diff --git a/HunYuanDiT/config_mt5.json b/HunYuanDiT/config_mt5.json
index d5c7028..825cb35 100644
--- a/HunYuanDiT/config_mt5.json
+++ b/HunYuanDiT/config_mt5.json
@@ -1,7 +1,7 @@
 {
   "_name_or_path": "mt5",
   "architectures": [
-    "MT5ForConditionalGeneration"
+    "MT5EncoderModel"
   ],
   "classifier_dropout": 0.0,
   "d_ff": 5120,
diff --git a/HunYuanDiT/loader.py b/HunYuanDiT/loader.py
index 4d9726f..8b89608 100644
--- a/HunYuanDiT/loader.py
+++ b/HunYuanDiT/loader.py
@@ -77,6 +77,5 @@ def load_hydit(model_path, model_conf):
 		load_device = load_device,
 		offload_device = offload_device,
 		current_device = "cpu",
-		size = 6 * (1024**3),
 	)
 	return model_patcher
diff --git a/HunYuanDiT/models/models.py b/HunYuanDiT/models/models.py
index 175bec9..e481206 100644
--- a/HunYuanDiT/models/models.py
+++ b/HunYuanDiT/models/models.py
@@ -244,9 +244,6 @@ class HunYuanDiT(nn.Module):
         self.final_layer = FinalLayer(hidden_size, hidden_size, patch_size, self.out_channels)
         self.unpatchify_channels = self.out_channels
 
-        # probably not needed when not training?
-        # self.initialize_weights()
-
     def forward_raw(self,
                 x,
                 t,
@@ -415,40 +412,6 @@ class HunYuanDiT(nn.Module):
         else:
                 return out
 
-
-    def initialize_weights(self):
-        # Initialize transformer layers:
-        def _basic_init(module):
-            if isinstance(module, nn.Linear):
-                torch.nn.init.xavier_uniform_(module.weight)
-                if module.bias is not None:
-                    nn.init.constant_(module.bias, 0)
-        self.apply(_basic_init)
-
-        # Initialize patch_embed like nn.Linear (instead of nn.Conv2d):
-        w = self.x_embedder.proj.weight.data
-        nn.init.xavier_uniform_(w.view([w.shape[0], -1]))
-        nn.init.constant_(self.x_embedder.proj.bias, 0)
-
-        # Initialize label embedding table:
-        nn.init.normal_(self.extra_embedder[0].weight, std=0.02)
-        nn.init.normal_(self.extra_embedder[2].weight, std=0.02)
-
-        # Initialize timestep embedding MLP:
-        nn.init.normal_(self.t_embedder.mlp[0].weight, std=0.02)
-        nn.init.normal_(self.t_embedder.mlp[2].weight, std=0.02)
-
-        # Zero-out adaLN modulation layers in HunYuanDiT blocks:
-        for block in self.blocks:
-            nn.init.constant_(block.default_modulation[-1].weight, 0)
-            nn.init.constant_(block.default_modulation[-1].bias, 0)
-
-        # Zero-out output layers:
-        nn.init.constant_(self.final_layer.adaLN_modulation[-1].weight, 0)
-        nn.init.constant_(self.final_layer.adaLN_modulation[-1].bias, 0)
-        nn.init.constant_(self.final_layer.linear.weight, 0)
-        nn.init.constant_(self.final_layer.linear.bias, 0)
-
     def unpatchify(self, x, h, w):
         """
         x: (N, T, patch_size**2 * C)