Merge pull request #10232 from akx/eff

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

modules/api/api.py

@@ -570,20 +570,20 @@ class Api:
             filename = create_embedding(**args) # create empty embedding
             sd_hijack.model_hijack.embedding_db.load_textual_inversion_embeddings() # reload embeddings so new one can be immediately used
             shared.state.end()
-            return CreateResponse(info = "create embedding filename: {filename}".format(filename = filename))
+            return CreateResponse(info=f"create embedding filename: {filename}")
         except AssertionError as e:
             shared.state.end()
-            return TrainResponse(info = "create embedding error: {error}".format(error = e))
+            return TrainResponse(info=f"create embedding error: {e}")
 
     def create_hypernetwork(self, args: dict):
         try:
             shared.state.begin()
             filename = create_hypernetwork(**args) # create empty embedding
             shared.state.end()
-            return CreateResponse(info = "create hypernetwork filename: {filename}".format(filename = filename))
+            return CreateResponse(info=f"create hypernetwork filename: {filename}")
         except AssertionError as e:
             shared.state.end()
-            return TrainResponse(info = "create hypernetwork error: {error}".format(error = e))
+            return TrainResponse(info=f"create hypernetwork error: {e}")
 
     def preprocess(self, args: dict):
         try:
@@ -593,13 +593,13 @@ class Api:
             return PreprocessResponse(info = 'preprocess complete')
         except KeyError as e:
             shared.state.end()
-            return PreprocessResponse(info = "preprocess error: invalid token: {error}".format(error = e))
+            return PreprocessResponse(info=f"preprocess error: invalid token: {e}")
         except AssertionError as e:
             shared.state.end()
-            return PreprocessResponse(info = "preprocess error: {error}".format(error = e))
+            return PreprocessResponse(info=f"preprocess error: {e}")
         except FileNotFoundError as e:
             shared.state.end()
-            return PreprocessResponse(info = 'preprocess error: {error}'.format(error = e))
+            return PreprocessResponse(info=f'preprocess error: {e}')
 
     def train_embedding(self, args: dict):
         try:
@@ -617,10 +617,10 @@ class Api:
                 if not apply_optimizations:
                     sd_hijack.apply_optimizations()
                 shared.state.end()
-            return TrainResponse(info = "train embedding complete: filename: {filename} error: {error}".format(filename = filename, error = error))
+            return TrainResponse(info=f"train embedding complete: filename: {filename} error: {error}")
         except AssertionError as msg:
             shared.state.end()
-            return TrainResponse(info = "train embedding error: {msg}".format(msg = msg))
+            return TrainResponse(info=f"train embedding error: {msg}")
 
     def train_hypernetwork(self, args: dict):
         try:
@@ -641,10 +641,10 @@ class Api:
                 if not apply_optimizations:
                     sd_hijack.apply_optimizations()
                 shared.state.end()
-            return TrainResponse(info="train embedding complete: filename: {filename} error: {error}".format(filename=filename, error=error))
+            return TrainResponse(info=f"train embedding complete: filename: {filename} error: {error}")
         except AssertionError as msg:
             shared.state.end()
-            return TrainResponse(info="train embedding error: {error}".format(error=error))
+            return TrainResponse(info=f"train embedding error: {error}")
 
     def get_memory(self):
         try:

modules/call_queue.py

@@ -60,7 +60,7 @@ def wrap_gradio_call(func, extra_outputs=None, add_stats=False):
             max_debug_str_len = 131072 # (1024*1024)/8
 
             print("Error completing request", file=sys.stderr)
-            argStr = f"Arguments: {str(args)} {str(kwargs)}"
+            argStr = f"Arguments: {args} {kwargs}"
             print(argStr[:max_debug_str_len], file=sys.stderr)
             if len(argStr) > max_debug_str_len:
                 print(f"(Argument list truncated at {max_debug_str_len}/{len(argStr)} characters)", file=sys.stderr)
@@ -73,7 +73,8 @@ def wrap_gradio_call(func, extra_outputs=None, add_stats=False):
             if extra_outputs_array is None:
                 extra_outputs_array = [None, '']
 
