Fix up string formatting/concatenation to f-strings where feasible
This commit is contained in:
Aarni Koskela
parent
8fb16ceb28
commit
3ba6c3c83c
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@ -570,20 +570,20 @@ class Api:
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filename = create_embedding(**args) # create empty embedding
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sd_hijack.model_hijack.embedding_db.load_textual_inversion_embeddings() # reload embeddings so new one can be immediately used
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shared.state.end()
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return CreateResponse(info = "create embedding filename: {filename}".format(filename = filename))
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return CreateResponse(info=f"create embedding filename: {filename}")
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except AssertionError as e:
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shared.state.end()
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return TrainResponse(info = "create embedding error: {error}".format(error = e))
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return TrainResponse(info=f"create embedding error: {e}")
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def create_hypernetwork(self, args: dict):
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try:
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shared.state.begin()
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filename = create_hypernetwork(**args) # create empty embedding
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shared.state.end()
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return CreateResponse(info = "create hypernetwork filename: {filename}".format(filename = filename))
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return CreateResponse(info=f"create hypernetwork filename: {filename}")
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except AssertionError as e:
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shared.state.end()
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return TrainResponse(info = "create hypernetwork error: {error}".format(error = e))
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return TrainResponse(info=f"create hypernetwork error: {e}")
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def preprocess(self, args: dict):
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try:
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@ -593,13 +593,13 @@ class Api:
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return PreprocessResponse(info = 'preprocess complete')
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except KeyError as e:
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shared.state.end()
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return PreprocessResponse(info = "preprocess error: invalid token: {error}".format(error = e))
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return PreprocessResponse(info=f"preprocess error: invalid token: {e}")
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except AssertionError as e:
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shared.state.end()
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return PreprocessResponse(info = "preprocess error: {error}".format(error = e))
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return PreprocessResponse(info=f"preprocess error: {e}")
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except FileNotFoundError as e:
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shared.state.end()
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return PreprocessResponse(info = 'preprocess error: {error}'.format(error = e))
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return PreprocessResponse(info=f'preprocess error: {e}')
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def train_embedding(self, args: dict):
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try:
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@ -617,10 +617,10 @@ class Api:
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if not apply_optimizations:
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sd_hijack.apply_optimizations()
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shared.state.end()
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return TrainResponse(info = "train embedding complete: filename: {filename} error: {error}".format(filename = filename, error = error))
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return TrainResponse(info=f"train embedding complete: filename: {filename} error: {error}")
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except AssertionError as msg:
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shared.state.end()
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return TrainResponse(info = "train embedding error: {msg}".format(msg = msg))
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return TrainResponse(info=f"train embedding error: {msg}")
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def train_hypernetwork(self, args: dict):
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try:
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@ -641,10 +641,10 @@ class Api:
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if not apply_optimizations:
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sd_hijack.apply_optimizations()
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shared.state.end()
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return TrainResponse(info="train embedding complete: filename: {filename} error: {error}".format(filename=filename, error=error))
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return TrainResponse(info=f"train embedding complete: filename: {filename} error: {error}")
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except AssertionError as msg:
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shared.state.end()
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return TrainResponse(info="train embedding error: {error}".format(error=error))
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return TrainResponse(info=f"train embedding error: {error}")
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def get_memory(self):
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try:
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@ -60,7 +60,7 @@ def wrap_gradio_call(func, extra_outputs=None, add_stats=False):
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max_debug_str_len = 131072 # (1024*1024)/8
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print("Error completing request", file=sys.stderr)
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argStr = f"Arguments: {str(args)} {str(kwargs)}"
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argStr = f"Arguments: {args} {kwargs}"
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print(argStr[:max_debug_str_len], file=sys.stderr)
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if len(argStr) > max_debug_str_len:
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print(f"(Argument list truncated at {max_debug_str_len}/{len(argStr)} characters)", file=sys.stderr)
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@ -73,7 +73,8 @@ def wrap_gradio_call(func, extra_outputs=None, add_stats=False):
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if extra_outputs_array is None:
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extra_outputs_array = [None, '']
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res = extra_outputs_array + [f"<div class='error'>{html.escape(type(e).__name__+': '+str(e))}</div>"]
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error_message = f'{type(e).__name__}: {e}'
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res = extra_outputs_array + [f"<div class='error'>{html.escape(error_message)}</div>"]
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shared.state.skipped = False
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shared.state.interrupted = False
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@ -156,13 +156,16 @@ class UpscalerESRGAN(Upscaler):
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def load_model(self, path: str):
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if "http" in path:
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filename = load_file_from_url(url=self.model_url, model_dir=self.model_path,
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file_name="%s.pth" % self.model_name,
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progress=True)
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filename = load_file_from_url(
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url=self.model_url,
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model_dir=self.model_path,
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file_name=f"{self.model_name}.pth",
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progress=True,
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)
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else:
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filename = path
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if not os.path.exists(filename) or filename is None:
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print("Unable to load %s from %s" % (self.model_path, filename))
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print(f"Unable to load {self.model_path} from {filename}")
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return None
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state_dict = torch.load(filename, map_location='cpu' if devices.device_esrgan.type == 'mps' else None)
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@ -38,7 +38,7 @@ class RRDBNet(nn.Module):
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elif upsample_mode == 'pixelshuffle':
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upsample_block = pixelshuffle_block
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else:
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raise NotImplementedError('upsample mode [{:s}] is not found'.format(upsample_mode))
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raise NotImplementedError(f'upsample mode [{upsample_mode}] is not found')
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if upscale == 3:
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upsampler = upsample_block(nf, nf, 3, act_type=act_type, convtype=convtype)
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else:
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@ -261,10 +261,10 @@ class Upsample(nn.Module):
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def extra_repr(self):
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if self.scale_factor is not None:
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info = 'scale_factor=' + str(self.scale_factor)
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info = f'scale_factor={self.scale_factor}'
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else:
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info = 'size=' + str(self.size)
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info += ', mode=' + self.mode
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info = f'size={self.size}'
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info += f', mode={self.mode}'
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return info
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@ -350,7 +350,7 @@ def act(act_type, inplace=True, neg_slope=0.2, n_prelu=1, beta=1.0):
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elif act_type == 'sigmoid': # [0, 1] range output
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layer = nn.Sigmoid()
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else:
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raise NotImplementedError('activation layer [{:s}] is not found'.format(act_type))
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raise NotImplementedError(f'activation layer [{act_type}] is not found')
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return layer
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@ -372,7 +372,7 @@ def norm(norm_type, nc):
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elif norm_type == 'none':
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def norm_layer(x): return Identity()
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else:
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raise NotImplementedError('normalization layer [{:s}] is not found'.format(norm_type))
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raise NotImplementedError(f'normalization layer [{norm_type}] is not found')
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return layer
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@ -388,7 +388,7 @@ def pad(pad_type, padding):
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elif pad_type == 'zero':
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layer = nn.ZeroPad2d(padding)
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else:
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raise NotImplementedError('padding layer [{:s}] is not implemented'.format(pad_type))
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raise NotImplementedError(f'padding layer [{pad_type}] is not implemented')
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return layer
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@ -432,7 +432,7 @@ def conv_block(in_nc, out_nc, kernel_size, stride=1, dilation=1, groups=1, bias=
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pad_type='zero', norm_type=None, act_type='relu', mode='CNA', convtype='Conv2D',
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spectral_norm=False):
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""" Conv layer with padding, normalization, activation """
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assert mode in ['CNA', 'NAC', 'CNAC'], 'Wrong conv mode [{:s}]'.format(mode)
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assert mode in ['CNA', 'NAC', 'CNAC'], f'Wrong conv mode [{mode}]'
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padding = get_valid_padding(kernel_size, dilation)
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p = pad(pad_type, padding) if pad_type and pad_type != 'zero' else None
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padding = padding if pad_type == 'zero' else 0
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@ -10,7 +10,8 @@ class ExtraNetworkHypernet(extra_networks.ExtraNetwork):
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additional = shared.opts.sd_hypernetwork
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if additional != "None" and additional in shared.hypernetworks and len([x for x in params_list if x.items[0] == additional]) == 0:
