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https://github.com/AUTOMATIC1111/stable-diffusion-webui.git
synced 2024-12-29 19:05:05 +08:00
fix F541 f-string without any placeholders
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5927d3fa95
commit
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@ -26,7 +26,7 @@ class LDSR:
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global cached_ldsr_model
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if shared.opts.ldsr_cached and cached_ldsr_model is not None:
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print(f"Loading model from cache")
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print("Loading model from cache")
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model: torch.nn.Module = cached_ldsr_model
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else:
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print(f"Loading model from {self.modelPath}")
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@ -382,7 +382,7 @@ class VQAutoEncoder(nn.Module):
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self.load_state_dict(torch.load(model_path, map_location='cpu')['params'])
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logger.info(f'vqgan is loaded from: {model_path} [params]')
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else:
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raise ValueError(f'Wrong params!')
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raise ValueError('Wrong params!')
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def forward(self, x):
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@ -431,7 +431,7 @@ class VQGANDiscriminator(nn.Module):
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elif 'params' in chkpt:
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self.load_state_dict(torch.load(model_path, map_location='cpu')['params'])
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else:
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raise ValueError(f'Wrong params!')
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raise ValueError('Wrong params!')
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def forward(self, x):
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return self.main(x)
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@ -277,7 +277,7 @@ def load_hypernetwork(filename):
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print(traceback.format_exc(), file=sys.stderr)
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else:
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if shared.loaded_hypernetwork is not None:
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print(f"Unloading hypernetwork")
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print("Unloading hypernetwork")
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shared.loaded_hypernetwork = None
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@ -417,7 +417,7 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, gradient_step,
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initial_step = hypernetwork.step or 0
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if initial_step >= steps:
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shared.state.textinfo = f"Model has already been trained beyond specified max steps"
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shared.state.textinfo = "Model has already been trained beyond specified max steps"
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return hypernetwork, filename
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scheduler = LearnRateScheduler(learn_rate, steps, initial_step)
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@ -599,7 +599,7 @@ def read_info_from_image(image):
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Negative prompt: {json_info["uc"]}
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Steps: {json_info["steps"]}, Sampler: {sampler}, CFG scale: {json_info["scale"]}, Seed: {json_info["seed"]}, Size: {image.width}x{image.height}, Clip skip: 2, ENSD: 31337"""
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except Exception:
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print(f"Error parsing NovelAI image generation parameters:", file=sys.stderr)
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print("Error parsing NovelAI image generation parameters:", file=sys.stderr)
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print(traceback.format_exc(), file=sys.stderr)
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return geninfo, items
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@ -172,7 +172,7 @@ class InterrogateModels:
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res += ", " + match
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except Exception:
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print(f"Error interrogating", file=sys.stderr)
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print("Error interrogating", file=sys.stderr)
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print(traceback.format_exc(), file=sys.stderr)
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res += "<error>"
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@ -137,15 +137,15 @@ def load_with_extra(filename, extra_handler=None, *args, **kwargs):
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except pickle.UnpicklingError:
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print(f"Error verifying pickled file from {filename}:", file=sys.stderr)
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print(traceback.format_exc(), file=sys.stderr)
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print(f"-----> !!!! The file is most likely corrupted !!!! <-----", file=sys.stderr)
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print(f"You can skip this check with --disable-safe-unpickle commandline argument, but that is not going to help you.\n\n", file=sys.stderr)
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print("-----> !!!! The file is most likely corrupted !!!! <-----", file=sys.stderr)
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print("You can skip this check with --disable-safe-unpickle commandline argument, but that is not going to help you.\n\n", file=sys.stderr)
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return None
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except Exception:
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print(f"Error verifying pickled file from {filename}:", file=sys.stderr)
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print(traceback.format_exc(), file=sys.stderr)
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print(f"\nThe file may be malicious, so the program is not going to read it.", file=sys.stderr)
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print(f"You can skip this check with --disable-safe-unpickle commandline argument.\n\n", file=sys.stderr)
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print("\nThe file may be malicious, so the program is not going to read it.", file=sys.stderr)
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print("You can skip this check with --disable-safe-unpickle commandline argument.\n\n", file=sys.stderr)
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return None
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return unsafe_torch_load(filename, *args, **kwargs)
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@ -117,13 +117,13 @@ def select_checkpoint():
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return checkpoint_info
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if len(checkpoints_list) == 0:
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print(f"No checkpoints found. When searching for checkpoints, looked at:", file=sys.stderr)
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print("No checkpoints found. When searching for checkpoints, looked at:", file=sys.stderr)
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if shared.cmd_opts.ckpt is not None:
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print(f" - file {os.path.abspath(shared.cmd_opts.ckpt)}", file=sys.stderr)
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print(f" - directory {model_path}", file=sys.stderr)
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if shared.cmd_opts.ckpt_dir is not None:
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print(f" - directory {os.path.abspath(shared.cmd_opts.ckpt_dir)}", file=sys.stderr)
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print(f"Can't run without a checkpoint. Find and place a .ckpt file into any of those locations. The program will exit.", file=sys.stderr)
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print("Can't run without a checkpoint. Find and place a .ckpt file into any of those locations. The program will exit.", file=sys.stderr)
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exit(1)
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checkpoint_info = next(iter(checkpoints_list.values()))
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@ -324,7 +324,7 @@ def load_model(checkpoint_info=None):
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script_callbacks.model_loaded_callback(sd_model)
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print(f"Model loaded.")
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print("Model loaded.")
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return sd_model
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@ -359,5 +359,5 @@ def reload_model_weights(sd_model=None, info=None):
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if not shared.cmd_opts.lowvram and not shared.cmd_opts.medvram:
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sd_model.to(devices.device)
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print(f"Weights loaded.")
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print("Weights loaded.")
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return sd_model
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@ -208,5 +208,5 @@ def reload_vae_weights(sd_model=None, vae_file="auto"):
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if not shared.cmd_opts.lowvram and not shared.cmd_opts.medvram:
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sd_model.to(devices.device)
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print(f"VAE Weights loaded.")
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print("VAE Weights loaded.")
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return sd_model
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@ -263,7 +263,7 @@ def train_embedding(embedding_name, learn_rate, batch_size, gradient_step, data_
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initial_step = embedding.step or 0
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if initial_step >= steps:
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shared.state.textinfo = f"Model has already been trained beyond specified max steps"
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shared.state.textinfo = "Model has already been trained beyond specified max steps"
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return embedding, filename
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scheduler = LearnRateScheduler(learn_rate, steps, initial_step)
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@ -140,7 +140,7 @@ class Script(scripts.Script):
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try:
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args = cmdargs(line)
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except Exception:
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print(f"Error parsing line [line] as commandline:", file=sys.stderr)
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print(f"Error parsing line {line} as commandline:", file=sys.stderr)
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print(traceback.format_exc(), file=sys.stderr)
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args = {"prompt": line}
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else:
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