fix num_processes, fix indent

library/train_util.py

@@ -1721,11 +1721,15 @@ def add_optimizer_arguments(parser: argparse.ArgumentParser):
         help='additional arguments for optimizer (like "weight_decay=0.01 betas=0.9,0.999 ...") / オプティマイザの追加引数（例： "weight_decay=0.01 betas=0.9,0.999 ..."）',
     )
 
-  parser.add_argument("--lr_scheduler_type", type=str, default="",
-                      help="custom scheduler module")
-  parser.add_argument("--lr_scheduler_args", type=str, default=None, nargs='*',
-                      help="additional arguments for scheduler (like \"weight_decay=0.01 betas=0.9,0.999 ...\") / スケジューラの追加引数（例： \"weight_decay=0.01 betas=0.9,0.999 ...\"）")
-  
+    parser.add_argument("--lr_scheduler_type", type=str, default="", help="custom scheduler module")
+    parser.add_argument(
+        "--lr_scheduler_args",
+        type=str,
+        default=None,
+        nargs="*",
+        help='additional arguments for scheduler (like "T_max=100") / スケジューラの追加引数（例： "T_max100"）',
+    )
+
     parser.add_argument(
         "--lr_scheduler",
         type=str,
@@ -2083,7 +2087,7 @@ def read_config_from_file(args: argparse.Namespace, parser: argparse.ArgumentPar
         # if value is dict, save all key and value into one dict
         for key, value in section_dict.items():
             ignore_nesting_dict[key] = value
-    
+
     config_args = argparse.Namespace(**ignore_nesting_dict)
     args = parser.parse_args(namespace=config_args)
     args.config_file = os.path.splitext(args.config_file)[0]
@@ -2290,26 +2294,9 @@ def get_optimizer(args, trainable_params):
 # This code can be removed when newer diffusers version (v0.12.1 or greater) is tested and implemented to sd-scripts
 
 
-def get_scheduler_fix(args,optimizer: Optimizer,num_processes:int):
+def get_scheduler_fix(args, optimizer: Optimizer, num_processes: int):
     """
     Unified API to get any scheduler from its name.
-    Args:
-        name (`str` or `SchedulerType`):
-            The name of the scheduler to use.
-        optimizer (`torch.optim.Optimizer`):
-            The optimizer that will be used during training.
-        num_warmup_steps (`int`, *optional*):
-            The number of warmup steps to do. This is not required by all schedulers (hence the argument being
-            optional), the function will raise an error if it's unset and the scheduler type requires it.
-        num_training_steps (`int``, *optional*):
-            The number of training steps to do. This is not required by all schedulers (hence the argument being
-            optional), the function will raise an error if it's unset and the scheduler type requires it.
-        num_cycles (`int`, *optional*):
-            The number of hard restarts used in `COSINE_WITH_RESTARTS` scheduler.
-        power (`float`, *optional*, defaults to 1.0):
-            Power factor. See `POLYNOMIAL` scheduler
-        last_epoch (`int`, *optional*, defaults to -1):
-            The index of the last epoch when resuming training.
     """
     name = args.lr_scheduler
     num_warmup_steps = args.lr_warmup_steps
@@ -2319,35 +2306,35 @@ def get_scheduler_fix(args,optimizer: Optimizer,num_processes:int):
 
     lr_scheduler_kwargs = {}  # get custom lr_scheduler kwargs
     if args.lr_scheduler_args is not None and len(args.lr_scheduler_args) > 0:
-      for arg in args.lr_scheduler_args:
-        key, value = arg.split('=')
+        for arg in args.lr_scheduler_args:
+            key, value = arg.split("=")
 
-        value = value.split(",")
-        for i in range(len(value)):
-          if value[i].lower() == "true" or value[i].lower() == "false":
-            value[i] = (value[i].lower() == "true")
-          else:
-            value[i] = ast.literal_eval(value[i])
-        if len(value) == 1:
-          value = value[0]
-        else:
-          value = list(value)  # some may use list?
+            value = value.split(",")
+            for i in range(len(value)):
+                if value[i].lower() == "true" or value[i].lower() == "false":
+                    value[i] = value[i].lower() == "true"
+                else:
+                    value[i] = ast.literal_eval(value[i])
+            if len(value) == 1:
+                value = value[0]
+            else:
+                value = list(value)  # some may use list?
 
-        lr_scheduler_kwargs[key] = value
+            lr_scheduler_kwargs[key] = value
 
     # using any lr_scheduler from other library
     if args.lr_scheduler_type:
-      lr_scheduler_type = args.lr_scheduler_type
-      print(f"use {lr_scheduler_type} | {lr_scheduler_kwargs} as lr_scheduler")
-      if "." not in lr_scheduler_type:  # default to use torch.optim
-        lr_scheduler_module = torch.optim.lr_scheduler
-      else:
-        values = lr_scheduler_type.split(".")
-        lr_scheduler_module = importlib.import_module(".".join(values[:-1]))
-        lr_scheduler_type = values[-1]
-      lr_scheduler_class = getattr(lr_scheduler_module, lr_scheduler_type)
-      lr_scheduler = lr_scheduler_class(optimizer, **lr_scheduler_kwargs)
-      return lr_scheduler
+        lr_scheduler_type = args.lr_scheduler_type
+        print(f"use {lr_scheduler_type} | {lr_scheduler_kwargs} as lr_scheduler")
+        if "." not in lr_scheduler_type:  # default to use torch.optim
+            lr_scheduler_module = torch.optim.lr_scheduler
+        else:
+            values = lr_scheduler_type.split(".")
+            lr_scheduler_module = importlib.import_module(".".join(values[:-1]))
+            lr_scheduler_type = values[-1]
+        lr_scheduler_class = getattr(lr_scheduler_module, lr_scheduler_type)
+        lr_scheduler = lr_scheduler_class(optimizer, **lr_scheduler_kwargs)
+        return lr_scheduler
 
     if name.startswith("adafactor"):
         assert (