype=torch.float16
+)
+vae = AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16)
+pipe = StableDiffusionXLControlNetPipeline.from_pretrained(
+    "stabilityai/stable-diffusion-xl-base-1.0",
+    controlnet=controlnet,
+    torch_dtype=torch.float16,
+)
+pipe.enable_model_cpu_offload()
+
+image = np.array(image)
+image = cv2.Canny(image, 100, 200)
+image = image[:, :, None]
+image = np.concatenate([image, image, image], axis=2)
+image = Image.fromarray(image)
+
+images = pipe(
+    prompt, image=image, controlnet_conditioning_scale=controlnet_conditioning_scale,
+    ).images
+
+image[0]_.save(f"hug_lab.png")
+```
+