diff --git a/.gitignore b/.gitignore
index 479088db7..bb17640a5 100644
--- a/.gitignore
+++ b/.gitignore
@@ -67,4 +67,3 @@ docs/source/distillation/examples/
 
 dmd_t2v_output/
 preprocess_output_text/
-wan2.2_train.sh
\ No newline at end of file
diff --git a/examples/inference/basic/basic.py b/examples/inference/basic/basic.py
index 316621eaf..f12a4969e 100644
--- a/examples/inference/basic/basic.py
+++ b/examples/inference/basic/basic.py
@@ -9,9 +9,9 @@ def main():
     # If a local path is provided, FastVideo will make a best effort
     # attempt to identify the optimal arguments.
     generator = VideoGenerator.from_pretrained(
-        "Wan-AI/Wan2.1-T2V-14B-Diffusers",
+        "Wan-AI/Wan2.1-T2V-1.3B-Diffusers",
         # FastVideo will automatically handle distributed setup
-        num_gpus=4,
+        num_gpus=1,
         use_fsdp_inference=True,
         dit_cpu_offload=False,
         vae_cpu_offload=False,
@@ -25,7 +25,9 @@ def main():
     # sampling_param.image_path = "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/astronaut.jpg"
     # Generate videos with the same simple API, regardless of GPU count
     prompt = (
-        "A watermelon wearing a helmet is crushed by a hydraulic press, causing it to flatten and burst open."
+        "A curious raccoon peers through a vibrant field of yellow sunflowers, its eyes "
+        "wide with interest. The playful yet serene atmosphere is complemented by soft "
+        "natural light filtering through the petals. Mid-shot, warm and cheerful tones."
     )
     video = generator.generate_video(prompt, output_path=OUTPUT_PATH, save_video=True)
     # video = generator.generate_video(prompt, sampling_param=sampling_param, output_path="wan_t2v_videos/")
@@ -33,7 +35,11 @@ def main():
     # Generate another video with a different prompt, without reloading the
     # model!
     prompt2 = (
-        "The video shows a green and orange object being flattened as if it were under a hydraulic press, with the press moving down and compressing the object")
+        "A majestic lion strides across the golden savanna, its powerful frame "
+        "glistening under the warm afternoon sun. The tall grass ripples gently in "
+        "the breeze, enhancing the lion's commanding presence. The tone is vibrant, "
+        "embodying the raw energy of the wild. Low angle, steady tracking shot, "
+        "cinematic.")
     video2 = generator.generate_video(prompt2, output_path=OUTPUT_PATH, save_video=True)
 
 
diff --git a/fastvideo/layers/layernorm.py b/fastvideo/layers/layernorm.py
index 66d5101d1..d93e1d966 100644
--- a/fastvideo/layers/layernorm.py
+++ b/fastvideo/layers/layernorm.py
@@ -276,4 +276,4 @@ class LayerNormScaleShift(nn.Module):
         if self.compute_dtype == torch.float32:
             output = output.to(x.dtype)
 
-        return output
\ No newline at end of file
+        return output
diff --git a/wan2.2_train.sh b/wan2.2_train.sh
deleted file mode 100644
index b83433860..000000000
--- a/wan2.2_train.sh
+++ /dev/null
@@ -1,57 +0,0 @@
-export WANDB_BASE_URL="https://api.wandb.ai"
-export WANDB_MODE=online
-export WANDB_API_KEY='your_wandb_api_key_here'
-# export FASTVIDEO_ATTENTION_BACKEND=TORCH_SDPA
-export TRITON_CACHE_DIR=/tmp/triton_cache
-# DATA_DIR=/mnt/sharefs/users/hao.zhang/Vchitect-2M/Wan-Syn-upload/latents_i2v/train/
-DATA_DIR=/mnt/weka/home/hao.zhang/wei/FastVideo/data/crush-smol_processed_t2v/combined_parquet_dataset
-# VALIDATION_DIR=/mnt/sharefs/users/hao.zhang/Vchitect-2M/mixkit/validation_8.json
-VALIDATION_DIR=/mnt/weka/home/hao.zhang/wei/FastVideo/data/crush-smol-single_processed_t2v/validation.json
-NUM_GPUS=8
-export FASTVIDEO_ATTENTION_BACKEND=FLASH_ATTN
-# export FASTVIDEO_ATTENTION_BACKEND=FLASH_ATTN
-# export CUDA_VISIBLE_DEVICES=4,5
-# IP=[MASTER NODE IP]
-CHECKPOINT_PATH="outputs_train_test/wan_finetune/checkpoint-10"
-# If you do not have 32 GPUs and to fit in memory, you can: 1. increase sp_size. 2. reduce num_latent_t
-torchrun --nnodes 1 --nproc_per_node $NUM_GPUS \
-    fastvideo/training/wan_training_pipeline.py \
-    --model_path Wan-AI/Wan2.2-T2V-A14B-Diffusers \
-    --inference_mode False \
-    --pretrained_model_name_or_path Wan-AI/Wan2.2-T2V-A14B-Diffusers \
-    --cache_dir "/home/ray/.cache" \
-    --data_path "$DATA_DIR" \
-    --validation_dataset_file  "$VALIDATION_DIR" \
-    --train_batch_size 1 \
-    --num_latent_t 16 \
-    --sp_size 4 \
-    --tp_size 1 \
-    --num_gpus $NUM_GPUS \
-    --hsdp_replicate_dim 1 \
-    --hsdp-shard-dim 8 \
-    --train_sp_batch_size 1 \
-    --dataloader_num_workers 4 \
-    --gradient_accumulation_steps 1 \
-    --max_train_steps 30000 \
-    --learning_rate 1e-5 \
-    --mixed_precision "bf16" \
-    --checkpointing_steps 1000 \
-    --validation_steps 30 \
-    --validation_sampling_steps "40" \
-    --log_validation True \
-    --checkpoints_total_limit 3 \
-    --ema_start_step 0 \
-    --training_cfg_rate 0.1 \
-    --seed 1024 \
-    --output_dir "outputs_train_test/wan_finetune_v1" \
-    --tracker_project_name VSA_finetune \
-    --num_height 448 \
-    --num_width 832 \
-    --num_frames 61 \
-    --flow_shift 5 \
-    --validation_guidance_scale "5.0" \
-    --num_euler_timesteps 50 \
-    --master_weight_type "fp32" \
-    --dit_precision "fp32" \
-    --weight_decay 0.01 \
-    --max_grad_norm 1.0
\ No newline at end of file