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 86fe2cfcf..96849a135 100644
--- a/vllm_omni/diffusion/models/hunyuan_image3/pipeline_hunyuan_image3.py
+++ b/vllm_omni/diffusion/models/hunyuan_image3/pipeline_hunyuan_image3.py
@@ -837,21 +837,17 @@ class HunyuanImage3Pipeline(
             cond_vae_images, cond_timestep, cond_vit_images = self._encode_cond_image(
                 batch_cond_image_info, cfg_factor[mode]
             )
-            # Pack vit kwargs. Siglip2 requires spatial_shapes and pixel_attention_mask
-            # at the forward boundary. transformers >=5.54 renamed the kwarg from
-            # `attention_mask` to `pixel_attention_mask` so the dict key must match
-            # the expected forward signature.
-            vit_kwargs = {"spatial_shapes": [], "pixel_attention_mask": []}
+            vit_kwargs = {"spatial_shapes": [], "attention_mask": []}
             for cond_image_info in batch_cond_image_info:
                 vit_kwargs["spatial_shapes"].append(
                     torch.stack([item.vision_encoder_kwargs["spatial_shapes"] for item in cond_image_info])
                 )
-                vit_kwargs["pixel_attention_mask"].append(
+                vit_kwargs["attention_mask"].append(
                     torch.stack([item.vision_encoder_kwargs["pixel_attention_mask"] for item in cond_image_info])
                 )
             if cfg_factor[mode] > 1:
                 vit_kwargs["spatial_shapes"] = vit_kwargs["spatial_shapes"] * cfg_factor[mode]
-                vit_kwargs["pixel_attention_mask"] = vit_kwargs["pixel_attention_mask"] * cfg_factor[mode]
+                vit_kwargs["attention_mask"] = vit_kwargs["attention_mask"] * cfg_factor[mode]
         else:
             cond_vae_images, cond_timestep, cond_vit_images = None, None, None
             vit_kwargs = None