From edb10092de516dda5271130ed53628387780a859 Mon Sep 17 00:00:00 2001 From: Shondoit Date: Thu, 12 Jan 2023 16:29:00 +0100 Subject: Add ability to choose using weighted loss or not --- modules/hypernetworks/hypernetwork.py | 13 +++++++++---- 1 file changed, 9 insertions(+), 4 deletions(-) (limited to 'modules/hypernetworks') diff --git a/modules/hypernetworks/hypernetwork.py b/modules/hypernetworks/hypernetwork.py index 9c79b7d0..f4fb69e0 100644 --- a/modules/hypernetworks/hypernetwork.py +++ b/modules/hypernetworks/hypernetwork.py @@ -496,7 +496,7 @@ def create_hypernetwork(name, enable_sizes, overwrite_old, layer_structure=None, shared.reload_hypernetworks() -def train_hypernetwork(id_task, hypernetwork_name, learn_rate, batch_size, gradient_step, data_root, log_directory, training_width, training_height, varsize, steps, clip_grad_mode, clip_grad_value, shuffle_tags, tag_drop_out, latent_sampling_method, create_image_every, save_hypernetwork_every, template_filename, preview_from_txt2img, preview_prompt, preview_negative_prompt, preview_steps, preview_sampler_index, preview_cfg_scale, preview_seed, preview_width, preview_height): +def train_hypernetwork(id_task, hypernetwork_name, learn_rate, batch_size, gradient_step, data_root, log_directory, training_width, training_height, varsize, steps, clip_grad_mode, clip_grad_value, shuffle_tags, tag_drop_out, latent_sampling_method, use_weight, create_image_every, save_hypernetwork_every, template_filename, 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. from modules import images @@ -554,7 +554,7 @@ def train_hypernetwork(id_task, hypernetwork_name, learn_rate, batch_size, gradi 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, varsize=varsize) + 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, varsize=varsize, use_weight=use_weight) if shared.opts.save_training_settings_to_txt: saved_params = dict( @@ -640,14 +640,19 @@ def train_hypernetwork(id_task, hypernetwork_name, learn_rate, batch_size, gradi with devices.autocast(): x = batch.latent_sample.to(devices.device, non_blocking=pin_memory) - w = batch.weight.to(devices.device, non_blocking=pin_memory) + if use_weight: + w = batch.weight.to(devices.device, non_blocking=pin_memory) if tag_drop_out != 0 or shuffle_tags: shared.sd_model.cond_stage_model.to(devices.device) c = shared.sd_model.cond_stage_model(batch.cond_text).to(devices.device, non_blocking=pin_memory) shared.sd_model.cond_stage_model.to(devices.cpu) else: c = stack_conds(batch.cond).to(devices.device, non_blocking=pin_memory) - loss = shared.sd_model.weighted_forward(x, c, w)[0] / gradient_step + if use_weight: + loss = shared.sd_model.weighted_forward(x, c, w)[0] / gradient_step + del w + else: + loss = shared.sd_model.forward(x, c)[0] / gradient_step del x del c -- cgit v1.2.3