monkeypatch updated get_scheduler for diffusers

enables use of "num_cycles" and "power" for cosine_with_restarts and polynomial learning rate schedulers

train_network.py

@@ -35,6 +35,75 @@ def generate_step_logs(args: argparse.Namespace, current_loss, avr_loss, lr_sche
   return logs
 
 
+# Monkeypatch newer get_scheduler() function overridng current version of diffusers.optimizer.get_scheduler
+# code is taken from https://github.com/huggingface/diffusers diffusers.optimizer, commit d87cc15977b87160c30abaace3894e802ad9e1e6
+# Which is a newer release of diffusers than currently packaged with sd-scripts
+# This code can be removed when newer diffusers version (v0.12.1 or greater) is tested and implemented to sd-scripts
+
+from typing import Optional, Union
+from torch.optim import Optimizer
+from diffusers.optimization import SchedulerType, TYPE_TO_SCHEDULER_FUNCTION
+
+def get_scheduler_fix(
+    name: Union[str, SchedulerType],
+    optimizer: Optimizer,
+    num_warmup_steps: Optional[int] = None,
+    num_training_steps: Optional[int] = None,
+    num_cycles: int = 1,
+    power: float = 1.0,
+):
+    """
+    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 = SchedulerType(name)
+    schedule_func = TYPE_TO_SCHEDULER_FUNCTION[name]
+    if name == SchedulerType.CONSTANT:
+        return schedule_func(optimizer)
+
+    # All other schedulers require `num_warmup_steps`
+    if num_warmup_steps is None:
+        raise ValueError(f"{name} requires `num_warmup_steps`, please provide that argument.")
+
+    if name == SchedulerType.CONSTANT_WITH_WARMUP:
+        return schedule_func(optimizer, num_warmup_steps=num_warmup_steps)
+
+    # All other schedulers require `num_training_steps`
+    if num_training_steps is None:
+        raise ValueError(f"{name} requires `num_training_steps`, please provide that argument.")
+
+    if name == SchedulerType.COSINE_WITH_RESTARTS:
+        return schedule_func(
+            optimizer, num_warmup_steps=num_warmup_steps, num_training_steps=num_training_steps, num_cycles=num_cycles
+        )
+
+    if name == SchedulerType.POLYNOMIAL:
+        return schedule_func(
+            optimizer, num_warmup_steps=num_warmup_steps, num_training_steps=num_training_steps, power=power
+        )
+
+    return schedule_func(optimizer, num_warmup_steps=num_warmup_steps, num_training_steps=num_training_steps)
+
+diffusers.optimization.get_scheduler = get_scheduler_fix
+
+
+
 def train(args):
   session_id = random.randint(0, 2**32)
   training_started_at = time.time()
@@ -157,7 +226,9 @@ def train(args):
 
   # lr schedulerを用意する
   lr_scheduler = diffusers.optimization.get_scheduler(
-      args.lr_scheduler, optimizer, num_warmup_steps=args.lr_warmup_steps, num_training_steps=args.max_train_steps * args.gradient_accumulation_steps)
+      args.lr_scheduler, optimizer, num_warmup_steps=args.lr_warmup_steps, 
+      num_training_steps=args.max_train_steps * args.gradient_accumulation_steps,
+      num_cycles = args.num_cycles, power = args.power)
 
   # 実験的機能：勾配も含めたfp16学習を行う　モデル全体をfp16にする
   if args.full_fp16:
@@ -460,6 +531,10 @@ if __name__ == '__main__':
                       help="only training Text Encoder part / Text Encoder関連部分のみ学習する")
   parser.add_argument("--training_comment", type=str, default=None,
                       help="arbitrary comment string stored in metadata / メタデータに記録する任意のコメント文字列")
+  parser.add_argument("--num_cycles", type=int, default=1,
+                      help="Number of restarts for cosine scheduler with restarts")
+  parser.add_argument("--power", type=float, default=1,
+                      help="Polynomial power for polynomial scheduler")
 
   args = parser.parse_args()
   train(args)