-            res = extra_outputs_array + [f"<div class='error'>{html.escape(type(e).__name__+': '+str(e))}</div>"]
+            error_message = f'{type(e).__name__}: {e}'
+            res = extra_outputs_array + [f"<div class='error'>{html.escape(error_message)}</div>"]
 
         shared.state.skipped = False
         shared.state.interrupted = False

modules/esrgan_model.py

@@ -156,13 +156,16 @@ class UpscalerESRGAN(Upscaler):
 
     def load_model(self, path: str):
         if "http" in path:
-            filename = load_file_from_url(url=self.model_url, model_dir=self.model_path,
-                                          file_name="%s.pth" % self.model_name,
-                                          progress=True)
+            filename = load_file_from_url(
+                url=self.model_url,
+                model_dir=self.model_path,
+                file_name=f"{self.model_name}.pth",
+                progress=True,
+            )
         else:
             filename = path
         if not os.path.exists(filename) or filename is None:
-            print("Unable to load %s from %s" % (self.model_path, filename))
+            print(f"Unable to load {self.model_path} from {filename}")
             return None
 
         state_dict = torch.load(filename, map_location='cpu' if devices.device_esrgan.type == 'mps' else None)

modules/esrgan_model_arch.py

@@ -38,7 +38,7 @@ class RRDBNet(nn.Module):
         elif upsample_mode == 'pixelshuffle':
             upsample_block = pixelshuffle_block
         else:
-            raise NotImplementedError('upsample mode [{:s}] is not found'.format(upsample_mode))
+            raise NotImplementedError(f'upsample mode [{upsample_mode}] is not found')
         if upscale == 3:
             upsampler = upsample_block(nf, nf, 3, act_type=act_type, convtype=convtype)
         else:
@@ -261,10 +261,10 @@ class Upsample(nn.Module):
 
     def extra_repr(self):
         if self.scale_factor is not None:
-            info = 'scale_factor=' + str(self.scale_factor)
+            info = f'scale_factor={self.scale_factor}'
         else:
-            info = 'size=' + str(self.size)
-        info += ', mode=' + self.mode
+            info = f'size={self.size}'
+        info += f', mode={self.mode}'
         return info
 
 
@@ -350,7 +350,7 @@ def act(act_type, inplace=True, neg_slope=0.2, n_prelu=1, beta=1.0):
     elif act_type == 'sigmoid':  # [0, 1] range output
         layer = nn.Sigmoid()
     else:
-        raise NotImplementedError('activation layer [{:s}] is not found'.format(act_type))
+        raise NotImplementedError(f'activation layer [{act_type}] is not found')
     return layer
 
 
@@ -372,7 +372,7 @@ def norm(norm_type, nc):
     elif norm_type == 'none':
         def norm_layer(x): return Identity()
     else:
-        raise NotImplementedError('normalization layer [{:s}] is not found'.format(norm_type))
+        raise NotImplementedError(f'normalization layer [{norm_type}] is not found')
     return layer
 
 
@@ -388,7 +388,7 @@ def pad(pad_type, padding):
     elif pad_type == 'zero':
         layer = nn.ZeroPad2d(padding)
     else:
-        raise NotImplementedError('padding layer [{:s}] is not implemented'.format(pad_type))
+        raise NotImplementedError(f'padding layer [{pad_type}] is not implemented')
     return layer
 
 
@@ -432,7 +432,7 @@ def conv_block(in_nc, out_nc, kernel_size, stride=1, dilation=1, groups=1, bias=
                pad_type='zero', norm_type=None, act_type='relu', mode='CNA', convtype='Conv2D',
                spectral_norm=False):
     """ Conv layer with padding, normalization, activation """
-    assert mode in ['CNA', 'NAC', 'CNAC'], 'Wrong conv mode [{:s}]'.format(mode)
+    assert mode in ['CNA', 'NAC', 'CNAC'], f'Wrong conv mode [{mode}]'
     padding = get_valid_padding(kernel_size, dilation)
     p = pad(pad_type, padding) if pad_type and pad_type != 'zero' else None
     padding = padding if pad_type == 'zero' else 0

modules/extra_networks_hypernet.py

@@ -10,7 +10,8 @@ class ExtraNetworkHypernet(extra_networks.ExtraNetwork):
         additional = shared.opts.sd_hypernetwork
 
