diff --git a/slide2vec/configs/default_model.yaml b/slide2vec/configs/default_model.yaml
index 3d7c02d..70068ae 100644
--- a/slide2vec/configs/default_model.yaml
+++ b/slide2vec/configs/default_model.yaml
@@ -13,7 +13,7 @@ model:
   arch: # architecture of custom model
   pretrained_weights: # path to the pretrained weights when using a custom model
   batch_size: 256
-  tile_size: ${tiling.params.tile_size}
+  input_size: ${tiling.params.tile_size}
   restrict_to_tissue: false # whether to restrict tile content to tissue pixels only when feeding tile through encoder
   patch_size: 256 # if level is "region", size used to unroll the region into patches
   token_size: 16 # size of the tokens used model is a custom pretrained ViT
diff --git a/slide2vec/main.py b/slide2vec/main.py
index d93c4ea..21757e4 100644
--- a/slide2vec/main.py
+++ b/slide2vec/main.py
@@ -93,8 +93,7 @@ def run_feature_extraction(config_file, output_dir, run_on_cpu: False):
         "torch.distributed.run",
         f"--master_port={free_port}",
         "--nproc_per_node=gpu",
-        "-m",
-        "slide2vec.embed",
+        "slide2vec/embed.py",
         "--config-file",
         os.path.abspath(config_file),
         "--output-dir",
@@ -103,8 +102,7 @@ def run_feature_extraction(config_file, output_dir, run_on_cpu: False):
     if run_on_cpu:
         cmd = [
             sys.executable,
-            "-m",
-            "slide2vec.embed",
+            "slide2vec/embed.py",
             "--config-file",
             os.path.abspath(config_file),
             "--output-dir",
@@ -127,11 +125,9 @@ def run_feature_extraction(config_file, output_dir, run_on_cpu: False):
 
 def run_feature_aggregation(config_file, output_dir, run_on_cpu: False):
     print("Running aggregate.py...")
-    # find a free port
     cmd = [
         sys.executable,
-        "-m",
-        "slide2vec.aggregate",
+        "slide2vec/aggregate.py",
         "--config-file",
         os.path.abspath(config_file),
         "--output-dir",
diff --git a/slide2vec/models/models.py b/slide2vec/models/models.py
index d3ec23b..7116857 100644
--- a/slide2vec/models/models.py
+++ b/slide2vec/models/models.py
@@ -63,7 +63,7 @@ def __init__(
                 model = PathoJEPA(
                     pretrained_weights=options.pretrained_weights,
                     arch=options.arch,
-                    input_size=options.tile_size,
+                    input_size=options.input_size,
                     patch_size=options.token_size,
                     normalize_embeddings=options.normalize_embeddings,
                 )
@@ -77,13 +77,13 @@ def __init__(
                 model = PandaViT(
                     arch="vit_small",
                     pretrained_weights=options.pretrained_weights,
-                    input_size=options.tile_size,
+                    input_size=options.input_size,
                 )
             elif options.name == "dino" and options.arch:
                 model = DINOViT(
                     arch=options.arch,
                     pretrained_weights=options.pretrained_weights,
-                    input_size=options.tile_size,
+                    input_size=options.input_size,
                     patch_size=options.token_size,
                 )
         elif options.level == "region":
@@ -117,7 +117,7 @@ def __init__(
                 tile_encoder = PathoJEPA(
                     pretrained_weights=options.pretrained_weights,
                     arch=options.arch,
-                    input_size=options.patch_size,
+                    input_size=options.input_size,
                     patch_size=options.token_size,
                     normalize_embeddings=options.normalize_embeddings,
                 )
@@ -131,13 +131,13 @@ def __init__(
                 tile_encoder = PandaViT(
                     arch="vit_small",
                     pretrained_weights=options.pretrained_weights,
-                    input_size=options.tile_size,
+                    input_size=options.input_size,
                 )
             elif options.name is None and options.arch:
                 tile_encoder = DINOViT(
                     arch=options.arch,
                     pretrained_weights=options.pretrained_weights,
-                    input_size=options.patch_size,
+                    input_size=options.input_size,
                     patch_size=options.token_size,
                 )
             model = RegionFeatureExtractor(tile_encoder, tile_size=options.patch_size)
@@ -861,7 +861,7 @@ class RegionFeatureExtractor(nn.Module):
     def __init__(
         self,
         tile_encoder: nn.Module,
-        tile_size: int = 256,
+        tile_size: int,
     ):
         super(RegionFeatureExtractor, self).__init__()
         self.tile_encoder = tile_encoder