[Fix]Ensure HuggingFace downloads complete before initialization.

vllm_omni/entrypoints/omni.py

@@ -42,6 +42,9 @@
     resolve_model_config_path,
 )
 from vllm_omni.inputs.data import OmniDiffusionSamplingParams, OmniPromptType, OmniSamplingParams
+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__)
@@ -68,14 +71,24 @@ 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.
+    return download_weights_from_hf_specific(
+        model_name_or_path=model_id,
+        cache_dir=None,
+        allow_patterns=["**/*.json", "**/*.bin", "**/*.safetensors", "**/*.pt", "**/*.txt", "**/*.model", "**/*.yaml"],
+        require_all=True,
+    )
 
 
 class OmniBase:

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 +36,7 @@ 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 download all patterns.
 
     Returns:
         str: The path to the downloaded model weights.
@@ -59,8 +61,8 @@ def download_weights_from_hf_specific(
                 **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)):
+            # we don't need to check the rest,unless require_all is set.
+            if not require_all and any(Path(hf_folder).glob(allow_pattern)):
                 break
         time_taken = time.perf_counter() - start_time
         if time_taken > 0.5: