diff --git a/diffsynth/diffusion/loss.py b/diffsynth/diffusion/loss.py
index bb12dfd..2e9d390 100644
--- a/diffsynth/diffusion/loss.py
+++ b/diffsynth/diffusion/loss.py
@@ -23,6 +23,7 @@ def FlowMatchSFTLoss(pipe: BasePipeline, **inputs):
 
 def DirectDistillLoss(pipe: BasePipeline, **inputs):
     pipe.scheduler.set_timesteps(inputs["num_inference_steps"])
+    pipe.scheduler.training = True
     models = {name: getattr(pipe, name) for name in pipe.in_iteration_models}
     for progress_id, timestep in enumerate(pipe.scheduler.timesteps):
         timestep = timestep.unsqueeze(0).to(dtype=pipe.torch_dtype, device=pipe.device)
diff --git a/examples/test/run.py b/examples/test/run.py
index 097800a..7c78043 100644
--- a/examples/test/run.py
+++ b/examples/test/run.py
@@ -5,8 +5,9 @@ def script_is_processed(output_path, script):
     return os.path.exists(os.path.join(output_path, script))
 
 
-def filter_unprocessed_tasks(script_path, output_path):
+def filter_unprocessed_tasks(script_path):
     tasks = []
+    output_path = os.path.join("data", script_path)
     for script in sorted(os.listdir(script_path)):
         if not script.endswith(".sh") and not script.endswith(".py"):
             continue
@@ -59,8 +60,8 @@ def run_train_multi_GPU(script_path, tasks):
         
 
 
-def run_train_single_GPU(script_path):
-    processes = [multiprocessing.Process(target=run_tasks_on_single_GPU, args=(script_path, i, 8)) for i in range(8)]
+def run_train_single_GPU(script_path, tasks):
+    processes = [multiprocessing.Process(target=run_tasks_on_single_GPU, args=(script_path, tasks, i, 8)) for i in range(8)]
     for p in processes:
         p.start()
     for p in processes:
@@ -85,8 +86,8 @@ if __name__ == "__main__":
     # run_train_single_GPU("examples/wanvideo/model_inference")
     # move_files("video_", "data/output/model_inference")
     # run_train_single_GPU("examples/wanvideo/model_training/lora")
-    # run_train_single_GPU("examples/wanvideo/model_training/validate_lora")
-    # move_files("video_", "data/output/validate_lora")
+    run_train_single_GPU("examples/wanvideo/model_training/validate_lora", filter_unprocessed_tasks("examples/wanvideo/model_training/validate_lora"))
+    move_files("video_", "data/output/validate_lora")
     # run_train_multi_GPU("examples/wanvideo/model_training/full")
     # run_train_single_GPU("examples/wanvideo/model_training/validate_full")
     # move_files("video_", "data/output/validate_full")
diff --git a/examples/wanvideo/model_training/special/direct_distill/Wan2.1-T2V-1.3B.sh b/examples/wanvideo/model_training/special/direct_distill/Wan2.1-T2V-1.3B.sh
new file mode 100644
index 0000000..73e85f3
--- /dev/null
+++ b/examples/wanvideo/model_training/special/direct_distill/Wan2.1-T2V-1.3B.sh
@@ -0,0 +1,14 @@
+accelerate launch --config_file examples/wanvideo/model_training/full/accelerate_config_14B.yaml examples/wanvideo/model_training/train.py \
+  --dataset_base_path data/example_video_dataset \
+  --dataset_metadata_path data/example_video_dataset/metadata_distill.csv \
+  --height 480 \
+  --width 832 \
+  --dataset_repeat 160 \
+  --model_id_with_origin_paths "Wan-AI/Wan2.1-T2V-1.3B:diffusion_pytorch_model*.safetensors,Wan-AI/Wan2.1-T2V-1.3B:models_t5_umt5-xxl-enc-bf16.pth,Wan-AI/Wan2.1-T2V-1.3B:Wan2.1_VAE.pth" \
+  --learning_rate 1e-5 \
+  --num_epochs 2 \
+  --remove_prefix_in_ckpt "pipe.dit." \
+  --output_path "./models/train/Wan2.1-T2V-1.3B_full_distill" \
+  --trainable_models "dit" \
+  --task "direct_distill" \
+  --extra_inputs "seed,rand_device,num_inference_steps,cfg_scale"
diff --git a/examples/wanvideo/model_training/special/direct_distill/validate.py b/examples/wanvideo/model_training/special/direct_distill/validate.py
new file mode 100644
index 0000000..6da0e1b
--- /dev/null
+++ b/examples/wanvideo/model_training/special/direct_distill/validate.py
@@ -0,0 +1,23 @@
+import torch
+from PIL import Image
+from diffsynth.utils.data import save_video, VideoData
+from diffsynth.pipelines.wan_video import WanVideoPipeline, ModelConfig
+
+
+pipe = WanVideoPipeline.from_pretrained(
+    torch_dtype=torch.bfloat16,
+    device="cuda",
+    model_configs=[
+        ModelConfig("models/train/Wan2.1-T2V-1.3B_full_distill/epoch-1.safetensors"),
+        ModelConfig(model_id="Wan-AI/Wan2.1-T2V-1.3B", origin_file_pattern="models_t5_umt5-xxl-enc-bf16.pth"),
+        ModelConfig(model_id="Wan-AI/Wan2.1-T2V-1.3B", origin_file_pattern="Wan2.1_VAE.pth"),
+    ],
+    tokenizer_config=ModelConfig(model_id="Wan-AI/Wan2.1-T2V-1.3B", origin_file_pattern="google/umt5-xxl/"),
+)
+
+video = pipe(
+    prompt="纪实摄影风格画面，一只活泼的小狗在绿茵茵的草地上迅速奔跑。小狗毛色棕黄，两只耳朵立起，神情专注而欢快。阳光洒在它身上，使得毛发看上去格外柔软而闪亮。背景是一片开阔的草地，偶尔点缀着几朵野花，远处隐约可见蓝天和几片白云。透视感鲜明，捕捉小狗奔跑时的动感和四周草地的生机。中景侧面移动视角。",
+    cfg_scale=1, num_inference_steps=4,
+    seed=0, tiled=True,
+)
+save_video(video, "video_distill_Wan2.1-T2V-1.3B.mp4", fps=15, quality=5)