Fix CI tests: Improve test isolation and mock handling

- Add numpy to requirements-nodes.txt
- Create conftest.py for test configuration
- Fix test_numpy_subprocess.py to import real numpy before any mocking
- Fix test_media.py to import real numpy early
- Fix test_image_node_sanitization.py to use MockTensor instead of torch.zeros

tests/conftest.py

@@ -0,0 +1,52 @@
+"""
+Pytest configuration file for tests.
+
+This module handles test isolation by ensuring that real packages are imported
+before any mocking occurs, and by providing cleanup fixtures.
+"""
+
+import sys
+import os
+
+# Add project root to path
+sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
+
+# Store references to real modules before any mocking
+# This ensures tests that need real numpy/PIL can use them
+_real_numpy = None
+_real_PIL = None
+
+def pytest_configure(config):
+    """Called after command line options have been parsed and all plugins loaded."""
+    global _real_numpy, _real_PIL
+    
+    # Import real modules and store references
+    try:
+        import numpy
+        _real_numpy = numpy
+    except ImportError:
+        pass
+    
+    try:
+        import PIL
+        import PIL.Image
+        import PIL.PngImagePlugin
+        _real_PIL = PIL
+    except ImportError:
+        pass
+
+
+def get_real_numpy():
+    """Get the real numpy module, not a mock."""
+    if _real_numpy is None:
+        import numpy
+        return numpy
+    return _real_numpy
+
+
+def get_real_PIL():
+    """Get the real PIL module, not a mock."""
+    if _real_PIL is None:
+        import PIL
+        return PIL
+    return _real_PIL

tests/test_image_node_sanitization.py

@@ -50,9 +50,29 @@ class TestDiscordImageNodeOptimization(unittest.TestCase):
         self.github_token = "ghp_sensitive12345"
 
     def test_save_images_sanitization(self):
-        # Create a mock image (needs to be proper shape for the node)
-        import torch
-        mock_image = torch.zeros((1, 64, 64, 3))
+        # Create a mock image tensor using numpy (torch not available in CI)
+        # The image_node iterates over images and accesses shape, so we need
+        # an object that supports iteration and has proper shape
+        import numpy as np
+        
+        # Create a simple class that mimics torch.Tensor behavior for the node
+        class MockTensor:
+            def __init__(self, data):
+                self._data = data
+                self.shape = data.shape
+            
+            def __len__(self):
+                return len(self._data)
+            
+            def __getitem__(self, idx):
+                return self._data[idx]
+            
+            def __iter__(self):
+                return iter(self._data)
+        
+        # Create a 1x64x64x3 "image batch" using numpy
+        image_data = np.zeros((1, 64, 64, 3), dtype=np.float32)
+        mock_image = MockTensor(image_data)
 
         # Create prompt and extra_pnginfo with sensitive data
         prompt = {

tests/test_media.py

@@ -3,7 +3,11 @@ import sys
 import unittest
 from unittest.mock import MagicMock, patch
 
-# Create a dummy torch module
+# IMPORTANT: Import real numpy FIRST before any mocking
+# This ensures test_power_of_two_math uses real numpy
+import numpy as np
+
+# Create a dummy torch module (torch is not installed in CI)
 mock_torch = MagicMock()
 sys.modules["torch"] = mock_torch
 sys.modules["folder_paths"] = MagicMock()

tests/test_numpy_subprocess.py

@@ -1,9 +1,18 @@
 import unittest
-import numpy as np
 import subprocess
 import sys
 import os
 
+# Ensure we get real numpy, not a mock from other test files
+# Remove any mocked numpy before importing
+if 'numpy' in sys.modules and hasattr(sys.modules['numpy'], '_mock_name'):
+    del sys.modules['numpy']
+
+import numpy as np
+
+# Verify numpy is real
+assert hasattr(np, 'arange'), "numpy.arange not found - numpy may be mocked"
+
 class TestNumpyToSubprocess(unittest.TestCase):
     """
     Verify that subprocess.Popen.stdin.write accepts numpy arrays directly.