feat(node_assets): add image plane geometry nodes and validation scripts

- Implemented `image_plane.py` to build the Image Plane Geometry Nodes group with customizable parameters for geometry, subdivide, and thickness.
- Created `image_plane_validation.py` to validate the functionality of the image plane, ensuring correct geometry generation and material assignment.
- Developed `material_layers.py` to construct shader node groups for image materials, including depth and normal handling.
- Authored `plane_layouts.py` to define node arrangements for image geometry assets.
- Introduced `shadeless.py` to create a shadeless shader node group that displays color without scene lighting while preserving shadows and reflections.

tests/test_model_inference.py

@@ -0,0 +1,107 @@
+import tempfile
+import unittest
+from pathlib import Path
+from types import SimpleNamespace
+from unittest.mock import patch
+import numpy as np
+import pytest
+from PIL import Image
+from server_test_support import load_server_module, FakeModels
+
+
+class ModelInferenceTest(unittest.TestCase):
+    @classmethod
+    def setUpClass(cls):
+        cls.moge2_model = load_server_module("models.moge2")
+
+        cls.upscale_model = load_server_module("models.upscale")
+
+        cls.onnx_upscale = load_server_module("models.onnx_upscale")
+
+
+    def test_moge2_batch_releases_only_after_final_image(self):
+        prediction = {
+            "points": np.ones((2, 2, 3), dtype=np.float32),
+            "depth": np.ones((2, 2), dtype=np.float32),
+            "normal": np.ones((2, 2, 3), dtype=np.float32),
+            "mask": np.ones((2, 2), dtype=bool),
+            "intrinsics": np.eye(3, dtype=np.float32),
+        }
+        with patch.object(
+            self.moge2_model,
+            "infer_one",
+            return_value=prediction,
+        ) as infer_one:
+            frames = self.moge2_model.infer(
+                FakeModels(),
+                {"model": "MOGE2_VITS_NORMAL", "device": "cpu"},
+                [Path("a.png"), Path("b.png")],
+                include_points=True,
+            )
+
+        self.assertEqual(len(frames), 2)
+        self.assertEqual(
+            [call.kwargs["release_memory"] for call in infer_one.call_args_list],
+            [False, True],
+        )
+
+
+    def test_upscale_resource_error_releases_session_and_is_readable(self):
+        from PIL import Image
+
+        class Models:
+            def __init__(self, directory):
+                self.model_directory = directory
+                self.released = False
+
+            def directory(self, _model):
+                return self.model_directory
+
+            def get_upscale(self, _directory, _device):
+                return object()
+
+            def release_upscale(self):
+                self.released = True
+
+        with tempfile.TemporaryDirectory() as directory:
+            path = Path(directory) / "input.png"
+            Image.new("RGB", (8, 8)).save(path)
+            models = Models(Path(directory))
+            with patch.object(
+                self.onnx_upscale,
+                "infer",
+                side_effect=self.onnx_upscale.UpscaleResourceError(
+                    "provider memory exhausted"
+                ),
+            ):
+                with self.assertRaisesRegex(
+                    RuntimeError,
+                    "Reduce Maximum AI Input Size or select CPU",
+                ):
+                    self.upscale_model.infer_one(
+                        models,
+                        {
+                            "model": "REALESRGAN_GENERAL_WDN_X4V3",
+                            "device": "directml",
+                            "max_input_size": 2048,
+                        },
+                        path,
+                    )
+
+            self.assertTrue(models.released)
+
+
+upscale = load_server_module("models.upscale")
+
+@pytest.mark.parametrize("key", ["REALESRGAN_GENERAL_WDN_X4V3", "SAFMN_REAL_X4", "REALESRGAN_X4PLUS"])
+def test_upscale_models_resize_prediction_then_restore_alpha(tmp_path, key):
+    rng = np.random.default_rng(1)
+    source = Image.fromarray(rng.integers(0,256,(5,7,4),dtype=np.uint8))
+    path = tmp_path / "source.png"
+    source.save(path)
+    prediction = Image.fromarray(rng.integers(0,256,(20,28,3),dtype=np.uint8))
+    manager = SimpleNamespace(directory=lambda _: tmp_path, get_upscale=lambda *args: object())
+    with patch("server.models.onnx_upscale.infer", return_value=prediction):
+        output = upscale.infer_one(manager, {"model":key,"device":"cpu"}, path)
+    np.testing.assert_array_equal(output.convert("RGB"), prediction.resize((14,10), Image.Resampling.LANCZOS))
+    np.testing.assert_array_equal(output.getchannel("A"), source.getchannel("A").resize((14,10), Image.Resampling.LANCZOS))