Add controlnet resize before canny tracing

app.py

@@ -480,7 +480,7 @@ def controlnet_edges(canny_input_prompt, input_image, input_low_threshold, input
     canny_2 = Image.fromarray(canny_1)
     canny_1 = Image.fromarray(canny_1)
 
-    if int(canny_input_rotate) > 0:
+    if canny_input_rotate and int(canny_input_rotate) > 0:
       canny_rotation = 360 - int(canny_input_rotate)
       canny_2 = canny_2.rotate(canny_rotation, resample=Image.BICUBIC)
       canny_1 = canny_1.rotate(canny_rotation, resample=Image.BICUBIC)
@@ -490,28 +490,41 @@ def controlnet_edges(canny_input_prompt, input_image, input_low_threshold, input
     limit_size = 768
     # limit_size = 32
 
+    # resize image
     if input_width > input_height:
         new_width = min(input_width, limit_size)
         new_height = int(new_width * input_height / input_width)
     else:
         new_height = min(input_height, limit_size)
         new_width = int(new_height * input_width / input_height)
-
     canny_2 = canny_2.resize((new_width, new_height))
     canny_1 = canny_1.resize((new_width, new_height))
 
+    # resize original input image
+    input_resize = np.array(input_image)
+    input_resize = Image.fromarray(input_resize)
+    input_resize = input_resize.resize((new_width, new_height))
+    # make canny image now, after resize
+    canny_resize = np.array(input_resize)
+    canny_resize = cv2.Canny(canny_resize, input_low_threshold, input_high_threshold)
+    canny_resize = canny_resize[:, :, None]
+    canny_resize = np.concatenate([canny_resize, canny_resize, canny_resize], axis=2)
+    if input_invert:
+      canny_resize = 255 - canny_resize
+    canny_resize = Image.fromarray(canny_resize)
+
     prompt = canny_input_prompt
     generator = torch.Generator(device="cpu").manual_seed(canny_input_seed)
 
     output_image = controlnet_pipe(
         prompt,
-        canny_2,
+        canny_resize,
         negative_prompt="monochrome, lowres, bad anatomy, worst quality, low quality",
         generator=generator,
         num_inference_steps=20,
     )
 
-    return [canny_1, output_image[0][0]]
+    return [canny_resize, output_image[0][0]]
     # return output_image[0][0]
 
 import random 
@@ -555,6 +568,7 @@ with gr.Blocks() as canny_blocks_interface:
 
 
 
+
 # ----- Old ControlNet Canny Gradio Setup without Block (working) -----------------------------------------------------------------
 
 # import gradio as gr