         if additional != "None" and additional in shared.hypernetworks and len([x for x in params_list if x.items[0] == additional]) == 0:
-            p.all_prompts = [x + f"<hypernet:{additional}:{shared.opts.extra_networks_default_multiplier}>" for x in p.all_prompts]
+            hypernet_prompt_text = f"<hypernet:{additional}:{shared.opts.extra_networks_default_multiplier}>"
+            p.all_prompts = [f"{prompt}{hypernet_prompt_text}" for prompt in p.all_prompts]
             params_list.append(extra_networks.ExtraNetworkParams(items=[additional, shared.opts.extra_networks_default_multiplier]))
 
         names = []

modules/generation_parameters_copypaste.py

@@ -269,8 +269,8 @@ Steps: 20, Sampler: Euler a, CFG scale: 7, Seed: 965400086, Size: 512x512, Model
         v = v[1:-1] if v[0] == '"' and v[-1] == '"' else v
         m = re_imagesize.match(v)
         if m is not None:
-            res[k+"-1"] = m.group(1)
-            res[k+"-2"] = m.group(2)
+            res[f"{k}-1"] = m.group(1)
+            res[f"{k}-2"] = m.group(2)
         else:
             res[k] = v
 

modules/hashes.py

@@ -13,7 +13,7 @@ cache_data = None
 
 
 def dump_cache():
-    with filelock.FileLock(cache_filename+".lock"):
+    with filelock.FileLock(f"{cache_filename}.lock"):
         with open(cache_filename, "w", encoding="utf8") as file:
             json.dump(cache_data, file, indent=4)
 
@@ -22,7 +22,7 @@ def cache(subsection):
     global cache_data
 
     if cache_data is None:
-        with filelock.FileLock(cache_filename+".lock"):
+        with filelock.FileLock(f"{cache_filename}.lock"):
             if not os.path.isfile(cache_filename):
                 cache_data = {}
             else:

modules/images.py

@@ -467,7 +467,7 @@ def get_next_sequence_number(path, basename):
     """
     result = -1
     if basename != '':
-        basename = basename + "-"
+        basename = f"{basename}-"
 
     prefix_length = len(basename)
     for p in os.listdir(path):
@@ -536,7 +536,7 @@ def save_image(image, path, basename, seed=None, prompt=None, extension='png', i
         add_number = opts.save_images_add_number or file_decoration == ''
 
         if file_decoration != "" and add_number:
-            file_decoration = "-" + file_decoration
+            file_decoration = f"-{file_decoration}"
 
         file_decoration = namegen.apply(file_decoration) + suffix
 
@@ -566,7 +566,7 @@ def save_image(image, path, basename, seed=None, prompt=None, extension='png', i
 
     def _atomically_save_image(image_to_save, filename_without_extension, extension):
         # save image with .tmp extension to avoid race condition when another process detects new image in the directory
-        temp_file_path = filename_without_extension + ".tmp"
+        temp_file_path = f"{filename_without_extension}.tmp"
         image_format = Image.registered_extensions()[extension]
 
         if extension.lower() == '.png':
@@ -626,7 +626,7 @@ def save_image(image, path, basename, seed=None, prompt=None, extension='png', i
     if opts.save_txt and info is not None:
         txt_fullfn = f"{fullfn_without_extension}.txt"
         with open(txt_fullfn, "w", encoding="utf8") as file:
-            file.write(info + "\n")
+            file.write(f"{info}\n")
     else:
         txt_fullfn = None
 

modules/interrogate.py

@@ -28,7 +28,7 @@ def category_types():
 def download_default_clip_interrogate_categories(content_dir):
     print("Downloading CLIP categories...")
 
-    tmpdir = content_dir + "_tmp"
+    tmpdir = f"{content_dir}_tmp"
     category_types = ["artists", "flavors", "mediums", "movements"]
 
     try:
@@ -214,7 +214,7 @@ class InterrogateModels:
                         if shared.opts.interrogate_return_ranks:
                             res += f", ({match}:{score/100:.3f})"
                         else:
-                            res += ", " + match
+                            res += f", {match}"
 
         except Exception:
             print("Error interrogating", file=sys.stderr)

