Accept canny directly, not image

app.py

@@ -53,14 +53,7 @@ def infer(
     progress=gr.Progress(track_tqdm=True)
 ):
     # Canny preprocessing
-    image_to_canny = load_image(image_in)
-    image_to_canny = np.array(image_to_canny)
-    image_to_canny = cv2.Canny(image_to_canny, low_threshold, high_threshold)
-    image_to_canny = image_to_canny[:, :, None]
-    image_to_canny = np.concatenate([image_to_canny, image_to_canny, image_to_canny], axis=2)
-    image_to_canny = Image.fromarray(image_to_canny)
-
-    control_image = image_to_canny
+    control_image = load_image(image_in)
  
     # Infer
     image = pipe(
@@ -104,11 +97,11 @@ with gr.Blocks(css=css) as demo:
                     with gr.Accordion("Advanced settings", open=False):
                         with gr.Column():
                             with gr.Row():
-                                inference_steps = gr.Slider(label="Inference steps", minimum=1, maximum=50, step=1, value=25)
+                                inference_steps = gr.Slider(label="Inference steps", minimum=1, maximum=100, step=1, value=50)
                                 guidance_scale = gr.Slider(label="Guidance scale", minimum=1.0, maximum=10.0, step=0.1, value=7.0)
                             control_weight = gr.Slider(label="Control Weight", minimum=0.0, maximum=1.0, step=0.01, value=0.7)
-                            low_threshold = gr.Slider(label="Canny Low Threshold", minimum=0, maximum=255, step=1, value=100)
-                            high_threshold = gr.Slider(label="Canny High Threshold", minimum=0, maximum=255, step=1, value=200)
+                            low_threshold = gr.Slider(label="Canny Low Threshold", minimum=0, maximum=2048, step=1, value=100)
+                            high_threshold = gr.Slider(label="Canny High Threshold", minimum=0, maximum=2048, step=1, value=200)
                     
                     submit_canny_btn = gr.Button("Submit")