diff --git a/nodes.py b/nodes.py
index c699d4d..cf2dd88 100644
--- a/nodes.py
+++ b/nodes.py
@@ -3345,103 +3345,6 @@ class WarpedBundleVideoImages:
 
         return (batched_images, first_image, last_image, int(batched_images.shape[0]), width, height, float(fps), )
 
-# class WarpedBundleVideoImagesFramepack:
-#     @classmethod
-#     def INPUT_TYPES(s):
-#         return {"required":
-#                     {"video_path": ("STRING", {"default": ""}),
-#                     "starting_index": ("INT", {"default": 0}),
-#                     "num_frames": ("INT", {"default": 301, "min": 5, "max": 1000001, "step": 4}),
-#                     "use_gpu": ("BOOLEAN", {"default": True}),
-#                     },
-#                 }
-#     RETURN_TYPES = ("IMAGE", "IMAGE", "IMAGE", "INT", "INT", "INT", "FLOAT", )
-#     RETURN_NAMES = ("image_batch", "first_image", "last_image", "num_frames", "width", "height", "fps", )
-#     FUNCTION = "generate"
-#
-#     CATEGORY = "Warped/Framepack/Video"
-#
-#     def generate(self, video_path, starting_index, num_frames, use_gpu=False):
-#         if use_gpu:
-#             device = mm.get_torch_device()
-#         else:
-#             device = get_offload_device()
-#
-#         print("WarpedBundleVideoImagesFramepack: device: {}".format(device))
-#
-#         cap = cv2.VideoCapture(video_path)
-#
-#         width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
-#         print('WarpedBundleVideoImagesFramepack: width = %d' % width)
-#         height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
-#         print('WarpedBundleVideoImagesFramepack: height = %d' % height)
-#         length = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))
-#         print('WarpedBundleVideoImagesFramepack: length = %d' % length)
-#         fps = float(cap.get(cv2.CAP_PROP_FPS))
-#         fps = round(fps)
-#         print('WarpedBundleVideoImagesFramepack: fps = %d' % fps)
-#
-#         batched_images = None
-#         # print_it = True
-#         last_image = None
-#         first_image = None
-#
-#         if starting_index > length:
-#             starting_index = 0
-#
-#         if ((starting_index + num_frames) > length) or (num_frames == 0):
-#             num_frames = length - starting_index
-#
-#         try:
-#             if starting_index > 0:
-#                 cap.set(cv2.CAP_PROP_POS_FRAMES, starting_index)
-#
-#             video_frames = []
-#             frame_count = 0
-#             for i in tqdm(range(num_frames), desc="Reading Video Frames: "):
-#                 frame_count += 1
-#
-#                 # Take each frame
-#                 _, frameorig = cap.read()
-#
-#                 color_coverted = cv2.cvtColor(frameorig, cv2.COLOR_BGR2RGB)
-#                 video_frames.append(color_coverted)
-#
-#             pil_image = Image.fromarray(video_frames[0])
-#             first_image = pil2tensorSwap(pil_image, device=device)
-#             first_image = first_image.to(get_offload_device())
-#
-#             pil_image = Image.fromarray(video_frames[len(video_frames) - 1])
-#             last_image = pil2tensorSwap(pil_image, device=device)
-#             last_image = last_image.to(get_offload_device())
-#
-#             for i in tqdm(range(len(video_frames)), desc="Preprocessing Video Frames: "):
-#                 pil_image = Image.fromarray(video_frames[i])
-#                 temp_image = pil2tensorSwap(pil_image, device=device)
-#
-#                 if len(temp_image.shape) < 4:
-#                     temp_image = temp_image.unsqueeze(0)
-#
-#                 if not batched_images is None:
-#                     batched_images = torch.cat((batched_images, temp_image), 0)
-#                 else:
-#                     batched_images = temp_image
-#
-#                 temp_image.to(get_offload_device())
-#                 temp_image = None
-#         except:
-#             print("WarpedBundleVideoImagesFramepack: Exception During Video File Read.")
-#         finally:
-#             cap.release()
-#
-#         batched_images = batched_images.to(get_offload_device())
-#
-#         mm.soft_empty_cache()
-#         gc.collect()
-#         time.sleep(1)
-#
-#         return (batched_images, first_image, last_image, int(batched_images.shape[0]), width, height, float(fps), )
-
 class WarpedWanImageToVideo:
     @classmethod
     def INPUT_TYPES(s):