Merge pull request #6782 from aria1th/fix-hypernetwork-loss (d6fa8e92) · Commits · github_fork / Stable Diffusion Webui

modules/hypernetworks/hypernetwork.py

+7 −6

Original line number	Diff line number	Diff line
		@@ -561,6 +561,7 @@ def train_hypernetwork(id_task, hypernetwork_name, learn_rate, batch_size, gradi
		_loss_step = 0 #internal
		# size = len(ds.indexes)
		# loss_dict = defaultdict(lambda : deque(maxlen = 1024))
		loss_logging = deque(maxlen=len(ds) * 3) # this should be configurable parameter, this is 3 * epoch(dataset size)
		# losses = torch.zeros((size,))
		# previous_mean_losses = [0]
		# previous_mean_loss = 0
		@@ -610,7 +611,7 @@ def train_hypernetwork(id_task, hypernetwork_name, learn_rate, batch_size, gradi
		# go back until we reach gradient accumulation steps
		if (j + 1) % gradient_step != 0:
		continue

		loss_logging.append(_loss_step)
		if clip_grad:
		clip_grad(weights, clip_grad_sched.learn_rate)

		@@ -644,7 +645,7 @@ def train_hypernetwork(id_task, hypernetwork_name, learn_rate, batch_size, gradi
		if shared.opts.training_enable_tensorboard:
		epoch_num = hypernetwork.step // len(ds)
		epoch_step = hypernetwork.step - (epoch_num * len(ds)) + 1
		mean_loss = sum(sum(x) for x in loss_dict.values()) / sum(len(x) for x in loss_dict.values())
		mean_loss = sum(loss_logging) / len(loss_logging)
		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, {
		@@ -689,9 +690,6 @@ def train_hypernetwork(id_task, hypernetwork_name, learn_rate, batch_size, gradi
		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)
		shared.sd_model.first_stage_model.to(devices.cpu)
		@@ -701,7 +699,10 @@ def train_hypernetwork(id_task, hypernetwork_name, learn_rate, batch_size, gradi
		hypernetwork.train()
		if image is not None:
		shared.state.assign_current_image(image)

		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)
		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}"

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