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Move TE/UN loss calc to train script

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Kohya S
2023-01-21 12:51:17 +09:00
parent 17089b1287
commit 22ee0ac467
2 changed files with 15 additions and 14 deletions
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12
library/train_util.py
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@@ -1423,17 +1423,5 @@ def save_state_on_train_end(args: argparse.Namespace, accelerator):
model_name = DEFAULT_LAST_OUTPUT_NAME if args.output_name is None else args.output_name model_name = DEFAULT_LAST_OUTPUT_NAME if args.output_name is None else args.output_name
accelerator.save_state(os.path.join(args.output_dir, LAST_STATE_NAME.format(model_name))) accelerator.save_state(os.path.join(args.output_dir, LAST_STATE_NAME.format(model_name)))
def generate_step_logs(args: argparse.Namespace, current_loss, avr_loss, lr_scheduler):
logs = {"loss/current": current_loss, "loss/average": avr_loss}
if args.network_train_unet_only:
logs["lr/unet"] = lr_scheduler.get_last_lr()[0]
elif args.network_train_text_encoder_only:
logs["lr/textencoder"] = lr_scheduler.get_last_lr()[0]
else:
logs["lr/textencoder"] = lr_scheduler.get_last_lr()[0]
logs["lr/unet"] = lr_scheduler.get_last_lr()[-1]
return logs
# endregion # endregion

17
train_network.py
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@@ -21,6 +21,20 @@ def collate_fn(examples):
return examples[0] return examples[0]
def generate_step_logs(args: argparse.Namespace, current_loss, avr_loss, lr_scheduler):
logs = {"loss/current": current_loss, "loss/average": avr_loss}
if args.network_train_unet_only:
logs["lr/unet"] = lr_scheduler.get_last_lr()[0]
elif args.network_train_text_encoder_only:
logs["lr/textencoder"] = lr_scheduler.get_last_lr()[0]
else:
logs["lr/textencoder"] = lr_scheduler.get_last_lr()[0]
logs["lr/unet"] = lr_scheduler.get_last_lr()[-1] # may be same to textencoder
return logs
def train(args): def train(args):
session_id = random.randint(0, 2**32) session_id = random.randint(0, 2**32)
training_started_at = time.time() training_started_at = time.time()
@@ -353,8 +367,7 @@ def train(args):
progress_bar.set_postfix(**logs) progress_bar.set_postfix(**logs)
if args.logging_dir is not None: if args.logging_dir is not None:
logs = train_util.generate_step_logs(args, current_loss, avr_loss, lr_scheduler) logs = generate_step_logs(args, current_loss, avr_loss, lr_scheduler)
accelerator.log(logs, step=global_step) accelerator.log(logs, step=global_step)
if global_step >= args.max_train_steps: if global_step >= args.max_train_steps: