diff --git a/.github/workflows/build-pipeline.yml b/.github/workflows/build-pipeline.yml
index 1287d3b..9233b18 100644
--- a/.github/workflows/build-pipeline.yml
+++ b/.github/workflows/build-pipeline.yml
@@ -26,7 +26,7 @@ jobs:
       - name: Install dependencies
         run: |
           python -m pip install --upgrade pip
-          pip install .[dev]
+          pip install .[dev] numpy torch pillow
       - name: Run Linting
         run: |
           ruff check .
diff --git a/README.md b/README.md
index 864f2b9..ede3c08 100644
--- a/README.md
+++ b/README.md
@@ -173,6 +173,16 @@ These nodes are very lightweight and require no additional dependencies.
 - Path conversions:
     - relative - Computes a relative path from a start path to a target path
     - expand_vars - Replaces environment variables in a path with their values
+- File loading:
+    - load STRING from file - Loads a text file and returns its content as a STRING
+    - load IMAGE from file (RGB) - Loads an image and returns RGB channels as a tensor
+    - load IMAGE+MASK from file (RGBA) - Loads an image and returns RGB channels as a tensor and alpha channel as a mask
+    - load MASK from alpha channel - Loads an image and extracts its alpha channel as a mask
+    - load MASK from greyscale/red - Loads an image and creates a mask from its greyscale or red channel
+- File saving:
+    - save STRING to file - Saves a string to a text file with optional directory creation
+    - save IMAGE to file - Saves an image tensor to a file in various formats (PNG, JPG, WEBP, JXL)
+    - save IMAGE+MASK to file - Saves an image with transparency using a mask as the alpha channel
 
 ### SET: Python set manipulation nodes (as a single variable)
 - Creation:
diff --git a/pyproject.toml b/pyproject.toml
index f55ac08..1d99235 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -4,7 +4,7 @@ build-backend = "setuptools.build_meta"
 
 [project]
 name = "basic_data_handling"
-version = "0.4.3"
+version = "0.4.4"
 description = """Basic Python functions for manipulating data that every programmer is used to, lightweight with no additional dependencies.
 
 Supported data types:
@@ -20,7 +20,7 @@ Feature categories:
 - Mathematical operations
 - Mathematical formula node in a safe implementation
 - String manipulation
-- File system path handling
+- File system path handling, including STRING, IMAGE and MASK load and save
 - SET operations"""
 authors = [
   {name = "StableLlama"}
diff --git a/src/basic_data_handling/path_nodes.py b/src/basic_data_handling/path_nodes.py
index a7af9e9..4e6fb9a 100644
--- a/src/basic_data_handling/path_nodes.py
+++ b/src/basic_data_handling/path_nodes.py
@@ -11,10 +11,79 @@ class IO:
         FLOAT = "FLOAT"
         STRING = "STRING"
         NUMBER = "FLOAT,INT"
+        IMAGE = "IMAGE"
+        MASK = "MASK"
         ANY = "*"
     ComfyNodeABC = object
 
 
+# helper functions:
+
+def load_image_helper(path: str):
+    """Helper function to load an image from a path"""
+    from PIL import Image, ImageOps
+    try:
+        import pillow_jxl  # noqa: F401 - imported but unused, kept for JPEG XL support
+    except ModuleNotFoundError:
+        pass
+
+    if not os.path.exists(path):
+        raise FileNotFoundError(f"Basic data handling: Image file not found: {path}")
+
+    # Open and process the image
+    img = Image.open(path)
+    img = ImageOps.exif_transpose(img)
+
+    return img
+
+
+def extract_mask_from_alpha(img):
+    """Extract a mask from the alpha channel of an image"""
+    import numpy as np
+    import torch
+
+    if 'A' in img.getbands():
+        alpha = np.array(img.getchannel('A')).astype(np.float32) / 255.0
+        mask_tensor = 1.0 - torch.from_numpy(alpha)
+    elif img.mode == 'P' and 'transparency' in img.info:
