diff --git a/__init__.py b/__init__.py
index b75da80..80acd8c 100644
--- a/__init__.py
+++ b/__init__.py
@@ -8,4 +8,4 @@ NODE_CLASS_MAPPINGS = {
 NODE_DISPLAY_NAME_MAPPINGS = {
     "PaletteTransfer": "Palette Transfer",
     "ColorPalette": "Color Palette",
-}
+}
\ No newline at end of file
diff --git a/color_transfer.py b/color_transfer.py
index 4b7eb87..c1f2473 100644
--- a/color_transfer.py
+++ b/color_transfer.py
@@ -1,22 +1,44 @@
 import numpy as np
-from sklearn.cluster import KMeans
+from sklearn.cluster import KMeans, MiniBatchKMeans
 import torch
 import ast
 
 
-def ColorClustering(image, k):
+def EuclideanDistance(current_colors, target_colors):
+    return np.linalg.norm(current_colors - target_colors, axis=1)
+
+
+def ManhattanDistance(current_colors, target_colors):
+    return np.sum(np.abs(current_colors - target_colors), axis=1)
+
+
+def ColorClustering(image, k, cluster_method):
     img_array = image.reshape((image.shape[0] * image.shape[1], 3))
-    kmeans = KMeans(n_clusters=k)
+
+    cluster_methods = {
+    "Kmeans": KMeans,
+    "Mini batch Kmeans": MiniBatchKMeans
+    }
+
+    kmeans = cluster_methods.get(cluster_method)(n_clusters=k)
+
     kmeans.fit(img_array)
     main_colors = kmeans.cluster_centers_
-    
     return image, main_colors.astype(int), kmeans
 
 
-def SwitchColors(image, current_colors, target_colors, kmeans):
+def SwitchColors(image, current_colors, target_colors, kmeans, distance_method):
     closest_colors = []
+
+    distance_methods = {
+    "Euclidean": EuclideanDistance,
+    "Manhattan": ManhattanDistance
+    }
+
+    distance_method = distance_methods.get(distance_method)
+
     for color in current_colors:
-        distances = np.linalg.norm(target_colors - color, axis=1)
+        distances = distance_method(color, target_colors)
         closest_color = target_colors[np.argmin(distances)]
         closest_colors.append(closest_color)
     closest_colors = np.array(closest_colors)
@@ -34,7 +56,9 @@ class PaletteTransferNode:
         data_in = {
             "required": {
                 "image": ("IMAGE",),
-                "colors": ("COLORS",)
+                "colors": ("COLORS",),
+                "cluster_method": (["Kmeans","Mini batch Kmeans"], {'default': 'Kmeans'}, ),
+                "distance_method": (["Euclidean", "Manhattan"], {'default': 'Euclidean'}, )
                 }
             }
         return data_in
@@ -44,7 +68,7 @@ class PaletteTransferNode:
     CATEGORY = "Palette Transfer"
 
 
-    def color_transfer(self, image, colors):
+    def color_transfer(self, image, colors, cluster_method, distance_method):
 
         if len(colors) == 0:
             return (image,)
@@ -54,8 +78,8 @@ class PaletteTransferNode:
             for image in image:
                 img = 255. * image.cpu().numpy()
 
-                img, current_colors, kmeans = ColorClustering(img, len(colors))
-                processed = SwitchColors(img, current_colors, colors, kmeans)
+                img, current_colors, kmeans = ColorClustering(img, len(colors), cluster_method)
+                processed = SwitchColors(img, current_colors, colors, kmeans, distance_method)
                 processedImages.append(processed)
             output = torch.cat(processedImages, dim=0)
 
@@ -76,4 +100,4 @@ class ColorPaletteNode:
     FUNCTION = "color_list"
 
     def color_list(self, colors):
-        return (ast.literal_eval(colors), )
\ No newline at end of file
+        return (ast.literal_eval(colors), )