diff --git a/__init__.py b/__init__.py
index a9b94c9..f1ce49c 100644
--- a/__init__.py
+++ b/__init__.py
@@ -43,6 +43,7 @@ NODE_CONFIG = {
     "AddLabel": {"class": AddLabel, "name": "Add Label"},
     "ColorMatch": {"class": ColorMatch, "name": "Color Match"},
     "CrossFadeImages": {"class": CrossFadeImages, "name": "Cross Fade Images"},
+    "CrossFadeImagesMulti": {"class": CrossFadeImagesMulti, "name": "Cross Fade Images Multi"},
     "GetImagesFromBatchIndexed": {"class": GetImagesFromBatchIndexed, "name": "Get Images From Batch Indexed"},
     "GetImageRangeFromBatch": {"class": GetImageRangeFromBatch, "name": "Get Image or Mask Range From Batch"},
     "GetImageSizeAndCount": {"class": GetImageSizeAndCount, "name": "Get Image Size & Count"},
diff --git a/nodes/image_nodes.py b/nodes/image_nodes.py
index 803df74..369f82e 100644
--- a/nodes/image_nodes.py
+++ b/nodes/image_nodes.py
@@ -1224,6 +1224,88 @@ class CrossFadeImages:
         beginning_images_1 = images_1[:transition_start_index]
         crossfade_images = torch.cat([beginning_images_1, crossfade_images], dim=0)
         return (crossfade_images, )
+    
+class CrossFadeImagesMulti:
+    RETURN_TYPES = ("IMAGE",)
+    FUNCTION = "crossfadeimages"
+    CATEGORY = "KJNodes/image"
+
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                 "inputcount": ("INT", {"default": 2, "min": 2, "max": 1000, "step": 1}),
+                 "image_1": ("IMAGE",),
+                 "image_2": ("IMAGE",),
+                 "interpolation": (["linear", "ease_in", "ease_out", "ease_in_out", "bounce", "elastic", "glitchy", "exponential_ease_out"],),
+                 "transitioning_frames": ("INT", {"default": 1,"min": 0, "max": 4096, "step": 1}),
+        },
+    } 
+    
+    def crossfadeimages(self, inputcount, transitioning_frames, interpolation, **kwargs):
+
+        def crossfade(images_1, images_2, alpha):
+            crossfade = (1 - alpha) * images_1 + alpha * images_2
+            return crossfade
+        def ease_in(t):
+            return t * t
+        def ease_out(t):
+            return 1 - (1 - t) * (1 - t)
+        def ease_in_out(t):
+            return 3 * t * t - 2 * t * t * t
+        def bounce(t):
+            if t < 0.5:
+                return self.ease_out(t * 2) * 0.5
+            else:
+                return self.ease_in((t - 0.5) * 2) * 0.5 + 0.5
+        def elastic(t):
+            return math.sin(13 * math.pi / 2 * t) * math.pow(2, 10 * (t - 1))
+        def glitchy(t):
+            return t + 0.1 * math.sin(40 * t)
+        def exponential_ease_out(t):
+            return 1 - (1 - t) ** 4
+
+        easing_functions = {
+            "linear": lambda t: t,
+            "ease_in": ease_in,
+            "ease_out": ease_out,
+            "ease_in_out": ease_in_out,
+            "bounce": bounce,
+            "elastic": elastic,
+            "glitchy": glitchy,
+            "exponential_ease_out": exponential_ease_out,
+        }
+
+        image_1 = kwargs["image_1"]
+        height = image_1.shape[1]
+        width = image_1.shape[2]
+
+        easing_function = easing_functions[interpolation]
+       
+        for c in range(1, inputcount):
+            frames = []
+            new_image = kwargs[f"image_{c + 1}"]
+            new_image_height = new_image.shape[1]
+            new_image_width = new_image.shape[2]
+
+            if new_image_height != height or new_image_width != width:
+                new_image = common_upscale(new_image.movedim(-1, 1), width, height, "lanczos", "disabled")
+                new_image = new_image.movedim(1, -1)  # Move channels back to the last dimension
+
+            last_frame_image_1 = image_1[-1]
+            first_frame_image_2 = new_image[0]
+
+            for frame in range(transitioning_frames):
+                t = frame / (transitioning_frames - 1)
+                alpha = easing_function(t)
+                alpha_tensor = torch.tensor(alpha, dtype=last_frame_image_1.dtype, device=last_frame_image_1.device)
+                frame_image = crossfade(last_frame_image_1, first_frame_image_2, alpha_tensor)
+                frames.append(frame_image)
+        
+            frames = torch.stack(frames)
+            image_1 = torch.cat((image_1, frames, new_image), dim=0)
+        
+        return image_1,
 
 class GetImageRangeFromBatch:
     
diff --git a/web/js/jsnodes.js b/web/js/jsnodes.js
index aa71369..5f7936f 100644
--- a/web/js/jsnodes.js
+++ b/web/js/jsnodes.js
@@ -32,6 +32,7 @@ app.registerExtension({
 			case "ImageBatchMulti":
 			case "ImageAddMulti":
 			case "ImageConcatMulti":
+			case "CrossFadeImagesMulti":
 				nodeType.prototype.onNodeCreated = function () {
 				this._type = "IMAGE"
 				this.inputs_offset = nodeData.name.includes("selective")?1:0