5 files changed, 17 insertions, 24 deletions
diff --git a/modules/textual_inversion/autocrop.py b/modules/textual_inversion/autocrop.py
index 68e1103c..7770d22f 100644
--- a/modules/textual_inversion/autocrop.py
+++ b/modules/textual_inversion/autocrop.py
@@ -1,10 +1,8 @@
 import cv2
 import requests
 import os
-from collections import defaultdict
-from math import log, sqrt
 import numpy as np
-from PIL import Image, ImageDraw
+from PIL import ImageDraw
 
 GREEN = "#0F0"
 BLUE = "#00F"
@@ -111,7 +109,7 @@ def focal_point(im, settings):
       if corner_centroid is not None:
         color = BLUE
         box = corner_centroid.bounding(max_size * corner_centroid.weight)
-        d.text((box[0], box[1]-15), "Edge: %.02f" % corner_centroid.weight, fill=color)
+        d.text((box[0], box[1]-15), f"Edge: {corner_centroid.weight:.02f}", fill=color)
         d.ellipse(box, outline=color)
         if len(corner_points) > 1:
           for f in corner_points:
@@ -119,7 +117,7 @@ def focal_point(im, settings):
       if entropy_centroid is not None:
         color = "#ff0"
         box = entropy_centroid.bounding(max_size * entropy_centroid.weight)
-        d.text((box[0], box[1]-15), "Entropy: %.02f" % entropy_centroid.weight, fill=color)
+        d.text((box[0], box[1]-15), f"Entropy: {entropy_centroid.weight:.02f}", fill=color)
         d.ellipse(box, outline=color)
         if len(entropy_points) > 1:
           for f in entropy_points:
@@ -127,7 +125,7 @@ def focal_point(im, settings):
       if face_centroid is not None:
         color = RED
         box = face_centroid.bounding(max_size * face_centroid.weight)
-        d.text((box[0], box[1]-15), "Face: %.02f" % face_centroid.weight, fill=color)
+        d.text((box[0], box[1]-15), f"Face: {face_centroid.weight:.02f}", fill=color)
         d.ellipse(box, outline=color)
         if len(face_points) > 1:
           for f in face_points:
@@ -185,7 +183,7 @@ def image_face_points(im, settings):
         try:
           faces = classifier.detectMultiScale(gray, scaleFactor=1.1,
             minNeighbors=7, minSize=(minsize, minsize), flags=cv2.CASCADE_SCALE_IMAGE)
-        except:
+        except Exception:
           continue
 
         if len(faces) > 0:
diff --git a/modules/textual_inversion/dataset.py b/modules/textual_inversion/dataset.py
index af9fbcf2..41610e03 100644
--- a/modules/textual_inversion/dataset.py
+++ b/modules/textual_inversion/dataset.py
@@ -72,7 +72,7 @@ class PersonalizedBase(Dataset):
             except Exception:
                 continue
 
-            text_filename = os.path.splitext(path)[0] + ".txt"
+            text_filename = f"{os.path.splitext(path)[0]}.txt"
             filename = os.path.basename(path)
 
             if os.path.exists(text_filename):
diff --git a/modules/textual_inversion/image_embedding.py b/modules/textual_inversion/image_embedding.py
index 5593f88c..ee0e850a 100644
--- a/modules/textual_inversion/image_embedding.py
+++ b/modules/textual_inversion/image_embedding.py
@@ -2,7 +2,7 @@ import base64
 import json
 import numpy as np
 import zlib
-from PIL import Image, PngImagePlugin, ImageDraw, ImageFont
+from PIL import Image, ImageDraw, ImageFont
 from fonts.ttf import Roboto
 import torch
 from modules.shared import opts
diff --git a/modules/textual_inversion/preprocess.py b/modules/textual_inversion/preprocess.py
index 4a29151d..d0cad09e 100644
--- a/modules/textual_inversion/preprocess.py
+++ b/modules/textual_inversion/preprocess.py
@@ -1,13 +1,9 @@
 import os
 from PIL import Image, ImageOps
 import math
-import platform
-import sys
 import tqdm
-import time
 
 from modules import paths, shared, images, deepbooru
-from modules.shared import opts, cmd_opts
 from modules.textual_inversion import autocrop
 
 
@@ -63,9 +59,9 @@ def save_pic_with_caption(image, index, params: PreprocessParams, existing_capti
     image.save(os.path.join(params.dstdir, f"{basename}.png"))
 
     if params.preprocess_txt_action == 'prepend' and existing_caption:
-        caption = existing_caption + ' ' + caption
+        caption = f"{existing_caption} {caption}"
     elif params.preprocess_txt_action == 'append' and existing_caption:
-        caption = caption + ' ' + existing_caption
+        caption = f"{caption} {existing_caption}"
     elif params.preprocess_txt_action == 'copy' and existing_caption:
         caption = existing_caption
 
@@ -174,7 +170,7 @@ def preprocess_work(process_src, process_dst, process_width, process_height, pre
         params.src = filename
 
         existing_caption = None
-        existing_caption_filename = os.path.splitext(filename)[0] + '.txt'
+        existing_caption_filename = f"{os.path.splitext(filename)[0]}.txt"
         if os.path.exists(existing_caption_filename):
             with open(existing_caption_filename, 'r', encoding="utf8") as file:
                 existing_caption = file.read()
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)