diff --git a/hyvideo/modules/fp8_optimization.py b/hyvideo/modules/fp8_optimization.py
index 3665a53..86b7af7 100644
--- a/hyvideo/modules/fp8_optimization.py
+++ b/hyvideo/modules/fp8_optimization.py
@@ -1,5 +1,4 @@
 import os
-
 import torch
 import torch.nn as nn
 from torch.nn import functional as F
@@ -85,22 +84,18 @@ def convert_fp8_linear(module, original_dtype):
     script_directory = os.path.dirname(os.path.abspath(__file__))
 
     # loading fp8 mapping file
-    #fp8_map_path = dit_weight_path.replace('.pt', '_map.pt')
     fp8_map_path = os.path.join(script_directory,"fp8_map.safetensors")
     if os.path.exists(fp8_map_path):
-        #fp8_map = torch.load(fp8_map_path, map_location=lambda storage, loc: storage)
-        fp8_map = load_torch_file(fp8_map_path)
+        fp8_map = load_torch_file(fp8_map_path, safe_load=True)
     else:
         raise ValueError(f"Invalid fp8_map path: {fp8_map_path}.")
 
-    fp8_layers = []
+    #fp8_layers = []
     for key, layer in module.named_modules():
         if isinstance(layer, nn.Linear) and ('double_blocks' in key or 'single_blocks' in key):
-            fp8_layers.append(key)
+            #fp8_layers.append(key)
             original_forward = layer.forward
-            layer.weight = torch.nn.Parameter(layer.weight.to(torch.float8_e4m3fn))
+            #layer.weight = torch.nn.Parameter(layer.weight.to(torch.float8_e4m3fn))
             setattr(layer, "fp8_scale", fp8_map[key].to(dtype=original_dtype))
             setattr(layer, "original_forward", original_forward)
             setattr(layer, "forward", lambda input, m=layer: fp8_linear_forward(m, original_dtype, input))
-    
-
diff --git a/nodes.py b/nodes.py
index ea8d1a0..9a55d54 100644
--- a/nodes.py
+++ b/nodes.py
@@ -280,7 +280,7 @@ class HyVideoModelLoader:
         base_dtype = {"fp8_e4m3fn": torch.float8_e4m3fn, "fp8_e4m3fn_fast": torch.float8_e4m3fn, "bf16": torch.bfloat16, "fp16": torch.float16, "fp32": torch.float32}[base_precision]
 
         model_path = folder_paths.get_full_path_or_raise("diffusion_models", model)
-        sd = load_torch_file(model_path, device=transformer_load_device)
+        sd = load_torch_file(model_path, device=transformer_load_device, safe_load=True)
 
         in_channels = out_channels = 16
         factor_kwargs = {"device": transformer_load_device, "dtype": base_dtype}
@@ -503,7 +503,7 @@ class HyVideoVAELoader:
         with open(os.path.join(script_directory, 'configs', 'hy_vae_config.json')) as f:
             vae_config = json.load(f)
         model_path = folder_paths.get_full_path("vae", model_name)
-        vae_sd = load_torch_file(model_path)
+        vae_sd = load_torch_file(model_path, safe_load=True)
 
         vae = AutoencoderKLCausal3D.from_config(vae_config)
         vae.load_state_dict(vae_sd)
@@ -1010,7 +1010,7 @@ class HyVideoTextEmbedsLoad:
 
     def load(self, embeds):
         embed_path = folder_paths.get_full_path_or_raise("hyvid_embeds", embeds)
-        loaded_tensors = load_torch_file(embed_path)
+        loaded_tensors = load_torch_file(embed_path, safe_load=True)
         # Reconstruct original dictionary with None for missing keys
         prompt_embeds_dict = {
             "prompt_embeds": loaded_tensors.get("prompt_embeds", None),