modules/models/diffusion/ddpm_edit.py

@@ -223,7 +223,7 @@ class DDPM(pl.LightningModule):
         for k in keys:
             for ik in ignore_keys:
                 if k.startswith(ik):
-                    print("Deleting key {} from state_dict.".format(k))
+                    print(f"Deleting key {k} from state_dict.")
                     del sd[k]
         missing, unexpected = self.load_state_dict(sd, strict=False) if not only_model else self.model.load_state_dict(
             sd, strict=False)
@@ -386,7 +386,7 @@ class DDPM(pl.LightningModule):
         _, loss_dict_no_ema = self.shared_step(batch)
         with self.ema_scope():
             _, loss_dict_ema = self.shared_step(batch)
-            loss_dict_ema = {key + '_ema': loss_dict_ema[key] for key in loss_dict_ema}
+            loss_dict_ema = {f"{key}_ema": loss_dict_ema[key] for key in loss_dict_ema}
         self.log_dict(loss_dict_no_ema, prog_bar=False, logger=True, on_step=False, on_epoch=True)
         self.log_dict(loss_dict_ema, prog_bar=False, logger=True, on_step=False, on_epoch=True)
 

modules/models/diffusion/uni_pc/uni_pc.py

@@ -94,7 +94,7 @@ class NoiseScheduleVP:
         """
 
         if schedule not in ['discrete', 'linear', 'cosine']:
-            raise ValueError("Unsupported noise schedule {}. The schedule needs to be 'discrete' or 'linear' or 'cosine'".format(schedule))
+            raise ValueError(f"Unsupported noise schedule {schedule}. The schedule needs to be 'discrete' or 'linear' or 'cosine'")
 
         self.schedule = schedule
         if schedule == 'discrete':
@@ -469,7 +469,7 @@ class UniPC:
             t = torch.linspace(t_T**(1. / t_order), t_0**(1. / t_order), N + 1).pow(t_order).to(device)
             return t
         else:
-            raise ValueError("Unsupported skip_type {}, need to be 'logSNR' or 'time_uniform' or 'time_quadratic'".format(skip_type))
+            raise ValueError(f"Unsupported skip_type {skip_type}, need to be 'logSNR' or 'time_uniform' or 'time_quadratic'")
 
     def get_orders_and_timesteps_for_singlestep_solver(self, steps, order, skip_type, t_T, t_0, device):
         """

modules/ngrok.py

@@ -7,8 +7,8 @@ def connect(token, port, region):
     else:
         if ':' in token:
             # token = authtoken:username:password
-            account = token.split(':')[1] + ':' + token.split(':')[-1]
-            token = token.split(':')[0]
+            token, username, password = token.split(':', 2)
+            account = f"{username}:{password}"
 
     config = conf.PyngrokConfig(
         auth_token=token, region=region

modules/paths.py

@@ -16,7 +16,7 @@ for possible_sd_path in possible_sd_paths:
         sd_path = os.path.abspath(possible_sd_path)
         break
 
-assert sd_path is not None, "Couldn't find Stable Diffusion in any of: " + str(possible_sd_paths)
+assert sd_path is not None, f"Couldn't find Stable Diffusion in any of: {possible_sd_paths}"
 
 path_dirs = [
     (sd_path, 'ldm', 'Stable Diffusion', []),

modules/processing.py

@@ -500,7 +500,7 @@ def create_infotext(p, all_prompts, all_seeds, all_subseeds, comments=None, iter
 
     generation_params_text = ", ".join([k if k == v else f'{k}: {generation_parameters_copypaste.quote(v)}' for k, v in generation_params.items() if v is not None])
 
-    negative_prompt_text = "\nNegative prompt: " + p.all_negative_prompts[index] if p.all_negative_prompts[index] else ""
+    negative_prompt_text = f"\nNegative prompt: {p.all_negative_prompts[index]}" if p.all_negative_prompts[index] else ""
 
     return f"{all_prompts[index]}{negative_prompt_text}\n{generation_params_text}".strip()
 
@@ -780,7 +780,16 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
 
     devices.torch_gc()
 
