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"""
Default values taken from
https://github.com/genmoai/mochi/blob/aba74c1b5e0755b1fa3343d9e4bd22e89de77ab1/demos/fine_tuner/configs/lora.yaml
when applicable.
"""
import argparse
def _get_model_args(parser: argparse.ArgumentParser) -> None:
parser.add_argument(
"--pretrained_model_name_or_path",
type=str,
default=None,
required=True,
help="Path to pretrained model or model identifier from huggingface.co/models.",
)
parser.add_argument(
"--revision",
type=str,
default=None,
required=False,
help="Revision of pretrained model identifier from huggingface.co/models.",
)
parser.add_argument(
"--variant",
type=str,
default=None,
help="Variant of the model files of the pretrained model identifier from huggingface.co/models, 'e.g.' fp16",
)
parser.add_argument(
"--cache_dir",
type=str,
default=None,
help="The directory where the downloaded models and datasets will be stored.",
)
parser.add_argument(
"--cast_dit",
action="store_true",
help="If we should cast DiT params to a lower precision.",
)
parser.add_argument(
"--compile_dit",
action="store_true",
help="If we should compile the DiT.",
)
def _get_dataset_args(parser: argparse.ArgumentParser) -> None:
parser.add_argument(
"--data_root",
type=str,
default=None,
help=("A folder containing the training data."),
)
parser.add_argument(
"--caption_dropout",
type=float,
default=None,
help=("Probability to drop out captions randomly."),
)
parser.add_argument(
"--dataloader_num_workers",
type=int,
default=0,
help="Number of subprocesses to use for data loading. 0 means that the data will be loaded in the main process.",
)
parser.add_argument(
"--pin_memory",
action="store_true",
help="Whether or not to use the pinned memory setting in pytorch dataloader.",
)
def _get_validation_args(parser: argparse.ArgumentParser) -> None:
parser.add_argument(
"--validation_prompt",
type=str,
default=None,
help="One or more prompt(s) that is used during validation to verify that the model is learning. Multiple validation prompts should be separated by the '--validation_prompt_seperator' string.",
)
parser.add_argument(
"--validation_images",
type=str,
default=None,
help="One or more image path(s)/URLs that is used during validation to verify that the model is learning. Multiple validation paths should be separated by the '--validation_prompt_seperator' string. These should correspond to the order of the validation prompts.",
)
parser.add_argument(
"--validation_prompt_separator",
type=str,
default=":::",
help="String that separates multiple validation prompts",
)
parser.add_argument(
"--num_validation_videos",
type=int,
default=1,
help="Number of videos that should be generated during validation per `validation_prompt`.",
)
parser.add_argument(
"--validation_epochs",
type=int,
default=50,
help="Run validation every X training steps. Validation consists of running the validation prompt `args.num_validation_videos` times.",
)
parser.add_argument(
"--enable_slicing",
action="store_true",
default=False,
help="Whether or not to use VAE slicing for saving memory.",
)
parser.add_argument(
"--enable_tiling",
action="store_true",
default=False,
help="Whether or not to use VAE tiling for saving memory.",
)
parser.add_argument(
"--enable_model_cpu_offload",
action="store_true",
default=False,
help="Whether or not to enable model-wise CPU offloading when performing validation/testing to save memory.",
)
parser.add_argument(
"--fps",
type=int,
default=30,
help="FPS to use when serializing the output videos.",
)
parser.add_argument(
"--height",
type=int,
default=480,
)
parser.add_argument(
"--width",
type=int,
default=848,
)
def _get_training_args(parser: argparse.ArgumentParser) -> None:
parser.add_argument("--seed", type=int, default=None, help="A seed for reproducible training.")
parser.add_argument("--rank", type=int, default=16, help="The rank for LoRA matrices.")
parser.add_argument(
"--lora_alpha",
type=int,
default=16,
help="The lora_alpha to compute scaling factor (lora_alpha / rank) for LoRA matrices.",
)
parser.add_argument(
"--target_modules",
nargs="+",
type=str,
default=["to_k", "to_q", "to_v", "to_out.0"],
help="Target modules to train LoRA for.",
)
parser.add_argument(
"--output_dir",
type=str,
default="mochi-lora",
help="The output directory where the model predictions and checkpoints will be written.",
)
parser.add_argument(
"--train_batch_size",
type=int,
default=4,
help="Batch size (per device) for the training dataloader.",
)
parser.add_argument("--num_train_epochs", type=int, default=1)
parser.add_argument(
"--max_train_steps",
type=int,
default=None,
help="Total number of training steps to perform. If provided, overrides `--num_train_epochs`.",
)
parser.add_argument(
"--gradient_checkpointing",
action="store_true",
help="Whether or not to use gradient checkpointing to save memory at the expense of slower backward pass.",
)
parser.add_argument(
"--learning_rate",
type=float,
default=2e-4,
help="Initial learning rate (after the potential warmup period) to use.",
)
parser.add_argument(
"--scale_lr",
action="store_true",
help="Scale the learning rate by the number of GPUs, gradient accumulation steps, and batch size.",
)
parser.add_argument(
"--lr_warmup_steps",
type=int,
default=200,
help="Number of steps for the warmup in the lr scheduler.",
)
parser.add_argument(
"--checkpointing_steps",
type=int,
default=None,
)
parser.add_argument(
"--resume_from_checkpoint",
type=str,
default=None,
)
def _get_optimizer_args(parser: argparse.ArgumentParser) -> None:
parser.add_argument(
"--optimizer",
type=lambda s: s.lower(),
default="adam",
choices=["adam", "adamw"],
help=("The optimizer type to use."),
)
parser.add_argument(
"--weight_decay",
type=float,
default=0.01,
help="Weight decay to use for optimizer.",
)
def _get_configuration_args(parser: argparse.ArgumentParser) -> None:
parser.add_argument("--tracker_name", type=str, default=None, help="Project tracker name")
parser.add_argument(
"--push_to_hub",
action="store_true",
help="Whether or not to push the model to the Hub.",
)
parser.add_argument(
"--hub_token",
type=str,
default=None,
help="The token to use to push to the Model Hub.",
)
parser.add_argument(
"--hub_model_id",
type=str,
default=None,
help="The name of the repository to keep in sync with the local `output_dir`.",
)
parser.add_argument(
"--allow_tf32",
action="store_true",
help=(
"Whether or not to allow TF32 on Ampere GPUs. Can be used to speed up training. For more information, see"
" https://pytorch.org/docs/stable/notes/cuda.html#tensorfloat-32-tf32-on-ampere-devices"
),
)
parser.add_argument("--report_to", type=str, default=None, help="If logging to wandb.")
def get_args():
parser = argparse.ArgumentParser(description="Simple example of a training script for Mochi-1.")
_get_model_args(parser)
_get_dataset_args(parser)
_get_training_args(parser)
_get_validation_args(parser)
_get_optimizer_args(parser)
_get_configuration_args(parser)
return parser.parse_args()
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