修改clip报错完成，修改upscale模块

tests/test_upscale_switcher.py

@@ -0,0 +1,84 @@
+import importlib.util
+import pathlib
+import sys
+import unittest
+from unittest import mock
+
+
+ROOT = pathlib.Path(__file__).resolve().parents[1]
+
+
+def load_plugin_package():
+    spec = importlib.util.spec_from_file_location(
+        "lls_node_test_upscale",
+        ROOT / "__init__.py",
+        submodule_search_locations=[str(ROOT)],
+    )
+    module = importlib.util.module_from_spec(spec)
+    sys.modules["lls_node_test_upscale"] = module
+    spec.loader.exec_module(module)
+    return module
+
+
+class RecordingUpscaleSwitcher:
+    def __init__(self, cls):
+        self._impl = cls()
+        self.calls = []
+
+    def _upscale_with_pytorch(self, image, scale, interpolation):
+        self.calls.append(("pytorch", image, scale, interpolation))
+        return ("PYTORCH_RESULT",)
+
+    def _upscale_with_model(self, image, model_name, tile, overlap):
+        self.calls.append(("model", image, model_name, tile, overlap))
+        return ("MODEL_RESULT",)
+
+
+class TestUpscaleSwitcher(unittest.TestCase):
+    def test_schema_defaults_to_pytorch_when_no_upscale_models_exist(self):
+        load_plugin_package()
+        from lls_node_test_upscale.upscale import nodes as upscale_nodes
+
+        with mock.patch.object(
+            upscale_nodes,
+            "_get_upscale_model_names",
+            return_value=[upscale_nodes.NO_UPSCALE_MODEL_PLACEHOLDER],
+        ):
+            schema = upscale_nodes.LLSUpscaleSwitcher.INPUT_TYPES()
+
+        self.assertEqual(schema["required"]["mode"][1]["default"], "pytorch")
+
+    def test_placeholder_model_name_falls_back_to_pytorch(self):
+        load_plugin_package()
+        from lls_node_test_upscale.upscale import nodes as upscale_nodes
+
+        recorder = RecordingUpscaleSwitcher(upscale_nodes.LLSUpscaleSwitcher)
+        with mock.patch.object(
+            upscale_nodes.LLSUpscaleSwitcher,
+            "_upscale_with_pytorch",
+            new=RecordingUpscaleSwitcher._upscale_with_pytorch,
+        ), mock.patch.object(
+            upscale_nodes.LLSUpscaleSwitcher,
+            "_upscale_with_model",
+            new=RecordingUpscaleSwitcher._upscale_with_model,
+        ):
+            result = upscale_nodes.LLSUpscaleSwitcher.upscale(
+                recorder,
+                image="image",
+                mode="upscale_model",
+                scale=2.0,
+                interpolation="bilinear",
+                model_name=upscale_nodes.NO_UPSCALE_MODEL_PLACEHOLDER,
+                tile=512,
+                overlap=32,
+            )
+
+        self.assertEqual(result, ("PYTORCH_RESULT",))
+        self.assertEqual(
+            recorder.calls,
+            [("pytorch", "image", 2.0, "bilinear")],
+        )
+
+
+if __name__ == "__main__":
+    unittest.main()

upscale/nodes.py

@@ -73,6 +73,14 @@ def _get_upscale_model_names() -> list[str]:
     return names if names else [NO_UPSCALE_MODEL_PLACEHOLDER]
 
 
+def _has_real_upscale_models(model_names: list[str]) -> bool:
+    return any(name and name != NO_UPSCALE_MODEL_PLACEHOLDER for name in model_names)
+
+
+def _is_missing_upscale_model_selection(model_name: str | None) -> bool:
+    return model_name in (None, "", NO_UPSCALE_MODEL_PLACEHOLDER)
+
+
 def _resolve_upscale_model_path(model_name: str) -> str:
     _require_comfy_modules()
     if model_name == NO_UPSCALE_MODEL_PLACEHOLDER:
@@ -212,10 +220,11 @@ class LLSUpscaleSwitcher:
     @classmethod
     def INPUT_TYPES(cls):
         model_names = _sorted_unique(_get_upscale_model_names())
+        default_mode = "upscale_model" if _has_real_upscale_models(model_names) else "pytorch"
         return {
             "required": {
                 "image": ("IMAGE",),
-                "mode": (UPSCALE_MODE_CHOICES, {"default": "upscale_model"}),
+                "mode": (UPSCALE_MODE_CHOICES, {"default": default_mode}),
                 "scale": ("FLOAT", {"default": 2.0, "min": 0.1, "max": 8.0, "step": 0.1}),
                 "interpolation": (INTERPOLATION_CHOICES, {"default": "bilinear"}),
                 "model_name": (model_names,),
@@ -229,6 +238,12 @@ class LLSUpscaleSwitcher:
             raise RuntimeError(f"Unsupported mode '{mode}'. Expected one of {UPSCALE_MODE_CHOICES}.")
         if mode == "pytorch":
             return self._upscale_with_pytorch(image=image, scale=scale, interpolation=interpolation)
+        if _is_missing_upscale_model_selection(model_name):
+            print(
+                "[LLS] WARNING: No valid upscale model is selected. "
+                "Falling back to PyTorch interpolation mode."
+            )
+            return self._upscale_with_pytorch(image=image, scale=scale, interpolation=interpolation)
         return self._upscale_with_model(image=image, model_name=model_name, tile=tile, overlap=overlap)
 
     def _upscale_with_pytorch(self, image, scale: float, interpolation: str):