From b85c2b5cf4a6809bc871718cf4680d49c3e95e94 Mon Sep 17 00:00:00 2001 From: timntorres Date: Thu, 5 Jan 2023 08:14:38 -0800 Subject: Clean up ti, add same behavior to hypernetwork. --- modules/hypernetworks/hypernetwork.py | 31 ++++++++++++++++++++++++++++++- 1 file changed, 30 insertions(+), 1 deletion(-) (limited to 'modules/hypernetworks') diff --git a/modules/hypernetworks/hypernetwork.py b/modules/hypernetworks/hypernetwork.py index 6a9b1398..d5985263 100644 --- a/modules/hypernetworks/hypernetwork.py +++ b/modules/hypernetworks/hypernetwork.py @@ -401,7 +401,33 @@ def create_hypernetwork(name, enable_sizes, overwrite_old, layer_structure=None, hypernet.save(fn) shared.reload_hypernetworks() +# Note: textual_inversion.py has a nearly identical function of the same name. +def save_settings_to_file(initial_step, num_of_dataset_images, hypernetwork_name, layer_structure, activation_func, weight_init, add_layer_norm, use_dropout, learn_rate, batch_size, data_root, log_directory, training_width, training_height, steps, create_image_every, save_hypernetwork_every, template_file, preview_from_txt2img, preview_prompt, preview_negative_prompt, preview_steps, preview_sampler_index, preview_cfg_scale, preview_seed, preview_width, preview_height): + checkpoint = sd_models.select_checkpoint() + model_name = checkpoint.model_name + model_hash = '[{}]'.format(checkpoint.hash) + # Starting index of preview-related arguments. + border_index = 19 + + # Get a list of the argument names, excluding default argument. + sig = inspect.signature(save_settings_to_file) + arg_names = [p.name for p in sig.parameters.values() if p.default == p.empty] + + # Create a list of the argument names to include in the settings string. + names = arg_names[:border_index] # Include all arguments up until the preview-related ones. + + # Include preview-related arguments if applicable. + if preview_from_txt2img: + names.extend(arg_names[border_index:]) + + # Build the settings string. + settings_str = "datetime : " + datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S") + "\n" + for name in names: + value = locals()[name] + settings_str += f"{name}: {value}\n" + with open(os.path.join(log_directory, 'settings.txt'), "a+") as fout: + fout.write(settings_str + "\n\n") def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, gradient_step, data_root, log_directory, training_width, training_height, steps, clip_grad_mode, clip_grad_value, shuffle_tags, tag_drop_out, latent_sampling_method, create_image_every, save_hypernetwork_every, template_file, preview_from_txt2img, preview_prompt, preview_negative_prompt, preview_steps, preview_sampler_index, preview_cfg_scale, preview_seed, preview_width, preview_height): # images allows training previews to have infotext. Importing it at the top causes a circular import problem. @@ -457,7 +483,10 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, gradient_step, pin_memory = shared.opts.pin_memory ds = modules.textual_inversion.dataset.PersonalizedBase(data_root=data_root, width=training_width, height=training_height, repeats=shared.opts.training_image_repeats_per_epoch, placeholder_token=hypernetwork_name, model=shared.sd_model, cond_model=shared.sd_model.cond_stage_model, device=devices.device, template_file=template_file, include_cond=True, batch_size=batch_size, gradient_step=gradient_step, shuffle_tags=shuffle_tags, tag_drop_out=tag_drop_out, latent_sampling_method=latent_sampling_method) - + + if shared.opts.save_training_settings_to_txt: + save_settings_to_file(initial_step, len(ds), hypernetwork_name, hypernetwork.layer_structure, hypernetwork.activation_func, hypernetwork.weight_init, hypernetwork.add_layer_norm, hypernetwork.use_dropout, learn_rate, batch_size, data_root, log_directory, training_width, training_height, steps, create_image_every, save_hypernetwork_every, template_file, preview_from_txt2img, preview_prompt, preview_negative_prompt, preview_steps, preview_sampler_index, preview_cfg_scale, preview_seed, preview_width, preview_height) + latent_sampling_method = ds.latent_sampling_method dl = modules.textual_inversion.dataset.PersonalizedDataLoader(ds, latent_sampling_method=latent_sampling_method, batch_size=ds.batch_size, pin_memory=pin_memory) -- cgit v1.2.3