import datetime import json import os saved_params_shared = { "batch_size", "clip_grad_mode", "clip_grad_value", "create_image_every", "data_root", "gradient_step", "initial_step", "latent_sampling_method", "learn_rate", "log_directory", "model_hash", "model_name", "num_of_dataset_images", "steps", "template_file", "training_height", "training_width", } saved_params_ti = { "embedding_name", "num_vectors_per_token", "save_embedding_every", "save_image_with_stored_embedding", } saved_params_hypernet = { "activation_func", "add_layer_norm", "hypernetwork_name", "layer_structure", "save_hypernetwork_every", "use_dropout", "weight_init", } saved_params_all = saved_params_shared | saved_params_ti | saved_params_hypernet saved_params_previews = { "preview_cfg_scale", "preview_height", "preview_negative_prompt", "preview_prompt", "preview_sampler_index", "preview_seed", "preview_steps", "preview_width", } def save_settings_to_file(log_directory, all_params): now = datetime.datetime.now() params = {"datetime": now.strftime("%Y-%m-%d %H:%M:%S")} keys = saved_params_all if all_params.get('preview_from_txt2img'): keys = keys | saved_params_previews params.update({k: v for k, v in all_params.items() if k in keys}) filename = f'settings-{now.strftime("%Y-%m-%d-%H-%M-%S")}.json' with open(os.path.join(log_directory, filename), "w") as file: json.dump(params, file, indent=4)