diff --git a/modules/hypernetworks/hypernetwork.py b/modules/hypernetworks/hypernetwork.py index 194679e8..83cbb4f0 100644 --- a/modules/hypernetworks/hypernetwork.py +++ b/modules/hypernetworks/hypernetwork.py @@ -24,7 +24,6 @@ from statistics import stdev, mean optimizer_dict = {optim_name : cls_obj for optim_name, cls_obj in inspect.getmembers(torch.optim, inspect.isclass) if optim_name != "Optimizer"} - class HypernetworkModule(torch.nn.Module): multiplier = 1.0 activation_dict = { @@ -498,6 +497,9 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, gradient_step, if clip_grad: clip_grad_sched = LearnRateScheduler(clip_grad_value, steps, initial_step, verbose=False) + if shared.opts.training_enable_tensorboard: + tensorboard_writer = textual_inversion.tensorboard_setup(log_directory) + # 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)}..." @@ -632,6 +634,14 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, gradient_step, save_hypernetwork(hypernetwork, checkpoint, hypernetwork_name, last_saved_file) hypernetwork.optimizer_state_dict = None # dereference it after saving, to save memory. + + + if shared.opts.training_enable_tensorboard: + epoch_num = hypernetwork.step // len(ds) + epoch_step = hypernetwork.step - (epoch_num * len(ds)) + 1 + + textual_inversion.tensorboard_add(tensorboard_writer, loss=mean_loss, global_step=hypernetwork.step, step=epoch_step, learn_rate=scheduler.learn_rate, epoch_num=epoch_num) + textual_inversion.write_loss(log_directory, "hypernetwork_loss.csv", hypernetwork.step, steps_per_epoch, { "loss": f"{loss_step:.7f}", "learn_rate": scheduler.learn_rate @@ -673,6 +683,9 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, gradient_step, processed = processing.process_images(p) image = processed.images[0] if len(processed.images) > 0 else None + + if shared.opts.training_enable_tensorboard and shared.opts.training_tensorboard_save_images: + textual_inversion.tensorboard_add_image(tensorboard_writer, f"Validation at epoch {epoch_num}", image, hypernetwork.step) if unload: shared.sd_model.cond_stage_model.to(devices.cpu) diff --git a/modules/shared.py b/modules/shared.py index 1c964237..b90ded52 100644 --- a/modules/shared.py +++ b/modules/shared.py @@ -373,6 +373,9 @@ options_templates.update(options_section(('training', "Training"), { "training_image_repeats_per_epoch": OptionInfo(1, "Number of repeats for a single input image per epoch; used only for displaying epoch number", gr.Number, {"precision": 0}), "training_write_csv_every": OptionInfo(500, "Save an csv containing the loss to log directory every N steps, 0 to disable"), "training_xattention_optimizations": OptionInfo(False, "Use cross attention optimizations while training"), + "training_enable_tensorboard": OptionInfo(False, "Enable tensorboard logging."), + "training_tensorboard_save_images": OptionInfo(False, "Save generated images within tensorboard."), + "training_tensorboard_flush_every": OptionInfo(120, "How often, in seconds, to flush the pending tensorboard events and summaries to disk."), })) options_templates.update(options_section(('sd', "Stable Diffusion"), { diff --git a/modules/textual_inversion/textual_inversion.py b/modules/textual_inversion/textual_inversion.py index e23906ca..85210b0e 100644 --- a/modules/textual_inversion/textual_inversion.py +++ b/modules/textual_inversion/textual_inversion.py @@ -12,6 +12,7 @@ import csv import safetensors.torch from PIL import Image, PngImagePlugin +from torch.utils.tensorboard import SummaryWriter from modules import shared, devices, sd_hijack, processing, sd_models, images, sd_samplers import modules.textual_inversion.dataset @@ -294,6 +295,30 @@ def write_loss(log_directory, filename, step, epoch_len, values): **values, }) +def tensorboard_setup(log_directory): + os.makedirs(os.path.join(log_directory, "tensorboard"), exist_ok=True) + return SummaryWriter( + log_dir=os.path.join(log_directory, "tensorboard"), + flush_secs=shared.opts.training_tensorboard_flush_every) + +def tensorboard_add(tensorboard_writer, loss, global_step, step, learn_rate, epoch_num): + tensorboard_add_scaler(tensorboard_writer, "Loss/train", loss, global_step) + tensorboard_add_scaler(tensorboard_writer, f"Loss/train/epoch-{epoch_num}", loss, step) + tensorboard_add_scaler(tensorboard_writer, "Learn rate/train", learn_rate, global_step) + tensorboard_add_scaler(tensorboard_writer, f"Learn rate/train/epoch-{epoch_num}", learn_rate, step) + +def tensorboard_add_scaler(tensorboard_writer, tag, value, step): + tensorboard_writer.add_scalar(tag=tag, + scalar_value=value, global_step=step) + +def tensorboard_add_image(tensorboard_writer, tag, pil_image, step): + # Convert a pil image to a torch tensor + img_tensor = torch.as_tensor(np.array(pil_image, copy=True)) + img_tensor = img_tensor.view(pil_image.size[1], pil_image.size[0], + len(pil_image.getbands())) + img_tensor = img_tensor.permute((2, 0, 1)) + + tensorboard_writer.add_image(tag, img_tensor, global_step=step) def validate_train_inputs(model_name, learn_rate, batch_size, gradient_step, data_root, template_file, template_filename, steps, save_model_every, create_image_every, log_directory, name="embedding"): assert model_name, f"{name} not selected" @@ -372,6 +397,9 @@ def train_embedding(embedding_name, learn_rate, batch_size, gradient_step, data_ # 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)}..." old_parallel_processing_allowed = shared.parallel_processing_allowed + + if shared.opts.training_enable_tensorboard: + tensorboard_writer = tensorboard_setup(log_directory) pin_memory = shared.opts.pin_memory @@ -535,6 +563,9 @@ def train_embedding(embedding_name, learn_rate, batch_size, gradient_step, data_ last_saved_image, last_text_info = images.save_image(image, images_dir, "", p.seed, p.prompt, shared.opts.samples_format, processed.infotexts[0], p=p, forced_filename=forced_filename, save_to_dirs=False) last_saved_image += f", prompt: {preview_text}" + if shared.opts.training_enable_tensorboard and shared.opts.training_tensorboard_save_images: + tensorboard_add_image(tensorboard_writer, f"Validation at epoch {epoch_num}", image, embedding.step) + if save_image_with_stored_embedding and os.path.exists(last_saved_file) and embedding_yet_to_be_embedded: last_saved_image_chunks = os.path.join(images_embeds_dir, f'{embedding_name}-{steps_done}.png')