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Running
on
Zero
Running
on
Zero
File size: 1,970 Bytes
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#!/usr/bin/env python
"""
Train LTXV models using configuration from YAML files.
This script provides a command-line interface for training LTXV models using
either LoRA fine-tuning or full model fine-tuning. It loads configuration from
a YAML file and passes it to the trainer.
Basic usage:
python scripts/train.py CONFIG_PATH [--disable-progress-bars]
For multi-GPU/FSDP training, configure and launch via Accelerate:
accelerate config
accelerate launch scripts/train.py CONFIG_PATH
"""
from pathlib import Path
import typer
import yaml
from rich.console import Console
from ltx_trainer.config import LtxTrainerConfig
from ltx_trainer.trainer import LtxvTrainer
console = Console()
app = typer.Typer(
pretty_exceptions_enable=False,
no_args_is_help=True,
help="Train LTXV models using configuration from YAML files.",
)
@app.command()
def main(
config_path: str = typer.Argument(..., help="Path to YAML configuration file"),
disable_progress_bars: bool = typer.Option(
False,
"--disable-progress-bars",
help="Disable progress bars (useful for multi-process runs)",
),
) -> None:
"""Train the model using the provided configuration file."""
# Load the configuration from the YAML file
config_path = Path(config_path)
if not config_path.exists():
typer.echo(f"Error: Configuration file {config_path} does not exist.")
raise typer.Exit(code=1)
with open(config_path, "r") as file:
config_data = yaml.safe_load(file)
# Convert the loaded data to the LtxTrainerConfig object
try:
trainer_config = LtxTrainerConfig(**config_data)
except Exception as e:
typer.echo(f"Error: Invalid configuration data: {e}")
raise typer.Exit(code=1) from e
# Initialize the training process
trainer = LtxvTrainer(trainer_config)
trainer.train(disable_progress_bars=disable_progress_bars)
if __name__ == "__main__":
app()
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