diff --git a/finetune/README.md b/finetune/README.md
index 1f6e50b..caad58f 100644
--- a/finetune/README.md
+++ b/finetune/README.md
@@ -23,6 +23,11 @@ the videos in the `videos` directory.
 └── videos.txt
 ```
 
+You can download [Disney Steamboat Willie](https://huggingface.co/datasets/Wild-Heart/Disney-VideoGeneration-Dataset)
+from here.
+
+The video fine-tuning dataset is used as a test for fine-tuning.
+
 ### Configuration Files and Execution
 
 `accelerate` configuration files are as follows:
diff --git a/finetune/README_zh.md b/finetune/README_zh.md
index 514ce72..195bc30 100644
--- a/finetune/README_zh.md
+++ b/finetune/README_zh.md
@@ -21,6 +21,10 @@
 └── videos.txt
 ```
 
+你可以从这里下载 [迪士尼汽船威利号](https://huggingface.co/datasets/Wild-Heart/Disney-VideoGeneration-Dataset)
+
+视频微调数据集作为测试微调。
+
 ### 配置文件和运行
 
 `accelerate` 配置文件如下:
diff --git a/finetune/finetune_multi_gpus_1.sh b/finetune/finetune_multi_gpus_1.sh
index 37e1fce..6ae55c5 100644
--- a/finetune/finetune_multi_gpus_1.sh
+++ b/finetune/finetune_multi_gpus_1.sh
@@ -2,9 +2,10 @@
 
 export MODEL_PATH="THUDM/CogVideoX-2b"
 export CACHE_PATH="~/.cache"
-export DATASET_PATH="disney"
+export DATASET_PATH="Disney-VideoGeneration-Dataset"
 export OUTPUT_PATH="cogvideox-lora-multi-gpu"
 export PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True
+export CUDA_VISIBLE_DEVICES=$CUDA_VISIBLE_DEVICES
 
 accelerate launch --config_file accelerate_config_machine_single.yaml --multi_gpu  --machine_rank 0 \
   train_cogvideox_lora.py \
diff --git a/finetune/finetune_single_gpu.sh b/finetune/finetune_single_gpu.sh
index 3f866fe..4accadf 100644
--- a/finetune/finetune_single_gpu.sh
+++ b/finetune/finetune_single_gpu.sh
@@ -2,9 +2,10 @@
 
 export MODEL_PATH="THUDM/CogVideoX-2b"
 export CACHE_PATH="~/.cache"
-export DATASET_PATH="disney"
+export DATASET_PATH="Disney-VideoGeneration-Dataset"
 export OUTPUT_PATH="cogvideox-lora-single-gpu"
 export PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True
+export CUDA_VISIBLE_DEVICES=$CUDA_VISIBLE_DEVICES
 
 accelerate launch --config_file accelerate_config_machine_single.yaml --multi_gpu \
   train_cogvideox_lora.py \
diff --git a/finetune/train_cogvideox_lora.py b/finetune/train_cogvideox_lora.py
index 12f3e9c..bba4d06 100644
--- a/finetune/train_cogvideox_lora.py
+++ b/finetune/train_cogvideox_lora.py
@@ -1073,7 +1073,6 @@ def main(args):
         target_modules=["to_k", "to_q", "to_v", "to_out.0"],
     )
     transformer.add_adapter(transformer_lora_config)
-
     if args.train_text_encoder:
         text_lora_config = LoraConfig(
             r=args.rank,
diff --git a/sat/README.md b/sat/README.md
index c07568f..48c4552 100644
--- a/sat/README.md
+++ b/sat/README.md
@@ -22,6 +22,7 @@ pip install -r requirements.txt
 ### 2. Download model weights
 