-    res = Processed(p, output_images, p.all_seeds[0], infotext(), comments="".join(["\n\n" + x for x in comments]), subseed=p.all_subseeds[0], index_of_first_image=index_of_first_image, infotexts=infotexts)
+    res = Processed(
+        p,
+        images_list=output_images,
+        seed=p.all_seeds[0],
+        info=infotext(),
+        comments="".join(f"\n\n{comment}" for comment in comments),
+        subseed=p.all_subseeds[0],
+        index_of_first_image=index_of_first_image,
+        infotexts=infotexts,
+    )
 
     if p.scripts is not None:
         p.scripts.postprocess(p, res)

modules/progress.py

@@ -96,7 +96,8 @@ def progressapi(req: ProgressRequest):
         if image is not None:
             buffered = io.BytesIO()
             image.save(buffered, format="png")
-            live_preview = 'data:image/png;base64,' + base64.b64encode(buffered.getvalue()).decode("ascii")
+            base64_image = base64.b64encode(buffered.getvalue()).decode('ascii')
+            live_preview = f"data:image/png;base64,{base64_image}"
             id_live_preview = shared.state.id_live_preview
         else:
             live_preview = None

modules/realesrgan_model.py

@@ -28,9 +28,9 @@ class UpscalerRealESRGAN(Upscaler):
             for scaler in scalers:
                 if scaler.local_data_path.startswith("http"):
                     filename = modelloader.friendly_name(scaler.local_data_path)
-                    local = next(iter([local_model for local_model in local_model_paths if local_model.endswith(filename + '.pth')]), None)
-                    if local:
-                        scaler.local_data_path = local
+                    local_model_candidates = [local_model for local_model in local_model_paths if local_model.endswith(f"{filename}.pth")]
+                    if local_model_candidates:
+                        scaler.local_data_path = local_model_candidates[0]
 
                 if scaler.name in opts.realesrgan_enabled_models:
                     self.scalers.append(scaler)
@@ -47,7 +47,7 @@ class UpscalerRealESRGAN(Upscaler):
 
         info = self.load_model(path)
         if not os.path.exists(info.local_data_path):
-            print("Unable to load RealESRGAN model: %s" % info.name)
+            print(f"Unable to load RealESRGAN model: {info.name}")
             return img
 
         upsampler = RealESRGANer(

modules/scripts.py

@@ -163,7 +163,8 @@ class Script:
         """helper function to generate id for a HTML element, constructs final id out of script name, tab and user-supplied item_id"""
 
         need_tabname = self.show(True) == self.show(False)
-        tabname = ('img2img' if self.is_img2img else 'txt2txt') + "_" if need_tabname else ""
+        tabkind = 'img2img' if self.is_img2img else 'txt2txt'
+        tabname = f"{tabkind}_" if need_tabname else ""
         title = re.sub(r'[^a-z_0-9]', '', re.sub(r'\s', '_', self.title().lower()))
 
         return f'script_{tabname}{title}_{item_id}'
@@ -526,7 +527,7 @@ def add_classes_to_gradio_component(comp):
     this adds gradio-* to the component for css styling (ie gradio-button to gr.Button), as well as some others
     """
 
-    comp.elem_classes = ["gradio-" + comp.get_block_name(), *(comp.elem_classes or [])]
+    comp.elem_classes = [f"gradio-{comp.get_block_name()}", *(comp.elem_classes or [])]
 
     if getattr(comp, 'multiselect', False):
         comp.elem_classes.append('multiselect')

modules/sd_hijack_clip_old.py

@@ -75,7 +75,8 @@ def forward_old(self: sd_hijack_clip.FrozenCLIPEmbedderWithCustomWordsBase, text
     self.hijack.comments += hijack_comments
 
     if len(used_custom_terms) > 0:
-        self.hijack.comments.append("Used embeddings: " + ", ".join([f'{word} [{checksum}]' for word, checksum in used_custom_terms]))
+        embedding_names = ", ".join(f"{word} [{checksum}]" for word, checksum in used_custom_terms)
+        self.hijack.comments.append(f"Used embeddings: {embedding_names}")
 
     self.hijack.fixes = hijack_fixes
     return self.process_tokens(remade_batch_tokens, batch_multipliers)

modules/sd_hijack_unet.py

@@ -18,7 +18,7 @@ class TorchHijackForUnet:
         if hasattr(torch, item):
             return getattr(torch, item)
 
-        raise AttributeError("'{}' object has no attribute '{}'".format(type(self).__name__, item))
+        raise AttributeError(f"'{type(self).__name__}' object has no attribute '{item}'")
 
     def cat(self, tensors, *args, **kwargs):
         if len(tensors) == 2:

modules/sd_models.py

@@ -47,7 +47,7 @@ class CheckpointInfo:
         self.model_name = os.path.splitext(name.replace("/", "_").replace("\\", "_"))[0]
         self.hash = model_hash(filename)
 
