diff --git a/vllm_omni/diffusion/models/hunyuan_image_3/hunyuan_image_3_transformer.py b/vllm_omni/diffusion/models/hunyuan_image_3/hunyuan_image_3_transformer.py
index 04b961c9a4..0c2f9e290a 100644
--- a/vllm_omni/diffusion/models/hunyuan_image_3/hunyuan_image_3_transformer.py
+++ b/vllm_omni/diffusion/models/hunyuan_image_3/hunyuan_image_3_transformer.py
@@ -1,11 +1,9 @@
 # SPDX-License-Identifier: Apache-2.0
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
 
-import glob
 import inspect
 import logging
 import math
-import os
 from collections.abc import Callable, Iterable
 from dataclasses import dataclass
 from typing import Any, cast
@@ -22,7 +20,6 @@
 from diffusers.utils.torch_utils import randn_tensor
 from einops import rearrange
 from PIL import Image
-from safetensors.torch import load_file
 from torch import nn
 from torchvision import transforms
 from transformers import PretrainedConfig, Siglip2ImageProcessorFast
@@ -357,14 +354,6 @@ def build_batch_2d_rope(
     return stacked_cos, stacked_sin
 
 
-def get_full_state_dict(model_path):
-    files = glob.glob(os.path.join(model_path, "*.safetensors"))
-    full_sd = {}
-    for f in files:
-        full_sd.update(load_file(f))
-    return full_sd
-
-
 def rotate_half(x):
     """Rotates half the hidden dims of the input."""
     x1 = x[..., : x.shape[-1] // 2]
@@ -1744,7 +1733,6 @@ def __init__(self, config: HunyuanImage3Config, prefix: str = ""):
         lora_vocab = (lora_config.lora_extra_vocab_size * (lora_config.max_loras or 1)) if lora_config else 0
         self.vocab_size = config.vocab_size + lora_vocab
         self.org_vocab_size = config.vocab_size
-        self.wte = nn.Embedding(config.vocab_size, config.hidden_size, self.padding_idx)
         if get_pp_group().is_first_rank or (config.tie_word_embeddings and get_pp_group().is_last_rank):
             self.embed_tokens = VocabParallelEmbedding(
                 self.vocab_size,
@@ -2047,7 +2035,7 @@ def forward(
         return_dict = return_dict if return_dict is not None else self.config.use_return_dict
 
         if inputs_embeds is None:
-            inputs_embeds = self.wte(input_ids)
+            inputs_embeds = self.embed_tokens(input_ids)
 
         # embed positions
         hidden_states = inputs_embeds
diff --git a/vllm_omni/diffusion/models/hunyuan_image_3/pipeline_hunyuan_image_3.py b/vllm_omni/diffusion/models/hunyuan_image_3/pipeline_hunyuan_image_3.py
index 9721f2edbc..e4b717a697 100644
--- a/vllm_omni/diffusion/models/hunyuan_image_3/pipeline_hunyuan_image_3.py
+++ b/vllm_omni/diffusion/models/hunyuan_image_3/pipeline_hunyuan_image_3.py
@@ -37,7 +37,6 @@
     UNetDown,
     UNetUp,
     build_batch_2d_rope,
-    get_full_state_dict,
     real_batched_index_select,
 )
 
@@ -113,10 +112,10 @@ def __init__(self, od_config: OmniDiffusionConfig) -> None:
         self.vllm_config = get_current_vllm_config()
         self.post_init()
 
-    def pre_load(self):
-        tp_rank = get_tensor_model_parallel_rank()
-        state_dict = get_full_state_dict(self.od_config.model)
-        non_layer_prefixes = [
+    def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]) -> set[str]:
+        skip_prefixes = ["lm_head."] if self.hf_config.tie_word_embeddings else []
+        # List of unexpected keywords in weight names
+        non_model_layer_prefixes = [
             "vae",
             "vision_model",
             "vision_aligner",
@@ -129,32 +128,15 @@ def pre_load(self):
             "time_embed_2",
             "final_layer.model",
         ]
-        filtered_sd = {}
-        for k, v in state_dict.items():
-            if any(k.startswith(prefix) for prefix in non_layer_prefixes):
-                filtered_sd[k] = v
-
-        missing, unexpected = self.load_state_dict(filtered_sd, strict=False)
-
-        for prefix in non_layer_prefixes:
-            if hasattr(self, prefix.split(".")[0]):
-                module = dict(self.named_modules()).get(prefix)
-                if module:
-                    module.to(f"{self.model.device.type}:{tp_rank}")
+        tp_rank = get_tensor_model_parallel_rank()
+        device_str = f"{self.model.device.type}:{tp_rank}"
+        named_modules = dict(self.named_modules())
+        for prefix in non_model_layer_prefixes:
+            mod = named_modules.get(prefix)
+            if mod:
+                mod.to(device_str)
 
-    def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]) -> set[str]:
-        self.pre_load()
-        skip_prefixes = ["lm_head."] if self.hf_config.tie_word_embeddings else []
-        # List of unexpected keywords in weight names
         unexpected_keywords = [
-            "vae",
-            "vision_aligner",
-            "vision_model",
-            "final_layer",
-            "patch_embed",
-            "timestep_emb",
-            "time_embed",
-            "time_embed_2",
             "guidance_emb",
             "timestep_r_emb",
         ]
@@ -892,7 +874,7 @@ def forward_call(
 
         custom_pos_emb = self.get_pos_emb(custom_pos_emb, position_ids)
 
-        inputs_embeds = self.model.wte(input_ids)
+        inputs_embeds = self.model.embed_tokens(input_ids)
 
         bsz, seq_len, n_embd = inputs_embeds.shape