diff --git a/nodes/image/FL_ImageSlicer.py b/nodes/image/FL_ImageSlicer.py
index ed5fbb2..f77fd31 100644
--- a/nodes/image/FL_ImageSlicer.py
+++ b/nodes/image/FL_ImageSlicer.py
@@ -8,8 +8,8 @@ class FL_ImageSlicer:
         return {
             "required": {
                 "image": ("IMAGE",),
-                "x_subdivisions": ("INT", {"default": 2, "min": 1, "max": 100, "step": 1}),
-                "y_subdivisions": ("INT", {"default": 2, "min": 1, "max": 100, "step": 1}),
+                "width_subdivisions": ("INT", {"default": 2, "min": 1, "max": 100, "step": 1}),
+                "height_subdivisions": ("INT", {"default": 2, "min": 1, "max": 100, "step": 1}),
             },
         }
 
@@ -17,7 +17,7 @@ class FL_ImageSlicer:
     FUNCTION = "slice_image"
     CATEGORY = "🏵️Fill Nodes/Image"
 
-    def slice_image(self, image, x_subdivisions, y_subdivisions):
+    def slice_image(self, image, width_subdivisions, height_subdivisions):
         # Convert from torch tensor to PIL Image
         pil_image = tensor_to_pil(image)
 
@@ -25,13 +25,13 @@ class FL_ImageSlicer:
         width, height = pil_image.size
 
         # Calculate slice dimensions
-        slice_width = width // x_subdivisions
-        slice_height = height // y_subdivisions
+        slice_width = width // width_subdivisions
+        slice_height = height // height_subdivisions
 
         # Slice the image
         slices = []
-        for y in range(y_subdivisions):
-            for x in range(x_subdivisions):
+        for y in range(height_subdivisions):
+            for x in range(width_subdivisions):
                 left = x * slice_width
                 upper = y * slice_height
                 right = left + slice_width
diff --git a/nodes/image/FL_SaveImages.py b/nodes/image/FL_SaveImages.py
index 0d00d32..afca817 100644
--- a/nodes/image/FL_SaveImages.py
+++ b/nodes/image/FL_SaveImages.py
@@ -1,4 +1,5 @@
 import os
+import re
 import torch
 from PIL import Image
 import numpy as np
@@ -14,6 +15,7 @@ class FL_SaveImages:
                 "folder_structure": ("STRING", {"default": "[]"}),
                 "file_name_template": ("STRING", {"default": "image_{index}.png"}),
                 "start_index": ("INT", {"default": 1, "min": 0, "max": 1000000}),
+                "auto_increment": ("BOOLEAN", {"default": True}),
             },
             "optional": {
                 "metadata": ("STRING", {"default": ""}),
@@ -25,24 +27,28 @@ class FL_SaveImages:
     OUTPUT_NODE = True
     CATEGORY = "🏵️Fill Nodes/Image"
 
-    def save_images(self, images, base_directory, folder_structure, file_name_template, start_index, metadata=""):
+    def save_images(self, images, base_directory, folder_structure, file_name_template, start_index, auto_increment=True, metadata=""):
         saved_paths = []
         folder_structure = json.loads(folder_structure)
 
         # Ensure base directory exists
         os.makedirs(base_directory, exist_ok=True)
 
+        # Create the full folder path based on the folder structure
+        full_folder_path = self.create_folder_path(base_directory, folder_structure)
+        os.makedirs(full_folder_path, exist_ok=True)
+
         batch_size = len(images)
         has_index_placeholder = "{index}" in file_name_template
 
+        # If auto_increment is enabled, find the next available index
+        if auto_increment and has_index_placeholder:
+            start_index = self.find_next_index(full_folder_path, file_name_template, start_index)
+
         for i, image in enumerate(images):
             # Convert the image tensor to a PIL Image
             img = Image.fromarray((image.cpu().numpy() * 255).astype(np.uint8))
 
-            # Create the full folder path based on the folder structure
-            full_folder_path = self.create_folder_path(base_directory, folder_structure)
-            os.makedirs(full_folder_path, exist_ok=True)
-
             # Determine the filename based on batch size and placeholder
             if batch_size > 1:
                 # For batches, always use an index
@@ -82,6 +88,26 @@ class FL_SaveImages:
 
         return (", ".join(saved_paths),)
 
+    def find_next_index(self, folder_path, file_name_template, min_index):
+        """Find the next available index by checking existing files in the folder."""
+        if not os.path.exists(folder_path):
+            return min_index
+
+        # Build a regex pattern from the template to extract existing indices
+        # Escape special regex chars, then replace {index} with a capture group
+        pattern_str = re.escape(file_name_template).replace(r"\{index\}", r"(\d+)")
+        pattern = re.compile(f"^{pattern_str}$")
+
+        max_found = min_index - 1
+        for filename in os.listdir(folder_path):
+            match = pattern.match(filename)
+            if match:
+                index = int(match.group(1))
+                if index > max_found:
+                    max_found = index
+
+        return max_found + 1
+
     def create_folder_path(self, base_directory, folder_structure):
         path = base_directory
         for folder in folder_structure:
diff --git a/pyproject.toml b/pyproject.toml
index d40d6e8..22a2c8d 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -1,7 +1,7 @@
 [project]
 name = "comfyui_fill-nodes"
 description = "Fill-Nodes is a versatile collection of custom nodes for ComfyUI that extends functionality across multiple domains. Features include advanced image processing (pixelation, slicing, masking), visual effects generation (glitch, halftone, pixel art), comprehensive file handling (PDF creation/extraction, Google Drive integration), AI model interfaces (GPT, DALL-E, Hugging Face), utility nodes for workflow enhancement, and specialized tools for video processing, captioning, and batch operations. The pack provides both practical workflow solutions and creative tools within a unified node collection."
-version = "2.1.6"
+version = "2.1.7"
 license = "LICENSE"
 dependencies = ["diffusers", "librosa", "sounddevice", "glitch_this", "PyOpenGL", "glfw", "scipy>=1.13.1", "requests", "aiohttp", "moviepy", "matplotlib", "reportlab", "openai", "PyPDF2", "pdf2image", "PyMuPDF", "reportlab", "PyPDF2", "ollama", "kornia", "opencv-python", "gdown", "open_clip_torch", "google-genai"]