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README.md

@@ -351,6 +351,7 @@ Note that this extension is hidden because it is only for developers. To see it
 import os
 import cv2
 import gradio as gr
+import numpy as np
 
 from modules import scripts
 from modules.shared_cmd_options import cmd_opts
@@ -423,7 +424,18 @@ class ControlNetExampleForge(scripts.Script):
         width = W * 8
 
         input_image = cv2.resize(input_image, (width, height))
-        canny_image = cv2.cvtColor(cv2.Canny(input_image, 100, 200), cv2.COLOR_GRAY2RGB)
+
+        # Below are two methods to preprocess images.
+        # Method 1: do it in your own way
+        canny_image_1 = cv2.cvtColor(cv2.Canny(input_image, 100, 200), cv2.COLOR_GRAY2RGB)
+
+        # Method 2: use built-in preprocessor
+        from modules_forge.shared import shared_preprocessors
+        canny_image_2 = shared_preprocessors['canny'](input_image, 100, 200)
+
+        # The two methods will give your same result
+        assert np.allclose(canny_image_1, canny_image_2)
+        canny_image = canny_image_1
 
         # Output preprocessor result. Now called every sampling. Cache in your own way.
         p.extra_result_images.append(canny_image)
@@ -437,7 +449,7 @@ class ControlNetExampleForge(scripts.Script):
         unet = apply_controlnet_advanced(unet=unet, controlnet=self.model, image_bhwc=control_image,
                                          strength=0.6, start_percent=0.0, end_percent=0.8,
                                          positive_advanced_weighting=None, negative_advanced_weighting=None,
-                                         frame_advanced_weighting=None)
+                                         advanced_frame_weighting=None)
 
         p.sd_model.forge_objects.unet = unet
 

extensions-builtin/sd_forge_controlnet_example/scripts/sd_forge_controlnet_example.py

@@ -3,6 +3,7 @@
 import os
 import cv2
 import gradio as gr
+import numpy as np
 
 from modules import scripts
 from modules.shared_cmd_options import cmd_opts
@@ -75,7 +76,18 @@ class ControlNetExampleForge(scripts.Script):
         width = W * 8
 
         input_image = cv2.resize(input_image, (width, height))
-        canny_image = cv2.cvtColor(cv2.Canny(input_image, 100, 200), cv2.COLOR_GRAY2RGB)
+
+        # Below are two methods to preprocess images.
+        # Method 1: do it in your own way
+        canny_image_1 = cv2.cvtColor(cv2.Canny(input_image, 100, 200), cv2.COLOR_GRAY2RGB)
+
+        # Method 2: use built-in preprocessor
+        from modules_forge.shared import shared_preprocessors
+        canny_image_2 = shared_preprocessors['canny'](input_image, 100, 200)
+
+        # The two methods will give your same result
+        assert np.allclose(canny_image_1, canny_image_2)
+        canny_image = canny_image_1
 
         # Output preprocessor result. Now called every sampling. Cache in your own way.
         p.extra_result_images.append(canny_image)

modules_forge/shared.py

@@ -0,0 +1,48 @@
+import cv2
+
+shared_preprocessors = {}
+
+
+class PreprocessorParameter:
+    def __init__(self, minimum=0.0, maximum=1.0, step=0.01, label='Parameter 1', value=0.5, visible=False):
+        self.gradio_update_kwargs = dict(
+            minimum=minimum, maximum=maximum, step=step, label=label, value=value, visible=visible
+        )
+
+
+class PreprocessorBase:
+    def __init__(self):
+        self.name = 'PreprocessorBase'
+        self.slider_1 = PreprocessorParameter()
+        self.slider_2 = PreprocessorParameter()
+        self.slider_3 = PreprocessorParameter()
+
+    def __call__(self, input_image, slider_1=None, slider_2=None, slider_3=None, **kwargs):
+        return input_image
+
+
+class PreprocessorNone(PreprocessorBase):
+    def __init__(self):
+        super().__init__()
+        self.name = 'None'
+
+    def __call__(self, input_image, slider_1=None, slider_2=None, slider_3=None, **kwargs):
+        return input_image
+
+
+shared_preprocessors['none'] = PreprocessorNone()
+
+
+class PreprocessorCanny(PreprocessorBase):
+    def __init__(self):
+        super().__init__()
+        self.name = 'canny'
+        self.slider_1 = PreprocessorParameter(minimum=0, maximum=256, step=1, value=100, label='Low Threshold', visible=True)
+        self.slider_2 = PreprocessorParameter(minimum=0, maximum=256, step=1, value=200, label='High Threshold', visible=True)
+
+    def __call__(self, input_image, slider_1=100, slider_2=200, slider_3=None, **kwargs):
+        canny_image = cv2.cvtColor(cv2.Canny(input_image, int(slider_1), int(slider_2)), cv2.COLOR_GRAY2RGB)
+        return canny_image
+
+
+shared_preprocessors['canny'] = PreprocessorCanny()