Update app.py

app.py

@@ -8,11 +8,9 @@ device = "cuda" if torch.cuda.is_available() else "cpu"
 
 if torch.cuda.is_available():
     torch.cuda.max_memory_allocated(device=device)
-    #torch.cuda.empty_cache()
     pipe = DiffusionPipeline.from_pretrained("stabilityai/sdxl-turbo", torch_dtype=torch.float16, variant="fp16", use_safetensors=True)
     pipe.enable_xformers_memory_efficient_attention()
     pipe = pipe.to(device)
-    #torch.cuda.empty_cache()
 else: 
     pipe = DiffusionPipeline.from_pretrained("stabilityai/sdxl-turbo", use_safetensors=True)
     pipe = pipe.to(device)
@@ -52,11 +50,17 @@ css="""
 }
 """
 
+if torch.cuda.is_available():
+    power_device = "GPU"
+else:
+    power_device = "CPU"
+
 with gr.Blocks(css=css) as demo:
     
     with gr.Column(elem_id="col-container"):
-        gr.Markdown("""
+        gr.Markdown(f"""
         # Text-to-Image Gradio Template
+        Currently running on {power_device}.
         """)
         
         with gr.Row():
@@ -99,7 +103,7 @@ with gr.Blocks(css=css) as demo:
                     minimum=256,
                     maximum=MAX_IMAGE_SIZE,
                     step=32,
-                    value=256,
+                    value=512,
                 )
                 
                 height = gr.Slider(
@@ -107,17 +111,17 @@ with gr.Blocks(css=css) as demo:
                     minimum=256,
                     maximum=MAX_IMAGE_SIZE,
                     step=32,
-                    value=256,
+                    value=512,
                 )
             
             with gr.Row():
                 
                 guidance_scale = gr.Slider(
                     label="Guidance scale",
-                    minimum=1,
-                    maximum=20,
+                    minimum=0.0,
+                    maximum=10.0,
                     step=0.1,
-                    value=5.0,
+                    value=0.0,
                 )
                 
                 num_inference_steps = gr.Slider(