diff --git a/multi_run.py b/multi_run.py
index bce16fd..5e996cf 100644
--- a/multi_run.py
+++ b/multi_run.py
@@ -1,4 +1,8 @@
-import shutil
+# Imports
+# =============================================================================
+from _paramters import main_arg_parser
+from main import run_lightning_loop
+
 import warnings
 
 from ml_lib.utils.config import Config
@@ -6,12 +10,6 @@ from ml_lib.utils.config import Config
 warnings.filterwarnings('ignore', category=FutureWarning)
 warnings.filterwarnings('ignore', category=UserWarning)
 
-# Imports
-# =============================================================================
-
-from main import run_lightning_loop
-from _paramters import main_arg_parser
-
 
 if __name__ == '__main__':
 
@@ -20,43 +18,59 @@ if __name__ == '__main__':
     config = Config().read_namespace(args)
 
     arg_dict = dict()
-    for seed in range(0, 3):
+    for seed in range(1):
         arg_dict.update(main_seed=seed)
-        for patch_size in [3, 5 , 9]:
-            for model in ['SequentialVisualTransformer']:
-                arg_dict.update(model_type=model, model_patch_size=patch_size)
-                raw_conf = dict(data_speed_amount=0.0, data_speed_min=0.0, data_speed_max=0.0,
-                                data_mask_ratio=0.0, data_noise_ratio=0.0, data_shift_ratio=0.0, data_loudness_ratio=0.0,
-                                data_stretch=False, train_epochs=401)
+        if False:
+            for patch_size in [3, 5 , 9]:
+                for model in ['SequentialVisualTransformer']:
+                    arg_dict.update(model_type=model, model_patch_size=patch_size)
+                    raw_conf = dict(data_speed_amount=0.0, data_speed_min=0.0, data_speed_max=0.0,
+                                    data_mask_ratio=0.0, data_noise_ratio=0.0, data_shift_ratio=0.0, data_loudness_ratio=0.0,
+                                    data_stretch=False, train_epochs=401)
 
-                all_conf = dict(data_speed_amount=0.4, data_speed_min=0.7, data_speed_max=1.7,
-                                data_mask_ratio=0.2, data_noise_ratio=0.4, data_shift_ratio=0.4, data_loudness_ratio=0.4,
-                                data_stretch=True, train_epochs=101)
+                    all_conf = dict(data_speed_amount=0.4, data_speed_min=0.7, data_speed_max=1.7,
+                                    data_mask_ratio=0.2, data_noise_ratio=0.4, data_shift_ratio=0.4, data_loudness_ratio=0.4,
+                                    data_stretch=True, train_epochs=101)
 
-                speed_conf = raw_conf.copy()
-                speed_conf.update(data_speed_amount=0.4, data_speed_min=0.7, data_speed_max=1.7,
-                                  data_stretch=True, train_epochs=101)
+                    speed_conf = raw_conf.copy()
+                    speed_conf.update(data_speed_amount=0.4, data_speed_min=0.7, data_speed_max=1.7,
+                                      data_stretch=True, train_epochs=101)
 
-                mask_conf = raw_conf.copy()
-                mask_conf.update(data_mask_ratio=0.2, data_stretch=True, train_epochs=101)
+                    mask_conf = raw_conf.copy()
+                    mask_conf.update(data_mask_ratio=0.2, data_stretch=True, train_epochs=101)
 
-                noise_conf = raw_conf.copy()
-                noise_conf.update(data_noise_ratio=0.4, data_stretch=True, train_epochs=101)
+                    noise_conf = raw_conf.copy()
+                    noise_conf.update(data_noise_ratio=0.4, data_stretch=True, train_epochs=101)
 
-                shift_conf = raw_conf.copy()
-                shift_conf.update(data_shift_ratio=0.4, data_stretch=True, train_epochs=101)
+                    shift_conf = raw_conf.copy()
+                    shift_conf.update(data_shift_ratio=0.4, data_stretch=True, train_epochs=101)
 
-                loudness_conf = raw_conf.copy()
-                loudness_conf.update(data_loudness_ratio=0.4, data_stretch=True, train_epochs=101)
+                    loudness_conf = raw_conf.copy()
+                    loudness_conf.update(data_loudness_ratio=0.4, data_stretch=True, train_epochs=101)
 
-                for dicts in [raw_conf, all_conf, speed_conf, mask_conf, noise_conf, shift_conf, loudness_conf]:
+                    for dicts in [raw_conf, all_conf, speed_conf, mask_conf, noise_conf, shift_conf, loudness_conf]:
 
-                    arg_dict.update(dicts)
-                    config = config.update(arg_dict)
-                    version_path = config.exp_path / config.version
-                    if version_path.exists():
-                        if not (version_path / 'weights.ckpt').exists():
-                            shutil.rmtree(version_path)
-                        else:
-                            continue
-                    run_lightning_loop(config)
+                        arg_dict.update(dicts)
+        if True:
+            for patch_size in [3, 7]:
+                for lat_dim in [4, 32]:
+                    for heads in [2, 4]:
+                        for embedding_size in [32, 64]:
+                            for attn_depth in [1, 3]:
+                                for model in ['SequentialVisualTransformer', 'VisualTransformer']:
+                                    arg_dict.update(
+                                        model_type=model,
+                                        model_patch_size=patch_size,
+                                        model_lat_dim=lat_dim,
+                                        model_heads=heads,
+                                        model_embedding_size=embedding_size,
+                                        model_attn_depth=attn_depth
+                                    )
+                                    config = config.update(arg_dict)
+                                    version_path = config.exp_path / config.version
+                                    if version_path.exists():
+                                        if not (version_path / 'weights.ckpt').exists():
+                                            shutil.rmtree(version_path)
+                                        else:
+                                            continue
+                                    run_lightning_loop(config)