Fix up string formatting/concatenation to f-strings where feasible

1 files changed, 6 insertions, 6 deletions

diff --git a/modules/textual_inversion/textual_inversion.py b/modules/textual_inversion/textual_inversion.py
index 379df243..4368eb63 100644
--- a/modules/textual_inversion/textual_inversion.py
+++ b/modules/textual_inversion/textual_inversion.py
@@ -69,7 +69,7 @@ class Embedding:
                 'hash': self.checksum(),

                 'optimizer_state_dict': self.optimizer_state_dict,

             }

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

+            torch.save(optimizer_saved_dict, f"{filename}.optim")

 

     def checksum(self):

         if self.cached_checksum is not None:

@@ -437,8 +437,8 @@ def train_embedding(id_task, embedding_name, learn_rate, batch_size, gradient_st
     optimizer = torch.optim.AdamW([embedding.vec], lr=scheduler.learn_rate, weight_decay=0.0)

     if shared.opts.save_optimizer_state:

         optimizer_state_dict = None

-        if os.path.exists(filename + '.optim'):

-            optimizer_saved_dict = torch.load(filename + '.optim', map_location='cpu')

+        if os.path.exists(f"{filename}.optim"):

+            optimizer_saved_dict = torch.load(f"{filename}.optim", map_location='cpu')

             if embedding.checksum() == optimizer_saved_dict.get('hash', None):

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

     

@@ -599,7 +599,7 @@ def train_embedding(id_task, embedding_name, learn_rate, batch_size, gradient_st
                         data = torch.load(last_saved_file)

                         info.add_text("sd-ti-embedding", embedding_to_b64(data))

 

-                        title = "<{}>".format(data.get('name', '???'))

+                        title = f"<{data.get('name', '???')}>"

 

                         try:

                             vectorSize = list(data['string_to_param'].values())[0].shape[0]

@@ -608,8 +608,8 @@ def train_embedding(id_task, embedding_name, learn_rate, batch_size, gradient_st
 

                         checkpoint = sd_models.select_checkpoint()

                         footer_left = checkpoint.model_name

-                        footer_mid = '[{}]'.format(checkpoint.shorthash)

-                        footer_right = '{}v {}s'.format(vectorSize, steps_done)

+                        footer_mid = f'[{checkpoint.shorthash}]'

+                        footer_right = f'{vectorSize}v {steps_done}s'

 

                         captioned_image = caption_image_overlay(image, title, footer_left, footer_mid, footer_right)

                         captioned_image = insert_image_data_embed(captioned_image, data)