-        self.sha256 = hashes.sha256_from_cache(self.filename, "checkpoint/" + name)
+        self.sha256 = hashes.sha256_from_cache(self.filename, f"checkpoint/{name}")
         self.shorthash = self.sha256[0:10] if self.sha256 else None
 
         self.title = name if self.shorthash is None else f'{name} [{self.shorthash}]'
@@ -69,7 +69,7 @@ class CheckpointInfo:
             checkpoint_alisases[id] = self
 
     def calculate_shorthash(self):
-        self.sha256 = hashes.sha256(self.filename, "checkpoint/" + self.name)
+        self.sha256 = hashes.sha256(self.filename, f"checkpoint/{self.name}")
         if self.sha256 is None:
             return
 

modules/sd_models_config.py

@@ -111,7 +111,7 @@ def find_checkpoint_config_near_filename(info):
     if info is None:
         return None
 
-    config = os.path.splitext(info.filename)[0] + ".yaml"
+    config = f"{os.path.splitext(info.filename)[0]}.yaml"
     if os.path.exists(config):
         return config
 

modules/sd_samplers_kdiffusion.py

@@ -198,7 +198,7 @@ class TorchHijack:
         if hasattr(torch, item):
             return getattr(torch, item)
 
-        raise AttributeError("'{}' object has no attribute '{}'".format(type(self).__name__, item))
+        raise AttributeError(f"'{type(self).__name__}' object has no attribute '{item}'")
 
     def randn_like(self, x):
         if self.sampler_noises:

modules/sd_vae.py

@@ -89,7 +89,7 @@ def refresh_vae_list():
 
 def find_vae_near_checkpoint(checkpoint_file):
     checkpoint_path = os.path.splitext(checkpoint_file)[0]
-    for vae_location in [checkpoint_path + ".vae.pt", checkpoint_path + ".vae.ckpt", checkpoint_path + ".vae.safetensors"]:
+    for vae_location in [f"{checkpoint_path}.vae.pt", f"{checkpoint_path}.vae.ckpt", f"{checkpoint_path}.vae.safetensors"]:
         if os.path.isfile(vae_location):
             return vae_location
 

modules/styles.py

@@ -74,7 +74,7 @@ class StyleDatabase:
     def save_styles(self, path: str) -> None:
         # Always keep a backup file around
         if os.path.exists(path):
-            shutil.copy(path, path + ".bak")
+            shutil.copy(path, f"{path}.bak")
 
         fd = os.open(path, os.O_RDWR|os.O_CREAT)
         with os.fdopen(fd, "w", encoding="utf-8-sig", newline='') as file:

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:

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):

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()

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)

modules/ui.py

@@ -101,7 +101,7 @@ def visit(x, func, path=""):
         for c in x.children:
             visit(c, func, path)
     elif x.label is not None:
-        func(path + "/" + str(x.label), x)
+        func(f"{path}/{x.label}", x)
 
 
 def add_style(name: str, prompt: str, negative_prompt: str):
@@ -166,7 +166,7 @@ def process_interrogate(interrogation_function, mode, ii_input_dir, ii_output_di
             img = Image.open(image)
             filename = os.path.basename(image)
             left, _ = os.path.splitext(filename)
-            print(interrogation_function(img), file=open(os.path.join(ii_output_dir, left + ".txt"), 'a'))
+            print(interrogation_function(img), file=open(os.path.join(ii_output_dir, f"{left}.txt"), 'a'))
 
         return [gr.update(), None]
 
@@ -182,29 +182,29 @@ def interrogate_deepbooru(image):
 
 
 def create_seed_inputs(target_interface):
-    with FormRow(elem_id=target_interface + '_seed_row', variant="compact"):
-        seed = (gr.Textbox if cmd_opts.use_textbox_seed else gr.Number)(label='Seed', value=-1, elem_id=target_interface + '_seed')
+    with FormRow(elem_id=f"{target_interface}_seed_row", variant="compact"):
+        seed = (gr.Textbox if cmd_opts.use_textbox_seed else gr.Number)(label='Seed', value=-1, elem_id=f"{target_interface}_seed")
         seed.style(container=False)
-        random_seed = ToolButton(random_symbol, elem_id=target_interface + '_random_seed', label='Random seed')
-        reuse_seed = ToolButton(reuse_symbol, elem_id=target_interface + '_reuse_seed', label='Reuse seed')
+        random_seed = ToolButton(random_symbol, elem_id=f"{target_interface}_random_seed", label='Random seed')
+        reuse_seed = ToolButton(reuse_symbol, elem_id=f"{target_interface}_reuse_seed", label='Reuse seed')
 
