diff --git a/nodes/image_nodes.py b/nodes/image_nodes.py
index b75e2dc..2d3ae65 100644
--- a/nodes/image_nodes.py
+++ b/nodes/image_nodes.py
@@ -1265,7 +1265,7 @@ class CrossFadeImages:
                  "images_1": ("IMAGE",),
                  "images_2": ("IMAGE",),
                  "interpolation": (["linear", "ease_in", "ease_out", "ease_in_out", "bounce", "elastic", "glitchy", "exponential_ease_out"],),
-                 "transition_start_index": ("INT", {"default": 1,"min": 0, "max": 4096, "step": 1}),
+                 "transition_start_index": ("INT", {"default": 1,"min": -4096, "max": 4096, "step": 1}),
                  "transitioning_frames": ("INT", {"default": 1,"min": 0, "max": 4096, "step": 1}),
                  "start_level": ("FLOAT", {"default": 0.0,"min": 0.0, "max": 1.0, "step": 0.01}),
                  "end_level": ("FLOAT", {"default": 1.0,"min": 0.0, "max": 1.0, "step": 0.01}),
@@ -1308,36 +1308,36 @@ class CrossFadeImages:
 
         crossfade_images = []
 
+        if transition_start_index < 0:
+            transition_start_index = len(images_1) + transition_start_index
+            if transition_start_index < 0:
+                raise ValueError("Transition start index is out of range for images_1.")
+            
+        transitioning_frames = min(transitioning_frames, len(images_1) - transition_start_index, len(images_2))
+
         alphas = torch.linspace(start_level, end_level, transitioning_frames)
         for i in range(transitioning_frames):
             alpha = alphas[i]
-            image1 = images_1[i + transition_start_index]
-            image2 = images_2[i + transition_start_index]
+            image1 = images_1[transition_start_index + i]
+            image2 = images_2[i]
             easing_function = easing_functions.get(interpolation)
             alpha = easing_function(alpha)  # Apply the easing function to the alpha value
 
             crossfade_image = crossfade(image1, image2, alpha)
             crossfade_images.append(crossfade_image)
-            
+
         # Convert crossfade_images to tensor
         crossfade_images = torch.stack(crossfade_images, dim=0)
-        # Get the last frame result of the interpolation
-        last_frame = crossfade_images[-1]
-        # Calculate the number of remaining frames from images_2
-        remaining_frames = len(images_2) - (transition_start_index + transitioning_frames)
-        # Crossfade the remaining frames with the last used alpha value
-        for i in range(remaining_frames):
-            alpha = alphas[-1]
-            image1 = images_1[i + transition_start_index + transitioning_frames]
-            image2 = images_2[i + transition_start_index + transitioning_frames]
-            easing_function = easing_functions.get(interpolation)
-            alpha = easing_function(alpha)  # Apply the easing function to the alpha value
 
-            crossfade_image = crossfade(image1, image2, alpha)
-            crossfade_images = torch.cat([crossfade_images, crossfade_image.unsqueeze(0)], dim=0)
-        # Append the beginning of images_1
+        # Append the beginning of images_1 (before the transition)
         beginning_images_1 = images_1[:transition_start_index]
         crossfade_images = torch.cat([beginning_images_1, crossfade_images], dim=0)
+
+        # Append the remaining frames of images_2 (after the transition)
+        remaining_images_2 = images_2[transitioning_frames:]
+        if len(remaining_images_2) > 0:
+            crossfade_images = torch.cat([crossfade_images, remaining_images_2], dim=0)
+
         return (crossfade_images, )
     
 class CrossFadeImagesMulti: