diff --git a/micro_sam/instance_segmentation.py b/micro_sam/instance_segmentation.py
index 504a5a4b..f79f7b46 100644
--- a/micro_sam/instance_segmentation.py
+++ b/micro_sam/instance_segmentation.py
@@ -1152,15 +1152,20 @@ def automatic_instance_segmentation(
     ndim: Optional[int] = None,
     tile_shape: Optional[Tuple[int, int]] = None,
     halo: Optional[Tuple[int, int]] = None,
+    use_amg: bool = False,
+    **generate_kwargs
 ):
     """
     """
-    predictor, decoder = get_predictor_and_decoder(model_type=model_type, checkpoint_path=checkpoint_path)
-    if tile_shape is None:
-        segmenter = InstanceSegmentationWithDecoder(predictor=predictor, decoder=decoder)
-    else:
-        segmenter = TiledInstanceSegmentationWithDecoder(predictor=predictor, decoder=decoder)
+    predictor, state = util.get_sam_model(model_type=model_type, checkpoint_path=checkpoint_path, return_state=True)
 
+    if "decoder_state" in state and not use_amg:  # AIS
+        predictor, decoder = get_predictor_and_decoder(model_type=model_type, checkpoint_path=checkpoint_path)
+        segmenter = get_amg(predictor=predictor, decoder=decoder, is_tiled=tile_shape is not None)
+    else:  # AMG
+        segmenter = get_amg(predictor=predictor, is_tiled=tile_shape is not None)
+
+    # Load the input image file.
     if isinstance(input_path, np.ndarray):
         image_data = input_path
     else:
@@ -1168,11 +1173,10 @@ def automatic_instance_segmentation(
 
     # Precompute the image embeddings.
     image_embeddings = util.precompute_image_embeddings(
-        predictor, image_data, embedding_path, ndim=ndim, tile_shape=tile_shape, halo=halo,
+        predictor=predictor, input_=image_data, save_path=embedding_path, ndim=ndim, tile_shape=tile_shape, halo=halo,
     )
 
     segmenter.initialize(image=image_data, image_embeddings=image_embeddings)
-    generate_kwargs = {}  # TODO: check how we can allow users to pass parameters.
     masks = segmenter.generate(**generate_kwargs)
 
     if len(masks) == 0:  # instance segmentation can have no masks, hence we just save empty labels
@@ -1187,6 +1191,12 @@ def automatic_instance_segmentation(
     else:
         instances = mask_data_to_segmentation(masks, with_background=True, min_object_size=0)
 
+    import napari
+    v = napari.Viewer()
+    v.add_image(image_data)
+    v.add_labels(instances)
+    napari.run()
+
     breakpoint()
 
 
@@ -1232,11 +1242,19 @@ def main():
     )
     parser.add_argument(
         "-n", "--ndim", type=int, default=None,
-        help="The number of spatial dimensions in the data. "
-        "Please specify this if your data has a channel dimension."
+        help="The number of spatial dimensions in the data. Please specify this if your data has a channel dimension."
     )
+    parser.add_argument(
+        "--amg", action="store_true", help="Whether to use automatic mask generation with the model."
+    )
+
+    args, parameter_args = parser.parse_known_args()
+    # NOTE: the script below allows the possibility to catch additional parsed arguments which correspond to
+    # the automatic segmentation post-processing parameters (eg. 'center_distance_threshold' in AIS)
+    generate_kwargs = {
+        parameter_args[i].lstrip("--"): float(parameter_args[i + 1]) for i in range(0, len(parameter_args), 2)
+    }
 
-    args = parser.parse_args()
     automatic_instance_segmentation(
         input_path=args.input_path,
         embedding_path=args.embedding_path,
@@ -1246,6 +1264,8 @@ def main():
         ndim=args.ndim,
         tile_shape=args.tile_shape,
         halo=args.halo,
+        use_amg=args.amg,
+        **generate_kwargs,
     )