| 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) | |