Version 0.4.7

pyproject.toml

@@ -4,7 +4,7 @@ build-backend = "setuptools.build_meta"
 
 [project]
 name = "basic_data_handling"
-version = "0.4.6"
+version = "0.4.7"
 description = """Basic Python functions for manipulating data that every programmer is used to, lightweight with no additional dependencies.
 
 Supported data types:

src/basic_data_handling/control_flow_nodes.py

@@ -308,6 +308,9 @@ class ExecutionOrder(ComfyNodeABC):
     the execution order of nodes in the workflow. You only need to chain this node
     with the other execution order nodes in the desired order and add any
     output of the nodes you want to force execution order on.
+
+    This node also passes through any input connected to "any node output" as
+    its second output.
     """
     @classmethod
     def INPUT_TYPES(cls):
@@ -318,13 +321,16 @@ def INPUT_TYPES(cls):
             }
         }
 
-    RETURN_TYPES = ("E/O",)
+    RETURN_TYPES = ("E/O", IO.ANY)
+    RETURN_NAMES = ("E/O", "passthrough")
+    FUNCTION = "execution_order"
     CATEGORY = "Basic/flow control"
     DESCRIPTION = cleandoc(__doc__ or "")
     FUNCTION = "execute"
 
-    def execute(self, **kwargs) -> tuple[Any]:
-        return (None,)
+    def execute(self, **kwargs: list[Any]) -> tuple[None, Any]:
+        any_node_output = kwargs.get('any node output', [])
+        return (None, any_node_output)
 
 
 NODE_CLASS_MAPPINGS = {

src/basic_data_handling/path_nodes.py

@@ -28,13 +28,15 @@ def load_image_helper(path: str):
         pass
 
     if not os.path.exists(path):
-        raise FileNotFoundError(f"Basic data handling: Image file not found: {path}")
+        return None
 
     # Open and process the image
-    img = Image.open(path)
-    img = ImageOps.exif_transpose(img)
-
-    return img
+    try:
+        img = Image.open(path)
+        img = ImageOps.exif_transpose(img)
+        return img
+    except Exception:
+        return None
 
 
 def extract_mask_from_alpha(img):
@@ -675,19 +677,33 @@ def INPUT_TYPES(cls):
             },
         }
 
-    RETURN_TYPES = (IO.STRING,)
-    RETURN_NAMES = ("text",)
+    RETURN_TYPES = (IO.STRING, IO.BOOLEAN)
+    RETURN_NAMES = ("text", "exists")
     CATEGORY = "Basic/Path"
     DESCRIPTION = cleandoc(__doc__ or "")
     FUNCTION = "load_text"
 
+    @classmethod
+    def IS_CHANGED(cls, path):
+        try:
+            if os.path.exists(path):
+                return os.path.getmtime(path)
+        except Exception:
+            pass
+        return float("NaN")  # Return NaN if file doesn't exist or can't access modification time
+
     def load_text(self, path: str):
-        if not os.path.exists(path):
-            raise FileNotFoundError(f"Basic data handling: String file not found: {path}")
+        exists = os.path.exists(path)
 
-        with open(path, "r", encoding="utf-8") as f:
-            text = f.read()
-        return (text,)
+        if not exists:
+            return ("", False)
+
+        try:
+            with open(path, "r", encoding="utf-8") as f:
+                text = f.read()
+            return (text, True)
+        except Exception:
+            return ("", False)
 
 
 class PathLoadImageRGB(ComfyNodeABC):
@@ -705,26 +721,40 @@ def INPUT_TYPES(cls):
             },
         }
 
-    RETURN_TYPES = (IO.IMAGE,)
-    RETURN_NAMES = ("image",)
+    RETURN_TYPES = (IO.IMAGE, IO.BOOLEAN)
+    RETURN_NAMES = ("image", "exists")
     CATEGORY = "Basic/Path"
     DESCRIPTION = cleandoc(__doc__ or "")
     FUNCTION = "load_image_rgb"
 
+    @classmethod
+    def IS_CHANGED(cls, path):
+        try:
+            if os.path.exists(path):
+                return os.path.getmtime(path)
+        except Exception:
+            pass
+        return float("NaN")  # Return NaN if file doesn't exist or can't access modification time
+
     def load_image_rgb(self, path: str):
         import numpy as np
         import torch
 
         img = load_image_helper(path)
 
