diff --git a/examples/2_evaluate_pretrained_policy.py b/examples/2_evaluate_pretrained_policy.py
index 5c1932de..b2fe1dba 100644
--- a/examples/2_evaluate_pretrained_policy.py
+++ b/examples/2_evaluate_pretrained_policy.py
@@ -18,6 +18,14 @@ from lerobot.common.policies.diffusion.modeling_diffusion import DiffusionPolicy
 output_directory = Path("outputs/eval/example_pusht_diffusion")
 output_directory.mkdir(parents=True, exist_ok=True)
 
+# Download the diffusion policy for pusht environment
+pretrained_policy_path = Path(snapshot_download("lerobot/diffusion_pusht"))
+# OR uncomment the following to evaluate a policy from the local outputs/train folder.
+# pretrained_policy_path = Path("outputs/train/example_pusht_diffusion")
+
+policy = DiffusionPolicy.from_pretrained(pretrained_policy_path)
+policy.eval()
+
 # Check if GPU is available
 if torch.cuda.is_available():
     device = torch.device("cuda")
@@ -28,13 +36,6 @@ else:
     # Decrease the number of reverse-diffusion steps (trades off a bit of quality for 10x speed)
     policy.diffusion.num_inference_steps = 10
 
-# Download the diffusion policy for pusht environment
-pretrained_policy_path = Path(snapshot_download("lerobot/diffusion_pusht"))
-# OR uncomment the following to evaluate a policy from the local outputs/train folder.
-# pretrained_policy_path = Path("outputs/train/example_pusht_diffusion")
-
-policy = DiffusionPolicy.from_pretrained(pretrained_policy_path)
-policy.eval()
 policy.to(device)
 
 # Initialize evaluation environment to render two observation types: