Update README.md

README.md

@@ -81,30 +81,19 @@ prompt: 1girl, solo, ball, swimsuit, bikini, mole, beachball, white bikini, brea
 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
-    if x.ndim == 2:
-        x = x[:, :, None]
-    assert x.ndim == 3
-    H, W, C = x.shape
-    assert C == 1 or C == 3 or C == 4
-    if C == 3:
-        return x
-    if C == 1:
-        return np.concatenate([x, x, x], axis=2)
-    if C == 4:
-        color = x[:, :, 0:3].astype(np.float32)
-        alpha = x[:, :, 3:4].astype(np.float32) / 255.0
-        y = color * alpha + 255.0 * (1.0 - alpha)
-        y = y.clip(0, 255).astype(np.uint8)
-        return y
 
 def nms(x, t, s):
     x = cv2.GaussianBlur(x.astype(np.float32), (0, 0), s)
@@ -150,22 +139,23 @@ pipe = StableDiffusionXLControlNetPipeline.from_pretrained(
 )
 
 # you can use either hed to generate a fake scribble given an image or a sketch image totally draw by yourself
-
 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=False)
+  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