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import os, sys
sys.path.insert(0, os.getcwd())
import argparse
def get_args():
parser = argparse.ArgumentParser()
parser.add_argument(
"base_model", help="The model which use it to train the dreambooth model",
default='', type=str
)
parser.add_argument(
"db_model", help="the dreambooth model you want to extract the locon",
default='', type=str
)
parser.add_argument(
"output_name", help="the output model",
default='./out.pt', type=str
)
parser.add_argument(
"--is_v2", help="Your base/db model is sd v2 or not",
default=False, action="store_true"
)
parser.add_argument(
"--device", help="Which device you want to use to extract the locon",
default='cpu', type=str
)
parser.add_argument(
"--mode",
help=(
'extraction mode, can be "fixed", "threshold", "ratio", "quantile". '
'If not "fixed", network_dim and conv_dim will be ignored'
),
default='fixed', type=str
)
parser.add_argument(
"--safetensors", help='use safetensors to save locon model',
default=False, action="store_true"
)
parser.add_argument(
"--linear_dim", help="network dim for linear layer in fixed mode",
default=1, type=int
)
parser.add_argument(
"--conv_dim", help="network dim for conv layer in fixed mode",
default=1, type=int
)
parser.add_argument(
"--linear_threshold", help="singular value threshold for linear layer in threshold mode",
default=0., type=float
)
parser.add_argument(
"--conv_threshold", help="singular value threshold for conv layer in threshold mode",
default=0., type=float
)
parser.add_argument(
"--linear_ratio", help="singular ratio for linear layer in ratio mode",
default=0., type=float
)
parser.add_argument(
"--conv_ratio", help="singular ratio for conv layer in ratio mode",
default=0., type=float
)
parser.add_argument(
"--linear_quantile", help="singular value quantile for linear layer quantile mode",
default=1., type=float
)
parser.add_argument(
"--conv_quantile", help="singular value quantile for conv layer quantile mode",
default=1., type=float
)
parser.add_argument(
"--use_sparse_bias", help="enable sparse bias",
default=False, action="store_true"
)
parser.add_argument(
"--sparsity", help="sparsity for sparse bias",
default=0.98, type=float
)
parser.add_argument(
"--disable_cp", help="don't use cp decomposition",
default=False, action="store_true"
)
return parser.parse_args()
ARGS = get_args()
from lycoris.utils import extract_diff
from lycoris.kohya_model_utils import load_models_from_stable_diffusion_checkpoint
import torch
from safetensors.torch import save_file
def main():
args = ARGS
base = load_models_from_stable_diffusion_checkpoint(args.is_v2, args.base_model)
db = load_models_from_stable_diffusion_checkpoint(args.is_v2, args.db_model)
linear_mode_param = {
'fixed': args.linear_dim,
'threshold': args.linear_threshold,
'ratio': args.linear_ratio,
'quantile': args.linear_quantile,
}[args.mode]
conv_mode_param = {
'fixed': args.conv_dim,
'threshold': args.conv_threshold,
'ratio': args.conv_ratio,
'quantile': args.conv_quantile,
}[args.mode]
state_dict = extract_diff(
base, db,
args.mode,
linear_mode_param, conv_mode_param,
args.device,
args.use_sparse_bias, args.sparsity,
not args.disable_cp
)
if args.safetensors:
save_file(state_dict, args.output_name)
else:
torch.save(state_dict, args.output_name)
if __name__ == '__main__':
main() |