diff --git a/inference/cli_demo.py b/inference/cli_demo.py
index 6e5ebf5..db281bb 100644
--- a/inference/cli_demo.py
+++ b/inference/cli_demo.py
@@ -36,7 +36,7 @@ def generate_video(
     model_path: str,
     lora_path: str = None,
     lora_rank: int = 128,
-    num_frames=81,
+    num_frames: int = 81,
     output_path: str = "./output.mp4",
     image_or_video_path: str = "",
     num_inference_steps: int = 50,
@@ -57,7 +57,7 @@ def generate_video(
     - lora_rank (int): The rank of the LoRA weights.
     - output_path (str): The path where the generated video will be saved.
     - num_inference_steps (int): Number of steps for the inference process. More steps can result in better quality.
-    - num_frames (int): Number of frames to generate.
+    - num_frames (int): Number of frames to generate. CogVideoX1.0 generates 49 frames for 6 seconds at 8 fps, while CogVideoX1.5 produces either 81 or 161 frames, corresponding to 5 seconds or 10 seconds at 16 fps.
     - guidance_scale (float): The scale for classifier-free guidance. Higher values can lead to better alignment with the prompt.
     - num_videos_per_prompt (int): Number of videos to generate per prompt.
     - dtype (torch.dtype): The data type for computation (default is torch.bfloat16).
@@ -99,9 +99,9 @@ def generate_video(
     # turn off if you have multiple GPUs or enough GPU memory(such as H100) and it will cost less time in inference
     # and enable to("cuda")
 
-    # pipe.to("cuda")
+    pipe.to("cuda")
 
-    pipe.enable_sequential_cpu_offload()
+    # pipe.enable_sequential_cpu_offload()
 
     pipe.vae.enable_slicing()
     pipe.vae.enable_tiling()
@@ -154,7 +154,7 @@ if __name__ == "__main__":
         help="The path of the image to be used as the background of the video",
     )
     parser.add_argument(
-        "--model_path", type=str, default="THUDM/CogVideoX-5b", help="Path of the pre-trained model use"
+        "--model_path", type=str, default="THUDM/CogVideoX1.5-5B", help="Path of the pre-trained model use"
     )
     parser.add_argument("--lora_path", type=str, default=None, help="The path of the LoRA weights to be used")
     parser.add_argument("--lora_rank", type=int, default=128, help="The rank of the LoRA weights")