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Merge pull request #366 from modelscope/swanlab

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Swanlab
This commit is contained in:
Zhongjie Duan
2025-02-27 19:32:23 +08:00
committed by GitHub
parent 34a9f82865 a57749ef60
commit 6fa8dbe077
2 changed files with 57 additions and 2 deletions
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31
diffsynth/trainers/text_to_image.py
View File

@@ -250,6 +250,18 @@ 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 +282,22 @@ def launch_training_task(model, args):
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_config]
else:
logger = []
# train
trainer = pl.Trainer(
max_epochs=args.max_epochs,
@@ -279,7 +307,8 @@ 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)

28
examples/wanvideo/train_wan_t2v.py
View File

@@ -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)