+        if img is None:
+            # Create an empty 1x1 image
+            empty_tensor = torch.zeros((1, 1, 1, 3), dtype=torch.float32)
+            return (empty_tensor, False)
+
         # Convert to RGB (removing alpha if present)
         img_rgb = img.convert("RGB")
 
         # Convert to tensor format expected by ComfyUI
         image_tensor = np.array(img_rgb).astype(np.float32) / 255.0
         image_tensor = torch.from_numpy(image_tensor)[None,]
 
-        return (image_tensor,)
+        return (image_tensor, True)
 
 
 class PathLoadImageRGBA(ComfyNodeABC):
@@ -743,18 +773,33 @@ def INPUT_TYPES(cls):
             },
         }
 
-    RETURN_TYPES = (IO.IMAGE, IO.MASK)
-    RETURN_NAMES = ("image", "mask")
+    RETURN_TYPES = (IO.IMAGE, IO.MASK, IO.BOOLEAN)
+    RETURN_NAMES = ("image", "mask", "exists")
     CATEGORY = "Basic/Path"
     DESCRIPTION = cleandoc(__doc__ or "")
     FUNCTION = "load_image_rgba"
 
+    @classmethod
+    def IS_CHANGED(cls, path):
+        try:
+            if os.path.exists(path):
+                return os.path.getmtime(path)
+        except Exception:
+            pass
+        return float("NaN")  # Return NaN if file doesn't exist or can't access modification time
+
     def load_image_rgba(self, path: str):
         import numpy as np
         import torch
 
         img = load_image_helper(path)
 
+        if img is None:
+            # Create empty 1x1 image and mask
+            empty_image = torch.zeros((1, 1, 1, 3), dtype=torch.float32)
+            empty_mask = torch.zeros((1, 1, 1), dtype=torch.float32)
+            return (empty_image, empty_mask, False)
+
         # Convert to RGB for the image
         img_rgb = img.convert("RGB")
 
@@ -765,7 +810,7 @@ def load_image_rgba(self, path: str):
         # Extract alpha channel as mask
         mask_tensor = extract_mask_from_alpha(img)
 
-        return (image_tensor, mask_tensor)
+        return (image_tensor, mask_tensor, True)
 
 
 class PathLoadMaskFromAlpha(ComfyNodeABC):
@@ -784,16 +829,33 @@ def INPUT_TYPES(cls):
             },
         }
 
-    RETURN_TYPES = (IO.MASK,)
-    RETURN_NAMES = ("mask",)
+    RETURN_TYPES = (IO.MASK, IO.BOOLEAN)
+    RETURN_NAMES = ("mask", "exists")
     CATEGORY = "Basic/Path"
     DESCRIPTION = cleandoc(__doc__ or "")
     FUNCTION = "load_mask_from_alpha"
 
+    @classmethod
+    def IS_CHANGED(cls, path):
+        try:
+            if os.path.exists(path):
+                return os.path.getmtime(path)
+        except Exception:
+            pass
+        return float("NaN")  # Return NaN if file doesn't exist or can't access modification time
+
     def load_mask_from_alpha(self, path: str):
+        import torch
+
         img = load_image_helper(path)
+
+        if img is None:
+            # Return empty 1x1 mask
+            empty_mask = torch.zeros((1, 1, 1), dtype=torch.float32)
+            return (empty_mask, False)
+
         mask_tensor = extract_mask_from_alpha(img)
-        return (mask_tensor,)
+        return (mask_tensor, True)
 
 
 class PathLoadMaskFromGreyscale(ComfyNodeABC):
@@ -815,21 +877,38 @@ def INPUT_TYPES(cls):
             },
         }
 
-    RETURN_TYPES = (IO.MASK,)
-    RETURN_NAMES = ("mask",)
+    RETURN_TYPES = (IO.MASK, IO.BOOLEAN)
+    RETURN_NAMES = ("mask", "exists")
     CATEGORY = "Basic/Path"
     DESCRIPTION = cleandoc(__doc__ or "")
     FUNCTION = "load_mask_from_greyscale"
 