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p.all_prompts = [x + f"<hypernet:{additional}:{shared.opts.extra_networks_default_multiplier}>" for x in p.all_prompts]
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hypernet_prompt_text = f"<hypernet:{additional}:{shared.opts.extra_networks_default_multiplier}>"
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p.all_prompts = [f"{prompt}{hypernet_prompt_text}" for prompt in p.all_prompts]
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params_list.append(extra_networks.ExtraNetworkParams(items=[additional, shared.opts.extra_networks_default_multiplier]))
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names = []
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@ -269,8 +269,8 @@ Steps: 20, Sampler: Euler a, CFG scale: 7, Seed: 965400086, Size: 512x512, Model
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v = v[1:-1] if v[0] == '"' and v[-1] == '"' else v
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m = re_imagesize.match(v)
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if m is not None:
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res[k+"-1"] = m.group(1)
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res[k+"-2"] = m.group(2)
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res[f"{k}-1"] = m.group(1)
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res[f"{k}-2"] = m.group(2)
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else:
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res[k] = v
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@ -13,7 +13,7 @@ cache_data = None
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def dump_cache():
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with filelock.FileLock(cache_filename+".lock"):
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with filelock.FileLock(f"{cache_filename}.lock"):
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with open(cache_filename, "w", encoding="utf8") as file:
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json.dump(cache_data, file, indent=4)
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@ -22,7 +22,7 @@ def cache(subsection):
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global cache_data
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if cache_data is None:
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with filelock.FileLock(cache_filename+".lock"):
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with filelock.FileLock(f"{cache_filename}.lock"):
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if not os.path.isfile(cache_filename):
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cache_data = {}
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else:
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@ -467,7 +467,7 @@ def get_next_sequence_number(path, basename):
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"""
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result = -1
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if basename != '':
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basename = basename + "-"
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basename = f"{basename}-"
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prefix_length = len(basename)
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for p in os.listdir(path):
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@ -536,7 +536,7 @@ def save_image(image, path, basename, seed=None, prompt=None, extension='png', i
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add_number = opts.save_images_add_number or file_decoration == ''
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if file_decoration != "" and add_number:
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file_decoration = "-" + file_decoration
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file_decoration = f"-{file_decoration}"
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file_decoration = namegen.apply(file_decoration) + suffix
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@ -566,7 +566,7 @@ def save_image(image, path, basename, seed=None, prompt=None, extension='png', i
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def _atomically_save_image(image_to_save, filename_without_extension, extension):
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# save image with .tmp extension to avoid race condition when another process detects new image in the directory
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temp_file_path = filename_without_extension + ".tmp"
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temp_file_path = f"{filename_without_extension}.tmp"
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image_format = Image.registered_extensions()[extension]
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if extension.lower() == '.png':
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@ -626,7 +626,7 @@ def save_image(image, path, basename, seed=None, prompt=None, extension='png', i
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if opts.save_txt and info is not None:
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txt_fullfn = f"{fullfn_without_extension}.txt"
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with open(txt_fullfn, "w", encoding="utf8") as file:
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file.write(info + "\n")
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file.write(f"{info}\n")
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else:
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txt_fullfn = None
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@ -28,7 +28,7 @@ def category_types():
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def download_default_clip_interrogate_categories(content_dir):
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print("Downloading CLIP categories...")
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tmpdir = content_dir + "_tmp"
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tmpdir = f"{content_dir}_tmp"
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category_types = ["artists", "flavors", "mediums", "movements"]
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try:
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@ -214,7 +214,7 @@ class InterrogateModels:
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if shared.opts.interrogate_return_ranks:
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res += f", ({match}:{score/100:.3f})"
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else:
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res += ", " + match
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res += f", {match}"
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except Exception:
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print("Error interrogating", file=sys.stderr)
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@ -223,7 +223,7 @@ class DDPM(pl.LightningModule):
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for k in keys:
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for ik in ignore_keys:
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if k.startswith(ik):
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print("Deleting key {} from state_dict.".format(k))
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print(f"Deleting key {k} from state_dict.")