 First, go to the SAT mirror to download the model weights. For the CogVideoX-2B model, please download as follows:
+
 ```shell
 mkdir CogVideoX-2b-sat
 cd CogVideoX-2b-sat
@@ -32,16 +33,14 @@ wget https://cloud.tsinghua.edu.cn/f/556a3e1329e74f1bac45/?dl=1
 mv 'index.html?dl=1' transformer.zip
 unzip transformer.zip
 ```
-For the CogVideoX-5B model, please download as follows (VAE files are the same):
-```shell
-mkdir CogVideoX-5b-sat
-cd CogVideoX-5b-sat
-wget https://cloud.tsinghua.edu.cn/f/fdba7608a49c463ba754/?dl=1
-mv 'index.html?dl=1' vae.zip
-unzip vae.zip
-```
-Then, you need to go to [Tsinghua Cloud Disk](https://cloud.tsinghua.edu.cn/d/fcef5b3904294a6885e5/?p=%2F&mode=list) to download our model and unzip it.
-After sorting, the complete model structure of the two models should be as follows:
+
+For the CogVideoX-5B model, please download the `transformers` file as follows link:
+(VAE files are the same as 2B)
+
++ [CogVideoX-5B](https://cloud.tsinghua.edu.cn/d/fcef5b3904294a6885e5/?p=%2F&mode=list)
++ [CogVideoX-5B-I2V](https://cloud.tsinghua.edu.cn/d/5cc62a2d6e7d45c0a2f6/?p=%2F1&mode=list)
+
+Next, you need to format the model files as follows:
 
 ```
 .
@@ -53,7 +52,8 @@ After sorting, the complete model structure of the two models should be as follo
     └── 3d-vae.pt
 ```
 
-Due to large size of model weight file, using `git lfs` is recommended. Installation of `git lfs` can be found [here](https://github.com/git-lfs/git-lfs?tab=readme-ov-file#installing) 
+Due to large size of model weight file, using `git lfs` is recommended. Installation of `git lfs` can be
+found [here](https://github.com/git-lfs/git-lfs?tab=readme-ov-file#installing)
 
 Next, clone the T5 model, which is not used for training and fine-tuning, but must be used.
 > T5 model is available on [Modelscope](https://modelscope.cn/models/ZhipuAI/CogVideoX-2b) as well.
@@ -160,14 +160,14 @@ model:
           ucg_rate: 0.1
           target: sgm.modules.encoders.modules.FrozenT5Embedder
           params:
-            model_dir: "{absolute_path/to/your/t5-v1_1-xxl}/t5-v1_1-xxl" # Absolute path to the CogVideoX-2b/t5-v1_1-xxl weights folder
+            model_dir: "t5-v1_1-xxl" # Absolute path to the CogVideoX-2b/t5-v1_1-xxl weights folder
             max_length: 226
 
   first_stage_config:
     target: vae_modules.autoencoder.VideoAutoencoderInferenceWrapper
     params:
       cp_size: 1
-      ckpt_path: "{absolute_path/to/your/t5-v1_1-xxl}/CogVideoX-2b-sat/vae/3d-vae.pt" # Absolute path to the CogVideoX-2b-sat/vae/3d-vae.pt folder
+      ckpt_path: "CogVideoX-2b-sat/vae/3d-vae.pt" # Absolute path to the CogVideoX-2b-sat/vae/3d-vae.pt folder
       ignore_keys: [ 'loss' ]
 
       loss_config:
@@ -254,13 +254,14 @@ args:
   sampling_num_frames: 13  # Must be 13, 11 or 9
   sampling_fps: 8
   fp16: True # For CogVideoX-2B
-#  bf16: True # For CogVideoX-5B
-  output_dir: outputs/ 
+  #  bf16: True # For CogVideoX-5B
+  output_dir: outputs/
   force_inference: True
 ```
 
-+ Modify `configs/test.txt` if multiple prompts is required, in which each line makes a prompt.  
-+ For better prompt formatting, refer to [convert_demo.py](../inference/convert_demo.py), for which you should set the OPENAI_API_KEY as your environmental variable.
++ Modify `configs/test.txt` if multiple prompts is required, in which each line makes a prompt.
++ For better prompt formatting, refer to [convert_demo.py](../inference/convert_demo.py), for which you should set the
+  OPENAI_API_KEY as your environmental variable.
 + Modify `input_type` in `configs/inference.yaml` if use command line as prompt iuput.
 
