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.

src/anyimage/server/models/upscale.py

@@ -13,6 +13,7 @@ def infer_one(
     image_path,
     cancel_check=None,
     preserve_alpha=False,
+    release_memory=True,
 ):
     from PIL import Image
 
@@ -30,7 +31,12 @@ def infer_one(
                 parameters.get("max_input_size", DEFAULT_MAX_AI_INPUT_SIZE),
             )
             source_alpha = _source_alpha(source) if preserve_alpha else None
-            prediction = infer(model, source, cancel_check=cancel_check)
+            prediction = infer(
+                model,
+                source,
+                cancel_check=cancel_check,
+                release_memory=release_memory,
+            )
             if source_alpha is None:
                 return prediction
             alpha = source_alpha.resize(