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- 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)
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