feat: Implement unified memory management for ONNX model inference

- Introduced a new memory management policy for ONNX Runtime to release temporary GPU memory after inference completion.
- Established a shared inference entry point for all ONNX models, differentiating between long-lived sessions and temporary memory arenas.
- Updated CUDA sessions to utilize a shrinkable memory arena, reclaiming GPU memory post-inference.
- Ensured consistent memory release semantics across BEN2, DA3, MoGe-2, and Upscale models while reusing existing sessions.
- Added lifecycle tests to validate temporary memory release and session reuse.
- Modified model adapters to integrate the new memory management features, ensuring backward compatibility with existing inference behavior.
- Enhanced unit tests to cover new memory release scenarios and verify correct behavior across different execution providers.

tests/test_model_ben2_onnx.py

@@ -2,6 +2,7 @@ import sys
 import unittest
 from pathlib import Path
 from types import SimpleNamespace
+from unittest.mock import patch
 
 import numpy as np
 from PIL import Image
@@ -20,6 +21,9 @@ import onnx_ben2
 
 
 class FakeSession:
+    def get_providers(self):
+        return ["CPUExecutionProvider"]
+
     def get_inputs(self):
         return [SimpleNamespace(name="input")]
 
@@ -34,6 +38,25 @@ class FakeSession:
 
 
 class Ben2OnnxTest(unittest.TestCase):
+    def test_infer_forwards_memory_release_boundary(self):
+        session = FakeSession()
+
+        with patch.object(
+            onnx_ben2,
+            "run_session",
+            side_effect=lambda model, names, feeds, **_options: model.run(
+                names, feeds
+            ),
+        ) as run:
+            result = onnx_ben2.infer(
+                session,
+                Image.new("RGB", (40, 20)),
+                release_memory=False,
+            )
+
+        self.assertEqual(result.size, (40, 20))
+        self.assertFalse(run.call_args.kwargs["release_memory"])
+
     def test_preprocess_matches_model_layout(self):
         image = Image.new("RGB", (40, 20), (64, 128, 192))
         result = onnx_ben2.preprocess(image)