Add extract locon tool

tools/extract_locon.py

@@ -0,0 +1,106 @@
+#
+# From: https://raw.githubusercontent.com/KohakuBlueleaf/LoCon/main/extract_locon.py
+#
+
+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", "percentile". '
+            'If not "fixed", network_dim and conv_dim will be ignored'
+        ),
+        default='fixed', type=str
+    )
+    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_percentile", help="singular value percentile for linear layer percentile mode",
+        default=1., type=float
+    )
+    parser.add_argument(
+        "--conv_percentile", help="singular value percentile for conv layer percentile mode",
+        default=1., type=float
+    )
+    return parser.parse_args()
+ARGS = get_args()
+
+from locon.utils import extract_diff
+from locon.kohya_model_utils import load_models_from_stable_diffusion_checkpoint
+
+import torch
+
+
+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,
+        'percentile': args.linear_percentile,
+    }[args.mode]
+    conv_mode_param = {
+        'fixed': args.conv_dim,
+        'threshold': args.conv_threshold,
+        'ratio': args.conv_ratio,
+        'percentile': args.conv_percentile,
+    }[args.mode]
+    
+    state_dict = extract_diff(
+        base, db,
+        args.mode,
+        linear_mode_param, conv_mode_param,
+        args.device
+    )
+    torch.save(state_dict, args.output_name)
+
+
+if __name__ == '__main__':
+    main()