+        alpha = np.array(img.convert('RGBA').getchannel('A')).astype(np.float32) / 255.0
+        mask_tensor = 1.0 - torch.from_numpy(alpha)
+    else:
+        # Create a blank mask if no alpha channel
+        mask_tensor = torch.zeros((img.height, img.width), dtype=torch.float32)
+
+    # Add batch dimension to mask
+    mask_tensor = mask_tensor.unsqueeze(0)
+
+    return mask_tensor
+
+
+def extract_mask_from_greyscale(img):
+    """Extract a mask from a greyscale image or the red channel of an RGB image"""
+    import numpy as np
+    import torch
+
+    if img.mode == 'L':
+        # Image is already greyscale
+        gray = np.array(img).astype(np.float32) / 255.0
+    elif img.mode == 'RGB' or img.mode == 'RGBA':
+        # Use the red channel of RGB or RGBA
+        gray = np.array(img.getchannel('R')).astype(np.float32) / 255.0
+    else:
+        # Convert to greyscale if it's another format
+        gray_img = img.convert('L')
+        gray = np.array(gray_img).astype(np.float32) / 255.0
+
+    # Convert to tensor and invert (white pixels in image = transparent in mask)
+    mask_tensor = 1.0 - torch.from_numpy(gray)
+
+    # Add batch dimension
+    mask_tensor = mask_tensor.unsqueeze(0)
+
+    return mask_tensor
+
+# the nodes:
+
 class PathAbspath(ComfyNodeABC):
     """
     Returns the absolute path of a file or directory.
@@ -271,6 +340,35 @@ def INPUT_TYPES(cls):
     FUNCTION = "glob_paths"
     OUTPUT_IS_LIST = (True,)
 
+    # Class variable to store the last matched paths
+    _last_matched_paths = {}
+
+    @classmethod
+    def IS_CHANGED(s, pattern: str, recursive: bool = False):
+        # Get current paths
+        current_paths = glob.glob(pattern, recursive=recursive)
+
+        # Create a key for this specific pattern and recursive setting
+        key = f"{pattern}_{recursive}"
+
+        # If we haven't seen this pattern before, store it and trigger recalculation
+        if key not in s._last_matched_paths:
+            s._last_matched_paths[key] = current_paths
+            return float("NaN")
+
+        # Compare with previous paths
+        previous_paths = s._last_matched_paths[key]
+        if previous_paths != current_paths:
+            # Update stored paths and trigger recalculation
+            s._last_matched_paths[key] = current_paths
+            return float("NaN")
+
+        # No changes, return a consistent value
+        import hashlib
+        m = hashlib.md5()
+        m.update(str(current_paths).encode())
+        return m.hexdigest()
+
     def glob_paths(self, pattern: str, recursive: bool = False) -> tuple[list[str]]:
         return (glob.glob(pattern, recursive=recursive),)
 
@@ -564,6 +662,415 @@ def split_ext(self, path: str) -> tuple[str, str]:
         return os.path.splitext(path)
 
 
+class PathLoadStringFile(ComfyNodeABC):
+    """
+    Loads a text file in UTF-8 encoding and returns its content as a STRING
+    without any further processing.
+    """
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "path": (IO.STRING, {"default": ""}),
+            },
+        }
+
+    RETURN_TYPES = (IO.STRING,)
+    RETURN_NAMES = ("text",)
+    CATEGORY = "Basic/Path"
+    DESCRIPTION = cleandoc(__doc__ or "")
+    FUNCTION = "load_text"
+
+    def load_text(self, path: str):
+        if not os.path.exists(path):
+            raise FileNotFoundError(f"Basic data handling: String file not found: {path}")
+
+        with open(path, "r", encoding="utf-8") as f:
+            text = f.read()
+        return (text,)
+
+
+class PathLoadImageRGB(ComfyNodeABC):
+    """
+    Loads an image from a file path and returns only the RGB channels.
+
+    This node loads an image from the specified path and processes it to
+    return only the RGB channels as a tensor, ignoring any alpha channel.