+    @classmethod
+    def IS_CHANGED(cls, path):
+        try:
+            if os.path.exists(path):
+                return os.path.getmtime(path)
+        except Exception:
+            pass
+        return float("NaN")  # Return NaN if file doesn't exist or can't access modification time
+
     def load_mask_from_greyscale(self, path: str, invert: bool = False):
+        import torch
+
         img = load_image_helper(path)
+
+        if img is None:
+            # Return empty 1x1 mask
+            empty_mask = torch.zeros((1, 1, 1), dtype=torch.float32)
+            return (empty_mask, False)
+
         mask_tensor = extract_mask_from_greyscale(img)
 
         # Optionally invert the mask (1.0 - mask)
         if invert:
             mask_tensor = 1.0 - mask_tensor
 
-        return (mask_tensor,)
+        return (mask_tensor, True)
 
 
 class PathSaveStringFile(ComfyNodeABC):
@@ -852,15 +931,16 @@ def INPUT_TYPES(cls):
             }
         }
 
-    RETURN_TYPES = (IO.BOOLEAN)
-    RETURN_NAMES = ("success")
+    RETURN_TYPES = (IO.BOOLEAN,)
+    RETURN_NAMES = ("success",)
     CATEGORY = "Basic/Path"
     DESCRIPTION = cleandoc(__doc__ or "")
     FUNCTION = "save_text"
     OUTPUT_NODE = True
 
     def save_text(self, text: str, path: str, create_dirs: bool = True, encoding: str = "utf-8"):
         if not path:
+            print("Basic data handling: Save failed - no path specified")
             return (False,)
 
         try:
@@ -872,6 +952,7 @@ def save_text(self, text: str, path: str, create_dirs: bool = True, encoding: st
             with open(path, "w", encoding=encoding) as f:
                 f.write(text)
 
+            print(f"Basic data handling: Successfully saved text to {path}")
             return (True,)
         except Exception as e:
             print(f"Basic data handling: Error saving text file: {e}")
@@ -899,15 +980,16 @@ def INPUT_TYPES(cls):
             }
         }
 
-    RETURN_TYPES = (IO.BOOLEAN)
-    RETURN_NAMES = ("success")
+    RETURN_TYPES = (IO.BOOLEAN,)
+    RETURN_NAMES = ("success",)
     CATEGORY = "Basic/Path"
     DESCRIPTION = cleandoc(__doc__ or "")
     FUNCTION = "save_image"
     OUTPUT_NODE = True
 
     def save_image(self, images, path: str, format: str = "png", quality: int = 95, create_dirs: bool = True):
         if not path:
+            print("Basic data handling: Save failed - no path specified")
             return (False,)
 
         # If the path doesn't have an extension or it doesn't match the format, add it
@@ -957,6 +1039,7 @@ def save_image(self, images, path: str, format: str = "png", quality: int = 95,
             else:
                 pil_img.save(path, format=format.upper())
 
+            print(f"Basic data handling: Successfully saved image to {path}")
             return (True,)
         except Exception as e:
             print(f"Basic data handling: Error saving image: {e}")
@@ -987,8 +1070,8 @@ def INPUT_TYPES(cls):
             }
         }
 
-    RETURN_TYPES = (IO.BOOLEAN)
-    RETURN_NAMES = ("success")
+    RETURN_TYPES = (IO.BOOLEAN,)
+    RETURN_NAMES = ("success",)
     CATEGORY = "Basic/Path"
     DESCRIPTION = cleandoc(__doc__ or "")
     FUNCTION = "save_image_with_mask"
@@ -998,6 +1081,7 @@ def save_image_with_mask(self, images, mask, path: str, format: str = "png",
                             quality: int = 95, invert_mask: bool = False,
                             create_dirs: bool = True):
         if not path:
+            print("Basic data handling: Save failed - no path specified")
             return (False,)
 
         # Check format compatibility - needs to support alpha channel
@@ -1065,6 +1149,7 @@ def save_image_with_mask(self, images, mask, path: str, format: str = "png",
             else:
                 pil_img_rgba.save(path, format=format.upper())
 
+            print(f"Basic data handling: Successfully saved image with mask to {path}")
             return (True,)
         except Exception as e:
             print(f"Basic data handling: Error saving image with mask: {e}")