Separate .optim file from model

Author: aria1th

modules/hypernetworks/hypernetwork.py

@@ -161,6 +161,7 @@ def weights(self):
 
     def save(self, filename):
         state_dict = {}
+        optimizer_saved_dict = {}
 
         for k, v in self.layers.items():
             state_dict[k] = (v[0].state_dict(), v[1].state_dict())
@@ -175,9 +176,10 @@ def save(self, filename):
         state_dict['sd_checkpoint'] = self.sd_checkpoint
         state_dict['sd_checkpoint_name'] = self.sd_checkpoint_name
         if self.optimizer_name is not None:
-            state_dict['optimizer_name'] = self.optimizer_name
+            optimizer_saved_dict['optimizer_name'] = self.optimizer_name
         if self.optimizer_state_dict:
-            state_dict['optimizer_state_dict'] = self.optimizer_state_dict
+            optimizer_saved_dict['optimizer_state_dict'] = self.optimizer_state_dict
+            torch.save(optimizer_saved_dict, filename + '.optim')
 
         torch.save(state_dict, filename)
 
@@ -198,9 +200,11 @@ def load(self, filename):
         print(f"Layer norm is set to {self.add_layer_norm}")
         self.use_dropout = state_dict.get('use_dropout', False)
         print(f"Dropout usage is set to {self.use_dropout}")
-        self.optimizer_name = state_dict.get('optimizer_name', 'AdamW')
+
+        optimizer_saved_dict = torch.load(self.filename + '.optim', map_location = 'cpu') if os.path.exists(self.filename + '.optim') else {}
+        self.optimizer_name = optimizer_saved_dict.get('optimizer_name', 'AdamW')
         print(f"Optimizer name is {self.optimizer_name}")
-        self.optimizer_state_dict = state_dict.get('optimizer_state_dict', None)
+        self.optimizer_state_dict = optimizer_saved_dict.get('optimizer_state_dict', None)
         if self.optimizer_state_dict:
             print("Loaded existing optimizer from checkpoint")
         else: