diff --git a/config/model_config.yaml b/config/model_config.yaml
index 7a4e605..ba8f983 100644
--- a/config/model_config.yaml
+++ b/config/model_config.yaml
@@ -1,10 +1,10 @@
-device: 0 # 0, cpu
+device: 1 # 0, cpu
 seq_len: 96 # should not be changed for the current datasets
 input_dim: 1 # or 1 depending on user, but is dynamically set
 noise_dim: 256
 cond_emb_dim: 64
 shuffle: True
-sparse_conditioning_loss_weight: 0.8 # sparse conditioning training sample weight for loss computation [0, 1]
+sparse_conditioning_loss_weight: 0.5 # sparse conditioning training sample weight for loss computation [0, 1]
 freeze_cond_after_warmup: False # specify whether to freeze conditioning module parameters after warmup epochs
 save_cycle: 200 # specify number of epochs to save model after
 
diff --git a/eval/evaluator.py b/eval/evaluator.py
index 6475cda..235e888 100644
--- a/eval/evaluator.py
+++ b/eval/evaluator.py
@@ -323,19 +323,22 @@ def create_visualizations(
             range_plot = plot_range_with_syn_values(
                 real_data_df, generated_samples_df, month, weekday
             )
-            writer.add_figure(f"Visualizations/Range_Plot_{i}", range_plot)
+            if range_plot is not None:
+                writer.add_figure(f"Visualizations/Range_Plot_{i}", range_plot)
 
             # Visualization 2: Plot closest real signals with synthetic values
             closest_plot = plot_syn_with_closest_real_ts(
                 real_data_df, generated_samples_df, month, weekday
             )
-            writer.add_figure(f"Visualizations/Closest_Real_TS_{i}", closest_plot)
+            if closest_plot is not None:
+                writer.add_figure(f"Visualizations/Closest_Real_TS_{i}", closest_plot)
 
         # Visualization 3: KDE plots for real and synthetic data
         real_data_array = np.stack(real_data_df["timeseries"])
         syn_data_array = np.stack(syn_data_df["timeseries"])
         kde_plot = visualization(real_data_array, syn_data_array, "kernel")
-        writer.add_figure(f"Visualizations/KDE", kde_plot)
+        if kde_plot is None:
+            writer.add_figure(f"Visualizations/KDE", kde_plot)
 
     def get_trained_model(self, dataset: Any) -> Any:
         """
diff --git a/eval/metrics.py b/eval/metrics.py
index 39d3fc7..b7f6917 100644
--- a/eval/metrics.py
+++ b/eval/metrics.py
@@ -384,7 +384,7 @@ def plot_range_with_syn_values(
                 alpha=0.6,
             )
 
-    plt.title(f"Range of Values and Synthetic Data for {weekday_name} in {month_name}")
+    plt.title(f"Range of Values and Synthetic Data for {weekday_name}s in {month_name}")
     plt.xlabel("Time of day")
     plt.ylabel("Electric load in kWh")
 
@@ -394,8 +394,7 @@ def plot_range_with_syn_values(
 
     plt.legend()
     plt.tight_layout()
-    plt.show()
-    return f
+    return plt.gcf()
 
 
 def plot_syn_with_closest_real_ts(
@@ -432,7 +431,7 @@ def plot_syn_with_closest_real_ts(
     syn_values = np.array([ts[:, dimension] for ts in syn_filtered_df["timeseries"]])
 
     # Generate timestamps at 15-minute intervals
-    timestamps = pd.date_range(start="00:00", end="23:45", freq="15T")
+    timestamps = pd.date_range(start="00:00", end="23:45", freq="15min")
 
     hourly_positions, hourly_labels = get_hourly_ticks(timestamps)
     month_name, weekday_name = get_month_weekday_names(month, weekday)
@@ -503,7 +502,7 @@ def plot_syn_with_closest_real_ts(
                 )
 
     plt.title(
-        f"Synthetic vs Closest Real Time Series for {weekday_name} in {month_name}"
+        f"Synthetic vs Closest Real Time Series for {weekday_name}s in {month_name}"
     )
     plt.xlabel("Time of day")
     plt.ylabel("Electric load in kWh")
@@ -513,5 +512,4 @@ def plot_syn_with_closest_real_ts(
 
     plt.legend()
     plt.tight_layout()
-    plt.show()
-    return f
+    return plt.gcf()
diff --git a/main.py b/main.py
index d26e4e0..fbda225 100644
--- a/main.py
+++ b/main.py
@@ -46,24 +46,24 @@ def main():
     # evaluate_individual_user_models("gpt", include_generation=False)
     # evaluate_individual_user_models("acgan", include_generation=True)
     # evaluate_individual_user_models("acgan", include_generation=False, normalization_method="date")
-    # evaluate_single_dataset_model(
-    #     "diffusion_ts",
-    #     # geography="california",
-    #     include_generation=True,
-    #     normalization_method="group",
-    # )
-    dataset_manager = PecanStreetDataManager(
+    evaluate_single_dataset_model(
+        "diffusion_ts",
         geography="california",
-        normalize=True,
         include_generation=True,
         normalization_method="group",
-        threshold=(-5, 5),
     )
-    non_pv_user_dataset = dataset_manager.create_non_pv_user_dataset()
-    generator = DataGenerator("diffusion_ts")
-    generator.load("checkpoints/2024-10-15_05-09-19/diffusion_ts_checkpoint_1000.pt")
-    evaluator = Evaluator(non_pv_user_dataset, "diffusion_ts")
-    evaluator.evaluate_model(model=generator.model, data_label="pre-loaded")
+    # dataset_manager = PecanStreetDataManager(
+    #     geography="california",
+    #     normalize=True,
+    #     include_generation=True,
+    #     normalization_method="group",
+    #     threshold=(-5, 5),
+    # )
+    # non_pv_user_dataset = dataset_manager.create_non_pv_user_dataset()
+    # generator = DataGenerator("diffusion_ts")
+    # generator.load("checkpoints/2024-10-15_05-09-44/diffusion_ts_checkpoint_1000.pt")
+    # evaluator = Evaluator(non_pv_user_dataset, "diffusion_ts")
+    # evaluator.evaluate_model(model=generator.model, data_label="pre-loaded")
 
 
 if __name__ == "__main__":