Update app.py

app.py

@@ -12,7 +12,7 @@ def initialize_auraflow_pipeline():
     ).to("cuda")
     return pipeline
 
-@spaces.GPU(duration=100)
+@spaces.GPU(duration=150)
 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)
@@ -40,7 +40,7 @@ with gr.Blocks() as demo:
     gr.HTML(
         """
     <h1 style='text-align: center'>
-    YOLOv9: Learning What You Want to Learn Using Programmable Gradient Information
+    AuraFlow v0.3
     </h1>
     """)
     gr.HTML(
@@ -53,9 +53,9 @@ with gr.Blocks() as demo:
     with gr.Row():
         with gr.Column():
             prompt_input = gr.Textbox(label="Prompt", placeholder="Enter your image prompt here...")
-            width_input = gr.Slider(minimum=512, maximum=2048, step=64, value=1536, label="Width")
-            height_input = gr.Slider(minimum=512, maximum=2048, step=64, value=768, label="Height")
-            steps_input = gr.Slider(minimum=10, maximum=100, step=1, value=50, label="Number of Inference Steps")
+            width_input = gr.Slider(minimum=256, maximum=1024, step=64, value=1024, label="Width")
+            height_input = gr.Slider(minimum=256, maximum=1024, step=64, value=1024, label="Height")
+            steps_input = gr.Slider(minimum=10, maximum=100, step=1, value=20, label="Number of Inference Steps")
             seed_input = gr.Number(label="Seed", value=1)
             guidance_input = gr.Slider(minimum=1, maximum=10, step=0.1, value=3.5, label="Guidance Scale")
             generate_btn = gr.Button("Generate Image")