diff --git a/src/trainers/diffusion_trainer.py b/src/trainers/diffusion_trainer.py
index f4dcfe7..ebe668b 100644
--- a/src/trainers/diffusion_trainer.py
+++ b/src/trainers/diffusion_trainer.py
@@ -28,6 +28,8 @@ class DiffusionTrainer:
         self.alpha = 1. - self.beta
         self.alpha_hat = torch.cumprod(self.alpha, dim=0)
 
+        self.best_score = None
+
     def noise_time_series(self, x: torch.tensor, t: int):
         """ Add noise to time series
         Args:
@@ -93,6 +95,7 @@ class DiffusionTrainer:
         self.data_processor = task.connect(self.data_processor, name="data_processor")
         
     def train(self, epochs: int, learning_rate: float, task: Task = None):
+        self.best_score = None
         optimizer = torch.optim.Adam(self.model.parameters(), lr=learning_rate)
         criterion = nn.MSELoss()
         self.model.to(self.device)
@@ -220,6 +223,9 @@ class DiffusionTrainer:
         all_crps = np.array(all_crps)
         mean_crps = all_crps.mean()
 
+        if self.best_score is None or mean_crps < self.best_score:
+            self.save_checkpoint(mean_crps, task, epoch)
+
         if task:
             task.get_logger().report_scalar(
                 title="CRPS",
@@ -227,4 +233,11 @@ class DiffusionTrainer:
                 value=mean_crps,
                 iteration=epoch
             )
+
+    def save_checkpoint(self, val_loss, task, iteration: int):
+        torch.save(self.model.state_dict(), "checkpoint.pt")
+        task.update_output_model(
+            model_path="checkpoint.pt", iteration=iteration, auto_delete_file=False
+        )
+        self.best_score = val_loss
         
diff --git a/src/training_scripts/diffusion_training.py b/src/training_scripts/diffusion_training.py
index 83f4b85..fd384f3 100644
--- a/src/training_scripts/diffusion_training.py
+++ b/src/training_scripts/diffusion_training.py
@@ -35,7 +35,7 @@ data_config.DAY_OF_WEEK = False
 
 data_config.NOMINAL_NET_POSITION = True
 
-data_config = Task.connect(data_config, name="data_features")
+data_config = task.connect(data_config, name="data_features")
 
 data_processor = DataProcessor(data_config, path="", lstm=False)
 data_processor.set_batch_size(8192)