diff --git a/examples/qwen_image/qwen_2511_fp8.py b/examples/qwen_image/qwen_2511_fp8.py
index b81618f9..907ddcd8 100755
--- a/examples/qwen_image/qwen_2511_fp8.py
+++ b/examples/qwen_image/qwen_2511_fp8.py
@@ -22,7 +22,7 @@
 # Suitable for RTX 30/40/50 consumer GPUs
 # pipe.enable_offload(
 #     cpu_offload=True,
-#     offload_granularity="block",
+#     offload_granularity="block", #["block", "phase"]
 #     text_encoder_offload=True,
 #     vae_offload=False,
 # )
diff --git a/examples/qwen_image/qwen_2511_with_distill_lora.py b/examples/qwen_image/qwen_2511_with_distill_lora.py
index 0e8736df..b1955f27 100755
--- a/examples/qwen_image/qwen_2511_with_distill_lora.py
+++ b/examples/qwen_image/qwen_2511_with_distill_lora.py
@@ -22,7 +22,7 @@
 # Suitable for RTX 30/40/50 consumer GPUs
 # pipe.enable_offload(
 #     cpu_offload=True,
-#     offload_granularity="block",
+#     offload_granularity="block", #["block", "phase"]
 #     text_encoder_offload=True,
 #     vae_offload=False,
 # )
diff --git a/lightx2v/models/networks/wan/lora_adapter.py b/lightx2v/models/networks/wan/lora_adapter.py
index 93a46923..0fd31887 100755
--- a/lightx2v/models/networks/wan/lora_adapter.py
+++ b/lightx2v/models/networks/wan/lora_adapter.py
@@ -1,17 +1,17 @@
-import gc
 import os
 
-import torch
 from loguru import logger
 from safetensors import safe_open
 
 from lightx2v.utils.envs import *
+from lightx2v.utils.lora_loader import LoRALoader
 
 
 class WanLoraWrapper:
     def __init__(self, wan_model):
         self.model = wan_model
         self.lora_metadata = {}
+        self.lora_loader = LoRALoader()
         self.override_dict = {}  # On CPU
 
     def load_lora(self, lora_path, lora_name=None):
@@ -41,91 +41,16 @@ def apply_lora(self, lora_name, alpha=1.0):
             return False
 
         lora_weights = self._load_lora_file(self.lora_metadata[lora_name]["path"])
+
         weight_dict = self.model.original_weight_dict
-        self._apply_lora_weights(weight_dict, lora_weights, alpha)
+        self.lora_loader.apply_lora(
+            weight_dict=weight_dict,
+            lora_weights=lora_weights,
+            alpha=alpha,
+            strength=alpha,
+        )
         self.model._apply_weights(weight_dict)
 
         logger.info(f"Applied LoRA: {lora_name} with alpha={alpha}")
         del lora_weights
         return True
-
-    @torch.no_grad()
-    def _apply_lora_weights(self, weight_dict, lora_weights, alpha):
-        lora_pairs = {}
-        lora_diffs = {}
-
-        def try_lora_pair(key, prefix, suffix_a, suffix_b, target_suffix):
-            if key.endswith(suffix_a):
-                base_name = key[len(prefix) :].replace(suffix_a, target_suffix)
-                pair_key = key.replace(suffix_a, suffix_b)
-                if pair_key in lora_weights:
-                    lora_pairs[base_name] = (key, pair_key)
-
-        def try_lora_diff(key, prefix, suffix, target_suffix):
-            if key.endswith(suffix):
-                base_name = key[len(prefix) :].replace(suffix, target_suffix)
-                lora_diffs[base_name] = key
-
-        prefixs = [
-            "",  # empty prefix
-            "diffusion_model.",
-        ]
-        for prefix in prefixs:
-            for key in lora_weights.keys():
-                if not key.startswith(prefix):
-                    continue
-
-                try_lora_pair(key, prefix, "lora_A.weight", "lora_B.weight", "weight")
-                try_lora_pair(key, prefix, "lora_down.weight", "lora_up.weight", "weight")
-                try_lora_diff(key, prefix, "diff", "weight")
-                try_lora_diff(key, prefix, "diff_b", "bias")
-                try_lora_diff(key, prefix, "diff_m", "modulation")
-
-        applied_count = 0
-        for name, param in weight_dict.items():
-            if name in lora_pairs:
-                if name not in self.override_dict:
-                    self.override_dict[name] = param.clone().cpu()
-                name_lora_A, name_lora_B = lora_pairs[name]
-                lora_A = lora_weights[name_lora_A].to(param.device, param.dtype)
-                lora_B = lora_weights[name_lora_B].to(param.device, param.dtype)
-                if param.shape == (lora_B.shape[0], lora_A.shape[1]):
-                    param += torch.matmul(lora_B, lora_A) * alpha
-                    applied_count += 1
-            elif name in lora_diffs:
-                if name not in self.override_dict:
-                    self.override_dict[name] = param.clone().cpu()
-
-                name_diff = lora_diffs[name]
-                lora_diff = lora_weights[name_diff].to(param.device, param.dtype)
-                if param.shape == lora_diff.shape:
-                    param += lora_diff * alpha
-                    applied_count += 1
-
-        logger.info(f"Applied {applied_count} LoRA weight adjustments")
-        if applied_count == 0:
-            logger.info(
-                "Warning: No LoRA weights were applied. Expected naming conventions: 'diffusion_model.<layer_name>.lora_A.weight' and 'diffusion_model.<layer_name>.lora_B.weight'. Please verify the LoRA weight file."
-            )
-
-    @torch.no_grad()
-    def remove_lora(self):
-        logger.info(f"Removing LoRA ...")
-
-        restored_count = 0
-        for k, v in self.override_dict.items():
-            self.model.original_weight_dict[k] = v.to(self.model.device)
-            restored_count += 1
-
-        logger.info(f"LoRA removed, restored {restored_count} weights")
-
-        self.model._apply_weights(self.model.original_weight_dict)
-
-        torch.cuda.empty_cache()
-        gc.collect()
-
-        self.lora_metadata = {}
-        self.override_dict = {}
-
-    def list_loaded_loras(self):
-        return list(self.lora_metadata.keys())