diff options
Diffstat (limited to 'modules/textual_inversion')
| -rw-r--r-- | modules/textual_inversion/autocrop.py | 6 | ||||
| -rw-r--r-- | modules/textual_inversion/dataset.py | 2 | ||||
| -rw-r--r-- | modules/textual_inversion/preprocess.py | 6 | ||||
| -rw-r--r-- | modules/textual_inversion/textual_inversion.py | 12 |
4 files changed, 13 insertions, 13 deletions
diff --git a/modules/textual_inversion/autocrop.py b/modules/textual_inversion/autocrop.py index 68e1103c..ba1bdcd4 100644 --- a/modules/textual_inversion/autocrop.py +++ b/modules/textual_inversion/autocrop.py @@ -111,7 +111,7 @@ 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 +119,7 @@ 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 +127,7 @@ 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:
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/preprocess.py b/modules/textual_inversion/preprocess.py index 4a29151d..da0bcb26 100644 --- a/modules/textual_inversion/preprocess.py +++ b/modules/textual_inversion/preprocess.py @@ -63,9 +63,9 @@ 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 +174,7 @@ 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..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)
|