diff --git a/xinfer/timm/timm_model.py b/xinfer/timm/timm_model.py
index 82d60da..d0a841b 100644
--- a/xinfer/timm/timm_model.py
+++ b/xinfer/timm/timm_model.py
@@ -21,7 +21,7 @@ def load_model(self, **kwargs):
         self.model = timm.create_model(self.model_id, pretrained=True, **kwargs).to(
             self.device, self.dtype
         )
-        self.model = torch.compile(self.model, mode="max-autotune")
+        # self.model = torch.compile(self.model, mode="max-autotune")
         self.model.eval()
 
     def preprocess(self, images: str | list[str]):
diff --git a/xinfer/viz.py b/xinfer/viz.py
index 858373b..1c93e27 100644
--- a/xinfer/viz.py
+++ b/xinfer/viz.py
@@ -66,6 +66,13 @@ def launch_gradio_demo():
     """
     available_models = [model.id for model in model_registry.list_models()]
 
+    # Add example image URLs
+    example_images = [
+        "https://raw.githubusercontent.com/dnth/x.infer/refs/heads/main/assets/demo/000b9c365c9e307a.jpg",
+        "https://raw.githubusercontent.com/dnth/x.infer/refs/heads/main/assets/demo/00aa2580828a9009.jpg",
+        "https://raw.githubusercontent.com/dnth/x.infer/refs/heads/main/assets/demo/0a6ee446579d2885.jpg",
+    ]
+
     def load_model_and_infer(model_id, image, prompt, device, dtype):
         model = create_model(model_id, device=device, dtype=dtype)
         model_info = model_registry.get_model_info(model_id)
@@ -92,19 +99,35 @@ def load_model_and_infer(model_id, image, prompt, device, dtype):
     with gr.Blocks() as demo:
         gr.Markdown("# x.infer Gradio Demo")
 
-        model_dropdown = gr.Dropdown(choices=available_models, label="Select a model")
-        image_input = gr.Image(type="filepath", label="Input Image")
-        prompt_input = gr.Textbox(
-            label="Prompt (for image-text to text models)", visible=False
-        )
-        device_dropdown = gr.Dropdown(
-            choices=["cuda", "cpu"], label="Device", value="cuda"
-        )
-        dtype_dropdown = gr.Dropdown(
-            choices=["float32", "float16", "bfloat16"], label="Dtype", value="float16"
-        )
-        run_button = gr.Button("Run Inference")
-        output = gr.Textbox(label="Result", lines=10)
+        with gr.Row():
+            # Left column: Input controls
+            with gr.Column(scale=1):
+                model_dropdown = gr.Dropdown(
+                    choices=available_models, label="Select a model"
+                )
+                with gr.Row():
+                    device_dropdown = gr.Dropdown(
+                        choices=["cuda", "cpu"], label="Device", value="cuda"
+                    )
+                    dtype_dropdown = gr.Dropdown(
+                        choices=["float32", "float16", "bfloat16"],
+                        label="Dtype",
+                        value="float16",
+                    )
+                prompt_input = gr.Textbox(
+                    label="Prompt (for image-text to text models)", visible=False
+                )
+                run_button = gr.Button("Run Inference", variant="primary")
+
+            # Right column: Image input
+            with gr.Column(scale=1):
+                image_input = gr.Image(type="filepath", label="Input Image", height=400)
+
+        # Results section
+        output = gr.Textbox(label="Result", lines=5)
+
+        # Add examples
+        gr.Examples(examples=example_images, inputs=image_input, label="Example Images")
 
         def update_prompt_visibility(model_id):
             model_info = model_registry.get_model_info(model_id)