diff --git a/utility_nodes/list_utility_nodes.py b/utility_nodes/list_utility_nodes.py
index d9c8252..fcfda3b 100644
--- a/utility_nodes/list_utility_nodes.py
+++ b/utility_nodes/list_utility_nodes.py
@@ -1,5 +1,6 @@
 import torch
 from comfy_api.latest import io
+from typing import Iterable
     
 class BatchFromImageList(io.ComfyNode):
     @classmethod
@@ -43,19 +44,23 @@ class ImageListFromBatch(io.ComfyNode):
     def execute(cls, images): # type: ignore
         image_list = list( i.unsqueeze(0) for i in images )
         return io.NodeOutput(image_list,) 
-   
+
+template = io.MatchType.Template("pick_from_list")
+
 class PickFromList(io.ComfyNode):
     @classmethod
     def define_schema(cls):
         return io.Schema(
             node_id      = "Pick from List",
             display_name = "Pick from List",
+            description  = "Given a comma separated list of indexes, return only those entries from the input list.",
             inputs       = [
-                io.AnyType.Input("anything"),
-                io.String.Input("indexes", display_name="indexes", tooltip="comma separated list of indexes. Whitespace stripped. Only these entries will be included. Zero indexed.")
+                io.MatchType.Input("anything", template=template, tooltip="note that a list is expected (or an image batch)"),
+                io.String.Input("indexes", display_name="indexes", tooltip="comma separated list of indexes. Whitespace stripped. Only these entries will be included."),
+                io.Combo.Input("indexing", options=['0','1'], default='0', tooltip="is the first item number 0 or number 1?")
             ],
             outputs      = [
-                io.String.Output("picks", display_name="picks", is_output_list=True)
+                io.MatchType.Output(template=template, display_name="picks", is_output_list=True)
             ],
             category     = "image_filter/helpers",
             is_input_list=True
@@ -63,19 +68,25 @@ class PickFromList(io.ComfyNode):
 
 
     @classmethod
-    def execute(cls, anything:list, indexes:list[str]): # type: ignore
+    def execute(cls, anything:list, indexes:list[str], indexing:list[str|int]=[0,]): # type: ignore
 
-        if len(anything)==1 and isinstance(anything[0],list): 
-            print("Warning: received list of lists. Processing just anything[0]")
-            anything = anything[0]
+        is_image_batch = False
+        if len(anything)==1:
+            if isinstance(anything[0],list): 
+                print("Warning: PickFromList received length 1 list of lists. Processing anything[0]")
+                anything = anything[0]
+            elif isinstance(anything[0], torch.Tensor) and len(anything[0].shape) == 4:
+                anything = anything[0]       # type: ignore
+                is_image_batch = True
 
         index_str:str = indexes[0]
+        offset:int    = int(indexing[0])
 
-        result = []
-        for x in [x.strip() for x in index_str.split(',')]:
-            try:
-                result.append(anything[int(x)])
-            except Exception as e:
-                print(f"{e} when processing {x} from {index_str}")
-
-        return io.NodeOutput(result, )
\ No newline at end of file
+        try:
+            index_ns = [int(x.strip())-offset for x in index_str.split(',') if x.strip()]
+            result   = [anything[x] for x in index_ns]
+            if is_image_batch: result = [torch.stack(result),]
+            return io.NodeOutput(result, )
+        except Exception as e:
+            print(f"{e} when processing {index_str}")
+            raise