diff --git a/diffsynth/trainers/text_to_image.py b/diffsynth/trainers/text_to_image.py
index 7b2690f..00a352e 100644
--- a/diffsynth/trainers/text_to_image.py
+++ b/diffsynth/trainers/text_to_image.py
@@ -250,18 +250,6 @@ def add_general_parsers(parser):
         default=None,
         help="Pretrained LoRA path. Required if the training is resumed.",
     )
-    parser.add_argument(
-        "--use_swanlab",
-        default=False,
-        action="store_true",
-        help="Whether to use SwanLab logger.",
-    )
-    parser.add_argument(
-        "--swanlab_mode",
-        default=None,
-        help="SwanLab mode (cloud or local).",
-    )
-    
     return parser
 
 
@@ -281,23 +269,6 @@ def launch_training_task(model, args):
         batch_size=args.batch_size,
         num_workers=args.dataloader_num_workers
     )
-
-    # set swanlab logger
-    if args.use_swanlab:
-        from swanlab.integration.pytorch_lightning import SwanLabLogger
-        swanlab_config = {"UPPERFRAMEWORK": "DiffSynth-Studio"}
-        swanlab_config.update(vars(args))
-        swanlab_logger = SwanLabLogger(
-            project="diffsynth_studio", 
-            name="diffsynth_studio",
-            config=swanlab_config,
-            mode=args.swanlab_mode,
-            logdir=args.output_path,
-        )
-        logger = [swanlab_logger]
-    else:
-        logger = []
-
     # train
     trainer = pl.Trainer(
         max_epochs=args.max_epochs,
@@ -308,7 +279,6 @@ def launch_training_task(model, args):
         default_root_dir=args.output_path,
         accumulate_grad_batches=args.accumulate_grad_batches,
         callbacks=[pl.pytorch.callbacks.ModelCheckpoint(save_top_k=-1)],
-        logger=logger,
     )
     trainer.fit(model=model, train_dataloaders=train_loader)
 
diff --git a/examples/wanvideo/train_wan_t2v.py b/examples/wanvideo/train_wan_t2v.py
index 4e7f8bc..39aa4c0 100644
--- a/examples/wanvideo/train_wan_t2v.py
+++ b/examples/wanvideo/train_wan_t2v.py
@@ -405,17 +405,6 @@ def parse_args():
         choices=["lora", "full"],
         help="Model structure to train. LoRA training or full training.",
     )
-    parser.add_argument(
-        "--use_swanlab",
-        default=False,
-        action="store_true",
-        help="Whether to use SwanLab logger.",
-    )
-    parser.add_argument(
-        "--swanlab_mode",
-        default=None,
-        help="SwanLab mode (cloud or local).",
-    )
     args = parser.parse_args()
     return args
 
@@ -473,20 +462,6 @@ def train(args):
         init_lora_weights=args.init_lora_weights,
         use_gradient_checkpointing=args.use_gradient_checkpointing
     )
-    if args.use_swanlab:
-        from swanlab.integration.pytorch_lightning import SwanLabLogger
-        swanlab_config = {"UPPERFRAMEWORK": "DiffSynth-Studio"}
-        swanlab_config.update(vars(args))
-        swanlab_logger = SwanLabLogger(
-            project="wan", 
-            name="wan",
-            config=swanlab_config,
-            mode=args.swanlab_mode,
-            logdir=args.output_path,
-        )
-        logger = [swanlab_logger]
-    else:
-        logger = []
     trainer = pl.Trainer(
         max_epochs=args.max_epochs,
         accelerator="gpu",
@@ -495,7 +470,6 @@ def train(args):
         default_root_dir=args.output_path,
         accumulate_grad_batches=args.accumulate_grad_batches,
         callbacks=[pl.pytorch.callbacks.ModelCheckpoint(save_top_k=-1)],
-        logger=logger,
     )
     trainer.fit(model, dataloader)