Merge pull request #366 from modelscope/swanlab

Swanlab

examples/wanvideo/train_wan_t2v.py

@@ -405,6 +405,17 @@ 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
 
@@ -462,6 +473,20 @@ 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",
@@ -469,7 +494,8 @@ def train(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, dataloader)