-        seed_checkbox = gr.Checkbox(label='Extra', elem_id=target_interface + '_subseed_show', value=False)
+        seed_checkbox = gr.Checkbox(label='Extra', elem_id=f"{target_interface}_subseed_show", value=False)
 
     # Components to show/hide based on the 'Extra' checkbox
     seed_extras = []
 
-    with FormRow(visible=False, elem_id=target_interface + '_subseed_row') as seed_extra_row_1:
+    with FormRow(visible=False, elem_id=f"{target_interface}_subseed_row") as seed_extra_row_1:
         seed_extras.append(seed_extra_row_1)
-        subseed = gr.Number(label='Variation seed', value=-1, elem_id=target_interface + '_subseed')
+        subseed = gr.Number(label='Variation seed', value=-1, elem_id=f"{target_interface}_subseed")
         subseed.style(container=False)
-        random_subseed = ToolButton(random_symbol, elem_id=target_interface + '_random_subseed')
-        reuse_subseed = ToolButton(reuse_symbol, elem_id=target_interface + '_reuse_subseed')
-        subseed_strength = gr.Slider(label='Variation strength', value=0.0, minimum=0, maximum=1, step=0.01, elem_id=target_interface + '_subseed_strength')
+        random_subseed = ToolButton(random_symbol, elem_id=f"{target_interface}_random_subseed")
+        reuse_subseed = ToolButton(reuse_symbol, elem_id=f"{target_interface}_reuse_subseed")
+        subseed_strength = gr.Slider(label='Variation strength', value=0.0, minimum=0, maximum=1, step=0.01, elem_id=f"{target_interface}_subseed_strength")
 
     with FormRow(visible=False) as seed_extra_row_2:
         seed_extras.append(seed_extra_row_2)
-        seed_resize_from_w = gr.Slider(minimum=0, maximum=2048, step=8, label="Resize seed from width", value=0, elem_id=target_interface + '_seed_resize_from_w')
-        seed_resize_from_h = gr.Slider(minimum=0, maximum=2048, step=8, label="Resize seed from height", value=0, elem_id=target_interface + '_seed_resize_from_h')
+        seed_resize_from_w = gr.Slider(minimum=0, maximum=2048, step=8, label="Resize seed from width", value=0, elem_id=f"{target_interface}_seed_resize_from_w")
+        seed_resize_from_h = gr.Slider(minimum=0, maximum=2048, step=8, label="Resize seed from height", value=0, elem_id=f"{target_interface}_seed_resize_from_h")
 
     random_seed.click(fn=lambda: -1, show_progress=False, inputs=[], outputs=[seed])
     random_subseed.click(fn=lambda: -1, show_progress=False, inputs=[], outputs=[subseed])
@@ -765,7 +765,7 @@ def create_ui():
                     )
                     button.click(
                         fn=lambda: None,
-                        _js="switch_to_"+name.replace(" ", "_"),
+                        _js=f"switch_to_{name.replace(' ', '_')}",
                         inputs=[],
                         outputs=[],
                     )
@@ -1462,18 +1462,18 @@ def create_ui():
         elif t == bool:
             comp = gr.Checkbox
         else:
-            raise Exception(f'bad options item type: {str(t)} for key {key}')
+            raise Exception(f'bad options item type: {t} for key {key}')
 
-        elem_id = "setting_"+key
+        elem_id = f"setting_{key}"
 
         if info.refresh is not None:
             if is_quicksettings:
                 res = comp(label=info.label, value=fun(), elem_id=elem_id, **(args or {}))
-                create_refresh_button(res, info.refresh, info.component_args, "refresh_" + key)
+                create_refresh_button(res, info.refresh, info.component_args, f"refresh_{key}")
             else:
                 with FormRow():
                     res = comp(label=info.label, value=fun(), elem_id=elem_id, **(args or {}))
-                    create_refresh_button(res, info.refresh, info.component_args, "refresh_" + key)
+                    create_refresh_button(res, info.refresh, info.component_args, f"refresh_{key}")
         else:
             res = comp(label=info.label, value=fun(), elem_id=elem_id, **(args or {}))
 