 ```yaml
@@ -408,28 +409,32 @@ python ../tools/convert_weight_sat2hf.py
 
 ### Exporting Huggingface Diffusers lora LoRA Weights from SAT Checkpoints
 
-After completing the training using the above steps, we get a SAT checkpoint with LoRA weights. You can find the file at `{args.save}/1000/1000/mp_rank_00_model_states.pt`.
+After completing the training using the above steps, we get a SAT checkpoint with LoRA weights. You can find the file
+at `{args.save}/1000/1000/mp_rank_00_model_states.pt`.
 
-The script for exporting LoRA weights can be found in the CogVideoX repository at `tools/export_sat_lora_weight.py`. After exporting, you can use `load_cogvideox_lora.py` for inference.
+The script for exporting LoRA weights can be found in the CogVideoX repository at `tools/export_sat_lora_weight.py`.
+After exporting, you can use `load_cogvideox_lora.py` for inference.
+
+Export command:
 
-#### Export command:
 ```bash
 python tools/export_sat_lora_weight.py --sat_pt_path {args.save}/{experiment_name}-09-09-21-10/1000/mp_rank_00_model_states.pt --lora_save_directory {args.save}/export_hf_lora_weights_1/
 ```
 
-This training mainly modified the following model structures. The table below lists the corresponding structure mappings for converting to the HF (Hugging Face) format LoRA structure. As you can see, LoRA adds a low-rank weight to the model's attention structure.
+This training mainly modified the following model structures. The table below lists the corresponding structure mappings
+for converting to the HF (Hugging Face) format LoRA structure. As you can see, LoRA adds a low-rank weight to the
+model's attention structure.
 
 ```
-
-    'attention.query_key_value.matrix_A.0': 'attn1.to_q.lora_A.weight',
-    'attention.query_key_value.matrix_A.1': 'attn1.to_k.lora_A.weight',
-    'attention.query_key_value.matrix_A.2': 'attn1.to_v.lora_A.weight',
-    'attention.query_key_value.matrix_B.0': 'attn1.to_q.lora_B.weight',
-    'attention.query_key_value.matrix_B.1': 'attn1.to_k.lora_B.weight',
-    'attention.query_key_value.matrix_B.2': 'attn1.to_v.lora_B.weight',
-    'attention.dense.matrix_A.0': 'attn1.to_out.0.lora_A.weight',
-    'attention.dense.matrix_B.0': 'attn1.to_out.0.lora_B.weight'
+'attention.query_key_value.matrix_A.0': 'attn1.to_q.lora_A.weight',
+'attention.query_key_value.matrix_A.1': 'attn1.to_k.lora_A.weight',
+'attention.query_key_value.matrix_A.2': 'attn1.to_v.lora_A.weight',
+'attention.query_key_value.matrix_B.0': 'attn1.to_q.lora_B.weight',
+'attention.query_key_value.matrix_B.1': 'attn1.to_k.lora_B.weight',
+'attention.query_key_value.matrix_B.2': 'attn1.to_v.lora_B.weight',
+'attention.dense.matrix_A.0': 'attn1.to_out.0.lora_A.weight',
+'attention.dense.matrix_B.0': 'attn1.to_out.0.lora_B.weight'
 ```
-  
+
 Using export_sat_lora_weight.py, you can convert the SAT checkpoint into the HF LoRA format.
 ![alt text](../resources/hf_lora_weights.png)
diff --git a/sat/README_ja.md b/sat/README_ja.md
index af48b9c..ee1abcd 100644
--- a/sat/README_ja.md
+++ b/sat/README_ja.md
@@ -32,18 +32,12 @@ mv 'index.html?dl=1' transformer.zip
 unzip transformer.zip
 ```
 
-CogVideoX-5B モデルの場合は、次のようにダウンロードしてください (VAE ファイルは同じです)。
+CogVideoX-5B モデルの `transformers` ファイルを以下のリンクからダウンロードしてください （VAE ファイルは 2B と同じです）：
 
-```shell
-mkdir CogVideoX-5b-sat
-cd CogVideoX-5b-sat
-wget https://cloud.tsinghua.edu.cn/f/fdba7608a49c463ba754/?dl=1
-mv 'index.html?dl=1' vae.zip
-unzip vae.zip
-```
++ [CogVideoX-5B](https://cloud.tsinghua.edu.cn/d/fcef5b3904294a6885e5/?p=%2F&mode=list)
++ [CogVideoX-5B-I2V](https://cloud.tsinghua.edu.cn/d/5cc62a2d6e7d45c0a2f6/?p=%2F1&mode=list)
 
-次に、[Tsinghua Cloud Disk](https://cloud.tsinghua.edu.cn/d/fcef5b3904294a6885e5/?p=%2F&mode=list) に移動してモデルをダウンロードし、解凍する必要があります。
-整理すると、2 つのモデルの完全なモデル構造は次のようになります。 モデル構造は次のようになります：
+次に、モデルファイルを以下の形式にフォーマットする必要があります：
 
