diff --git a/ldm/modules/lora_manager.py b/ldm/modules/lora_manager.py
index c1719a6db6..bb63f501e7 100644
--- a/ldm/modules/lora_manager.py
+++ b/ldm/modules/lora_manager.py
@@ -98,7 +98,7 @@ def load_lora(
                 value.shape[1], value.shape[0], (1, 1), bias=False)
         else:
             print(
-                f">> Encoundered unknown lora layer module in {name}: {type(value).__name__}")
+                f">> Encountered unknown lora layer module in {name}: {type(value).__name__}")
             continue
 
         with torch.no_grad():
@@ -125,7 +125,6 @@ class LoraManager:
 
     def __init__(self, pipe):
         self.lora_path = Path(global_models_dir(), 'lora')
-        self.lora_match = re.compile(r"<lora:([^>]+)>")
         self.unet = pipe.unet
         self.text_encoder = pipe.text_encoder
         self.device = torch.device(choose_torch_device())
@@ -208,30 +207,31 @@ class LoraManager:
         if lora_name in self.loras:
             del self.loras[lora_name]
 
+    # Define a lora to be loaded
+    # Can be used to define a lora to be loaded outside of prompts
     def set_lora(self, name, mult: float = 1.0):
         self.loras_to_load[name] = {"mult": mult}
 
-    def set_lora_from_prompt(self, match):
-        match = match.split(':')
-        name = match[0]
-
-        mult = 1.0
-        if len(match) == 2:
-            mult = float(match[1])
-
-        self.set_lora(name, mult)
-
+    # Load the lora from a prompt, syntax is <lora:lora_name:multiplier>
+    # Multiplier should be a value between 0.0 and 1.0
     def configure_prompt(self, prompt: str) -> str:
         self.applied_loras = {}
         self.loras_to_load = {}
 
-        for match in re.findall(self.lora_match, prompt):
-            self.set_lora_from_prompt(match)
+        lora_match = re.compile(r"<lora:([^>]+)>")
 
-        def found(m):
-            return ""
+        for match in re.findall(lora_match, prompt):
+            match = match.split(':')
+            name = match[0]
 
-        return re.sub(self.lora_match, found, prompt)
+            mult = 1.0
+            if len(match) == 2:
+                mult = float(match[1])
+
+            self.set_lora(name, mult)
+
+        # remove lora and return prompt to avoid the lora prompt causing issues in inference
+        return re.sub(lora_match, "", prompt)
 
     def __del__(self):
         del self.loras