| 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) |
|
|