 ```
 .
@@ -55,8 +49,9 @@ unzip vae.zip
     └── 3d-vae.pt
 ```
 
+モデルの重みファイルが大きいため、`git lfs`を使用することをお勧めいたします。`git lfs`
+のインストールについては、[こちら](https://github.com/git-lfs/git-lfs?tab=readme-ov-file#installing)をご参照ください。
 
-モデルの重みファイルが大きいため、`git lfs`を使用することをお勧めいたします。`git lfs`のインストールについては、[こちら](https://github.com/git-lfs/git-lfs?tab=readme-ov-file#installing)をご参照ください。
 ```shell
 git lfs install
 ```
@@ -166,14 +161,14 @@ model:
           ucg_rate: 0.1
           target: sgm.modules.encoders.modules.FrozenT5Embedder
           params:
-            model_dir: "{absolute_path/to/your/t5-v1_1-xxl}/t5-v1_1-xxl" # CogVideoX-2b/t5-v1_1-xxlフォルダの絶対パス
+            model_dir: "t5-v1_1-xxl" # CogVideoX-2b/t5-v1_1-xxlフォルダの絶対パス
             max_length: 226
 
   first_stage_config:
     target: vae_modules.autoencoder.VideoAutoencoderInferenceWrapper
     params:
       cp_size: 1
-      ckpt_path: "{absolute_path/to/your/t5-v1_1-xxl}/CogVideoX-2b-sat/vae/3d-vae.pt" # CogVideoX-2b-sat/vae/3d-vae.ptフォルダの絶対パス
+      ckpt_path: "CogVideoX-2b-sat/vae/3d-vae.pt" # CogVideoX-2b-sat/vae/3d-vae.ptフォルダの絶対パス
       ignore_keys: [ 'loss' ]
 
       loss_config:
@@ -244,6 +239,7 @@ model:
           exp: 5
           num_steps: 50
 ```
+
 ### 4. `configs/inference.yaml` ファイルを変更します。
 
 ```yaml
@@ -259,7 +255,7 @@ args:
   sampling_num_frames: 13  # Must be 13, 11 or 9
   sampling_fps: 8
   fp16: True # For CogVideoX-2B
-#  bf16: True # For CogVideoX-5B
+  #  bf16: True # For CogVideoX-5B
   output_dir: outputs/
   force_inference: True
 ```
@@ -417,25 +413,23 @@ python ../tools/convert_weight_sat2hf.py
 
 LoRAウェイトをエクスポートするためのスクリプトは、CogVideoXリポジトリの `tools/export_sat_lora_weight.py` にあります。エクスポート後、`load_cogvideox_lora.py` を使用して推論を行うことができます。
 
-#### エクスポートコマンド:
+エクスポートコマンド:
+
 ```bash
 python tools/export_sat_lora_weight.py --sat_pt_path {args.save}/{experiment_name}-09-09-21-10/1000/mp_rank_00_model_states.pt --lora_save_directory {args.save}/export_hf_lora_weights_1/
 ```
 
 このトレーニングでは主に以下のモデル構造が変更されました。以下の表は、HF (Hugging Face) 形式のLoRA構造に変換する際の対応関係を示しています。ご覧の通り、LoRAはモデルの注意メカニズムに低ランクの重みを追加しています。
 
-
-
 ```
-
-    'attention.query_key_value.matrix_A.0': 'attn1.to_q.lora_A.weight',
-    'attention.query_key_value.matrix_A.1': 'attn1.to_k.lora_A.weight',
-    'attention.query_key_value.matrix_A.2': 'attn1.to_v.lora_A.weight',
-    'attention.query_key_value.matrix_B.0': 'attn1.to_q.lora_B.weight',
-    'attention.query_key_value.matrix_B.1': 'attn1.to_k.lora_B.weight',
-    'attention.query_key_value.matrix_B.2': 'attn1.to_v.lora_B.weight',
-    'attention.dense.matrix_A.0': 'attn1.to_out.0.lora_A.weight',
-    'attention.dense.matrix_B.0': 'attn1.to_out.0.lora_B.weight'
+'attention.query_key_value.matrix_A.0': 'attn1.to_q.lora_A.weight',
+'attention.query_key_value.matrix_A.1': 'attn1.to_k.lora_A.weight',
+'attention.query_key_value.matrix_A.2': 'attn1.to_v.lora_A.weight',
+'attention.query_key_value.matrix_B.0': 'attn1.to_q.lora_B.weight',
+'attention.query_key_value.matrix_B.1': 'attn1.to_k.lora_B.weight',
+'attention.query_key_value.matrix_B.2': 'attn1.to_v.lora_B.weight',
+'attention.dense.matrix_A.0': 'attn1.to_out.0.lora_A.weight',
+'attention.dense.matrix_B.0': 'attn1.to_out.0.lora_B.weight'
 ```
   
 export_sat_lora_weight.py を使用して、SATチェックポイントをHF LoRA形式に変換できます。
diff --git a/sat/README_zh.md b/sat/README_zh.md
index a67c70e..c605da8 100644
--- a/sat/README_zh.md
+++ b/sat/README_zh.md
@@ -162,14 +162,14 @@ model:
           ucg_rate: 0.1
           target: sgm.modules.encoders.modules.FrozenT5Embedder
           params:
-            model_dir: "{absolute_path/to/your/t5-v1_1-xxl}/t5-v1_1-xxl" # CogVideoX-2b/t5-v1_1-xxl权重文件夹的绝对路径
+            model_dir: "t5-v1_1-xxl" # CogVideoX-2b/t5-v1_1-xxl 权重文件夹的绝对路径
             max_length: 226
 
   first_stage_config:
     target: vae_modules.autoencoder.VideoAutoencoderInferenceWrapper
     params:
       cp_size: 1
-      ckpt_path: "{absolute_path/to/your/t5-v1_1-xxl}/CogVideoX-2b-sat/vae/3d-vae.pt" # CogVideoX-2b-sat/vae/3d-vae.pt文件夹的绝对路径
+      ckpt_path: "CogVideoX-2b-sat/vae/3d-vae.pt" # CogVideoX-2b-sat/vae/3d-vae.pt文件夹的绝对路径
       ignore_keys: [ 'loss' ]
 
