Fix model complexity causing crash

frigate/detectors/detection_runners.py

@@ -21,21 +21,25 @@ def is_arm64_platform() -> bool:
     return machine in ("aarch64", "arm64", "armv8", "armv7l")
 
 
-def get_ort_session_options() -> ort.SessionOptions | None:
+def get_ort_session_options(
+    is_complex_model: bool = False,
+) -> ort.SessionOptions | None:
     """Get ONNX Runtime session options with appropriate settings.
 
-    On ARM/RKNN platforms, use basic optimizations to avoid graph fusion issues
-    that can break certain models. On amd64, use default optimizations for better performance.
-    """
-    sess_options = None
+    Args:
+        is_complex_model: Whether the model needs basic optimization to avoid graph fusion issues.
 
-    if is_arm64_platform():
+    Returns:
+        SessionOptions with appropriate optimization level, or None for default settings.
+    """
+    if is_complex_model:
         sess_options = ort.SessionOptions()
         sess_options.graph_optimization_level = (
             ort.GraphOptimizationLevel.ORT_ENABLE_BASIC
         )
+        return sess_options
 
-    return sess_options
+    return None
 
 
 # Import OpenVINO only when needed to avoid circular dependencies
@@ -103,6 +107,21 @@ class BaseModelRunner(ABC):
 class ONNXModelRunner(BaseModelRunner):
     """Run ONNX models using ONNX Runtime."""
 
+    @staticmethod
+    def is_cpu_complex_model(model_type: str) -> bool:
+        """Check if model needs basic optimization level to avoid graph fusion issues.
+
+        Some models (like Jina-CLIP) have issues with aggressive optimizations like
+        SimplifiedLayerNormFusion that create or expect nodes that don't exist.
+        """
+        # Import here to avoid circular imports
+        from frigate.embeddings.types import EnrichmentModelTypeEnum
+
+        return model_type in [
+            EnrichmentModelTypeEnum.jina_v1.value,
+            EnrichmentModelTypeEnum.jina_v2.value,
+        ]
+
     @staticmethod
     def is_migraphx_complex_model(model_type: str) -> bool:
         # Import here to avoid circular imports
@@ -496,7 +515,9 @@ def get_optimized_runner(
     return ONNXModelRunner(
         ort.InferenceSession(
             model_path,
-            sess_options=get_ort_session_options(),
+            sess_options=get_ort_session_options(
+                ONNXModelRunner.is_cpu_complex_model(model_type)
+            ),
             providers=providers,
             provider_options=options,
         )