diff --git a/inference/cli_demo.py b/inference/cli_demo.py
index 9c59b00..41f4267 100644
--- a/inference/cli_demo.py
+++ b/inference/cli_demo.py
@@ -14,7 +14,10 @@ To run the script, use the following command with appropriate arguments:
 $ python cli_demo.py --prompt "A girl riding a bike." --model_path THUDM/CogVideoX1.5-5b --generate_type "t2v"
 ```
 
+You can change `pipe.enable_sequential_cpu_offload()` to `pipe.enable_model_cpu_offload()` to speed up inference, but this will use more GPU memory
+
 Additional options are available to specify the model path, guidance scale, number of inference steps, video generation type, and output paths.
+
 """
 
 import argparse
@@ -22,6 +25,7 @@ import logging
 from typing import Literal, Optional
 
 import torch
+
 from diffusers import (
     CogVideoXDPMScheduler,
     CogVideoXImageToVideoPipeline,
@@ -121,7 +125,7 @@ def generate_video(
     # If you're using with lora, add this code
     if lora_path:
         pipe.load_lora_weights(lora_path, weight_name="pytorch_lora_weights.safetensors", adapter_name="test_1")
-        pipe.fuse_lora(components=["transformer"], lora_scale=1/lora_rank)
+        pipe.fuse_lora(components=["transformer"], lora_scale=1 / lora_rank)
 
     # 2. Set Scheduler.
     # Can be changed to `CogVideoXDPMScheduler` or `CogVideoXDDIMScheduler`.
@@ -134,8 +138,9 @@ def generate_video(
     # 3. Enable CPU offload for the model.
     # 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.enable_model_cpu_offload()
     pipe.enable_sequential_cpu_offload()
     pipe.vae.enable_slicing()
     pipe.vae.enable_tiling()