diff --git a/py/gradient_map.py b/py/gradient_map.py
new file mode 100644
index 0000000..29e116a
--- /dev/null
+++ b/py/gradient_map.py
@@ -0,0 +1,85 @@
+import torch
+from PIL import Image
+import numpy as np
+from .imagefunc import *
+
+NODE_NAME = 'GradientMap'
+
+class GradientMap:
+
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "image": ("IMAGE",),
+                "start_color": ("STRING", {"default": "#015A52"}),
+                "mid_color": ("STRING", {"default": "#02AF9F"}),
+                "end_color": ("STRING", {"default": "#7FFFEC"}),
+                "mid_point": ("FLOAT", {"default": 0.6, "min": 0.0, "max": 1.0, "step": 0.01}),
+                "opacity": ("INT", {"default": 100, "min": 0, "max": 100, "step": 1}),
+            },
+            "optional": {
+                "layer_mask": ("MASK",),
+            }
+        }
+
+    RETURN_TYPES = ("IMAGE", "IMAGE")
+    RETURN_NAMES = ("image", "gradient")
+    FUNCTION = 'apply_gradient_map'
+    CATEGORY = '😺dzNodes/LayerStyle'
+
+    def apply_gradient_map(self, image, start_color, mid_color, end_color, mid_point, opacity, layer_mask=None):
+        def create_gradient_array(start_color, mid_color, end_color, mid_point):
+            start_rgb = Hex_to_RGB(start_color)
+            mid_rgb = Hex_to_RGB(mid_color)
+            end_rgb = Hex_to_RGB(end_color)
+            
+            mid_index = int(255 * mid_point)
+            gradient1 = np.array([np.linspace(start_rgb[i], mid_rgb[i], mid_index + 1) for i in range(3)]).T
+            gradient2 = np.array([np.linspace(mid_rgb[i], end_rgb[i], 256 - mid_index) for i in range(3)]).T
+            return np.vstack((gradient1[:-1], gradient2))
+
+        gradient_array = create_gradient_array(start_color, mid_color, end_color, mid_point)
+
+        gradient_image = Image.fromarray(np.uint8(gradient_array.reshape(1, -1, 3).repeat(50, axis=0)))
+        gradient_tensor = pil2tensor(gradient_image)
+        ret_images = []
+        for img in image:
+            pil_image = tensor2pil(img)
+            
+            # Convert to grayscale to get luminance
+            gray_image = np.array(pil_image.convert('L'))
+            
+            # Apply gradient map
+            gradient_mapped = gradient_array[gray_image]
+            
+            # Preserve luminance of original image
+            original_array = np.array(pil_image)
+            luminance = np.sum(original_array * [0.299, 0.587, 0.114], axis=2, keepdims=True) / 255.0
+            gradient_mapped = gradient_mapped * luminance + original_array * (1 - luminance)
+            
+            gradient_mapped_image = Image.fromarray(np.uint8(gradient_mapped))
+            
+            # Apply opacity
+            if opacity < 100:
+                gradient_mapped_image = Image.blend(pil_image, gradient_mapped_image, opacity / 100)
+            
+            # Apply mask if provided
+            if layer_mask is not None:
+                mask = tensor2pil(layer_mask).convert('L')
+                pil_image.paste(gradient_mapped_image, (0, 0), mask)
+            else:
+                pil_image = gradient_mapped_image
+            
+            ret_images.append(pil2tensor(pil_image))
+
+        log(f"{NODE_NAME} Processed {len(ret_images)} image(s).", message_type='finish')
+        return (torch.cat(ret_images, dim=0), gradient_tensor)
+    
+NODE_CLASS_MAPPINGS = {
+    "LayerStyle: Gradient Map": GradientMap
+}
+
+NODE_DISPLAY_NAME_MAPPINGS = {
+    "LayerStyle: Gradient Map": "LayerStyle: Gradient Map"
+}
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