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Improve argument handling (#209)

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This commit is contained in:
Aryan
2025-01-11 06:18:13 +05:30
committed by GitHub
parent 81bf590a91
commit d3b797ee35
1 changed files with 11 additions and 17 deletions
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finetrainers/args.py
+11 -17
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@@ -455,7 +455,7 @@ def _add_model_arguments(parser: argparse.ArgumentParser) -> None:
parser.add_argument( parser.add_argument(
"--pretrained_model_name_or_path", "--pretrained_model_name_or_path",
type=str, type=str,
default=None, required=True,
help="Path to pretrained model or model identifier from huggingface.co/models.", help="Path to pretrained model or model identifier from huggingface.co/models.",
) )
parser.add_argument( parser.add_argument(
@@ -505,7 +505,7 @@ def _add_dataset_arguments(parser: argparse.ArgumentParser) -> None:
parser.add_argument( parser.add_argument(
"--data_root", "--data_root",
type=str, type=str,
default=None, required=True,
help=("A folder containing the training data."), help=("A folder containing the training data."),
) )
parser.add_argument( parser.add_argument(
@@ -632,19 +632,19 @@ def _add_diffusion_arguments(parser: argparse.ArgumentParser) -> None:
type=str, type=str,
default="none", default="none",
choices=["sigma_sqrt", "logit_normal", "mode", "cosmap", "none"], choices=["sigma_sqrt", "logit_normal", "mode", "cosmap", "none"],
help=('We default to the "none" weighting scheme for uniform sampling and uniform loss'), help='We default to the "none" weighting scheme for uniform sampling and uniform loss',
) )
parser.add_argument( parser.add_argument(
"--flow_logit_mean", "--flow_logit_mean",
type=float, type=float,
default=0.0, default=0.0,
help="mean to use when using the `'logit_normal'` weighting scheme.", help="Mean to use when using the `'logit_normal'` weighting scheme.",
) )
parser.add_argument( parser.add_argument(
"--flow_logit_std", "--flow_logit_std",
type=float, type=float,
default=1.0, default=1.0,
help="std to use when using the `'logit_normal'` weighting scheme.", help="Standard deviation to use when using the `'logit_normal'` weighting scheme.",
) )
parser.add_argument( parser.add_argument(
"--flow_mode_scale", "--flow_mode_scale",
@@ -659,7 +659,7 @@ def _add_training_arguments(parser: argparse.ArgumentParser) -> None:
parser.add_argument( parser.add_argument(
"--training_type", "--training_type",
type=str, type=str,
default=None, required=True,
help="Type of training to perform. Choose between ['lora']", help="Type of training to perform. Choose between ['lora']",
) )
parser.add_argument("--seed", type=int, default=None, help="A seed for reproducible training.") parser.add_argument("--seed", type=int, default=None, help="A seed for reproducible training.")
@@ -676,10 +676,10 @@ def _add_training_arguments(parser: argparse.ArgumentParser) -> None:
parser.add_argument( parser.add_argument(
"--batch_size", "--batch_size",
type=int, type=int,
default=4, default=1,
help="Batch size (per device) for the training dataloader.", help="Batch size (per device) for the training dataloader.",
) )
parser.add_argument("--train_epochs", type=int, default=1) parser.add_argument("--train_epochs", type=int, default=1, help="Number of training epochs.")
parser.add_argument( parser.add_argument(
"--train_steps", "--train_steps",
type=int, type=int,
@@ -735,13 +735,11 @@ def _add_training_arguments(parser: argparse.ArgumentParser) -> None:
parser.add_argument( parser.add_argument(
"--enable_slicing", "--enable_slicing",
action="store_true", action="store_true",
default=False,
help="Whether or not to use VAE slicing for saving memory.", help="Whether or not to use VAE slicing for saving memory.",
) )
parser.add_argument( parser.add_argument(
"--enable_tiling", "--enable_tiling",
action="store_true", action="store_true",
default=False,
help="Whether or not to use VAE tiling for saving memory.", help="Whether or not to use VAE tiling for saving memory.",
) )
@@ -756,7 +754,6 @@ def _add_optimizer_arguments(parser: argparse.ArgumentParser) -> None:
parser.add_argument( parser.add_argument(
"--scale_lr", "--scale_lr",
action="store_true", action="store_true",
default=False,
help="Scale the learning rate by the number of GPUs, gradient accumulation steps, and batch size.", help="Scale the learning rate by the number of GPUs, gradient accumulation steps, and batch size.",
) )
parser.add_argument( parser.add_argument(
@@ -877,7 +874,6 @@ def _add_validation_arguments(parser: argparse.ArgumentParser) -> None:
parser.add_argument( parser.add_argument(
"--enable_model_cpu_offload", "--enable_model_cpu_offload",
action="store_true", action="store_true",
default=False,
help="Whether or not to enable model-wise CPU offloading when performing validation/testing to save memory.", help="Whether or not to enable model-wise CPU offloading when performing validation/testing to save memory.",
) )
@@ -904,7 +900,7 @@ def _add_miscellaneous_arguments(parser: argparse.ArgumentParser) -> None:
parser.add_argument( parser.add_argument(
"--output_dir", "--output_dir",
type=str, type=str,
default="finetrainer-training", default="finetrainers-training",
help="The output directory where the model predictions and checkpoints will be written.", help="The output directory where the model predictions and checkpoints will be written.",
) )
parser.add_argument( parser.add_argument(
@@ -931,10 +927,8 @@ def _add_miscellaneous_arguments(parser: argparse.ArgumentParser) -> None:
"--report_to", "--report_to",
type=str, type=str,
default="none", default="none",
help=( choices=["none", "wandb"],
'The integration to report the results and logs to. Supported platforms are `"tensorboard"`' help="The integration to report the results and logs to.",
' (default), `"wandb"` and `"comet_ml"`. Use `"all"` to report to all integrations.'
),
) )