diff --git a/examples/distill/SFWan2.1-T2V/distill_dmd_t2v_1.3B.sh b/examples/distill/SFWan2.1-T2V/distill_dmd_t2v_1.3B.sh
index a50741ec7..f0d3d1c43 100644
--- a/examples/distill/SFWan2.1-T2V/distill_dmd_t2v_1.3B.sh
+++ b/examples/distill/SFWan2.1-T2V/distill_dmd_t2v_1.3B.sh
@@ -16,7 +16,7 @@ export NCCL_P2P_DISABLE=1
 export TORCH_NCCL_ENABLE_MONITORING=0
 # different cache dir for different processes
 export TRITON_CACHE_DIR=/tmp/triton_cache_${SLURM_PROCID}
-export MASTER_PORT=29502
+export MASTER_PORT=29503
 export TOKENIZERS_PARALLELISM=false
 export WANDB_API_KEY="50632ebd88ffd970521cec9ab4a1a2d7e85bfc45"
 export WANDB_BASE_URL="https://api.wandb.ai"
@@ -24,11 +24,11 @@ export WANDB_MODE=online
 export FASTVIDEO_ATTENTION_BACKEND=FLASH_ATTN
 
 # Configs
-NUM_GPUS=1
+NUM_GPUS=4
 
 # Model paths for Self-Forcing DMD distillation:
 GENERATOR_MODEL_PATH="wlsaidhi/SFWan2.1-T2V-1.3B-Diffusers"
-REAL_SCORE_MODEL_PATH="Wan-AI/Wan2.1-T2V-1.3B-Diffusers"  # Teacher model
+REAL_SCORE_MODEL_PATH="Wan-AI/Wan2.1-T2V-14B-Diffusers"  # Teacher model
 FAKE_SCORE_MODEL_PATH="Wan-AI/Wan2.1-T2V-1.3B-Diffusers"  # Critic model
 
 DATA_DIR="data/crush-smol_processed_t2v/combined_parquet_dataset/"
@@ -58,11 +58,11 @@ training_args=(
 
 # Parallel arguments
 parallel_args=(
-  --num_gpus 1 # 64
+  --num_gpus $NUM_GPUS # 64
   --sp_size 1
   --tp_size 1
   --hsdp_replicate_dim 1 # 64
-  --hsdp_shard_dim 1
+  --hsdp_shard_dim $NUM_GPUS
 )
 
 # Model arguments
@@ -84,7 +84,7 @@ dataset_args=(
 validation_args=(
   --log_validation
   --validation_dataset_file "$VALIDATION_DATASET_FILE"
-  --validation_steps 20
+  --validation_steps 50
   --validation_sampling_steps "4"
   --validation_guidance_scale "6.0" # not used for dmd inference
 )
@@ -93,8 +93,8 @@ validation_args=(
 optimizer_args=(
   --learning_rate 1e-5
   --mixed_precision "bf16"
-  --training_state_checkpointing_steps 200
-  --weight_only_checkpointing_steps 200
+  --training_state_checkpointing_steps 500
+  --weight_only_checkpointing_steps 500
   --weight_decay 0.01
   --betas '0.0,0.999'
   --max_grad_norm 1.0
diff --git a/examples/inference/basic/basic.py b/examples/inference/basic/basic.py
index f12a4969e..316621eaf 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-1.3B-Diffusers",
+        "Wan-AI/Wan2.1-T2V-14B-Diffusers",
         # FastVideo will automatically handle distributed setup
-        num_gpus=1,
+        num_gpus=4,
         use_fsdp_inference=True,
         dit_cpu_offload=False,
         vae_cpu_offload=False,
@@ -25,9 +25,7 @@ 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 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."
+        "A watermelon wearing a helmet is crushed by a hydraulic press, causing it to flatten and burst open."
     )
     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/")
@@ -35,11 +33,7 @@ def main():
     # Generate another video with a different prompt, without reloading the
     # model!
     prompt2 = (
-        "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.")
+        "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")
     video2 = generator.generate_video(prompt2, output_path=OUTPUT_PATH, save_video=True)