Allow special args for schedulers

jobs/process/BaseSDTrainProcess.py

@@ -553,13 +553,17 @@ class BaseSDTrainProcess(BaseTrainProcess):
                                   optimizer_params=self.train_config.optimizer_params)
         self.optimizer = optimizer
 
+        lr_scheduler_params = self.train_config.lr_scheduler_params
+
+        # make sure it had bare minimum
+        if 'max_iterations' not in lr_scheduler_params:
+            lr_scheduler_params['total_iters'] = self.train_config.steps
+
         lr_scheduler = get_lr_scheduler(
             self.train_config.lr_scheduler,
             optimizer,
-            max_iterations=self.train_config.steps,
-            lr_min=self.train_config.lr / 100,
+            **lr_scheduler_params
         )
-
         self.lr_scheduler = lr_scheduler
 
         ### HOOK ###
@@ -601,6 +605,8 @@ class BaseSDTrainProcess(BaseTrainProcess):
         # zero any gradients
         optimizer.zero_grad()
 
+        self.lr_scheduler.step(self.step_num)
+
         # self.step_num = 0
         for step in range(self.step_num, self.train_config.steps):
             with torch.no_grad():

toolkit/config_modules.py

@@ -71,7 +71,9 @@ class TrainConfig:
         self.unet_lr = kwargs.get('unet_lr', self.lr)
         self.text_encoder_lr = kwargs.get('text_encoder_lr', self.lr)
         self.optimizer = kwargs.get('optimizer', 'adamw')
+        self.optimizer_params = kwargs.get('optimizer_params', {})
         self.lr_scheduler = kwargs.get('lr_scheduler', 'constant')
+        self.lr_scheduler_params = kwargs.get('lr_scheduler_params', {})
         self.max_denoising_steps: int = kwargs.get('max_denoising_steps', 50)
         self.batch_size: int = kwargs.get('batch_size', 1)
         self.dtype: str = kwargs.get('dtype', 'fp32')
@@ -80,7 +82,6 @@ class TrainConfig:
         self.train_text_encoder = kwargs.get('train_text_encoder', True)
         self.min_snr_gamma = kwargs.get('min_snr_gamma', None)
         self.noise_offset = kwargs.get('noise_offset', 0.0)
-        self.optimizer_params = kwargs.get('optimizer_params', {})
         self.skip_first_sample = kwargs.get('skip_first_sample', False)
         self.gradient_checkpointing = kwargs.get('gradient_checkpointing', True)
         self.weight_jitter = kwargs.get('weight_jitter', 0.0)

toolkit/scheduler.py

@@ -5,27 +5,34 @@ from typing import Optional
 def get_lr_scheduler(
         name: Optional[str],
         optimizer: torch.optim.Optimizer,
-        max_iterations: Optional[int],
-        lr_min: Optional[float],
         **kwargs,
 ):
     if name == "cosine":
+        if 'total_iters' in kwargs:
+            kwargs['T_max'] = kwargs.pop('total_iters')
         return torch.optim.lr_scheduler.CosineAnnealingLR(
-            optimizer, T_max=max_iterations, eta_min=lr_min, **kwargs
+            optimizer,  **kwargs
         )
     elif name == "cosine_with_restarts":
+        if 'total_iters' in kwargs:
+            kwargs['T_0'] = kwargs.pop('total_iters')
         return torch.optim.lr_scheduler.CosineAnnealingWarmRestarts(
-            optimizer, T_0=max_iterations, T_mult=2, eta_min=lr_min, **kwargs
+            optimizer, **kwargs
         )
     elif name == "step":
+
         return torch.optim.lr_scheduler.StepLR(
-            optimizer, step_size=max_iterations // 100, gamma=0.999, **kwargs
+            optimizer, **kwargs
         )
     elif name == "constant":
-        return torch.optim.lr_scheduler.ConstantLR(optimizer, factor=1, **kwargs)
+        if 'facor' not in kwargs:
+            kwargs['factor'] = 1.0
+
+        return torch.optim.lr_scheduler.ConstantLR(optimizer, **kwargs)
     elif name == "linear":
+
         return torch.optim.lr_scheduler.LinearLR(
-            optimizer, start_factor=0.5, end_factor=0.5, total_iters=max_iterations, **kwargs
+            optimizer, **kwargs
         )
     else:
         raise ValueError(