       loss_config:
@@ -294,9 +294,9 @@ bash inference.sh
 ```
 .
 ├── labels
-│   ├── 1.txt
-│   ├── 2.txt
-│   ├── ...
+│   ├── 1.txt
+│   ├── 2.txt
+│   ├── ...
 └── videos
     ├── 1.mp4
     ├── 2.mp4
diff --git a/sat/configs/cogvideox_5b_lora.yaml b/sat/configs/cogvideox_5b_lora.yaml
index 79577f1..f3ac66c 100644
--- a/sat/configs/cogvideox_5b_lora.yaml
+++ b/sat/configs/cogvideox_5b_lora.yaml
@@ -1,7 +1,7 @@
 model:
   scale_factor: 0.7 # different from cogvideox_2b_infer.yaml
   disable_first_stage_autocast: true
-  not_trainable_prefixes: ['all'] ## Using Lora
+  not_trainable_prefixes: ['all'] # Using Lora
   log_keys:
     - txt
 
@@ -53,7 +53,7 @@ model:
             hidden_size_head: 64
             text_length: 226
 
-        lora_config: ## Using Lora
+        lora_config: # Using Lora
           target: sat.model.finetune.lora2.LoraMixin
           params:
             r: 128
diff --git a/sat/sgm/modules/attention.py b/sat/sgm/modules/attention.py
index 1304eaa..bb24157 100644
--- a/sat/sgm/modules/attention.py
+++ b/sat/sgm/modules/attention.py
@@ -225,7 +225,7 @@ class CrossAttention(nn.Module):
 
         q, k, v = map(lambda t: rearrange(t, "b n (h d) -> b h n d", h=h), (q, k, v))
 
-        ## old
+        # old
         """
         sim = einsum('b i d, b j d -> b i j', q, k) * self.scale
         del q, k
@@ -241,7 +241,7 @@ class CrossAttention(nn.Module):
 
         out = einsum('b i j, b j d -> b i d', sim, v)
         """
-        ## new
+        # new
         with sdp_kernel(**BACKEND_MAP[self.backend]):
             # print("dispatching into backend", self.backend, "q/k/v shape: ", q.shape, k.shape, v.shape)
             out = F.scaled_dot_product_attention(q, k, v, attn_mask=mask)  # scale is dim_head ** -0.5 per default
diff --git a/sat/sgm/modules/diffusionmodules/openaimodel.py b/sat/sgm/modules/diffusionmodules/openaimodel.py
index cb5be79..3f0b83c 100644
--- a/sat/sgm/modules/diffusionmodules/openaimodel.py
+++ b/sat/sgm/modules/diffusionmodules/openaimodel.py
@@ -34,7 +34,6 @@ def convert_module_to_f32(x):
     pass
 
 
-## go
 class AttentionPool2d(nn.Module):
     """
     Adapted from CLIP: https://github.com/openai/CLIP/blob/main/clip/model.py
diff --git a/sat/vae_modules/attention.py b/sat/vae_modules/attention.py
index 52bbba5..041df77 100644
--- a/sat/vae_modules/attention.py
+++ b/sat/vae_modules/attention.py
@@ -225,7 +225,7 @@ class CrossAttention(nn.Module):
 
         q, k, v = map(lambda t: rearrange(t, "b n (h d) -> b h n d", h=h), (q, k, v))
 
-        ## old
+        # old
         """
         sim = einsum('b i d, b j d -> b i j', q, k) * self.scale
         del q, k
@@ -241,7 +241,7 @@ class CrossAttention(nn.Module):
 
         out = einsum('b i j, b j d -> b i d', sim, v)
         """
-        ## new
+        # new
         with sdp_kernel(**BACKEND_MAP[self.backend]):
             # print("dispatching into backend", self.backend, "q/k/v shape: ", q.shape, k.shape, v.shape)
             out = F.scaled_dot_product_attention(q, k, v, attn_mask=mask)  # scale is dim_head ** -0.5 per default
diff --git a/tools/llm_flux_cogvideox/generate.sh b/tools/llm_flux_cogvideox/generate.sh
index ca8fc1b..c455273 100644
