4 files changed, 11 insertions, 11 deletions
diff --git a/modules/textual_inversion/autocrop.py b/modules/textual_inversion/autocrop.py
index 8e667a4d..75705459 100644
--- a/modules/textual_inversion/autocrop.py
+++ b/modules/textual_inversion/autocrop.py
@@ -77,27 +77,27 @@ def focal_point(im, settings):
     pois = []
 
     weight_pref_total = 0
-    if len(corner_points) > 0:
+    if corner_points:
       weight_pref_total += settings.corner_points_weight
-    if len(entropy_points) > 0:
+    if entropy_points:
       weight_pref_total += settings.entropy_points_weight
-    if len(face_points) > 0:
+    if face_points:
       weight_pref_total += settings.face_points_weight
 
     corner_centroid = None
-    if len(corner_points) > 0:
+    if corner_points:
       corner_centroid = centroid(corner_points)
       corner_centroid.weight = settings.corner_points_weight / weight_pref_total
       pois.append(corner_centroid)
 
     entropy_centroid = None
-    if len(entropy_points) > 0:
+    if entropy_points:
       entropy_centroid = centroid(entropy_points)
       entropy_centroid.weight = settings.entropy_points_weight / weight_pref_total
       pois.append(entropy_centroid)
 
     face_centroid = None
-    if len(face_points) > 0:
+    if face_points:
       face_centroid = centroid(face_points)
       face_centroid.weight = settings.face_points_weight / weight_pref_total
       pois.append(face_centroid)
@@ -187,7 +187,7 @@ def image_face_points(im, settings):
         except Exception:
           continue
 
-        if len(faces) > 0:
+        if faces:
           rects = [[f[0], f[1], f[0] + f[2], f[1] + f[3]] for f in faces]
           return [PointOfInterest((r[0] +r[2]) // 2, (r[1] + r[3]) // 2, size=abs(r[0]-r[2]), weight=1/len(rects)) for r in rects]
     return []
diff --git a/modules/textual_inversion/dataset.py b/modules/textual_inversion/dataset.py
index b9621fc9..7ee05061 100644
--- a/modules/textual_inversion/dataset.py
+++ b/modules/textual_inversion/dataset.py
@@ -32,7 +32,7 @@ class DatasetEntry:
 
 class PersonalizedBase(Dataset):
     def __init__(self, data_root, width, height, repeats, flip_p=0.5, placeholder_token="*", model=None, cond_model=None, device=None, template_file=None, include_cond=False, batch_size=1, gradient_step=1, shuffle_tags=False, tag_drop_out=0, latent_sampling_method='once', varsize=False, use_weight=False):
-        re_word = re.compile(shared.opts.dataset_filename_word_regex) if len(shared.opts.dataset_filename_word_regex) > 0 else None
+        re_word = re.compile(shared.opts.dataset_filename_word_regex) if shared.opts.dataset_filename_word_regex else None
 
         self.placeholder_token = placeholder_token
 
diff --git a/modules/textual_inversion/preprocess.py b/modules/textual_inversion/preprocess.py
index a009d8e8..0d4c3f84 100644
--- a/modules/textual_inversion/preprocess.py
+++ b/modules/textual_inversion/preprocess.py
@@ -47,7 +47,7 @@ def save_pic_with_caption(image, index, params: PreprocessParams, existing_capti
         caption += shared.interrogator.generate_caption(image)
 
     if params.process_caption_deepbooru:
-        if len(caption) > 0:
+        if caption:
             caption += ", "
         caption += deepbooru.model.tag_multi(image)
 
@@ -67,7 +67,7 @@ def save_pic_with_caption(image, index, params: PreprocessParams, existing_capti
 
     caption = caption.strip()
 
-    if len(caption) > 0:
+    if caption:
         with open(os.path.join(params.dstdir, f"{basename}.txt"), "w", encoding="utf8") as file:
             file.write(caption)
 
diff --git a/modules/textual_inversion/textual_inversion.py b/modules/textual_inversion/textual_inversion.py
index 8da050ca..bb6f211c 100644
--- a/modules/textual_inversion/textual_inversion.py
+++ b/modules/textual_inversion/textual_inversion.py
@@ -251,7 +251,7 @@ class EmbeddingDatabase:
         if self.previously_displayed_embeddings != displayed_embeddings:
             self.previously_displayed_embeddings = displayed_embeddings
             print(f"Textual inversion embeddings loaded({len(self.word_embeddings)}): {', '.join(self.word_embeddings.keys())}")
-            if len(self.skipped_embeddings) > 0:
+            if self.skipped_embeddings:
                 print(f"Textual inversion embeddings skipped({len(self.skipped_embeddings)}): {', '.join(self.skipped_embeddings.keys())}")
 
     def find_embedding_at_position(self, tokens, offset):