Fix log off by 1 (9ceef81f) · Commits · github_fork / Stable Diffusion Webui

modules/hypernetworks/hypernetwork.py

+7 −5

Original line number	Diff line number	Diff line
		@@ -428,6 +428,8 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log

		optimizer.step()

		steps_done = hypernetwork.step + 1

		if torch.isnan(losses[hypernetwork.step % losses.shape[0]]):
		raise RuntimeError("Loss diverged.")

		@@ -438,9 +440,9 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log
		dataset_loss_info = f"dataset loss:{mean(previous_mean_losses):.3f}" + u"\u00B1" + f"({std / (len(previous_mean_losses) ** 0.5):.3f})"
		pbar.set_description(dataset_loss_info)

		if hypernetwork.step > 0 and hypernetwork_dir is not None and hypernetwork.step % save_hypernetwork_every == 0:
		if hypernetwork_dir is not None and steps_done % save_hypernetwork_every == 0:
		# Before saving, change name to match current checkpoint.
		hypernetwork.name = f'{hypernetwork_name}-{hypernetwork.step}'
		hypernetwork.name = f'{hypernetwork_name}-{steps_done}'
		last_saved_file = os.path.join(hypernetwork_dir, f'{hypernetwork.name}.pt')
		hypernetwork.save(last_saved_file)

		@@ -449,8 +451,8 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log
		"learn_rate": scheduler.learn_rate
		})

		if hypernetwork.step > 0 and images_dir is not None and hypernetwork.step % create_image_every == 0:
		forced_filename = f'{hypernetwork_name}-{hypernetwork.step}'
		if images_dir is not None and steps_done % create_image_every == 0:
		forced_filename = f'{hypernetwork_name}-{steps_done}'
		last_saved_image = os.path.join(images_dir, forced_filename)

		optimizer.zero_grad()

modules/textual_inversion/learn_schedule.py

+1 −1

Original line number	Diff line number	Diff line
		@@ -52,7 +52,7 @@ class LearnRateScheduler:
		self.finished = False

		def apply(self, optimizer, step_number):
		if step_number <= self.end_step:
		if step_number < self.end_step:
		return

		try:

modules/textual_inversion/textual_inversion.py

+12 −12

Original line number	Diff line number	Diff line
		@@ -184,9 +184,8 @@ def write_loss(log_directory, filename, step, epoch_len, values):
		if shared.opts.training_write_csv_every == 0:
		return

		if step % shared.opts.training_write_csv_every != 0:
		if (step + 1) % shared.opts.training_write_csv_every != 0:
		return

		write_csv_header = False if os.path.exists(os.path.join(log_directory, filename)) else True

		with open(os.path.join(log_directory, filename), "a+", newline='') as fout:
		@@ -196,11 +195,11 @@ def write_loss(log_directory, filename, step, epoch_len, values):
		csv_writer.writeheader()

		epoch = step // epoch_len
		epoch_step = step - epoch * epoch_len
		epoch_step = step % epoch_len

		csv_writer.writerow({
		"step": step + 1,
		"epoch": epoch + 1,
		"epoch": epoch,
		"epoch_step": epoch_step + 1,
		**values,
		})
		@@ -282,15 +281,16 @@ def train_embedding(embedding_name, learn_rate, batch_size, data_root, log_direc
		loss.backward()
		optimizer.step()

		steps_done = embedding.step + 1

		epoch_num = embedding.step // len(ds)
		epoch_step = embedding.step - (epoch_num * len(ds)) + 1
		epoch_step = embedding.step % len(ds)

		pbar.set_description(f"[Epoch {epoch_num}: {epoch_step}/{len(ds)}]loss: {losses.mean():.7f}")
		pbar.set_description(f"[Epoch {epoch_num}: {epoch_step+1}/{len(ds)}]loss: {losses.mean():.7f}")

		if embedding.step > 0 and embedding_dir is not None and embedding.step % save_embedding_every == 0:
		if embedding_dir is not None and steps_done % save_embedding_every == 0:
		# Before saving, change name to match current checkpoint.
		embedding.name = f'{embedding_name}-{embedding.step}'
		embedding.name = f'{embedding_name}-{steps_done}'
		last_saved_file = os.path.join(embedding_dir, f'{embedding.name}.pt')
		embedding.save(last_saved_file)
		embedding_yet_to_be_embedded = True
		@@ -300,8 +300,8 @@ def train_embedding(embedding_name, learn_rate, batch_size, data_root, log_direc
		"learn_rate": scheduler.learn_rate
		})

		if embedding.step > 0 and images_dir is not None and embedding.step % create_image_every == 0:
		forced_filename = f'{embedding_name}-{embedding.step}'
		if images_dir is not None and steps_done % create_image_every == 0:
		forced_filename = f'{embedding_name}-{steps_done}'
		last_saved_image = os.path.join(images_dir, forced_filename)
		p = processing.StableDiffusionProcessingTxt2Img(
		sd_model=shared.sd_model,
		@@ -334,7 +334,7 @@ def train_embedding(embedding_name, learn_rate, batch_size, data_root, log_direc

		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}-{embedding.step}.png')
		last_saved_image_chunks = os.path.join(images_embeds_dir, f'{embedding_name}-{steps_done}.png')

		info = PngImagePlugin.PngInfo()
		data = torch.load(last_saved_file)
		@@ -350,7 +350,7 @@ def train_embedding(embedding_name, learn_rate, batch_size, data_root, log_direc
		checkpoint = sd_models.select_checkpoint()
		footer_left = checkpoint.model_name
		footer_mid = '[{}]'.format(checkpoint.hash)
		footer_right = '{}v {}s'.format(vectorSize, embedding.step)
		footer_right = '{}v {}s'.format(vectorSize, steps_done)

		captioned_image = caption_image_overlay(image, title, footer_left, footer_mid, footer_right)
		captioned_image = insert_image_data_embed(captioned_image, data)

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