Update app.py

app.py

@@ -14,9 +14,6 @@ from diffusers.models import SD3ControlNetModel, SD3MultiControlNetModel
 from diffusers.utils import load_image
 
 # load pipeline
-global pipe_canny
-global pipe_tile
-global pipe
 controlnet_canny = SD3ControlNetModel.from_pretrained("InstantX/SD3-Controlnet-Canny")
 controlnet_tile = SD3ControlNetModel.from_pretrained("InstantX/SD3-Controlnet-Tile")
 
@@ -42,14 +39,17 @@ def resize_image(input_path, output_path, target_height):
 def show_hidden():
     return gr.update(visible=True)
 
-def load_pipeline(control_type):
+def load_pipeline(control_type, progress=gr.Progress(track_tqdm=True)):
+    global pipe_canny, pipe_tile
     if control_type == "canny":
+        global pipe_canny
         pipe_canny = StableDiffusion3ControlNetPipeline.from_pretrained(
             "stabilityai/stable-diffusion-3-medium-diffusers",
             controlnet=controlnet_canny
         )
     elif control_type == "tile":
-       pipe_tile = StableDiffusion3ControlNetPipeline.from_pretrained(
+        global pipe_tile
+        pipe_tile = StableDiffusion3ControlNetPipeline.from_pretrained(
             "stabilityai/stable-diffusion-3-medium-diffusers",
             controlnet=controlnet_tile
         )
@@ -57,7 +57,7 @@ def load_pipeline(control_type):
 
 @spaces.GPU(duration=90)
 def infer(image_in, prompt, control_type, inference_steps, guidance_scale, control_weight, progress=gr.Progress(track_tqdm=True)):
-    
+    global pipe, pipe_canny, pipe_tile
     n_prompt = 'NSFW, nude, naked, porn, ugly'
 
     if control_type == "canny":
@@ -135,8 +135,9 @@ with gr.Blocks(css=css) as demo:
                             control_weight = gr.Slider(label="Control Weight", minimum=0.0, maximum=1.0, step=0.01, value=0.7)
                     
                     submit_canny_btn = gr.Button("Submit")
-                    models = gr.Textbox(visible=False)
+                    
                 with gr.Column():
+                    models = gr.Textbox(label="Plug-in pipes", visible=False)
                     result = gr.Image(label="Result")
                     canny_used = gr.Image(label="Preprocessed Canny", visible=False)