diff --git a/examples/offline_inference/hunyuan_image3/end2end.py b/examples/offline_inference/hunyuan_image3/end2end.py
index fc4b75e78d5..8993c8dadca 100644
--- a/examples/offline_inference/hunyuan_image3/end2end.py
+++ b/examples/offline_inference/hunyuan_image3/end2end.py
@@ -149,7 +149,7 @@ def main():
     for p in prompts:
         token_ids = build_prompt_tokens(p, tokenizer, task=task, sys_type=args.sys_type)
 
-        prompt_dict: dict = {"prompt_token_ids": token_ids}
+        prompt_dict: dict = {"prompt_token_ids": token_ids, "prompt": p}
 
         if args.modality == "text2img":
             prompt_dict["modalities"] = ["image"]
diff --git a/vllm_omni/diffusion/models/hunyuan_image3/hunyuan_fused_moe.py b/vllm_omni/diffusion/models/hunyuan_image3/hunyuan_fused_moe.py
index 3f69cd00af2..63ba0d724c6 100644
--- a/vllm_omni/diffusion/models/hunyuan_image3/hunyuan_fused_moe.py
+++ b/vllm_omni/diffusion/models/hunyuan_image3/hunyuan_fused_moe.py
@@ -29,12 +29,6 @@ class HunyuanFusedMoEDefault(FusedMoE):
     def __init__(self, *, prefix: str = "", **kwargs: Any) -> None:
         super().__init__(prefix=prefix, **kwargs)
         self._prefix = prefix
-        self._init_hook_handle = self.register_forward_pre_hook(self._initialize_kernel_hook, with_kwargs=True)
-
-    def _initialize_kernel_hook(self, module: Any, args: Any, kwargs: Any) -> None:
-        if self.quant_method:
-            self.quant_method.process_weights_after_loading(self)
-        self._init_hook_handle.remove()
 
     def forward(self, hidden_states: Any, router_logits: Any) -> Any:
         _set_forward_context_num_tokens(hidden_states.shape[0])
diff --git a/vllm_omni/diffusion/models/hunyuan_image3/hunyuan_image3_transformer.py b/vllm_omni/diffusion/models/hunyuan_image3/hunyuan_image3_transformer.py
index e1acc4cdc65..cdd79b9fddb 100644
--- a/vllm_omni/diffusion/models/hunyuan_image3/hunyuan_image3_transformer.py
+++ b/vllm_omni/diffusion/models/hunyuan_image3/hunyuan_image3_transformer.py
@@ -1514,7 +1514,6 @@ def __init__(
             top_k=top_k,
             hidden_size=config.hidden_size,
             intermediate_size=intermediate_size,
-            reduce_results=False,
             renormalize=top_k > 1,
             quant_config=quant_config,
             prefix=f"{prefix}.experts",
@@ -1532,11 +1531,6 @@ def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
         # router_logits: (num_tokens, n_experts)
         router_logits, _ = self.gate(hidden_states)
         final_hidden_states = self.experts(hidden_states=hidden_states, router_logits=router_logits)
-        if self.shared_mlp is not None:
-            final_hidden_states = final_hidden_states[0] + final_hidden_states[1]
-
-        if self.tp_size > 1:
-            final_hidden_states = self.experts.maybe_all_reduce_tensor_model_parallel(final_hidden_states)
 
         return final_hidden_states.view(orig_shape)
 
diff --git a/vllm_omni/diffusion/models/hunyuan_image3/pipeline_hunyuan_image3.py b/vllm_omni/diffusion/models/hunyuan_image3/pipeline_hunyuan_image3.py
index dbbad4e7197..0646d77e6df 100644
--- a/vllm_omni/diffusion/models/hunyuan_image3/pipeline_hunyuan_image3.py
+++ b/vllm_omni/diffusion/models/hunyuan_image3/pipeline_hunyuan_image3.py
@@ -11,7 +11,6 @@
 from diffusers.schedulers.scheduling_flow_match_euler_discrete import FlowMatchEulerDiscreteScheduler
 from transformers.generation.configuration_utils import GenerationConfig
 from transformers.generation.utils import ALL_CACHE_NAMES, GenerationMixin
-from transformers.models.siglip2 import Siglip2VisionConfig, Siglip2VisionModel
 from transformers.utils.generic import ModelOutput
 from vllm.config.vllm import get_current_vllm_config
 from vllm.model_executor.models.utils import AutoWeightsLoader, WeightsMapper
@@ -22,6 +21,7 @@
 from vllm_omni.diffusion.model_loader.diffusers_loader import DiffusersPipelineLoader
 from vllm_omni.diffusion.profiler.diffusion_pipeline_profiler import DiffusionPipelineProfilerMixin
 from vllm_omni.diffusion.request import OmniDiffusionRequest
+from vllm_omni.model_executor.models.hunyuan_image3.siglip2 import Siglip2VisionTransformer
 
 from .autoencoder import AutoencoderKLConv3D
 from .hunyuan_image3_tokenizer import TokenizerWrapper
@@ -111,9 +111,7 @@ def __init__(self, od_config: OmniDiffusionConfig) -> None:
         self._pipeline = None
         self._tkwrapper = TokenizerWrapper(od_config.model)
         self.image_processor = HunyuanImage3ImageProcessor(self.hf_config)
-        self.hf_config.vit.pop("use_return_dict", None)
-        vision_config = Siglip2VisionConfig(**self.hf_config.vit)
-        self.vision_model = Siglip2VisionModel(vision_config).vision_model
+        self.vision_model = Siglip2VisionTransformer(self.hf_config.vit)
         # self.vision_model = vision_model.vision_model
         self.vision_aligner = LightProjector(self.hf_config.vit_aligner)
         self.timestep_emb = TimestepEmbedder(hidden_size=self.hf_config.hidden_size)