add min_diff, clamp_quantile args

based on https://github.com/bmaltais/kohya_ss/pull/1332 a9ec90c40a

networks/extract_lora_from_models.py

@@ -13,8 +13,8 @@ from library import sai_model_spec, model_util, sdxl_model_util
 import lora
 
 
-CLAMP_QUANTILE = 0.99
-MIN_DIFF = 1e-1
+#CLAMP_QUANTILE = 0.99
+#MIN_DIFF = 1e-1
 
 
 def save_to_file(file_name, model, state_dict, dtype):
@@ -29,7 +29,7 @@ def save_to_file(file_name, model, state_dict, dtype):
         torch.save(model, file_name)
 
 
-def svd(model_org=None, model_tuned=None, save_to=None, dim=4, v2=None, sdxl=None, conv_dim=None, v_parameterization=None, device=None, save_precision=None, no_metadata=False):
+def svd(model_org=None, model_tuned=None, save_to=None, dim=4, v2=None, sdxl=None, conv_dim=None, v_parameterization=None, device=None, save_precision=None, clamp_quantile=0.99, min_diff=0.01, no_metadata=False):
     def str_to_dtype(p):
         if p == "float":
             return torch.float
@@ -91,9 +91,9 @@ def svd(model_org=None, model_tuned=None, save_to=None, dim=4, v2=None, sdxl=Non
         diff = module_t.weight - module_o.weight
 
         # Text Encoder might be same
-        if not text_encoder_different and torch.max(torch.abs(diff)) > MIN_DIFF:
+        if not text_encoder_different and torch.max(torch.abs(diff)) > min_diff:
             text_encoder_different = True
-            print(f"Text encoder is different. {torch.max(torch.abs(diff))} > {MIN_DIFF}")
+            print(f"Text encoder is different. {torch.max(torch.abs(diff))} > {min_diff}")
 
         diff = diff.float()
         diffs[lora_name] = diff
@@ -149,7 +149,7 @@ def svd(model_org=None, model_tuned=None, save_to=None, dim=4, v2=None, sdxl=Non
             Vh = Vh[:rank, :]
 
             dist = torch.cat([U.flatten(), Vh.flatten()])
-            hi_val = torch.quantile(dist, CLAMP_QUANTILE)
+            hi_val = torch.quantile(dist, clamp_quantile)
             low_val = -hi_val
 
             U = U.clamp(low_val, hi_val)
@@ -252,6 +252,18 @@ def setup_parser() -> argparse.ArgumentParser:
         help="dimension (rank) of LoRA for Conv2d-3x3 (default None, disabled) / LoRAのConv2d-3x3の次元数（rank）（デフォルトNone、適用なし）",
     )
     parser.add_argument("--device", type=str, default=None, help="device to use, cuda for GPU / 計算を行うデバイス、cuda でGPUを使う")
+    parser.add_argument(
+        "--clamp_quantile",
+        type=float,
+        default=0.99,
+        help="Quantile clamping value, float, (0-1). Default = 0.99",
+    )
+    parser.add_argument(
+        "--min_diff",
+        type=float,
+        default=0.01,
+        help="Minimum difference between finetuned model and base to consider them different enough to extract, float, (0-1). Default = 0.01",
+    )
     parser.add_argument(
         "--no_metadata",
         action="store_true",