diff --git a/vllm_omni/entrypoints/omni.py b/vllm_omni/entrypoints/omni.py
index 9e49f951e20..510813c6abd 100644
--- a/vllm_omni/entrypoints/omni.py
+++ b/vllm_omni/entrypoints/omni.py
@@ -10,6 +10,7 @@
 from concurrent.futures import ThreadPoolExecutor, as_completed
 from typing import Any, Literal, overload
 
+import huggingface_hub
 from omegaconf import OmegaConf
 from tqdm.auto import tqdm
 from vllm import SamplingParams
@@ -40,6 +41,9 @@
 )
 from vllm_omni.inputs.data import OmniDiffusionSamplingParams, OmniPromptType, OmniSamplingParams
 from vllm_omni.metrics import OrchestratorAggregator, StageRequestStats
+from vllm_omni.model_executor.model_loader.weight_utils import (
+    download_weights_from_hf_specific,
+)
 from vllm_omni.outputs import OmniRequestOutput
 
 logger = init_logger(__name__)
@@ -66,14 +70,28 @@ def _dummy_snapshot_download(model_id):
 
 
 def omni_snapshot_download(model_id) -> str:
+    # If it's already a local path, just return it
+    if os.path.exists(model_id):
+        return model_id
     # TODO: this is just a workaround for quickly use modelscope, we should support
     # modelscope in weight loading feature instead of using `snapshot_download`
     if os.environ.get("VLLM_USE_MODELSCOPE", False):
         from modelscope.hub.snapshot_download import snapshot_download
 
         return snapshot_download(model_id)
-    else:
-        return _dummy_snapshot_download(model_id)
+    # For other cases (Hugging Face), perform a real download to ensure all
+    # necessary files (including *.pt for audio/diffusion) are available locally
+    # before stage workers are spawned. This prevents initialization timeouts.
+    try:
+        return download_weights_from_hf_specific(
+            model_name_or_path=model_id,
+            cache_dir=None,
+            allow_patterns=["*"],
+            require_all=True,
+        )
+    except huggingface_hub.errors.RepositoryNotFoundError:
+        logger.warning(f"Repository not found for '{model_id}'.")
+        return model_id
 
 
 class OmniBase:
diff --git a/vllm_omni/model_executor/model_loader/weight_utils.py b/vllm_omni/model_executor/model_loader/weight_utils.py
index 7432ad9a2a4..c5225ec0287 100644
--- a/vllm_omni/model_executor/model_loader/weight_utils.py
+++ b/vllm_omni/model_executor/model_loader/weight_utils.py
@@ -20,6 +20,7 @@ def download_weights_from_hf_specific(
     allow_patterns: list[str],
     revision: str | None = None,
     ignore_patterns: str | list[str] | None = None,
+    require_all: bool = False,
 ) -> str:
     """Download model weights from Hugging Face Hub. Users can specify the
     allow_patterns to download only the necessary weights.
@@ -35,6 +36,9 @@ def download_weights_from_hf_specific(
         ignore_patterns (Optional[Union[str, list[str]]]): The patterns to
             filter out the weight files. Files matched by any of the patterns
             will be ignored.
+        require_all (bool): If True, will iterate through and download files
+            matching all patterns in allow_patterns. If False, will stop after
+            the first pattern that matches any files.
 
     Returns:
         str: The path to the downloaded model weights.
@@ -48,20 +52,31 @@ def download_weights_from_hf_specific(
     # downloading the same model weights at the same time.
     with get_lock(model_name_or_path, cache_dir):
         start_time = time.perf_counter()
-        for allow_pattern in allow_patterns:
+        if require_all:
             hf_folder = snapshot_download(
                 model_name_or_path,
-                allow_patterns=allow_pattern,
+                allow_patterns=allow_patterns,
                 ignore_patterns=ignore_patterns,
                 cache_dir=cache_dir,
                 revision=revision,
                 local_files_only=local_only,
                 **download_kwargs,
             )
-            # If we have downloaded weights for this allow_pattern,
-            # we don't need to check the rest.
-            if any(Path(hf_folder).glob(allow_pattern)):
-                break
+        else:
+            for allow_pattern in allow_patterns:
+                hf_folder = snapshot_download(
+                    model_name_or_path,
+                    allow_patterns=allow_pattern,
+                    ignore_patterns=ignore_patterns,
+                    cache_dir=cache_dir,
+                    revision=revision,
+                    local_files_only=local_only,
+                    **download_kwargs,
+                )
+                # If we have downloaded weights for this allow_pattern,
+                # we don't need to check the rest, unless require_all is set.
+                if any(Path(hf_folder).glob(allow_pattern)):
+                    break
         time_taken = time.perf_counter() - start_time
         if time_taken > 0.5:
             logger.info(