@@ -1545,7 +1545,7 @@ def create_ui():
                         current_tab.__exit__()
 
                     gr.Group()
-                    current_tab = gr.TabItem(elem_id="settings_{}".format(elem_id), label=text)
+                    current_tab = gr.TabItem(elem_id=f"settings_{elem_id}", label=text)
                     current_tab.__enter__()
                     current_row = gr.Column(variant='compact')
                     current_row.__enter__()
@@ -1664,7 +1664,7 @@ def create_ui():
             for interface, label, ifid in interfaces:
                 if label in shared.opts.hidden_tabs:
                     continue
-                with gr.TabItem(label, id=ifid, elem_id='tab_' + ifid):
+                with gr.TabItem(label, id=ifid, elem_id=f"tab_{ifid}"):
                     interface.render()
 
         if os.path.exists(os.path.join(script_path, "notification.mp3")):
@@ -1771,10 +1771,10 @@ def create_ui():
 
     def loadsave(path, x):
         def apply_field(obj, field, condition=None, init_field=None):
-            key = path + "/" + field
+            key = f"{path}/{field}"
 
             if getattr(obj, 'custom_script_source', None) is not None:
-              key = 'customscript/' + obj.custom_script_source + '/' + key
+              key = f"customscript/{obj.custom_script_source}/{key}"
 
             if getattr(obj, 'do_not_save_to_config', False):
                 return

modules/ui_extensions.py

@@ -61,7 +61,8 @@ def save_config_state(name):
     if not name:
         name = "Config"
     current_config_state["name"] = name
-    filename = os.path.join(config_states_dir, datetime.now().strftime("%Y_%m_%d-%H_%M_%S") + "_" + name + ".json")
+    timestamp = datetime.now().strftime('%Y_%m_%d-%H_%M_%S')
+    filename = os.path.join(config_states_dir, f"{timestamp}_{name}.json")
     print(f"Saving backup of webui/extension state to {filename}.")
     with open(filename, "w", encoding="utf-8") as f:
         json.dump(current_config_state, f)

modules/ui_extra_networks.py

@@ -69,7 +69,9 @@ class ExtraNetworksPage:
         pass
 
     def link_preview(self, filename):
-        return "./sd_extra_networks/thumb?filename=" + urllib.parse.quote(filename.replace('\\', '/')) + "&mtime=" + str(os.path.getmtime(filename))
+        quoted_filename = urllib.parse.quote(filename.replace('\\', '/'))
+        mtime = os.path.getmtime(filename)
+        return f"./sd_extra_networks/thumb?filename={quoted_filename}&mtime={mtime}"
 
     def search_terms_from_path(self, filename, possible_directories=None):
         abspath = os.path.abspath(filename)

scripts/custom_code.py

@@ -77,7 +77,7 @@ return process_images(p)
         module.display = display
 
         indent = " " * indent_level
-        indented = code.replace('\n', '\n' + indent)
+        indented = code.replace('\n', f"\n{indent}")
         body = f"""def __webuitemp__():
 {indent}{indented}
 __webuitemp__()"""

scripts/loopback.py

@@ -84,7 +84,7 @@ class Script(scripts.Script):
                     p.color_corrections = initial_color_corrections
 
                 if append_interrogation != "None":
-                    p.prompt = original_prompt + ", " if original_prompt != "" else ""
+                    p.prompt = f"{original_prompt}, " if original_prompt else ""
                     if append_interrogation == "CLIP":
                         p.prompt += shared.interrogator.interrogate(p.init_images[0])
                     elif append_interrogation == "DeepBooru":

scripts/xyz_grid.py

@@ -439,7 +439,7 @@ class Script(scripts.Script):
         z_type.change(fn=select_axis, inputs=[z_type,z_values_dropdown], outputs=[fill_z_button,z_values,z_values_dropdown])
 
         def get_dropdown_update_from_params(axis,params):
-            val_key = axis + " Values"
+            val_key = f"{axis} Values"
             vals = params.get(val_key,"")
             valslist = [x.strip() for x in chain.from_iterable(csv.reader(StringIO(vals))) if x]
             return gr.update(value = valslist)