diff --git a/__init__.py b/__init__.py
index f338853..0c9c7f1 100644
--- a/__init__.py
+++ b/__init__.py
@@ -51,6 +51,7 @@ NODE_CONFIG = {
     "GetImagesFromBatchIndexed": {"class": GetImagesFromBatchIndexed, "name": "Get Images From Batch Indexed"},
     "GetImageRangeFromBatch": {"class": GetImageRangeFromBatch, "name": "Get Image or Mask Range From Batch"},
     "GetLatentRangeFromBatch": {"class": GetLatentRangeFromBatch, "name": "Get Latent Range From Batch"},
+    "GetLatentSizeAndCount": {"class": GetLatentSizeAndCount, "name": "Get Latent Size & Count"},
     "GetImageSizeAndCount": {"class": GetImageSizeAndCount, "name": "Get Image Size & Count"},
     "FastPreview": {"class": FastPreview, "name": "Fast Preview"},
     "ImageBatchFilter": {"class": ImageBatchFilter, "name": "Image Batch Filter"},
diff --git a/nodes/image_nodes.py b/nodes/image_nodes.py
index fa70f33..72b128a 100644
--- a/nodes/image_nodes.py
+++ b/nodes/image_nodes.py
@@ -1,3 +1,5 @@
+from itertools import count
+from turtle import width
 import numpy as np
 import time
 import torch
@@ -795,6 +797,36 @@ and passes it through unchanged.
             "text": [f"{count}x{width}x{height}"]}, 
             "result": (image, width, height, count) 
         }
+
+class GetLatentSizeAndCount:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": {
+            "latent": ("LATENT",),
+        }}
+
+    RETURN_TYPES = ("LATENT","INT", "INT", "INT", "INT", "INT")
+    RETURN_NAMES = ("latent", "batch_size", "channels", "frames", "width", "height")
+    FUNCTION = "getsize"
+    CATEGORY = "KJNodes/image"
+    DESCRIPTION = """
+Returns latent tensor dimensions,  
+and passes the latent through unchanged.  
+
+"""
+    def getsize(self, latent):
+        if len(latent["samples"].shape) == 5:
+            B, C, T, H, W = latent["samples"].shape
+        elif len(latent["samples"].shape) == 4:
+            B, C, H, W = latent["samples"].shape
+            T = 0
+        else:
+            raise ValueError("Invalid latent shape")
+
+        return {"ui": {
+            "text": [f"{B}x{C}x{T}x{H}x{W}"]}, 
+            "result": (latent, B, C, T, H, W) 
+        }
     
 class ImageBatchRepeatInterleaving:
     
diff --git a/web/js/jsnodes.js b/web/js/jsnodes.js
index ff820b8..2e67693 100644
--- a/web/js/jsnodes.js
+++ b/web/js/jsnodes.js
@@ -191,6 +191,33 @@ app.registerExtension({
 				}
 				break;
 
+			case "GetLatentSizeAndCount":
+				const onGetLatentConnectInput = nodeType.prototype.onConnectInput;
+				nodeType.prototype.onConnectInput = function (targetSlot, type, output, originNode, originSlot) {
+					console.log(this)
+					const v = onGetLatentConnectInput? onGetLatentConnectInput.apply(this, arguments): undefined
+					//console.log(this)
+					this.outputs[1]["label"] = "width"
+					this.outputs[2]["label"] = "height" 
+					this.outputs[3]["label"] = "count"
+					return v;
+				}
+				//const onGetImageSizeExecuted = nodeType.prototype.onExecuted;
+				const onGetLatentSizeExecuted = nodeType.prototype.onAfterExecuteNode;
+				nodeType.prototype.onExecuted = function(message) {
+					console.log(this)
+					const r = onGetLatentSizeExecuted? onGetLatentSizeExecuted.apply(this,arguments): undefined
+					let values = message["text"].toString().split('x').map(Number);
+					console.log(values)
+					this.outputs[1]["label"] = values[0] + " batch"
+					this.outputs[2]["label"] = values[1] + " channels" 
+					this.outputs[3]["label"] = values[2] + " frames" 
+					this.outputs[4]["label"] = values[3] + " height" 
+					this.outputs[5]["label"] = values[4] + " width" 
+					return r
+				}
+				break;
+
 			case "PreviewAnimation":
 				const onPreviewAnimationConnectInput = nodeType.prototype.onConnectInput;
 				nodeType.prototype.onConnectInput = function (targetSlot, type, output, originNode, originSlot) {