apply dadaptation

train_network.py

@@ -37,6 +37,9 @@ def generate_step_logs(args: argparse.Namespace, current_loss, avr_loss, lr_sche
     logs["lr/textencoder"] = lr_scheduler.get_last_lr()[0]
     logs["lr/unet"] = lr_scheduler.get_last_lr()[-1]          # may be same to textencoder
 
+  if args.use_dadaptation_optimizer: # tracking d*lr value of unet.
+    logs["lr/d*lr"] = lr_scheduler.optimizers[-1].param_groups[0]['d']*lr_scheduler.optimizers[-1].param_groups[0]['lr']
+
   return logs
 
 
@@ -223,6 +226,18 @@ def train(args):
       raise ImportError("No lion_pytorch / lion_pytorch がインストールされていないようです")
     print("use Lion optimizer")
     optimizer_class = lion_pytorch.Lion
+  elif args.use_dadaptation_optimizer:
+    try:
+      import dadaptation
+    except ImportError:
+      raise ImportError("No dadaptation / dadaptation がインストールされていないようです")
+    print("use dadaptation optimizer")
+    optimizer_class = dadaptation.DAdaptAdam
+    if args.network_dim > args.network_alpha:
+      print('network dimension is greater than network alpha. It possibly makes network blow up.')
+    if args.learning_rate <= 0.1 or args.text_encoder_lr <= 0.1 or args.unet_lr <= 0.1:
+      print('learning rate is too low. If using dadaptaion, set learning rate around 1.0.')
+      print('recommend option: lr=1.0, unet_lr=1.0, txtencoder_lr=0.5')
   else:
     optimizer_class = torch.optim.AdamW