diff --git a/comfy_extras/nodes_images.py b/comfy_extras/nodes_images.py
index 1ac740d1d..e48b2ea2d 100644
--- a/comfy_extras/nodes_images.py
+++ b/comfy_extras/nodes_images.py
@@ -136,7 +136,7 @@ class ImageFromBatch(IO.ComfyNode):
             category="image/batch",
             inputs=[
                 IO.Image.Input("image"),
-                IO.Int.Input("batch_index", default=0, min=0, max=4095),
+                IO.Int.Input("batch_index", default=0, min=-MAX_RESOLUTION, max=MAX_RESOLUTION),
                 IO.Int.Input("length", default=1, min=1, max=4096),
             ],
             outputs=[IO.Image.Output()],
@@ -145,7 +145,9 @@ class ImageFromBatch(IO.ComfyNode):
     @classmethod
     def execute(cls, image, batch_index, length) -> IO.NodeOutput:
         s_in = image
-        batch_index = min(s_in.shape[0] - 1, batch_index)
+        if batch_index < 0:
+            batch_index += s_in.shape[0]
+        batch_index = max(0, min(s_in.shape[0] - 1, batch_index))
         length = min(s_in.shape[0] - batch_index, length)
         s = s_in[batch_index:batch_index + length].clone()
         return IO.NodeOutput(s)
diff --git a/nodes.py b/nodes.py
index 2b63f9fbb..2768e1016 100644
--- a/nodes.py
+++ b/nodes.py
@@ -1221,7 +1221,7 @@ class LatentFromBatch:
     @classmethod
     def INPUT_TYPES(s):
         return {"required": { "samples": ("LATENT",),
-                              "batch_index": ("INT", {"default": 0, "min": 0, "max": 63}),
+                              "batch_index": ("INT", {"default": 0, "min": -MAX_RESOLUTION, "max": MAX_RESOLUTION}),
                               "length": ("INT", {"default": 1, "min": 1, "max": 64}),
                               }}
     RETURN_TYPES = ("LATENT",)
@@ -1232,7 +1232,9 @@ class LatentFromBatch:
     def frombatch(self, samples, batch_index, length):
         s = samples.copy()
         s_in = samples["samples"]
-        batch_index = min(s_in.shape[0] - 1, batch_index)
+        if batch_index < 0:
+            batch_index += s_in.shape[0]
+        batch_index = max(0, min(s_in.shape[0] - 1, batch_index))
         length = min(s_in.shape[0] - batch_index, length)
         s["samples"] = s_in[batch_index:batch_index + length].clone()
         if "noise_mask" in samples: