format by black, add ja comment

networks/extract_lora_from_models.py

@@ -29,7 +29,21 @@ 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, clamp_quantile=0.99, min_diff=0.01, 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
@@ -39,9 +53,7 @@ def svd(model_org=None, model_tuned=None, save_to=None, dim=4, v2=None, sdxl=Non
             return torch.bfloat16
         return None
 
-    assert v2 != sdxl or (
-        not v2 and not sdxl
-    ), "v2 and sdxl cannot be specified at the same time / v2とsdxlは同時に指定できません"
+    assert v2 != sdxl or (not v2 and not sdxl), "v2 and sdxl cannot be specified at the same time / v2とsdxlは同時に指定できません"
     if v_parameterization is None:
         v_parameterization = v2
 
@@ -199,9 +211,7 @@ def svd(model_org=None, model_tuned=None, save_to=None, dim=4, v2=None, sdxl=Non
 
     if not no_metadata:
         title = os.path.splitext(os.path.basename(save_to))[0]
-        sai_metadata = sai_model_spec.build_metadata(
-            None, v2, v_parameterization, sdxl, True, False, time.time(), title=title
-        )
+        sai_metadata = sai_model_spec.build_metadata(None, v2, v_parameterization, sdxl, True, False, time.time(), title=title)
         metadata.update(sai_metadata)
 
     lora_network_save.save_weights(save_to, save_dtype, metadata)
@@ -242,7 +252,11 @@ def setup_parser() -> argparse.ArgumentParser:
         help="Stable Diffusion tuned model, LoRA is difference of `original to tuned`: ckpt or safetensors file / 派生モデル（生成されるLoRAは元→派生の差分になります）、ckptまたはsafetensors",
     )
     parser.add_argument(
-        "--save_to", type=str, default=None, required=True, help="destination file name: ckpt or safetensors file / 保存先のファイル名、ckptまたはsafetensors"
+        "--save_to",
+        type=str,
+        default=None,
+        required=True,
+        help="destination file name: ckpt or safetensors file / 保存先のファイル名、ckptまたはsafetensors",
     )
     parser.add_argument("--dim", type=int, default=4, help="dimension (rank) of LoRA (default 4) / LoRAの次元数（rank）（デフォルト4）")
     parser.add_argument(
@@ -256,13 +270,14 @@ def setup_parser() -> argparse.ArgumentParser:
         "--clamp_quantile",
         type=float,
         default=0.99,
-        help="Quantile clamping value, float, (0-1). Default = 0.99",
+        help="Quantile clamping value, float, (0-1). Default = 0.99 / 値をクランプするための分位点、float、(0-1)。デフォルトは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",
+        help="Minimum difference between finetuned model and base to consider them different enough to extract, float, (0-1). Default = 0.01 /"
+        + "LoRAを抽出するために元モデルと派生モデルの差分の最小値、float、(0-1)。デフォルトは0.01",
     )
     parser.add_argument(
         "--no_metadata",