Update app.py

app.py

@@ -12,7 +12,7 @@ def initialize_auraflow_pipeline():
     ).to("cuda")
     return pipeline
 
-@spaces.GPU()
+@spaces.GPU(duration=200)
 def generate_image(pipeline, prompt, width, height, num_inference_steps, seed, guidance_scale):
     """Generate an image using the AuraFlowPipeline."""
     generator = torch.Generator().manual_seed(seed)
@@ -37,8 +37,19 @@ def gradio_generate_image(prompt, width, height, num_inference_steps, seed, guid
 
 # Create Gradio Blocks
 with gr.Blocks() as demo:
-    gr.Markdown("# AuraFlow Image Generation")
-    
+    gr.HTML(
+        """
+    <h1 style='text-align: center'>
+    YOLOv9: Learning What You Want to Learn Using Programmable Gradient Information
+    </h1>
+    """)
+    gr.HTML(
+        """
+        <h3 style='text-align: center'>
+        Follow me for more!
+        <a href='https://twitter.com/kadirnar_ai' target='_blank'>Twitter</a> | <a href='https://github.com/kadirnar' target='_blank'>Github</a> | <a href='https://www.linkedin.com/in/kadir-nar/' target='_blank'>Linkedin</a>  | <a href='https://www.huggingface.co/kadirnar/' target='_blank'>HuggingFace</a>
+        </h3>
+        """)
     with gr.Row():
         with gr.Column():
             prompt_input = gr.Textbox(label="Prompt", placeholder="Enter your image prompt here...")