fix swanlab after test

diffsynth/trainers/text_to_image.py

@@ -250,6 +250,17 @@ 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
 
 
@@ -270,6 +281,20 @@ def launch_training_task(model, args):
         num_workers=args.dataloader_num_workers
     )
     # train
+    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 = None
     trainer = pl.Trainer(
         max_epochs=args.max_epochs,
         accelerator="gpu",
@@ -278,7 +303,8 @@ def launch_training_task(model, args):
         strategy=args.training_strategy,
         default_root_dir=args.output_path,
         accumulate_grad_batches=args.accumulate_grad_batches,
-        callbacks=[pl.pytorch.callbacks.ModelCheckpoint(save_top_k=-1)]
+        callbacks=[pl.pytorch.callbacks.ModelCheckpoint(save_top_k=-1)],
+        logger=logger,
     )
     trainer.fit(model=model, train_dataloaders=train_loader)
 

examples/wanvideo/README.md

@@ -132,8 +132,8 @@ CUDA_VISIBLE_DEVICES="0" python examples/wanvideo/train_wan_t2v.py \
   --steps_per_epoch 500 \
   --max_epochs 10 \
   --learning_rate 1e-4 \
-  --lora_rank 4 \
+  --lora_rank 16 \
-  --lora_alpha 4 \
+  --lora_alpha 16 \
   --lora_target_modules "q,k,v,o,ffn.0,ffn.2" \
   --accumulate_grad_batches 1 \
   --use_gradient_checkpointing

examples/wanvideo/train_wan_t2v.py

@@ -423,6 +423,17 @@ def parse_args():
         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).",
+    )
     args = parser.parse_args()
     return args
 
@@ -481,6 +492,20 @@ def train(args):
         use_gradient_checkpointing=args.use_gradient_checkpointing,
         pretrained_lora_path=args.pretrained_lora_path,
     )
+    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 = None
     trainer = pl.Trainer(
         max_epochs=args.max_epochs,
         accelerator="gpu",
@@ -489,6 +514,7 @@ 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)