+    """
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "path": (IO.STRING, {"default": ""}),
+            },
+        }
+
+    RETURN_TYPES = (IO.IMAGE,)
+    RETURN_NAMES = ("image",)
+    CATEGORY = "Basic/Path"
+    DESCRIPTION = cleandoc(__doc__ or "")
+    FUNCTION = "load_image_rgb"
+
+    def load_image_rgb(self, path: str):
+        import numpy as np
+        import torch
+
+        img = load_image_helper(path)
+
+        # 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,)
+
+
+class PathLoadImageRGBA(ComfyNodeABC):
+    """
+    Loads an image from a file path and returns RGB channels and Alpha as a mask.
+
+    This node loads an image from the specified path and processes it to
+    return the RGB channels as a tensor and the Alpha channel as a mask tensor.
+    If the image has no alpha channel, a blank mask is returned.
+    """
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "path": (IO.STRING, {"default": ""}),
+            },
+        }
+
+    RETURN_TYPES = (IO.IMAGE, IO.MASK)
+    RETURN_NAMES = ("image", "mask")
+    CATEGORY = "Basic/Path"
+    DESCRIPTION = cleandoc(__doc__ or "")
+    FUNCTION = "load_image_rgba"
+
+    def load_image_rgba(self, path: str):
+        import numpy as np
+        import torch
+
+        img = load_image_helper(path)
+
+        # Convert to RGB for the image
+        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,]
+
+        # Extract alpha channel as mask
+        mask_tensor = extract_mask_from_alpha(img)
+
+        return (image_tensor, mask_tensor)
+
+
+class PathLoadMaskFromAlpha(ComfyNodeABC):
+    """
+    Loads a mask from the alpha channel of an image.
+
+    This node loads an image from the specified path and extracts the alpha
+    channel to use as a mask. If the image has no alpha channel, a blank mask
+    is returned.
+    """
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "path": (IO.STRING, {"default": ""}),
+            },
+        }
+
+    RETURN_TYPES = (IO.MASK,)
+    RETURN_NAMES = ("mask",)
+    CATEGORY = "Basic/Path"
+    DESCRIPTION = cleandoc(__doc__ or "")
+    FUNCTION = "load_mask_from_alpha"
+
+    def load_mask_from_alpha(self, path: str):
+        img = load_image_helper(path)
+        mask_tensor = extract_mask_from_alpha(img)
+        return (mask_tensor,)
+
+
+class PathLoadMaskFromGreyscale(ComfyNodeABC):
+    """
+    Loads a mask from a greyscale image or the red channel of an RGB image.
+
+    This node loads an image from the specified path and creates a mask from it.
+    If the image is greyscale, the intensity is used directly.
+    If the image is RGB, the red channel is used.
+    """
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "path": (IO.STRING, {"default": ""}),
+            },
+            "optional": {
+                "invert": (IO.BOOLEAN, {"default": False}),
+            },
+        }
+
+    RETURN_TYPES = (IO.MASK,)
+    RETURN_NAMES = ("mask",)
+    CATEGORY = "Basic/Path"
+    DESCRIPTION = cleandoc(__doc__ or "")
+    FUNCTION = "load_mask_from_greyscale"
+
+    def load_mask_from_greyscale(self, path: str, invert: bool = False):
+        img = load_image_helper(path)
+        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,)
+
+
+class PathSaveStringFile(ComfyNodeABC):
+    """
+    Saves a string to a text file.
+
+    This node takes a string and saves it to the specified path as a text file.
+    Optionally, you can choose to create the directory if it doesn't exist.
+    """
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "text": (IO.STRING, {"default": ""}),
+                "path": (IO.STRING, {"default": ""}),
+            },
+            "optional": {
+                "create_dirs": (IO.BOOLEAN, {"default": True}),
+                "encoding": (IO.STRING, {"default": "utf-8"}),
+            }
+        }
+
+    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:
+            return (False,)
+
+        try:
+            # Create directories if needed
+            directory = os.path.dirname(path)
+            if directory and create_dirs and not os.path.exists(directory):
+                os.makedirs(directory)
+
+            with open(path, "w", encoding=encoding) as f:
+                f.write(text)
+
+            return (True,)
+        except Exception as e:
+            print(f"Basic data handling: Error saving text file: {e}")
+            return (False,)
+
+
+class PathSaveImageRGB(ComfyNodeABC):
+    """
+    Saves an image to a file.
