diff options
Diffstat (limited to 'modules/textual_inversion/textual_inversion.py')
| -rw-r--r-- | modules/textual_inversion/textual_inversion.py | 15 |
1 files changed, 7 insertions, 8 deletions
diff --git a/modules/textual_inversion/textual_inversion.py b/modules/textual_inversion/textual_inversion.py index 379df243..9ed9ba45 100644 --- a/modules/textual_inversion/textual_inversion.py +++ b/modules/textual_inversion/textual_inversion.py @@ -1,7 +1,6 @@ import os
import sys
import traceback
-import inspect
from collections import namedtuple
import torch
@@ -69,7 +68,7 @@ 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 +436,8 @@ 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)
@@ -599,17 +598,17 @@ 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]
- except Exception as e:
+ except Exception:
vectorSize = '?'
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)
|