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"""
Train a diffusion model on images.
"""
import argparse
from guided_diffusion import dist_util, logger
from guided_diffusion.image_datasets import load_data
from guided_diffusion.resample import create_named_schedule_sampler
from guided_diffusion.script_util import (
model_and_diffusion_defaults,
create_model_and_diffusion,
args_to_dict,
add_dict_to_argparser,
)
from guided_diffusion.train_util import TrainLoop
def main():
args = create_argparser().parse_args()
dist_util.setup_dist()
logger.configure()
logger.log("creating model and diffusion...")
model, diffusion = create_model_and_diffusion(
**args_to_dict(args, model_and_diffusion_defaults().keys())
)
model.to(dist_util.dev())
schedule_sampler = create_named_schedule_sampler(args.schedule_sampler, diffusion)
logger.log("creating data loader...")
data = load_data(
data_dir=args.data_dir,
batch_size=args.batch_size,
image_size=args.image_size,
class_cond=args.class_cond,
)
logger.log("training...")
TrainLoop(
model=model,
diffusion=diffusion,
data=data,
batch_size=args.batch_size,
microbatch=args.microbatch,
lr=args.lr,
ema_rate=args.ema_rate,
log_interval=args.log_interval,
save_interval=args.save_interval,
resume_checkpoint=args.resume_checkpoint,
use_fp16=args.use_fp16,
fp16_scale_growth=args.fp16_scale_growth,
schedule_sampler=schedule_sampler,
weight_decay=args.weight_decay,
lr_anneal_steps=args.lr_anneal_steps,
).run_loop()
def create_argparser():
defaults = dict(
data_dir="",
schedule_sampler="uniform",
lr=1e-4,
weight_decay=0.0,
lr_anneal_steps=0,
batch_size=1,
microbatch=-1, # -1 disables microbatches
ema_rate="0.9999", # comma-separated list of EMA values
log_interval=10,
save_interval=10000,
resume_checkpoint="",
use_fp16=False,
fp16_scale_growth=1e-3,
)
defaults.update(model_and_diffusion_defaults())
parser = argparse.ArgumentParser()
add_dict_to_argparser(parser, defaults)
return parser
if __name__ == "__main__":
main()