diff --git a/src/maxdiffusion/loaders/lora_base.py b/src/maxdiffusion/loaders/lora_base.py
index f22696d3c..f52ef1531 100644
--- a/src/maxdiffusion/loaders/lora_base.py
+++ b/src/maxdiffusion/loaders/lora_base.py
@@ -24,6 +24,17 @@ class LoRABaseMixin:
   _lora_lodable_modules = []
   num_fused_loras = 0
 
+  def __init__(self):
+    self._fused_lora_keys = set()
+
+  def _check_and_record_lora(self, lora_key):
+    """Return True if this LoRA was already merged (duplicate). Records it otherwise."""
+    if lora_key in self._fused_lora_keys:
+      return True
+    self._fused_lora_keys.add(lora_key)
+    self.num_fused_loras += 1
+    return False
+
   def load_lora_weights(self, **kwargs):
     raise NotImplementedError("`load_lora_weights()` is not implemented.")
 
diff --git a/src/maxdiffusion/loaders/ltx2_lora_nnx_loader.py b/src/maxdiffusion/loaders/ltx2_lora_nnx_loader.py
index a3c4d0d38..7261fa917 100644
--- a/src/maxdiffusion/loaders/ltx2_lora_nnx_loader.py
+++ b/src/maxdiffusion/loaders/ltx2_lora_nnx_loader.py
@@ -54,6 +54,11 @@ def translate_fn(nnx_path_str):
       max_logging.log("No LoRA weight name provided; skipping LoRA load.")
       return pipeline
 
+    lora_key = (lora_model_path, transformer_weight_name)
+    if self._check_and_record_lora(lora_key):
+      max_logging.log(f"WARNING: LoRA '{lora_model_path}' already merged — skipping to avoid double-application.")
+      return pipeline
+
     h_state_dict, _ = lora_loader.lora_state_dict(lora_model_path, weight_name=transformer_weight_name, **kwargs)
     transformer_state_dict = {}
     connector_state_dict = {}
diff --git a/src/maxdiffusion/loaders/wan_lora_nnx_loader.py b/src/maxdiffusion/loaders/wan_lora_nnx_loader.py
index a34c0f1a1..7e6598730 100644
--- a/src/maxdiffusion/loaders/wan_lora_nnx_loader.py
+++ b/src/maxdiffusion/loaders/wan_lora_nnx_loader.py
@@ -50,6 +50,11 @@ def load_lora_weights(
     def translate_fn(nnx_path_str):
       return lora_conversion_utils.translate_wan_nnx_path_to_diffusers_lora(nnx_path_str, scan_layers=scan_layers)
 
+    lora_key = (lora_model_path, transformer_weight_name)
+    if self._check_and_record_lora(lora_key):
+      max_logging.log(f"WARNING: LoRA '{lora_model_path}' already merged — skipping to avoid double-application.")
+      return pipeline
+
     if hasattr(pipeline, "transformer") and transformer_weight_name:
       max_logging.log(f"Merging LoRA into transformer with rank={rank}")
       h_state_dict, _ = lora_loader.lora_state_dict(lora_model_path, weight_name=transformer_weight_name, **kwargs)
@@ -91,7 +96,10 @@ def translate_fn(nnx_path_str: str):
       return lora_conversion_utils.translate_wan_nnx_path_to_diffusers_lora(nnx_path_str, scan_layers=scan_layers)
 
     # Handle high noise model
-    if hasattr(pipeline, "high_noise_transformer") and high_noise_weight_name:
+    high_key = (lora_model_path, high_noise_weight_name, "high_noise")
+    if self._check_and_record_lora(high_key):
+      max_logging.log(f"WARNING: LoRA '{lora_model_path}' already merged into high_noise_transformer — skipping.")
+    elif hasattr(pipeline, "high_noise_transformer") and high_noise_weight_name:
       max_logging.log(f"Merging LoRA into high_noise_transformer with rank={rank}")
       h_state_dict, _ = lora_loader.lora_state_dict(lora_model_path, weight_name=high_noise_weight_name, **kwargs)
       h_state_dict = lora_conversion_utils.preprocess_wan_lora_dict(h_state_dict)
@@ -100,7 +108,10 @@ def translate_fn(nnx_path_str: str):
       max_logging.log("high_noise_transformer not found or no weight name provided for LoRA.")
 
     # Handle low noise model
-    if hasattr(pipeline, "low_noise_transformer") and low_noise_weight_name:
+    low_key = (lora_model_path, low_noise_weight_name, "low_noise")
+    if self._check_and_record_lora(low_key):
+      max_logging.log(f"WARNING: LoRA '{lora_model_path}' already merged into low_noise_transformer — skipping.")
+    elif hasattr(pipeline, "low_noise_transformer") and low_noise_weight_name:
       max_logging.log(f"Merging LoRA into low_noise_transformer with rank={rank}")
       l_state_dict, _ = lora_loader.lora_state_dict(lora_model_path, weight_name=low_noise_weight_name, **kwargs)
       l_state_dict = lora_conversion_utils.preprocess_wan_lora_dict(l_state_dict)