Fix the image dims

app.py

@@ -48,12 +48,12 @@ def infer(
     inference_steps=25,
     guidance_scale=7.0,
     control_weight=0.7,
-    low_threshold=100,
-    high_threshold=200,
     progress=gr.Progress(track_tqdm=True)
 ):
     # Canny preprocessing
     control_image = load_image(image_in)
+    control_image = np.concatenate([control_image[:, :, None]] * 3, axis=2)
+    control_image = Image.fromarray(control_image)
  
     # Infer
     image = pipe(
@@ -65,7 +65,7 @@ def infer(
         guidance_scale=guidance_scale,
     ).images[0]
 
-    image_redim, w, h = resize_image(image_in, "resized_input.jpg", 1024)
+    _, w, h = resize_image(image_in, "resized_input.jpg", 1024)
     image = image.resize((w, h), Image.LANCZOS)
     
     return image, gr.update(value=control_image , visible=True)
@@ -100,8 +100,6 @@ with gr.Blocks(css=css) as demo:
                                 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=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")
                     
@@ -112,11 +110,10 @@ with gr.Blocks(css=css) as demo:
 
     submit_canny_btn.click(
         fn=infer,
-        inputs=[image_in, prompt, negative_prompt, inference_steps, guidance_scale, control_weight, low_threshold, high_threshold],
+        inputs=[image_in, prompt, negative_prompt, inference_steps, guidance_scale, control_weight],
         outputs=[result, canny_used],
         api_name="predict",
         show_api=True
     )
 
 demo.queue().launch(show_api=True)
-