+
+    This node takes an image tensor and saves it to the specified path.
+    Supports various image formats like PNG, JPG, WEBP, JXL (if pillow-jxl is installed), etc.
+    """
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "images": (IO.IMAGE,),
+                "path": (IO.STRING, {"default": ""}),
+            },
+            "optional": {
+                "format": (IO.STRING, {"default": "png"}),
+                "quality": (IO.INT, {"default": 95, "min": 1, "max": 100}),
+                "create_dirs": (IO.BOOLEAN, {"default": True}),
+            }
+        }
+
+    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:
+            return (False,)
+
+        # If the path doesn't have an extension or it doesn't match the format, add it
+        if not path.lower().endswith(f".{format.lower()}"):
+            path = f"{path}.{format.lower()}"
+
+        try:
+            import numpy as np
+            from PIL import Image
+
+            # Check if pillow_jxl is available for JXL support
+            has_jxl_support = False
+            try:
+                import pillow_jxl # noqa: F401 - imported but unused, kept for JPEG XL support
+                has_jxl_support = True
+            except ModuleNotFoundError:
+                # pillow_jxl is not installed
+                if format.lower() == "jxl":
+                    print("Basic data handling: JPEG XL format requested but pillow_jxl module is not installed. "
+                          "Please install it with 'pip install pillow-jxl-plugin'.")
+                    return (False,)
+
+            # Create directories if needed
+            directory = os.path.dirname(path)
+            if directory and create_dirs and not os.path.exists(directory):
+                os.makedirs(directory)
+
+            # Convert from tensor format back to PIL Image
+            # Extract the first image from the batch
+            i = 0
+            img_tensor = images[i].cpu().numpy()
+
+            # Convert to uint8 format for PIL
+            img_np = (img_tensor * 255).astype(np.uint8)
+
+            # Create PIL image
+            pil_img = Image.fromarray(img_np)
+
+            # Save the image
+            if format.lower() == "jpg" or format.lower() == "jpeg":
+                pil_img.save(path, format="JPEG", quality=quality)
+            elif format.lower() == "webp":
+                pil_img.save(path, format="WEBP", quality=quality)
+            elif format.lower() == "jxl" and has_jxl_support:
+                # JPEG XL specific options
+                pil_img.save(path, format="JXL", quality=quality)
+            else:
+                pil_img.save(path, format=format.upper())
+
+            return (True,)
+        except Exception as e:
+            print(f"Basic data handling: Error saving image: {e}")
+            return (False,)
+
+
+class PathSaveImageRGBA(ComfyNodeABC):
+    """
+    Saves an image with a mask to a file with transparency.
+
+    This node takes an image tensor and a mask tensor and saves them to the
+    specified path as an image with transparency, where the mask defines the
+    alpha channel.
+    """
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "images": (IO.IMAGE,),
+                "mask": (IO.MASK,),
+                "path": (IO.STRING, {"default": ""}),
+            },
+            "optional": {
+                "format": (IO.STRING, {"default": "png"}),
+                "quality": (IO.INT, {"default": 95, "min": 1, "max": 100}),
+                "invert_mask": (IO.BOOLEAN, {"default": False}),
+                "create_dirs": (IO.BOOLEAN, {"default": True}),
+            }
+        }
+
+    RETURN_TYPES = (IO.BOOLEAN)
+    RETURN_NAMES = ("success")
+    CATEGORY = "Basic/Path"
+    DESCRIPTION = cleandoc(__doc__ or "")
+    FUNCTION = "save_image_with_mask"
+    OUTPUT_NODE = True
+
+    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:
+            return (False,)
+
+        # Check format compatibility - needs to support alpha channel
+        if format.lower() in ["jpg", "jpeg"]:
+            print("Basic data handling: JPEG format doesn't support transparency. Using PNG instead.")
