diff --git a/kohya_gui/dreambooth_gui.py b/kohya_gui/dreambooth_gui.py
index f28d8265..615db99b 100644
--- a/kohya_gui/dreambooth_gui.py
+++ b/kohya_gui/dreambooth_gui.py
@@ -763,11 +763,11 @@ def train_model(
             reg_factor = 1
         else:
             log.warning(
-                "Regularisation images are used... Will double the number of steps required..."
+                "Regularization images are used... Will double the number of steps required..."
             )
             reg_factor = 2
 
-        log.info(f"Regulatization factor: {reg_factor}")
+        log.info(f"Regularization factor: {reg_factor}")
 
         if max_train_steps == 0:
             # calculate max_train_steps
diff --git a/kohya_gui/lora_gui.py b/kohya_gui/lora_gui.py
index 7978b6ca..4b44e7ed 100644
--- a/kohya_gui/lora_gui.py
+++ b/kohya_gui/lora_gui.py
@@ -1052,11 +1052,11 @@ def train_model(
             reg_factor = 1
         else:
             log.warning(
-                "Regularisation images are used... Will double the number of steps required..."
+                "Regularization images are used... Will double the number of steps required..."
             )
             reg_factor = 2
 
-        log.info(f"Regulatization factor: {reg_factor}")
+        log.info(f"Regularization factor: {reg_factor}")
 
         if max_train_steps == 0:
             # calculate max_train_steps
diff --git a/kohya_gui/textual_inversion_gui.py b/kohya_gui/textual_inversion_gui.py
index f8545cac..83dc2c79 100644
--- a/kohya_gui/textual_inversion_gui.py
+++ b/kohya_gui/textual_inversion_gui.py
@@ -648,11 +648,11 @@ def train_model(
             reg_factor = 1
         else:
             log.warning(
-                "Regularisation images are used... Will double the number of steps required..."
+                "Regularization images are used... Will double the number of steps required..."
             )
             reg_factor = 2
 
-        log.info(f"Regulatization factor: {reg_factor}")
+        log.info(f"Regularization factor: {reg_factor}")
 
         if max_train_steps == 0:
             # calculate max_train_steps