diff --git a/vllm_omni/diffusion/models/omnigen2/pipeline_omnigen2.py b/vllm_omni/diffusion/models/omnigen2/pipeline_omnigen2.py
index 32b874c6535..2d370aea19c 100644
--- a/vllm_omni/diffusion/models/omnigen2/pipeline_omnigen2.py
+++ b/vllm_omni/diffusion/models/omnigen2/pipeline_omnigen2.py
@@ -36,6 +36,7 @@
     OmniGen2Transformer2DModel,
 )
 from vllm_omni.diffusion.request import OmniDiffusionRequest
+from vllm_omni.diffusion.utils.tf_utils import get_transformer_config_kwargs
 from vllm_omni.inputs.data import OmniTextPrompt
 from vllm_omni.model_executor.model_loader.weight_utils import (
     download_weights_from_hf_specific,
@@ -673,41 +674,7 @@ def __init__(
             self.device
         )
 
-        transformer_config_path = os.path.join(model, "transformer", "config.json")
-        transformer_kwargs = {}
-
-        if os.path.exists(transformer_config_path):
-            with open(transformer_config_path) as f:
-                transformer_config = json.load(f)
-
-            param_mapping = {
-                "patch_size": "patch_size",
-                "in_channels": "in_channels",
-                "out_channels": "out_channels",
-                "hidden_size": "hidden_size",
-                "num_layers": "num_layers",
-                "num_refiner_layers": "num_refiner_layers",
-                "num_attention_heads": "num_attention_heads",
-                "num_kv_heads": "num_kv_heads",
-                "multiple_of": "multiple_of",
-                "ffn_dim_multiplier": "ffn_dim_multiplier",
-                "norm_eps": "norm_eps",
-                "axes_dim_rope": "axes_dim_rope",
-                "axes_lens": "axes_lens",
-                "text_feat_dim": "text_feat_dim",
-                "timestep_scale": "timestep_scale",
-            }
-
-            for config_key, param_name in param_mapping.items():
-                if config_key in transformer_config:
-                    value = transformer_config[config_key]
-                    # Handle tuple parameters (axes_dim_rope, axes_lens)
-                    if isinstance(value, list) and param_name in (
-                        "axes_dim_rope",
-                        "axes_lens",
-                    ):
-                        value = tuple(value)
-                    transformer_kwargs[param_name] = value
+        transformer_kwargs = get_transformer_config_kwargs(od_config.tf_model_config, OmniGen2Transformer2DModel)
         self.transformer = OmniGen2Transformer2DModel(**transformer_kwargs)
         self.mllm = Qwen2_5_VLForConditionalGeneration.from_pretrained(
             model, subfolder="mllm", local_files_only=local_files_only