Update app.py

app.py

@@ -40,15 +40,16 @@ def resize_image(input_path, output_path, target_height):
 
 def load_pipeline(control_type):
     if control_type == "canny":
-        pipe_canny = StableDiffusion3ControlNetPipeline.from_pretrained(
+        global 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 = StableDiffusion3ControlNetPipeline.from_pretrained(
             "stabilityai/stable-diffusion-3-medium-diffusers",
             controlnet=controlnet_tile
         )
+    return gr.update(value="pipeline ready", visible=True)
 
 @spaces.GPU(duration=90)
 def infer(image_in, prompt, control_type, inference_steps, guidance_scale, control_weight, progress=gr.Progress(track_tqdm=True)):
@@ -56,7 +57,7 @@ def infer(image_in, prompt, control_type, inference_steps, guidance_scale, contr
     n_prompt = 'NSFW, nude, naked, porn, ugly'
 
     if control_type == "canny":
-        pipe = pipe_canny
+        global pipe = pipe_canny
         pipe.to("cuda", torch.float16)
         # Canny preprocessing
         image_to_canny = load_image(image_in)
@@ -69,7 +70,7 @@ def infer(image_in, prompt, control_type, inference_steps, guidance_scale, contr
         control_image = image_to_canny
     
     elif control_type == "tile":
-        pipe = pipe_tile
+        global pipe = pipe_tile
         pipe.to("cuda", torch.float16)
         control_image = load_image(image_in)
  
@@ -130,15 +131,17 @@ 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():
                     result = gr.Image(label="Result")
                     canny_used = gr.Image(label="Preprocessed Canny", visible=False)
+                    
 
 
     submit_canny_btn.click(
         fn = load_pipeline,
         inputs = [control_type],
-        outputs = None
+        outputs = [models]
     ).then(
         fn = infer,
         inputs = [image_in, prompt, control_type, inference_steps, guidance_scale, control_weight],