diff --git a/ldm/modules/embedding_manager.py b/ldm/modules/embedding_manager.py
index 677bc4ad3a4..128b575e9bb 100644
--- a/ldm/modules/embedding_manager.py
+++ b/ldm/modules/embedding_manager.py
@@ -215,11 +215,13 @@ def save(self, ckpt_path):
             ckpt_path,
         )
 
-    def load(self, ckpt_path):
+    def load(self, ckpt_path, full=True):
         ckpt = torch.load(ckpt_path, map_location='cpu')
-
-        self.string_to_token_dict = ckpt['string_to_token']
-        self.string_to_param_dict = ckpt['string_to_param']
+        self.string_to_token_dict = ckpt["string_to_token"]
+        self.string_to_param_dict = ckpt["string_to_param"]
+        if not full:
+            for key, value in self.string_to_param_dict.items():
+                self.string_to_param_dict[key] = torch.nn.Parameter(value.half())
 
     def get_embedding_norms_squared(self):
         all_params = torch.cat(
diff --git a/ldm/simplet2i.py b/ldm/simplet2i.py
index ffec5fda2b4..8c793a2b92a 100644
--- a/ldm/simplet2i.py
+++ b/ldm/simplet2i.py
@@ -488,7 +488,7 @@ def load_model(self):
                 )
                 model = self._load_model_from_config(config, self.weights)
                 if self.embedding_path is not None:
-                    model.embedding_manager.load(self.embedding_path)
+                    model.embedding_manager.load(self.embedding_path, self.full_precision)
                 self.model = model.to(self.device)
                 # model.to doesn't change the cond_stage_model.device used to move the tokenizer output, so set it here
                 self.model.cond_stage_model.device = self.device