--- a/tools/llm_flux_cogvideox/generate.sh
+++ b/tools/llm_flux_cogvideox/generate.sh
@@ -1,12 +1,20 @@
 #!/bin/bash
 
-NUM_VIDEOS=100
+NUM_VIDEOS=10
 INFERENCE_STEPS=50
 GUIDANCE_SCALE=7.0
 OUTPUT_DIR_PREFIX="outputs/gpu_"
 LOG_DIR_PREFIX="logs/gpu_"
 
-CUDA_DEVICES=${CUDA_VISIBLE_DEVICES:-"0,1,2,3,4,5,6,7"}
+VIDEO_MODEL_PATH="/share/official_pretrains/hf_home/CogVideoX-5b-I2V"
+LLM_MODEL_PATH="/share/home/zyx/Models/Meta-Llama-3.1-8B-Instruct"
+IMAGE_MODEL_PATH = "share/home/zyx/Models/FLUX.1-dev"
+
+#VIDEO_MODEL_PATH="THUDM/CogVideoX-5B-I2V"
+#LLM_MODEL_PATH="THUDM/glm-4-9b-chat"
+#IMAGE_MODEL_PATH = "black-forest-labs/FLUX.1-dev"
+
+CUDA_DEVICES=${CUDA_VISIBLE_DEVICES:-"0"}
 
 IFS=',' read -r -a GPU_ARRAY <<< "$CUDA_DEVICES"
 
@@ -15,6 +23,9 @@ do
     GPU=${GPU_ARRAY[$i]}
     echo "Starting task on GPU $GPU..."
     CUDA_VISIBLE_DEVICES=$GPU nohup python3 llm_flux_cogvideox.py \
+    --caption_generator_model_id $LLM_MODEL_PATH \
+    --image_generator_model_id $IMAGE_MODEL_PATH \
+    --model_path $VIDEO_MODEL_PATH \
     --num_videos $NUM_VIDEOS \
     --image_generator_num_inference_steps $INFERENCE_STEPS \
     --guidance_scale $GUIDANCE_SCALE \
diff --git a/tools/llm_flux_cogvideox/llm_flux_cogvideox.py b/tools/llm_flux_cogvideox/llm_flux_cogvideox.py
index 92c1a38..8e97888 100644
--- a/tools/llm_flux_cogvideox/llm_flux_cogvideox.py
+++ b/tools/llm_flux_cogvideox/llm_flux_cogvideox.py
@@ -48,6 +48,7 @@ There are a few rules to follow:
 - If the user mentions to summarize the prompt in [X] words, make sure to not exceed the limit.
 
 You responses should just be the video generation prompt. Here are examples:
+- “A lone figure stands on a city rooftop at night, gazing up at the full moon. The moon glows brightly, casting a gentle light over the quiet cityscape. Below, the windows of countless homes shine with warm lights, creating a contrast between the bustling life below and the peaceful solitude above. The scene captures the essence of the Mid-Autumn Festival, where despite the distance, the figure feels connected to loved ones through the shared beauty of the moonlit sky.”
 - "A detailed wooden toy ship with intricately carved masts and sails is seen gliding smoothly over a plush, blue carpet that mimics the waves of the sea. The ship's hull is painted a rich brown, with tiny windows. The carpet, soft and textured, provides a perfect backdrop, resembling an oceanic expanse. Surrounding the ship are various other toys and children's items, hinting at a playful environment. The scene captures the innocence and imagination of childhood, with the toy ship's journey symbolizing endless adventures in a whimsical, indoor setting."
 - "A street artist, clad in a worn-out denim jacket and a colorful banana, stands before a vast concrete wall in the heart, holding a can of spray paint, spray-painting a colorful bird on a mottled wall"
 """.strip()
@@ -172,7 +173,7 @@ def main(args: Dict[str, Any]) -> None:
 
     captions = []
     for i in range(args.num_videos):
-        num_words = random.choice([100, 150, 200])
+        num_words = random.choice([50, 75, 100])
         user_prompt = USER_PROMPT.format(num_words)
 
         messages = [