diff --git a/model_loading.py b/model_loading.py
index c3591c0..b8bb57a 100644
--- a/model_loading.py
+++ b/model_loading.py
@@ -182,16 +182,7 @@ class DownloadAndLoadCogVideoModel:
         if block_edit is not None:
             transformer = remove_specific_blocks(transformer, block_edit)
         
-        #fp8
-        if fp8_transformer == "enabled" or fp8_transformer == "fastmode":
-            for name, param in transformer.named_parameters():
-                params_to_keep = {"patch_embed", "lora", "pos_embedding"}
-                if not any(keyword in name for keyword in params_to_keep):
-                    param.data = param.data.to(torch.float8_e4m3fn)
         
-            if fp8_transformer == "fastmode":
-                from .fp8_optimization import convert_fp8_linear
-                convert_fp8_linear(transformer, dtype)
 
         with open(scheduler_path) as f:
             scheduler_config = json.load(f)
@@ -221,8 +212,7 @@ class DownloadAndLoadCogVideoModel:
                 adapter_list = []
                 adapter_weights = []
                 for l in lora:
-                    if l["fuse_lora"]:
-                        fuse = True
+                    fuse = True if l["fuse_lora"] else False
                     lora_sd = load_torch_file(l["path"])             
                     for key, val in lora_sd.items():
                         if "lora_B" in key:
@@ -240,8 +230,17 @@ class DownloadAndLoadCogVideoModel:
                     pipe.set_adapters(adapter_list, adapter_weights=adapter_weights)
                 if fuse:
                     pipe.fuse_lora(lora_scale=1 / lora_rank, components=["transformer"])
+
+        #fp8
+        if fp8_transformer == "enabled" or fp8_transformer == "fastmode":
+            for name, param in pipe.transformer.named_parameters():
+                params_to_keep = {"patch_embed", "lora", "pos_embedding"}
+                if not any(keyword in name for keyword in params_to_keep):
+                    param.data = param.data.to(torch.float8_e4m3fn)
         
-        
+            if fp8_transformer == "fastmode":
+                from .fp8_optimization import convert_fp8_linear
+                convert_fp8_linear(pipe.transformer, dtype)
 
         if enable_sequential_cpu_offload:
             pipe.enable_sequential_cpu_offload()