+            format = "png"
+
+        # If the path doesn't have an extension or it doesn't match the format, add it
+        if not path.lower().endswith(f".{format.lower()}"):
+            path = f"{path}.{format.lower()}"
+
+        try:
+            import numpy as np
+            from PIL import Image
+
+            # Check if pillow_jxl is available for JXL support
+            has_jxl_support = False
+            try:
+                import pillow_jxl # noqa: F401 - imported but unused, kept for JPEG XL support
+                has_jxl_support = True
+            except ModuleNotFoundError:
+                # pillow_jxl is not installed
+                if format.lower() == "jxl":
+                    print("Basic data handling: JPEG XL format requested but pillow_jxl module is not installed. "
+                          "Please install it with 'pip install pillow-jxl-plugin'.")
+                    return (False,)
+
+            # Create directories if needed
+            directory = os.path.dirname(path)
+            if directory and create_dirs and not os.path.exists(directory):
+                os.makedirs(directory)
+
+            # Convert from tensor format back to PIL Image
+            # Extract the first image from the batch
+            i = 0
+            img_tensor = images[i].cpu().numpy()
+            mask_tensor = mask[i].cpu()
+
+            # Invert the mask if needed (1.0 becomes transparent, 0.0 becomes opaque)
+            if invert_mask:
+                mask_tensor = 1.0 - mask_tensor
+
+            # Convert to alpha channel (0-255)
+            alpha_np = (255.0 * (1.0 - mask_tensor.numpy())).astype(np.uint8)
+
+            # Convert to uint8 format for PIL
+            img_np = (img_tensor * 255).astype(np.uint8)
+
+            # Create PIL image (RGB)
+            pil_img = Image.fromarray(img_np)
+
+            # Create alpha channel image
+            alpha_img = Image.fromarray(alpha_np, mode='L')
+
+            # Convert to RGBA and add alpha channel
+            pil_img_rgba = pil_img.convert("RGBA")
+            pil_img_rgba.putalpha(alpha_img)
+
+            # Save the image
+            if format.lower() == "webp":
+                pil_img_rgba.save(path, format="WEBP", quality=quality)
+            elif format.lower() == "jxl" and has_jxl_support:
+                # JPEG XL supports alpha channel
+                pil_img_rgba.save(path, format="JXL", quality=quality)
+            else:
+                pil_img_rgba.save(path, format=format.upper())
+
+            return (True,)
+        except Exception as e:
+            print(f"Basic data handling: Error saving image with mask: {e}")
+            return (False,)
+
+
 NODE_CLASS_MAPPINGS = {
     "Basic data handling: PathAbspath": PathAbspath,
     "Basic data handling: PathBasename": PathBasename,
@@ -585,6 +1092,14 @@ def split_ext(self, path: str) -> tuple[str, str]:
     "Basic data handling: PathRelative": PathRelative,
     "Basic data handling: PathSplit": PathSplit,
     "Basic data handling: PathSplitExt": PathSplitExt,
+    "Basic data handling: PathLoadStringFile": PathLoadStringFile,
+    "Basic data handling: PathLoadImageRGB": PathLoadImageRGB,
+    "Basic data handling: PathLoadImageRGBA": PathLoadImageRGBA,
+    "Basic data handling: PathLoadMaskFromAlpha": PathLoadMaskFromAlpha,
+    "Basic data handling: PathLoadMaskFromGreyscale": PathLoadMaskFromGreyscale,
+    "Basic data handling: PathSaveStringFile": PathSaveStringFile,
+    "Basic data handling: PathSaveImageRGB": PathSaveImageRGB,
+    "Basic data handling: PathSaveImageRGBA": PathSaveImageRGBA,
 }
 
 NODE_DISPLAY_NAME_MAPPINGS = {
@@ -608,4 +1123,12 @@ def split_ext(self, path: str) -> tuple[str, str]:
