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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(
"--is_sdxl",
help="Your base/db model is sdxl 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 "full", "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=0.0,
type=float,
)
parser.add_argument(
"--conv_threshold",
help="singular value threshold for conv layer in threshold mode",
default=0.0,
type=float,
)
parser.add_argument(
"--linear_ratio",
help="singular ratio for linear layer in ratio mode",
default=0.0,
type=float,
)
parser.add_argument(
"--conv_ratio",
help="singular ratio for conv layer in ratio mode",
default=0.0,
type=float,
)
parser.add_argument(
"--linear_quantile",
help="singular value quantile for linear layer quantile mode",
default=1.0,
type=float,
)
parser.add_argument(
"--conv_quantile",
help="singular value quantile for conv layer quantile mode",
default=1.0,
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
from lycoris.kohya.sdxl_model_util import load_models_from_sdxl_checkpoint
import torch
from safetensors.torch import save_file
def main():
args = ARGS
if args.is_sdxl:
base = load_models_from_sdxl_checkpoint(None, args.base_model, args.device)
db = load_models_from_sdxl_checkpoint(None, args.db_model, args.device)
else:
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,
"full": None,
}[args.mode]
conv_mode_param = {
"fixed": args.conv_dim,
"threshold": args.conv_threshold,
"ratio": args.conv_ratio,
"quantile": args.conv_quantile,
"full": None,
}[args.mode]
if args.is_sdxl:
db_tes = [db[0], db[1]]
db_unet = db[3]
base_tes = [base[0], base[1]]
base_unet = base[3]
else:
db_tes = [db[0]]
db_unet = db[2]
base_tes = [base[0]]
base_unet = base[2]
state_dict = extract_diff(
base_tes,
db_tes,
base_unet,
db_unet,
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() |