textual_inversion/logging.py: clean up duplicate key from sets (and sort them) (Ruff B033)
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@ -2,11 +2,51 @@ import datetime
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import json
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import json
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import os
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import os
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saved_params_shared = {"model_name", "model_hash", "initial_step", "num_of_dataset_images", "learn_rate", "batch_size", "clip_grad_mode", "clip_grad_value", "gradient_step", "data_root", "log_directory", "training_width", "training_height", "steps", "create_image_every", "template_file", "gradient_step", "latent_sampling_method"}
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saved_params_shared = {
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saved_params_ti = {"embedding_name", "num_vectors_per_token", "save_embedding_every", "save_image_with_stored_embedding"}
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"batch_size",
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saved_params_hypernet = {"hypernetwork_name", "layer_structure", "activation_func", "weight_init", "add_layer_norm", "use_dropout", "save_hypernetwork_every"}
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"clip_grad_mode",
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"clip_grad_value",
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"create_image_every",
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"data_root",
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"gradient_step",
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"initial_step",
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"latent_sampling_method",
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"learn_rate",
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"log_directory",
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"model_hash",
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"model_name",
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"num_of_dataset_images",
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"steps",
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"template_file",
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"training_height",
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"training_width",
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}
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saved_params_ti = {
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"embedding_name",
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"num_vectors_per_token",
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"save_embedding_every",
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"save_image_with_stored_embedding",
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}
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saved_params_hypernet = {
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"activation_func",
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"add_layer_norm",
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"hypernetwork_name",
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"layer_structure",
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"save_hypernetwork_every",
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"use_dropout",
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"weight_init",
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}
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saved_params_all = saved_params_shared | saved_params_ti | saved_params_hypernet
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saved_params_all = saved_params_shared | saved_params_ti | saved_params_hypernet
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saved_params_previews = {"preview_prompt", "preview_negative_prompt", "preview_steps", "preview_sampler_index", "preview_cfg_scale", "preview_seed", "preview_width", "preview_height"}
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saved_params_previews = {
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"preview_cfg_scale",
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"preview_height",
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"preview_negative_prompt",
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"preview_prompt",
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"preview_sampler_index",
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"preview_seed",
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"preview_steps",
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"preview_width",
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}
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def save_settings_to_file(log_directory, all_params):
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def save_settings_to_file(log_directory, all_params):
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