     "Basic data handling: PathRelative": "relative",
     "Basic data handling: PathSplit": "split",
     "Basic data handling: PathSplitExt": "splitext",
+    "Basic data handling: PathLoadStringFile": "load STRING from file",
+    "Basic data handling: PathLoadImageRGB": "load IMAGE from file (RGB)",
+    "Basic data handling: PathLoadImageRGBA": "load IMAGE+MASK from file (RGBA)",
+    "Basic data handling: PathLoadMaskFromAlpha": "load MASK from alpha channel",
+    "Basic data handling: PathLoadMaskFromGreyscale": "load MASK from greyscale/red",
+    "Basic data handling: PathSaveStringFile": "save STRING to file",
+    "Basic data handling: PathSaveImageRGB": "save IMAGE to file",
+    "Basic data handling: PathSaveImageRGBA": "save IMAGE+MASK to file",
 }
diff --git a/tests/test_path_nodes.py b/tests/test_path_nodes.py
index 0052253..dab0b9c 100644
--- a/tests/test_path_nodes.py
+++ b/tests/test_path_nodes.py
@@ -1,12 +1,17 @@
 import os
 import pytest
 import platform
+import numpy as np
+import torch
+from PIL import Image
 
 from src.basic_data_handling.path_nodes import (
     PathJoin, PathAbspath, PathExists, PathIsFile, PathIsDir, PathGetSize,
     PathSplit, PathSplitExt, PathBasename, PathDirname, PathGetExtension,
     PathSetExtension, PathNormalize, PathRelative, PathGlob, PathExpandVars, PathGetCwd,
-    PathListDir, PathIsAbsolute, PathCommonPrefix,
+    PathListDir, PathIsAbsolute, PathCommonPrefix, PathLoadStringFile, PathSaveStringFile,
+    PathLoadImageRGB, PathSaveImageRGB, PathLoadImageRGBA, PathSaveImageRGBA,
+    PathLoadMaskFromAlpha, PathLoadMaskFromGreyscale,
 )
 
 
@@ -21,6 +26,39 @@ def test_path_join():
     nested_path = os.path.join("folder", "subfolder", "file.txt")
     assert node.join_paths("folder", os.path.join("subfolder", "file.txt")) == (nested_path,)
 
+def test_path_load_save_string_file(tmp_path):
+    # Test saving a string to a file
+    save_node = PathSaveStringFile()
+    test_string = "This is a test string\nwith multiple lines"
+    file_path = str(tmp_path / "test_string.txt")
+
+    # Save the string
+    assert save_node.save_text(test_string, file_path) == (True,)
+
+    # Verify file exists
+    assert os.path.exists(file_path)
+
+    # Test loading the string back
+    load_node = PathLoadStringFile()
+    loaded_string = load_node.load_text(file_path)
+    assert loaded_string == (test_string,)
+
+    # Test creating directories when saving
+    nested_path = str(tmp_path / "nested" / "dir" / "test.txt")
+    assert save_node.save_text("Test with nested dirs", nested_path) == (True,)
+    assert os.path.exists(nested_path)
+
+    # Test different encodings
+    utf8_text = "UTF-8 text with special chars: 你好, ñ, é, ö"
+    utf8_path = str(tmp_path / "utf8_test.txt")
+    assert save_node.save_text(utf8_text, utf8_path, encoding="utf-8") == (True,)
+    assert load_node.load_text(utf8_path) == (utf8_text,)
+
+    # Test error handling
+    with pytest.raises(FileNotFoundError):
+        load_node.load_text(str(tmp_path / "nonexistent.txt"))
+
+
 
 def test_path_abspath():
     node = PathAbspath()
@@ -217,6 +255,193 @@ def test_path_set_extension():
     assert node.set_extension("", ".txt") == (".txt",)
 
 
+def test_path_load_mask_nodes(tmp_path, monkeypatch):
+    # Create test images
+    img_size = (64, 64)
+
+    # Image with alpha channel
+    rgba_img = Image.new('RGBA', img_size, color=(255, 0, 0, 128))  # Semi-transparent red
+    rgba_path = str(tmp_path / "alpha_mask.png")
+    rgba_img.save(rgba_path)
+
+    # Grayscale image
+    gray_img = Image.new('L', img_size)
+    # Create a gradient from black to white
+    for y in range(img_size[1]):
+        for x in range(img_size[0]):
