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