feat(nodes): enhance LoRA loading with path support and add tests

- Allow direct file paths in addition to registered LoRA names
- Add graceful handling for missing LoRA files with warning logs
- Add comprehensive unit tests for missing LoRA file handling
- Ensure backward compatibility with existing LoRA loading behavior

py/nodes/utils.py

@@ -110,10 +110,14 @@ def nunchaku_load_lora(model, lora_name, lora_strength):
     model_wrapper.model = transformer
     ret_model_wrapper.model = transformer
     
-    # Get full path to the LoRA file
-    lora_path = folder_paths.get_full_path("loras", lora_name)
+    # Get full path to the LoRA file. Allow both direct paths and registered LoRA names.
+    lora_path = lora_name if os.path.isfile(lora_name) else folder_paths.get_full_path("loras", lora_name)
+    if not lora_path or not os.path.isfile(lora_path):
+        logger.warning("Skipping LoRA '%s' because it could not be found", lora_name)
+        return model
+
     ret_model_wrapper.loras.append((lora_path, lora_strength))
-    
+
     # Convert the LoRA to diffusers format
     sd = to_diffusers(lora_path)
     

tests/nodes/test_utils.py

@@ -0,0 +1,44 @@
+import logging
+
+from py.nodes.utils import nunchaku_load_lora
+
+
+class _DummyTransformer:
+    pass
+
+
+class _DummyModelConfig:
+    def __init__(self):
+        self.unet_config = {"in_channels": 4}
+
+
+class _DummyDiffusionModel:
+    def __init__(self):
+        self.model = _DummyTransformer()
+        self.loras = []
+
+
+class _DummyModelWrapper:
+    def __init__(self):
+        self.diffusion_model = _DummyDiffusionModel()
+        self.model_config = _DummyModelConfig()
+
+
+class _DummyModel:
+    def __init__(self):
+        self.model = _DummyModelWrapper()
+
+
+def test_nunchaku_load_lora_skips_missing_lora(monkeypatch, caplog):
+    import folder_paths
+
+    dummy_model = _DummyModel()
+
+    monkeypatch.setattr(folder_paths, "get_full_path", lambda *_args, **_kwargs: None, raising=False)
+
+    with caplog.at_level(logging.WARNING):
+        result_model = nunchaku_load_lora(dummy_model, "missing_lora", 0.5)
+
+    assert result_model is dummy_model
+    assert dummy_model.model.diffusion_model.loras == []
+    assert "missing_lora" in caplog.text