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

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This commit is contained in:
Aarni Koskela
2023-05-09 22:17:58 +03:00
parent 8fb16ceb28
commit 3ba6c3c83c
34 changed files with 121 additions and 101 deletions
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6
modules/textual_inversion/autocrop.py
View File

@@ -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:

2
modules/textual_inversion/dataset.py
View File

@@ -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):

6
modules/textual_inversion/preprocess.py
View File

@@ -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()

12
modules/textual_inversion/textual_inversion.py
View File

@@ -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)