feat: extract attribute canny_high_threshold

extras/preprocessors.py

@@ -3,25 +3,25 @@ import numpy as np
 import modules.advanced_parameters as advanced_parameters
 
 
-def centered_canny(x: np.ndarray, canny_low_threshold):
+def centered_canny(x: np.ndarray, canny_low_threshold, canny_high_threshold):
     assert isinstance(x, np.ndarray)
     assert x.ndim == 2 and x.dtype == np.uint8
 
-    y = cv2.Canny(x, int(canny_low_threshold), int(advanced_parameters.canny_high_threshold))
+    y = cv2.Canny(x, int(canny_low_threshold), int(canny_high_threshold))
     y = y.astype(np.float32) / 255.0
     return y
 
 
-def centered_canny_color(x: np.ndarray, canny_low_threshold):
+def centered_canny_color(x: np.ndarray, canny_low_threshold, canny_high_threshold):
     assert isinstance(x, np.ndarray)
     assert x.ndim == 3 and x.shape[2] == 3
 
-    result = [centered_canny(x[..., i], canny_low_threshold) for i in range(3)]
+    result = [centered_canny(x[..., i], canny_low_threshold, canny_high_threshold) for i in range(3)]
     result = np.stack(result, axis=2)
     return result
 
 
-def pyramid_canny_color(x: np.ndarray, canny_low_threshold):
+def pyramid_canny_color(x: np.ndarray, canny_low_threshold, canny_high_threshold):
     assert isinstance(x, np.ndarray)
     assert x.ndim == 3 and x.shape[2] == 3
 
@@ -31,7 +31,7 @@ def pyramid_canny_color(x: np.ndarray, canny_low_threshold):
     for k in [0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0]:
         Hs, Ws = int(H * k), int(W * k)
         small = cv2.resize(x, (Ws, Hs), interpolation=cv2.INTER_AREA)
-        edge = centered_canny_color(small, canny_low_threshold)
+        edge = centered_canny_color(small, canny_low_threshold, canny_high_threshold)
         if acc_edge is None:
             acc_edge = edge
         else:
@@ -54,11 +54,11 @@ def norm255(x, low=4, high=96):
     return x * 255.0
 
 
-def canny_pyramid(x, canny_low_threshold):
+def canny_pyramid(x, canny_low_threshold, canny_high_threshold):
     # For some reasons, SAI's Control-lora Canny seems to be trained on canny maps with non-standard resolutions.
     # Then we use pyramid to use all resolutions to avoid missing any structure in specific resolutions.
 
-    color_canny = pyramid_canny_color(x, canny_low_threshold)
+    color_canny = pyramid_canny_color(x, canny_low_threshold, canny_high_threshold)
     result = np.sum(color_canny, axis=2)
 
     return norm255(result, low=1, high=99).clip(0, 255).astype(np.uint8)

modules/advanced_parameters.py

@@ -1,18 +1,18 @@
-controlnet_softness, canny_high_threshold, \
+controlnet_softness, \
     refiner_swap_method, \
     freeu_enabled, freeu_b1, freeu_b2, freeu_s1, freeu_s2, \
     debugging_inpaint_preprocessor, inpaint_disable_initial_latent, inpaint_engine, inpaint_strength, inpaint_respective_field, \
-    inpaint_mask_upload_checkbox, invert_mask_checkbox, inpaint_erode_or_dilate = [None] * 16
+    inpaint_mask_upload_checkbox, invert_mask_checkbox, inpaint_erode_or_dilate = [None] * 15
 
 
 def set_all_advanced_parameters(*args):
-    global controlnet_softness, canny_high_threshold, \
+    global controlnet_softness, \
         refiner_swap_method, \
         freeu_enabled, freeu_b1, freeu_b2, freeu_s1, freeu_s2, \
         debugging_inpaint_preprocessor, inpaint_disable_initial_latent, inpaint_engine, inpaint_strength, inpaint_respective_field, \
         inpaint_mask_upload_checkbox, invert_mask_checkbox, inpaint_erode_or_dilate
 
-    controlnet_softness, canny_high_threshold, \
+    controlnet_softness, \
         refiner_swap_method, \
         freeu_enabled, freeu_b1, freeu_b2, freeu_s1, freeu_s2, \
         debugging_inpaint_preprocessor, inpaint_disable_initial_latent, inpaint_engine, inpaint_strength, inpaint_respective_field, \

modules/async_worker.py

@@ -155,6 +155,7 @@ def worker():
         debugging_cn_preprocessor = args.pop()
         skipping_cn_preprocessor = args.pop()
         canny_low_threshold = args.pop()
+        canny_high_threshold = args.pop()
 
         cn_tasks = {x: [] for x in flags.ip_list}
         for _ in range(4):
@@ -647,7 +648,7 @@ def worker():
                 cn_img = resize_image(HWC3(cn_img), width=width, height=height)
 
                 if not skipping_cn_preprocessor:
-                    cn_img = preprocessors.canny_pyramid(cn_img, canny_low_threshold)
+                    cn_img = preprocessors.canny_pyramid(cn_img, canny_low_threshold, canny_high_threshold)
 
                 cn_img = HWC3(cn_img)
                 task[0] = core.numpy_to_pytorch(cn_img)

webui.py

@@ -446,7 +446,7 @@ with shared.gradio_root:
                         freeu_s2 = gr.Slider(label='S2', minimum=0, maximum=4, step=0.01, value=0.95)
                         freeu_ctrls = [freeu_enabled, freeu_b1, freeu_b2, freeu_s1, freeu_s2]
 
-                adps = [controlnet_softness, canny_high_threshold, refiner_swap_method]
+                adps = [controlnet_softness, refiner_swap_method]
                 adps += freeu_ctrls
                 adps += inpaint_ctrls
 
@@ -528,7 +528,7 @@ with shared.gradio_root:
         ctrls += [sampler_name, scheduler_name]
         ctrls += [overwrite_step, overwrite_switch, overwrite_width, overwrite_height, overwrite_vary_strength]
         ctrls += [overwrite_upscale_strength, mixing_image_prompt_and_vary_upscale, mixing_image_prompt_and_inpaint]
-        ctrls += [debugging_cn_preprocessor, skipping_cn_preprocessor, canny_low_threshold]
+        ctrls += [debugging_cn_preprocessor, skipping_cn_preprocessor, canny_low_threshold, canny_high_threshold]
         ctrls += ip_ctrls
 
         state_is_generating = gr.State(False)