global init

check_app.py

@@ -23,6 +23,18 @@ MAX_SEED = np.iinfo(np.int32).max
 MAX_IMAGE_SIZE = 1024
 gallery : gr.Gallery = None
 
+def initialize_components():
+    global gallery
+    
+    gallery = gr.Gallery(
+    label="Image Gallery",
+    show_label=True,
+    columns=4,
+    rows=3,
+    height=600,
+    object_fit="contain"
+    )
+    
 class ProgressPipeline(DiffusionPipeline):
     def __init__(self, original_pipeline):
         super().__init__()
@@ -200,6 +212,8 @@ def create_pipeline_logic(prompt_text, model_name, negative_prompt="",  seed=42,
     )
     return f"Seed: {gen_seed}", image
 def main():
+    if gallery is None:
+        initialize_components()
     with gr.Blocks() as app:
         gr.Markdown("# Dynamic Multiple Model Image Generation")
 
@@ -255,28 +269,23 @@ def main():
                 )
         
         for model_name, config in MODEL_CONFIGS.items():
-            global gallery
+            #global gallery
             with gr.Tab(model_name):
                 button = gr.Button(f"Run {model_name}")
                 output = gr.Textbox(label="Status")
                 img = gr.Image(label=model_name, height=300)
 
                 button.click(fn=create_pipeline_logic, inputs=[prompt_text, gr.Text(value= model_name,visible=False), negative_prompt,
-            seed,
-            randomize_seed,
-            width,
-            height,
-            guidance_scale,
-            num_inference_steps], outputs=[output, img, gallery])
-           
-            gallery = gr.Gallery(
-            label="Image Gallery",
-            show_label=True,
-            columns=4,
-            rows=3,
-            height=600,
-            object_fit="contain"
-        )
+                        seed,
+                        randomize_seed,
+                        width,
+                        height,
+                        guidance_scale,
+                        num_inference_steps], outputs=[output, img, gallery])
+
+                gallery.render()
+                
+        
 
     app.launch()