update swanlab log

diffsynth/trainers/text_to_image.py

@@ -256,29 +256,11 @@ def add_general_parsers(parser):
         action="store_true",
         help="Whether to use SwanLab logger.",
     )
-    parser.add_argument(
-        "--swanlab_project",
-        type=str,
-        default="diffsynth_studio",
-        help="SwanLab project name.",
-    )
-    parser.add_argument(
-        "--swanlab_name",
-        type=str,
-        default="diffsynth_studio_train",
-        help="SwanLab experimentname.",
-    )
     parser.add_argument(
         "--swanlab_mode",
         default=None,
         help="SwanLab mode (cloud or local).",
     )
-    parser.add_argument(
-        "--swanlab_logdir",
-        type=str,
-        default=None,
-        help="SwanLab local log directory.",
-    )
     
     return parser
 
@@ -301,18 +283,20 @@ def launch_training_task(model, args):
     )
 
     # set swanlab logger
-    swanlab_logger = None
     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=args.swanlab_project, 
-            name=args.swanlab_name,
+            project="diffsynth_studio", 
+            name="diffsynth_studio",
             config=swanlab_config,
             mode=args.swanlab_mode,
-            logdir=args.swanlab_logdir,
+            logdir=args.output_path,
         )
+        logger = [swanlab_config]
+    else:
+        logger = []
 
     # train
     trainer = pl.Trainer(
@@ -324,7 +308,7 @@ 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=[swanlab_logger],
+        logger=logger,
     )
     trainer.fit(model=model, train_dataloaders=train_loader)