From ab27c111d06ec920791c73eea25ad9a61671852e Mon Sep 17 00:00:00 2001 From: Muhammad Rizqi Nur Date: Sat, 29 Oct 2022 18:09:17 +0700 Subject: Add input validations before loading dataset for training --- modules/hypernetworks/hypernetwork.py | 38 ++++++++++++++++++++--------------- 1 file changed, 22 insertions(+), 16 deletions(-) (limited to 'modules/hypernetworks') diff --git a/modules/hypernetworks/hypernetwork.py b/modules/hypernetworks/hypernetwork.py index 2e84583b..38f35c58 100644 --- a/modules/hypernetworks/hypernetwork.py +++ b/modules/hypernetworks/hypernetwork.py @@ -332,7 +332,9 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log # images allows training previews to have infotext. Importing it at the top causes a circular import problem. from modules import images - assert hypernetwork_name, 'hypernetwork not selected' + save_hypernetwork_every = save_hypernetwork_every or 0 + create_image_every = create_image_every or 0 + textual_inversion.validate_train_inputs(hypernetwork_name, learn_rate, batch_size, data_root, template_file, steps, save_hypernetwork_every, create_image_every, log_directory, name="hypernetwork") path = shared.hypernetworks.get(hypernetwork_name, None) shared.loaded_hypernetwork = Hypernetwork() @@ -358,39 +360,43 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log else: images_dir = None + hypernetwork = shared.loaded_hypernetwork + + ititial_step = hypernetwork.step or 0 + if ititial_step > steps: + shared.state.textinfo = f"Model has already been trained beyond specified max steps" + return hypernetwork, filename + + scheduler = LearnRateScheduler(learn_rate, steps, ititial_step) + + # dataset loading may take a while, so input validations and early returns should be done before this shared.state.textinfo = f"Preparing dataset from {html.escape(data_root)}..." with torch.autocast("cuda"): 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, device=devices.device, template_file=template_file, include_cond=True, batch_size=batch_size) + if unload: shared.sd_model.cond_stage_model.to(devices.cpu) shared.sd_model.first_stage_model.to(devices.cpu) - hypernetwork = shared.loaded_hypernetwork - weights = hypernetwork.weights() - for weight in weights: - weight.requires_grad = True - size = len(ds.indexes) loss_dict = defaultdict(lambda : deque(maxlen = 1024)) losses = torch.zeros((size,)) previous_mean_losses = [0] previous_mean_loss = 0 print("Mean loss of {} elements".format(size)) - - last_saved_file = "" - last_saved_image = "" - forced_filename = "" - - ititial_step = hypernetwork.step or 0 - if ititial_step > steps: - return hypernetwork, filename - - scheduler = LearnRateScheduler(learn_rate, steps, ititial_step) + + weights = hypernetwork.weights() + for weight in weights: + weight.requires_grad = True # if optimizer == "AdamW": or else Adam / AdamW / SGD, etc... optimizer = torch.optim.AdamW(weights, lr=scheduler.learn_rate) steps_without_grad = 0 + last_saved_file = "" + last_saved_image = "" + forced_filename = "" + pbar = tqdm.tqdm(enumerate(ds), total=steps - ititial_step) for i, entries in pbar: hypernetwork.step = i + ititial_step -- cgit v1.2.3