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del sd[k]
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missing, unexpected = self.load_state_dict(sd, strict=False) if not only_model else self.model.load_state_dict(
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sd, strict=False)
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@ -386,7 +386,7 @@ class DDPM(pl.LightningModule):
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_, loss_dict_no_ema = self.shared_step(batch)
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with self.ema_scope():
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_, loss_dict_ema = self.shared_step(batch)
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loss_dict_ema = {key + '_ema': loss_dict_ema[key] for key in loss_dict_ema}
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loss_dict_ema = {f"{key}_ema": loss_dict_ema[key] for key in loss_dict_ema}
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self.log_dict(loss_dict_no_ema, prog_bar=False, logger=True, on_step=False, on_epoch=True)
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self.log_dict(loss_dict_ema, prog_bar=False, logger=True, on_step=False, on_epoch=True)
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@ -94,7 +94,7 @@ class NoiseScheduleVP:
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"""
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if schedule not in ['discrete', 'linear', 'cosine']:
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raise ValueError("Unsupported noise schedule {}. The schedule needs to be 'discrete' or 'linear' or 'cosine'".format(schedule))
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raise ValueError(f"Unsupported noise schedule {schedule}. The schedule needs to be 'discrete' or 'linear' or 'cosine'")
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self.schedule = schedule
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if schedule == 'discrete':
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@ -469,7 +469,7 @@ class UniPC:
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t = torch.linspace(t_T**(1. / t_order), t_0**(1. / t_order), N + 1).pow(t_order).to(device)
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return t
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else:
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raise ValueError("Unsupported skip_type {}, need to be 'logSNR' or 'time_uniform' or 'time_quadratic'".format(skip_type))
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raise ValueError(f"Unsupported skip_type {skip_type}, need to be 'logSNR' or 'time_uniform' or 'time_quadratic'")
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def get_orders_and_timesteps_for_singlestep_solver(self, steps, order, skip_type, t_T, t_0, device):
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"""
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@ -7,8 +7,8 @@ def connect(token, port, region):
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else:
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if ':' in token:
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# token = authtoken:username:password
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account = token.split(':')[1] + ':' + token.split(':')[-1]
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token = token.split(':')[0]
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token, username, password = token.split(':', 2)
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account = f"{username}:{password}"
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config = conf.PyngrokConfig(
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auth_token=token, region=region
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@ -16,7 +16,7 @@ for possible_sd_path in possible_sd_paths:
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sd_path = os.path.abspath(possible_sd_path)
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break
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assert sd_path is not None, "Couldn't find Stable Diffusion in any of: " + str(possible_sd_paths)
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assert sd_path is not None, f"Couldn't find Stable Diffusion in any of: {possible_sd_paths}"
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path_dirs = [
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(sd_path, 'ldm', 'Stable Diffusion', []),
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@ -500,7 +500,7 @@ def create_infotext(p, all_prompts, all_seeds, all_subseeds, comments=None, iter
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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])
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negative_prompt_text = "\nNegative prompt: " + p.all_negative_prompts[index] if p.all_negative_prompts[index] else ""
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negative_prompt_text = f"\nNegative prompt: {p.all_negative_prompts[index]}" if p.all_negative_prompts[index] else ""
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return f"{all_prompts[index]}{negative_prompt_text}\n{generation_params_text}".strip()
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@ -780,7 +780,16 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
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devices.torch_gc()
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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)
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res = Processed(
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p,
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images_list=output_images,
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seed=p.all_seeds[0],
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info=infotext(),
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comments="".join(f"\n\n{comment}" for comment in comments),
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subseed=p.all_subseeds[0],
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index_of_first_image=index_of_first_image,
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infotexts=infotexts,
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)
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if p.scripts is not None:
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p.scripts.postprocess(p, res)
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@ -96,7 +96,8 @@ def progressapi(req: ProgressRequest):
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if image is not None:
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buffered = io.BytesIO()
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image.save(buffered, format="png")
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live_preview = 'data:image/png;base64,' + base64.b64encode(buffered.getvalue()).decode("ascii")
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base64_image = base64.b64encode(buffered.getvalue()).decode('ascii')
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live_preview = f"data:image/png;base64,{base64_image}"
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id_live_preview = shared.state.id_live_preview
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else:
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live_preview = None
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@ -28,9 +28,9 @@ class UpscalerRealESRGAN(Upscaler):
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for scaler in scalers:
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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(
|
||||
|
|
|
|||
|
|
@ -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')
|
||||
|
|
|
|||
|
|
@ -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)
|
||||
|
|
|
|||
|
|
@ -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:
|
||||
|
|
|
|||
|
|
@ -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
|
||||
|
||||
|
|
|
|||
|
|
@ -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
|
||||
|
||||
|
|
|
|||
|
|
@ -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:
|
||||
|
|
|
|||
|
|
@ -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
|
||||
|
||||
|
|
|
|||
|
|
@ -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:
|
||||
|
|
|
|||
|
|
@ -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:
|
||||
|
|
|
|||
|
|
@ -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):
|
||||
|
|
|
|||
|
|
@ -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()
|
||||
|
|
|
|||
|
|
@ -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)
|
||||
|
|
|
|||
|
|
@ -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
|
||||
|
|
|
|||
|
|
@ -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)
|
||||
|
|
|
|||
|
|
@ -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)
|
||||
|
|
|
|||
|
|
@ -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__()"""
|
||||
|
|
|
|||
|
|
@ -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":
|
||||
|
|
|
|||
|
|
@ -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)
|
||||
|
|
|
|||
Loading…
Reference in New Issue