diff --git a/trident/slide_encoder_models/load.py b/trident/slide_encoder_models/load.py
index 86cca219..abf9cc8c 100644
--- a/trident/slide_encoder_models/load.py
+++ b/trident/slide_encoder_models/load.py
@@ -5,8 +5,7 @@
 from abc import abstractmethod
 from einops import rearrange
 from typing import Optional, Tuple
-
-from trident.IO import get_weights_path
+from trident.IO import get_weights_path, has_internet_connection
 
 """
 This file contains 10+ pretrained slide encoders, all loadable via the encoder_factory() function.
@@ -50,13 +49,16 @@ def encoder_factory(model_name: str, pretrained: bool = True, freeze: bool = Tru
 
 
 class BaseSlideEncoder(torch.nn.Module):
-    
-    def __init__(self, freeze: bool = True, **build_kwargs: dict) -> None:
+
+    _has_internet = has_internet_connection()
+
+    def __init__(self, weights_path: Optional[str] = None, freeze: bool = True, **build_kwargs: dict) -> None:
         """
         Parent class for all pretrained slide encoders.
         """
         super().__init__()
         self.enc_name = None
+        self.weights_path: Optional[str] = weights_path
         self.model, self.precision, self.embedding_dim = self._build(**build_kwargs)
 
         # Set all parameters to be non-trainable
@@ -71,7 +73,33 @@ def forward(self, batch):
         """
         z = self.model(batch)
         return z
-        
+
+
+    def ensure_valid_weights_path(self, weights_path):
+        if weights_path and not (os.path.isfile(weights_path) or os.path.isdir(weights_path)):
+            raise FileNotFoundError(f"Expected checkpoint at '{weights_path}', but the file was not found.")
+
+    def _get_weights_path(self):
+        """ If self.weights_path is provided, use it. If not provided, check the model registry.
+        If path in model registry is empty, auto-download from huggingface else, use the path from the registry.
+        """
+        if self.weights_path:
+            self.ensure_valid_weights_path(self.weights_path)
+            return self.weights_path
+        else:
+            weights_path = get_weights_path('slide', self.enc_name)
+            self.ensure_valid_weights_path(weights_path)
+            return weights_path
+
+
+    def ensure_has_internet(self, enc_name):
+        if not BaseSlideEncoder._has_internet:
+            raise FileNotFoundError(
+                f"Internet connection does seem not available. Auto checkpoint download is disabled."
+                f"To proceed, please manually download: {enc_name},\n"
+                f"and place it in the model registry in:\n`trident/slide_encoder_models/local_ckpts.json`"
+            )
+
     @abstractmethod
     def _build(self, **build_kwargs):
         """
@@ -410,8 +438,14 @@ def __init__(self, **build_kwargs):
     def _build(self, pretrained=True):
         self.enc_name = 'titan'
         assert pretrained, "TitanSlideEncoder has no non-pretrained models. Please load with pretrained=True."
-        from transformers import AutoModel 
-        model = AutoModel.from_pretrained('MahmoodLab/TITAN', trust_remote_code=True)
+        from transformers import AutoModel
+
+        weights_path = self._get_weights_path()
+        if weights_path:
+            model = AutoModel.from_pretrained(weights_path, trust_remote_code=True)
+        else:
+            self.ensure_has_internet(self.enc_name)
+            model = AutoModel.from_pretrained('MahmoodLab/TITAN', trust_remote_code=True)
         precision = torch.float16
         embedding_dim = 768
         return model, precision, embedding_dim
diff --git a/trident/slide_encoder_models/local_ckpts.json b/trident/slide_encoder_models/local_ckpts.json
index ca493010..5ec97705 100644
--- a/trident/slide_encoder_models/local_ckpts.json
+++ b/trident/slide_encoder_models/local_ckpts.json
@@ -1,4 +1,5 @@
 {
     "chief": "./CHIEF",
-    "madeleine": "./MADELEINE"
+    "madeleine": "./MADELEINE",
+    "titan": ""
 }
\ No newline at end of file