+            gray_img.putpixel((x, y), int(255 * (x / img_size[0])))
+    gray_path = str(tmp_path / "gray_mask.png")
+    gray_img.save(gray_path)
+
+    # Mock the helper functions
+    def mock_load_image_helper(path):
+        if not os.path.exists(path):
+            raise FileNotFoundError(f"Image file not found: {path}")
+        return Image.open(path)
+
+    def mock_extract_mask_from_alpha(img):
+        if 'A' in img.getbands():
+            alpha = np.array(img.getchannel('A')).astype(np.float32) / 255.0
+            mask_tensor = 1.0 - torch.from_numpy(alpha)
+            return mask_tensor.unsqueeze(0)
+        return torch.zeros((1, img.height, img.width), dtype=torch.float32)
+
+    def mock_extract_mask_from_greyscale(img):
+        if img.mode == 'L':
+            gray = np.array(img).astype(np.float32) / 255.0
+        else:
+            gray = np.array(img.getchannel('R')).astype(np.float32) / 255.0
+        mask_tensor = 1.0 - torch.from_numpy(gray)
+        return mask_tensor.unsqueeze(0)
+
+    monkeypatch.setattr("src.basic_data_handling.path_nodes.load_image_helper", mock_load_image_helper)
+    monkeypatch.setattr("src.basic_data_handling.path_nodes.extract_mask_from_alpha", mock_extract_mask_from_alpha)
+    monkeypatch.setattr("src.basic_data_handling.path_nodes.extract_mask_from_greyscale", mock_extract_mask_from_greyscale)
+
+    # Test loading mask from alpha channel
+    alpha_node = PathLoadMaskFromAlpha()
+    alpha_mask = alpha_node.load_mask_from_alpha(rgba_path)
+
+    # Verify the mask shape
+    assert isinstance(alpha_mask, tuple)
+    assert len(alpha_mask) == 1
+    assert isinstance(alpha_mask[0], torch.Tensor)
+    assert alpha_mask[0].shape == (1, img_size[1], img_size[0])
+
+    # Test loading mask from grayscale image
+    gray_node = PathLoadMaskFromGreyscale()
+    gray_mask = gray_node.load_mask_from_greyscale(gray_path)
+
+    # Verify the mask shape
+    assert isinstance(gray_mask, tuple)
+    assert len(gray_mask) == 1
+    assert isinstance(gray_mask[0], torch.Tensor)
+    assert gray_mask[0].shape == (1, img_size[1], img_size[0])
+
+    # Test with invert option
+    inverted_mask = gray_node.load_mask_from_greyscale(gray_path, invert=True)
+    assert torch.allclose(inverted_mask[0], 1.0 - gray_mask[0])
+
+    # Test error handling
+    with pytest.raises(FileNotFoundError):
+        alpha_node.load_mask_from_alpha(str(tmp_path / "nonexistent.png"))
+
+
+def test_path_load_save_image_rgba(tmp_path, monkeypatch):
+    # Create a test image with transparency
+    img_size = (64, 64)
+    test_img = Image.new('RGBA', img_size, color=(255, 0, 0, 128))  # Semi-transparent red
+    img_path = str(tmp_path / "test_rgba.png")
+    test_img.save(img_path)
+
+    # Mock the load_image_helper and extraction functions
+    def mock_load_image_helper(path):
+        if not os.path.exists(path):
+            raise FileNotFoundError(f"Image file not found: {path}")
+        return Image.open(path)
+
+    def mock_extract_mask_from_alpha(img):
+        if 'A' in img.getbands():
+            alpha = np.array(img.getchannel('A')).astype(np.float32) / 255.0
+            mask_tensor = 1.0 - torch.from_numpy(alpha)
+            return mask_tensor.unsqueeze(0)
+        return torch.zeros((1, img.height, img.width), dtype=torch.float32)
+
+    monkeypatch.setattr("src.basic_data_handling.path_nodes.load_image_helper", mock_load_image_helper)
+    monkeypatch.setattr("src.basic_data_handling.path_nodes.extract_mask_from_alpha", mock_extract_mask_from_alpha)
+
+    # Test loading an image with alpha
+    load_node = PathLoadImageRGBA()
+    loaded_img, loaded_mask = load_node.load_image_rgba(img_path)
+
