2023-01-05 22:52:06 -07:00
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import datetime
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import json
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import os
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2023-06-13 04:07:39 -06:00
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saved_params_shared = {
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"batch_size",
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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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2023-01-05 22:52:06 -07:00
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saved_params_all = saved_params_shared | saved_params_ti | saved_params_hypernet
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2023-06-13 04:07:39 -06:00
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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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2023-01-05 22:52:06 -07:00
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def save_settings_to_file(log_directory, all_params):
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now = datetime.datetime.now()
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params = {"datetime": now.strftime("%Y-%m-%d %H:%M:%S")}
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keys = saved_params_all
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if all_params.get('preview_from_txt2img'):
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keys = keys | saved_params_previews
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params.update({k: v for k, v in all_params.items() if k in keys})
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filename = f'settings-{now.strftime("%Y-%m-%d-%H-%M-%S")}.json'
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with open(os.path.join(log_directory, filename), "w") as file:
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json.dump(params, file, indent=4)
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