diff --git a/model_loading.py b/model_loading.py
index 7c79d71..1fcfbb6 100644
--- a/model_loading.py
+++ b/model_loading.py
@@ -201,21 +201,6 @@ class DownloadAndLoadCogVideoModel:
 
         if block_edit is not None:
             transformer = remove_specific_blocks(transformer, block_edit)
-        
-        #fp8
-        if fp8_transformer == "enabled" or fp8_transformer == "fastmode":
-            params_to_keep = {"patch_embed", "lora", "pos_embedding", "time_embedding"}
-            if "1.5" in model:
-                    params_to_keep.update({"norm1.linear.weight", "norm_k", "norm_q","ofs_embedding", "norm_final", "norm_out", "proj_out"}) 
-            for name, param in transformer.named_parameters():
-                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
-                if "1.5" in model:
-                    params_to_keep.update({"ff"}) #otherwise NaNs
-                convert_fp8_linear(transformer, dtype, params_to_keep=params_to_keep)
 
         with open(scheduler_path) as f:
             scheduler_config = json.load(f)
@@ -267,6 +252,21 @@ class DownloadAndLoadCogVideoModel:
                     if any(item in lora[-1]["path"].lower() for item in dimension_loras):
                         lora_scale = lora_scale / lora_rank
                     pipe.fuse_lora(lora_scale=lora_scale, components=["transformer"])
+
+        #fp8
+        if fp8_transformer == "enabled" or fp8_transformer == "fastmode":
+            params_to_keep = {"patch_embed", "lora", "pos_embedding", "time_embedding"}
+            if "1.5" in model:
+                    params_to_keep.update({"norm1.linear.weight", "norm_k", "norm_q","ofs_embedding", "norm_final", "norm_out", "proj_out"}) 
+            for name, param in pipe.transformer.named_parameters():
+                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
+                if "1.5" in model:
+                    params_to_keep.update({"ff"}) #otherwise NaNs
+                convert_fp8_linear(pipe.transformer, dtype, params_to_keep=params_to_keep)
         
         if enable_sequential_cpu_offload:
             pipe.enable_sequential_cpu_offload()