From 3ba6c3c83c0983a025c7bddc08bb7f49481b3cbb Mon Sep 17 00:00:00 2001 From: Aarni Koskela Date: Tue, 9 May 2023 22:17:58 +0300 Subject: Fix up string formatting/concatenation to f-strings where feasible --- modules/textual_inversion/autocrop.py | 6 +++--- modules/textual_inversion/dataset.py | 2 +- modules/textual_inversion/preprocess.py | 6 +++--- modules/textual_inversion/textual_inversion.py | 12 ++++++------ 4 files changed, 13 insertions(+), 13 deletions(-) (limited to 'modules/textual_inversion') 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) -- cgit v1.2.1