VictorMylle/Kohya-ss-sd-scripts
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add logging args for training scripts

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Kohya S
2024-02-08 21:16:42 +09:00
parent 9b8ea12d34
commit 055f02e1e1
9 changed files with 147 additions and 48 deletions
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25
sdxl_train.py
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@@ -20,10 +20,14 @@ from diffusers import DDPMScheduler
from library import sdxl_model_util
import library.train_util as train_util
from library.utils import setup_logging
from library.utils import setup_logging, add_logging_arguments
setup_logging()
import logging
logger = logging.getLogger(__name__)
import library.config_util as config_util
import library.sdxl_train_util as sdxl_train_util
from library.config_util import (
@@ -96,7 +100,9 @@ def train(args):
train_util.prepare_dataset_args(args, True)
sdxl_train_util.verify_sdxl_training_args(args)
assert not args.weighted_captions, "weighted_captions is not supported currently / weighted_captionsは現在サポートされていません"
assert (
not args.weighted_captions
), "weighted_captions is not supported currently / weighted_captionsは現在サポートされていません"
assert (
not args.train_text_encoder or not args.cache_text_encoder_outputs
), "cache_text_encoder_outputs is not supported when training text encoder / text encoderを学習するときはcache_text_encoder_outputsはサポートされていません"
@@ -367,7 +373,9 @@ def train(args):
args.max_train_steps = args.max_train_epochs * math.ceil(
len(train_dataloader) / accelerator.num_processes / args.gradient_accumulation_steps
)
accelerator.print(f"override steps. steps for {args.max_train_epochs} epochs is / 指定エポックまでのステップ数: {args.max_train_steps}")
accelerator.print(
f"override steps. steps for {args.max_train_epochs} epochs is / 指定エポックまでのステップ数: {args.max_train_steps}"
)
# データセット側にも学習ステップを送信
train_dataset_group.set_max_train_steps(args.max_train_steps)
@@ -437,7 +445,9 @@ def train(args):
accelerator.print(f" num examples / サンプル数: {train_dataset_group.num_train_images}")
accelerator.print(f" num batches per epoch / 1epochのバッチ数: {len(train_dataloader)}")
accelerator.print(f" num epochs / epoch数: {num_train_epochs}")
accelerator.print(f" batch size per device / バッチサイズ: {', '.join([str(d.batch_size) for d in train_dataset_group.datasets])}")
accelerator.print(
f" batch size per device / バッチサイズ: {', '.join([str(d.batch_size) for d in train_dataset_group.datasets])}"
)
# accelerator.print(
# f" total train batch size (with parallel & distributed & accumulation) / 総バッチサイズ（並列学習、勾配合計含む）: {total_batch_size}"
# )
@@ -457,7 +467,7 @@ def train(args):
if accelerator.is_main_process:
init_kwargs = {}
if args.wandb_run_name:
init_kwargs['wandb'] = {'name': args.wandb_run_name}
init_kwargs["wandb"] = {"name": args.wandb_run_name}
if args.log_tracker_config is not None:
init_kwargs = toml.load(args.log_tracker_config)
accelerator.init_trackers("finetuning" if args.log_tracker_name is None else args.log_tracker_name, init_kwargs=init_kwargs)
@@ -734,6 +744,7 @@ def train(args):
def setup_parser() -> argparse.ArgumentParser:
parser = argparse.ArgumentParser()
add_logging_arguments(parser)
train_util.add_sd_models_arguments(parser)
train_util.add_dataset_arguments(parser, True, True, True)
train_util.add_training_arguments(parser, False)
@@ -756,7 +767,9 @@ def setup_parser() -> argparse.ArgumentParser:
help="learning rate for text encoder 2 (BiG-G) / text encoder 2 (BiG-G)の学習率",
)
parser.add_argument("--diffusers_xformers", action="store_true", help="use xformers by diffusers / Diffusersでxformersを使用する")
parser.add_argument(
"--diffusers_xformers", action="store_true", help="use xformers by diffusers / Diffusersでxformersを使用する"
)
parser.add_argument("--train_text_encoder", action="store_true", help="train text encoder / text encoderも学習する")
parser.add_argument(
"--no_half_vae",