diff --git a/trap/config.py b/trap/config.py
index ef83b91..180d00f 100644
--- a/trap/config.py
+++ b/trap/config.py
@@ -156,6 +156,28 @@ inference_parser.add_argument("--smooth-predictions",
                     help="Smooth the predicted tracks",
                     action='store_true')
 
+inference_parser.add_argument('--prediction-horizon',
+                    help='Trajectron.incremental_forward parameter',
+                    type=int,
+                    default=30)
+inference_parser.add_argument('--num-samples',
+                    help='Trajectron.incremental_forward parameter',
+                    type=int,
+                    default=5)
+inference_parser.add_argument("--full-dist",
+                    help="Trajectron.incremental_forward parameter",
+                    type=bool,
+                    default=False)
+inference_parser.add_argument("--gmm-mode",
+                    help="Trajectron.incremental_forward parameter",
+                    type=bool,
+                    default=True)
+inference_parser.add_argument("--z-mode",
+                    help="Trajectron.incremental_forward parameter",
+                    type=bool,
+                    default=False)
+
+
 # Internal connections.
 
 connection_parser.add_argument('--zmq-trajectory-addr',
diff --git a/trap/prediction_server.py b/trap/prediction_server.py
index a3c9a27..275a451 100644
--- a/trap/prediction_server.py
+++ b/trap/prediction_server.py
@@ -191,7 +191,7 @@ class PredictionServer:
 
         # You need to have at least acceleration, so you want 2 timesteps of prior data, e.g. [0, 1],
         # so that you can immediately start incremental inference from the 3rd timestep onwards.
-        init_timestep = 1
+        init_timestep = 2
 
         eval_scene = eval_env.scenes[scene_idx]
         online_env = create_online_env(eval_env, hyperparams, scene_idx, init_timestep)
@@ -314,24 +314,25 @@ class PredictionServer:
                 maps = get_maps_for_input(input_dict, eval_scene, hyperparams)
             # print(maps)
 
-            robot_present_and_future = None
-            if eval_scene.robot is not None and hyperparams['incl_robot_node']:
-                robot_present_and_future = eval_scene.robot.get(np.array([timestep,
-                                                                        timestep + hyperparams['prediction_horizon']]),
-                                                                hyperparams['state'][eval_scene.robot.type],
-                                                                padding=0.0)
-                robot_present_and_future = np.stack([robot_present_and_future, robot_present_and_future], axis=0)
-                # robot_present_and_future += adjustment
+            # robot_present_and_future = None
+            # if eval_scene.robot is not None and hyperparams['incl_robot_node']:
+            #     robot_present_and_future = eval_scene.robot.get(np.array([timestep,
+            #                                                             timestep + hyperparams['prediction_horizon']]),
+            #                                                     hyperparams['state'][eval_scene.robot.type],
+            #                                                     padding=0.0)
+            #     robot_present_and_future = np.stack([robot_present_and_future, robot_present_and_future], axis=0)
+            #     # robot_present_and_future += adjustment
 
             start = time.time()
             with warnings.catch_warnings():
                 warnings.simplefilter('ignore') # prevent deluge of UserWarning from torch's rrn.py
                 dists, preds = trajectron.incremental_forward(input_dict,
                                                             maps,
-                                                            prediction_horizon=125, # TODO: make variable
-                                                            num_samples=5, # TODO: make variable
-                                                            robot_present_and_future=robot_present_and_future,
-                                                            full_dist=True)
+                                                            prediction_horizon=self.config.prediction_horizon, # TODO: make variable
+                                                            num_samples=self.config.num_samples, # TODO: make variable
+                                                            full_dist=self.config.full_dist,
+                                                            gmm_mode=self.config.gmm_mode,
+                                                            z_mode=self.config.z_mode)
             end = time.time()
             logger.debug("took %.2f s (= %.2f Hz) w/ %d nodes and %d edges" % (end - start,
                                                                             1. / (end - start), len(trajectron.nodes),
diff --git a/trap/renderer.py b/trap/renderer.py
index 0be8c4c..453de58 100644
--- a/trap/renderer.py
+++ b/trap/renderer.py
@@ -99,7 +99,7 @@ class Renderer:
             if first_time is None:
                     first_time = frame.time
 
-            decorate_frame(frame, prediction_frame, first_time)
+            decorate_frame(frame, prediction_frame, first_time, self.config)
 
             img_path = (self.config.output_dir / f"{i:05d}.png").resolve()
 
@@ -132,7 +132,7 @@ colorset = [(0, 0, 0),
  ]
 
 
-def decorate_frame(frame: Frame, prediction_frame: Frame, first_time) -> np.array:
+def decorate_frame(frame: Frame, prediction_frame: Frame, first_time: float, config: Namespace) -> np.array:
     frame.img
 
     overlay = np.zeros(frame.img.shape, np.uint8)
@@ -225,8 +225,16 @@ def decorate_frame(frame: Frame, prediction_frame: Frame, first_time) -> np.arra
         cv2.putText(img, f"h: {np.average([len(t.history or []) for t in prediction_frame.tracks.values()])}", (580,17),  cv2.FONT_HERSHEY_PLAIN, 1, info_color, 1)
         cv2.putText(img, f"ph: {np.average([len(t.predictor_history or []) for t in prediction_frame.tracks.values()])}", (660,17),  cv2.FONT_HERSHEY_PLAIN, 1, info_color, 1)
         cv2.putText(img, f"p: {np.average([len(t.predictions or []) for t in prediction_frame.tracks.values()])}", (740,17),  cv2.FONT_HERSHEY_PLAIN, 1, info_color, 1)
+    
+    options = []
+    for option in ['prediction_horizon','num_samples','full_dist','gmm_mode','z_mode', 'model_dir']:
+        options.append(f"{option}: {config.__dict__[option]}")
+    
 
-    return img    
+    cv2.putText(img, options.pop(-1), (20,img.shape[0]-30),  cv2.FONT_HERSHEY_PLAIN, 1, base_color, 1)
+    cv2.putText(img, " | ".join(options), (20,img.shape[0]-10),  cv2.FONT_HERSHEY_PLAIN, 1, base_color, 1)
+
+    return img
 
     
 def run_renderer(config: Namespace, is_running: Event):