use hash to check valid optim

1 files changed, 9 insertions, 4 deletions

diff --git a/modules/hypernetworks/hypernetwork.py b/modules/hypernetworks/hypernetwork.py
index 63c25de8..4230b8cf 100644
--- a/modules/hypernetworks/hypernetwork.py
+++ b/modules/hypernetworks/hypernetwork.py
@@ -177,12 +177,13 @@ class Hypernetwork:
         state_dict['sd_checkpoint_name'] = self.sd_checkpoint_name

         if self.optimizer_name is not None:

             optimizer_saved_dict['optimizer_name'] = self.optimizer_name

+

+        torch.save(state_dict, filename)

         if self.optimizer_state_dict:

+            optimizer_saved_dict['hash'] = sd_models.model_hash(filename)

             optimizer_saved_dict['optimizer_state_dict'] = self.optimizer_state_dict

             torch.save(optimizer_saved_dict, filename + '.optim')

 

-        torch.save(state_dict, filename)

-

     def load(self, filename):

         self.filename = filename

         if self.name is None:

@@ -204,7 +205,10 @@ class Hypernetwork:
         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 = optimizer_saved_dict.get('optimizer_state_dict', None)

+        if sd_models.model_hash(filename) == optimizer_saved_dict.get('hash', None):

+            self.optimizer_state_dict = optimizer_saved_dict.get('optimizer_state_dict', None)

+        else:

+            self.optimizer_state_dict = None

         if self.optimizer_state_dict:

             print("Loaded existing optimizer from checkpoint")

         else:

@@ -229,7 +233,7 @@ def list_hypernetworks(path):
         name = os.path.splitext(os.path.basename(filename))[0]

         # Prevent a hypothetical "None.pt" from being listed.

         if name != "None":

-            res[name] = filename

+            res[name + f"({sd_models.model_hash(filename)})"] = filename

     return res

 

 

@@ -375,6 +379,7 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log
     else:

         hypernetwork_dir = None

 

+    hypernetwork_name = hypernetwork_name.rsplit('(', 1)[0]

     if create_image_every > 0:

         images_dir = os.path.join(log_directory, "images")

         os.makedirs(images_dir, exist_ok=True)