Update README.md

README.md

@@ -54,10 +54,17 @@ Use the code below to get started with the model.
 ```python
 from diffusers import ControlNetModel, StableDiffusionXLControlNetPipeline, AutoencoderKL
 from diffusers import DDIMScheduler, EulerAncestralDiscreteScheduler
+from controlnet_aux import PidiNetDetector, HEDdetector
+from diffusers.utils import load_image
+from huggingface_hub import HfApi
+from pathlib import Path
 from PIL import Image
 import torch
 import numpy as np
 import cv2
+import os
+
+
 
 def HWC3(x):
     assert x.dtype == np.uint8
@@ -126,17 +133,19 @@ if random.random() > 0.5:
   # Method 1 
   # if you use hed, you should provide an image, the image can be real or anime, you extract its hed lines and use it as the scribbles
   # The detail about hed detect you can refer to https://github.com/lllyasviel/ControlNet/blob/main/gradio_fake_scribble2image.py
-  # I provide a pseudo-code here
-  #     img = cv2.imread(img_path)
-  #     hed_img = apply_hed(img)
-  #     cv2.imwrite("a hed detect path for an image", hed_img)
-  
-  controlnet_img = Image.open("a hed detect path for an image")
+  # Below is a example using diffusers HED detector
+
+  image_path = Image.open("your image path, the image can be real or anime, HED detector will extract its edge boundery")
+  processor = HEDdetector.from_pretrained('lllyasviel/Annotators')
+  controlnet_img = processor(image_path, scribble=True)
+  controlnet_img.save("a hed detect path for an image")
+
+  # following is some processing to simulate human sketch draw, different threshold can generate different width of lines
   controlnet_img = np.array(controlnet_img)
   controlnet_img = nms(controlnet_img, 127, 3)
   controlnet_img = cv2.GaussianBlur(controlnet_img, (0, 0), 3)
-  
-  # different threshold for different lines
+
+  # higher threshold, thiner line
   random_val = int(round(random.uniform(0.01, 0.10), 2) * 255)
   controlnet_img[controlnet_img > random_val] = 255
   controlnet_img[controlnet_img < 255] = 0