diff --git a/pyproject.toml b/pyproject.toml
index 0caf3d4..171b6fc 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -1,7 +1,7 @@
 [project]
 name = "WanVideoLooper"
 description = "This is a set of custom nodes for ComfyUI designed for generating sequential long video clips based on the Wan 2.2 model architecture, handling continuity between segments and offering advanced control over the sampling process"
-version = "1.0.8"
+version = "1.0.9"
 license = {file = "LICENSE"}
 dependencies = [
     "color-match",
diff --git a/wan_looper_node.py b/wan_looper_node.py
index 9ad8513..ddb0c53 100644
--- a/wan_looper_node.py
+++ b/wan_looper_node.py
@@ -287,27 +287,34 @@ def _apply_color_match(target_batch_tensor, reference_tensor, strength=1.0, meth
         return target_batch_tensor
 
     _log(f"Applying internal color match (strength: {strength:.2f}) using {method} method on GPU.")
-    
+
+    original_device = target_batch_tensor.device
+    gpu_device = comfy.model_management.intermediate_device()
+
+    # Move tensors to GPU for processing
+    target_gpu = target_batch_tensor.to(gpu_device)
+    ref_gpu = reference_tensor.to(gpu_device)
+
     # Ensure tensors are in (B, C, H, W) format
-    if target_batch_tensor.dim() == 4 and target_batch_tensor.shape[-1] == 3:
-        target_batch_tensor = target_batch_tensor.permute(0, 3, 1, 2)
-    if reference_tensor.dim() == 4 and reference_tensor.shape[-1] == 3:
-        reference_tensor = reference_tensor.permute(0, 3, 1, 2)
+    if target_gpu.dim() == 4 and target_gpu.shape[-1] == 3:
+        target_gpu = target_gpu.permute(0, 3, 1, 2)
+    if ref_gpu.dim() == 4 and ref_gpu.shape[-1] == 3:
+        ref_gpu = ref_gpu.permute(0, 3, 1, 2)
 
     if method == "MKL":
-        matched_batch = _gpu_mkl_color_transfer(target_batch_tensor, reference_tensor)
-    else: # Default to Reinhard
-        matched_batch = _reinhard_color_transfer(target_batch_tensor, reference_tensor)
-    
-    # Blend the original and color-matched images
-    final_result = target_batch_tensor + strength * (matched_batch - target_batch_tensor)
+        matched_batch = _gpu_mkl_color_transfer(target_gpu, ref_gpu)
+    else:  # Default to Reinhard
+        matched_batch = _reinhard_color_transfer(target_gpu, ref_gpu)
+
+    # Blend the original and color-matched images on the GPU
+    final_result = target_gpu + strength * (matched_batch - target_gpu)
     final_result = torch.clamp(final_result, 0.0, 1.0)
 
     # Permute back to (B, H, W, C)
     if final_result.dim() == 4 and final_result.shape[1] == 3:
         final_result = final_result.permute(0, 2, 3, 1)
 
-    return final_result
+    return final_result.to(original_device)
 
 
 # ====================================================================================================
@@ -536,13 +543,18 @@ class WanVideoLooper:
                 
                 # Only run segment matching if color_match is on AND color_match_lastframe is OFF
                 if color_match_method != "Disabled" and color_match_reference_frame == "Sequential" and reference_for_this_loop is not None:
-                    _log(f"Segment {i+1}: Applying segment-to-segment color match.")
-                    decoded_image_batch = _apply_color_match(
-                        target_batch_tensor=decoded_image_batch,
+                    _log(f"Segment {i+1}: Applying segment-to-segment color match to the first frame.")
+                    first_frame = decoded_image_batch[0:1]
+                    rest_of_frames = decoded_image_batch[1:]
+                    
+                    matched_first_frame = _apply_color_match(
+                        target_batch_tensor=first_frame,
                         reference_tensor=reference_for_this_loop,
                         strength=color_match_strength,
                         method=color_match_method
                     )
+                    
+                    decoded_image_batch = torch.cat([matched_first_frame, rest_of_frames], dim=0)
 
                 previous_loop_last_frame = next_reference_frame
             else: