VictorMylle/Kohya-ss-sd-scripts
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Merge branch 'main' into original-u-net

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Kohya S
2023-06-16 20:59:34 +09:00
parent 449ad7502c 038c09f552
commit 19dfa24abb
10 changed files with 532 additions and 247 deletions
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56
fine_tune.py
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@@ -42,33 +42,37 @@ def train(args):
tokenizer = train_util.load_tokenizer(args)
blueprint_generator = BlueprintGenerator(ConfigSanitizer(False, True, True))
if args.dataset_config is not None:
print(f"Load dataset config from {args.dataset_config}")
user_config = config_util.load_user_config(args.dataset_config)
ignored = ["train_data_dir", "in_json"]
if any(getattr(args, attr) is not None for attr in ignored):
print(
"ignore following options because config file is found: {0} / 設定ファイルが利用されるため以下のオプションは無視されます: {0}".format(
", ".join(ignored)
# データセットを準備する
if args.dataset_class is None:
blueprint_generator = BlueprintGenerator(ConfigSanitizer(False, True, True))
if args.dataset_config is not None:
print(f"Load dataset config from {args.dataset_config}")
user_config = config_util.load_user_config(args.dataset_config)
ignored = ["train_data_dir", "in_json"]
if any(getattr(args, attr) is not None for attr in ignored):
print(
"ignore following options because config file is found: {0} / 設定ファイルが利用されるため以下のオプションは無視されます: {0}".format(
", ".join(ignored)
)
)
)
else:
user_config = {
"datasets": [
{
"subsets": [
{
"image_dir": args.train_data_dir,
"metadata_file": args.in_json,
}
]
}
]
}
else:
user_config = {
"datasets": [
{
"subsets": [
{
"image_dir": args.train_data_dir,
"metadata_file": args.in_json,
}
]
}
]
}
blueprint = blueprint_generator.generate(user_config, args, tokenizer=tokenizer)
train_dataset_group = config_util.generate_dataset_group_by_blueprint(blueprint.dataset_group)
blueprint = blueprint_generator.generate(user_config, args, tokenizer=tokenizer)
train_dataset_group = config_util.generate_dataset_group_by_blueprint(blueprint.dataset_group)
else:
train_dataset_group = train_util.load_arbitrary_dataset(args, tokenizer)
current_epoch = Value("i", 0)
current_step = Value("i", 0)
@@ -393,7 +397,7 @@ def train(args):
current_loss = loss.detach().item() # 平均なのでbatch sizeは関係ないはず
if args.logging_dir is not None:
logs = {"loss": current_loss, "lr": float(lr_scheduler.get_last_lr()[0])}
if args.optimizer_type.lower().startswith("DAdapt".lower()): # tracking d*lr value
if args.optimizer_type.lower().startswith("DAdapt".lower()) or args.optimizer_type.lower() == "Prodigy": # tracking d*lr value
logs["lr/d*lr"] = (
lr_scheduler.optimizers[0].param_groups[0]["d"] * lr_scheduler.optimizers[0].param_groups[0]["lr"]
)