Update app.py

app.py

@@ -14,19 +14,11 @@ from diffusers.models import SD3ControlNetModel, SD3MultiControlNetModel
 from diffusers.utils import load_image
 
 # load pipeline
+global pipe_canny
+global pipe_tile
 controlnet_canny = SD3ControlNetModel.from_pretrained("InstantX/SD3-Controlnet-Canny")
 controlnet_tile = SD3ControlNetModel.from_pretrained("InstantX/SD3-Controlnet-Tile")
 
-pipe_canny = StableDiffusion3ControlNetPipeline.from_pretrained(
-            "stabilityai/stable-diffusion-3-medium-diffusers",
-            controlnet=controlnet_canny
-        )
-
-pipe_tile = StableDiffusion3ControlNetPipeline.from_pretrained(
-            "stabilityai/stable-diffusion-3-medium-diffusers",
-            controlnet=controlnet_tile
-        )
-
 def resize_image(input_path, output_path, target_height):
     # Open the input image
     img = Image.open(input_path)
@@ -46,8 +38,20 @@ def resize_image(input_path, output_path, target_height):
 
     return output_path, new_width, target_height
 
+def load_pipeline(control_type):
+    if control_type == "canny":
+        pipe_canny = StableDiffusion3ControlNetPipeline.from_pretrained(
+            "stabilityai/stable-diffusion-3-medium-diffusers",
+            controlnet=controlnet_canny
+        )
+    elif control_type == "tile":
+        pipe_tile = StableDiffusion3ControlNetPipeline.from_pretrained(
+            "stabilityai/stable-diffusion-3-medium-diffusers",
+            controlnet=controlnet_tile
+        )
+
 @spaces.GPU(duration=90)
-def infer_canny(image_in, prompt, control_type, inference_steps, guidance_scale, control_weight, progress=gr.Progress(track_tqdm=True)):
+def infer(image_in, prompt, control_type, inference_steps, guidance_scale, control_weight, progress=gr.Progress(track_tqdm=True)):
     
     n_prompt = 'NSFW, nude, naked, porn, ugly'
 
@@ -132,7 +136,11 @@ with gr.Blocks(css=css) as demo:
 
 
     submit_canny_btn.click(
-        fn = infer_canny,
+        fn = load_pipeline,
+        inputs = [control_type],
+        outputs = None
+    ).then(
+        fn = infer,
         inputs = [image_in, prompt, control_type, inference_steps, guidance_scale, control_weight],
         outputs = [result, canny_used],
         show_api=False