+    # Verify that the returned objects are tensors with the right shapes
+    assert isinstance(loaded_img, torch.Tensor)
+    assert isinstance(loaded_mask, torch.Tensor)
+    assert loaded_img.shape == (1, img_size[1], img_size[0], 3)  # (batch, height, width, channels)
+    assert loaded_mask.shape == (1, img_size[1], img_size[0])    # (batch, height, width)
+
+    # Test saving an image with mask
+    save_node = PathSaveImageRGBA()
+    output_path = str(tmp_path / "output_rgba")
+
+    # Create a simple red test image tensor and a gradient mask
+    red_img = torch.zeros(1, img_size[1], img_size[0], 3)
+    red_img[0, :, :, 0] = 1.0  # Red channel set to 1
+
+    # Create a gradient mask (0.0 to 1.0 from left to right)
+    mask = torch.zeros(1, img_size[1], img_size[0])
+    for i in range(img_size[0]):
+        mask[0, :, i] = i / img_size[0]
+
+    # Save the image with mask
+    assert save_node.save_image_with_mask(red_img, mask, output_path) == (True,)
+
+    # Verify the image was saved with the correct extension
+    assert os.path.exists(output_path + ".png")
+
+    # Test with invert_mask option
+    assert save_node.save_image_with_mask(red_img, mask, str(tmp_path / "inverted"), invert_mask=True) == (True,)
+    assert os.path.exists(str(tmp_path / "inverted.png"))
+
+    # Test with JPEG format (should switch to PNG for transparency)
+    assert save_node.save_image_with_mask(red_img, mask, str(tmp_path / "jpeg_test"), format="jpg") == (True,)
+    # Should be saved as PNG despite the request for JPEG
+    assert os.path.exists(str(tmp_path / "jpeg_test.png"))
+
+
+def test_path_load_save_image_rgb(tmp_path, monkeypatch):
+    # Create a test image
+    img_size = (64, 64)
+    test_img = Image.new('RGB', img_size, color='red')
+    img_path = str(tmp_path / "test_rgb.png")
+    test_img.save(img_path)
+
+    # Mock the load_image_helper function to avoid PIL import issues in testing
+    def mock_load_image_helper(path):
+        if not os.path.exists(path):
+            raise FileNotFoundError(f"Image file not found: {path}")
+        return Image.open(path)
+
+    monkeypatch.setattr("src.basic_data_handling.path_nodes.load_image_helper", mock_load_image_helper)
+
+    # Test loading an image
+    load_node = PathLoadImageRGB()
+    loaded_img = load_node.load_image_rgb(img_path)
+
+    # Verify that the returned object is a tensor with the right shape
+    assert isinstance(loaded_img, tuple)
+    assert len(loaded_img) == 1
+    assert isinstance(loaded_img[0], torch.Tensor)
+    assert loaded_img[0].shape == (1, img_size[1], img_size[0], 3)  # (batch, height, width, channels)
+
+    # Test saving an image
+    save_node = PathSaveImageRGB()
+    output_path = str(tmp_path / "output_rgb")
+
+    # Create a simple red test image tensor
+    red_img = torch.zeros(1, img_size[1], img_size[0], 3)
+    red_img[0, :, :, 0] = 1.0  # Red channel set to 1
+
+    # Save the image
+    assert save_node.save_image(red_img, output_path) == (True,)
+
+    # Verify the image was saved with the correct extension
+    assert os.path.exists(output_path + ".png")
+
+    # Test different formats
+    assert save_node.save_image(red_img, str(tmp_path / "jpeg_test"), format="jpg") == (True,)
+    assert os.path.exists(str(tmp_path / "jpeg_test.jpg"))
+
+    # Test with directories that don't exist
+    nested_path = str(tmp_path / "nested" / "images" / "test_rgb")
+    assert save_node.save_image(red_img, nested_path) == (True,)
+    assert os.path.exists(nested_path + ".png")
+
+    # Test error handling for loading
+    with pytest.raises(FileNotFoundError):
+        load_node.load_image_rgb(str(tmp_path / "nonexistent.png"))
+
+
 
 def test_path_normalize():
     node = PathNormalize()