Fix Lora Extract GUI issue

.release

@@ -1 +1 @@
-v22.6.1
+v22.6.2

README.md

@@ -503,6 +503,9 @@ masterpiece, best quality, 1boy, in business suit, standing at street, looking b
 
 
 ## Change History
+* 2024/02/17 (v22.6.2)
+- Fix issue with Lora Extract GUI
+  
 * 2024/02/15 (v22.6.1)
 - Add support for multi-gpu parameters in the GUI under the "Parameters > Advanced" tab.
 - Significant rewrite of how parameters are created in the code. I hope I did not break anything in the process... Will make the code easier to update.

library/extract_lora_gui.py

@@ -95,7 +95,7 @@ def extract_lora(
 
 def gradio_extract_lora_tab(headless=False):
     def change_sdxl(sdxl):
-        return gr(visible=sdxl), gr(visible=sdxl)
+        return gr.Dropdown(visible=sdxl), gr.Dropdown(visible=sdxl)
             
     
     

tools/extract_locon.py

@@ -1,4 +1,5 @@
 import os, sys
+
 sys.path.insert(0, os.getcwd())
 import argparse
 
@@ -6,87 +7,125 @@ import argparse
 def get_args():
     parser = argparse.ArgumentParser()
     parser.add_argument(
-        "base_model", help="The model which use it to train the dreambooth model",
+        "base_model",
-        default='', type=str
+        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",
+        "db_model",
-        default='', type=str
+        help="the dreambooth model you want to extract the locon",
+        default="",
+        type=str,
     )
     parser.add_argument(
-        "output_name", help="the output model",
+        "output_name", help="the output model", default="./out.pt", type=str
-        default='./out.pt', type=str
     )
     parser.add_argument(
-        "--is_v2", help="Your base/db model is sd v2 or not",
+        "--is_v2",
-        default=False, action="store_true"
+        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",
+        "--is_sdxl",
-        default='cpu', type=str
+        help="Your base/db model is sdxl or not",
+        default=False,
+        action="store_true",
     )
     parser.add_argument(
-        "--mode", 
+        "--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". '
+            'extraction mode, can be "full", "fixed", "threshold", "ratio", "quantile". '
             'If not "fixed", network_dim and conv_dim will be ignored'
         ),
-        default='fixed', type=str
+        default="fixed",
+        type=str,
     )
     parser.add_argument(
-        "--safetensors", help='use safetensors to save locon model',
+        "--safetensors",
-        default=True, action="store_true"
+        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",
+        "--linear_dim",
-        default=1, type=int
+        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",
+        "--conv_dim",
-        default=1, type=int
+        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",
+        "--linear_threshold",
-        default=0., type=float
+        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",
+        "--conv_threshold",
-        default=0., type=float
+        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",
+        "--linear_ratio",
-        default=0., type=float
+        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",
+        "--conv_ratio",
-        default=0., type=float
+        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",
+        "--linear_quantile",
-        default=1., type=float
+        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",
+        "--conv_quantile",
-        default=1., type=float
+        help="singular value quantile for conv layer quantile mode",
+        default=1.0,
+        type=float,
     )
     parser.add_argument(
-        "--use_sparse_bias", help="enable sparse bias",
+        "--use_sparse_bias",
-        default=False, action="store_true"
+        help="enable sparse bias",
+        default=False,
+        action="store_true",
     )
     parser.add_argument(
-        "--sparsity", help="sparsity for sparse bias",
+        "--sparsity", help="sparsity for sparse bias", default=0.98, type=float
-        default=0.98, type=float
     )
     parser.add_argument(
-        "--disable_cp", help="don't use cp decomposition",
+        "--disable_cp",
-        default=False, action="store_true"
+        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
@@ -94,36 +133,58 @@ from safetensors.torch import save_file
 
 def main():
     args = ARGS
-    base = load_models_from_stable_diffusion_checkpoint(args.is_v2, args.base_model)
+    if args.is_sdxl:
-    db = load_models_from_stable_diffusion_checkpoint(args.is_v2, args.db_model)
+        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,
+        "fixed": args.linear_dim,
-        'threshold': args.linear_threshold,
+        "threshold": args.linear_threshold,
-        'ratio': args.linear_ratio,
+        "ratio": args.linear_ratio,
-        'quantile': args.linear_quantile,
+        "quantile": args.linear_quantile,
+        "full": None,
     }[args.mode]
     conv_mode_param = {
-        'fixed': args.conv_dim,
+        "fixed": args.conv_dim,
-        'threshold': args.conv_threshold,
+        "threshold": args.conv_threshold,
-        'ratio': args.conv_ratio,
+        "ratio": args.conv_ratio,
-        'quantile': args.conv_quantile,
+        "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, db,
+        base_tes,
+        db_tes,
+        base_unet,
+        db_unet,
         args.mode,
-        linear_mode_param, conv_mode_param,
+        linear_mode_param,
-        args.device, 
+        conv_mode_param,
-        args.use_sparse_bias, args.sparsity,
+        args.device,
-        not args.disable_cp
+        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__':
+if __name__ == "__main__":
     main()