Revert "Add cleanup after training" (ab05a74e) · Commits · github_fork / Stable Diffusion Webui

modules/hypernetworks/hypernetwork.py

+96 −105

Original line number	Original line	Diff line number	Diff line
	@@ -398,8 +398,6 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log
	forced_filename = "<none>"		forced_filename = "<none>"

	pbar = tqdm.tqdm(enumerate(ds), total=steps - ititial_step)		pbar = tqdm.tqdm(enumerate(ds), total=steps - ititial_step)

	try:
	for i, entries in pbar:		for i, entries in pbar:
	hypernetwork.step = i + ititial_step		hypernetwork.step = i + ititial_step
	if len(loss_dict) > 0:		if len(loss_dict) > 0:
	@@ -512,13 +510,6 @@ Last saved hypernetwork: {html.escape(last_saved_file)}<br/>
	Last saved image: {html.escape(last_saved_image)}<br/>		Last saved image: {html.escape(last_saved_image)}<br/>
	</p>		</p>
	"""		"""
	finally:
	if weights:
	for weight in weights:
	weight.requires_grad = False
	if unload:
	shared.sd_model.cond_stage_model.to(devices.device)
	shared.sd_model.first_stage_model.to(devices.device)

	report_statistics(loss_dict)		report_statistics(loss_dict)
	checkpoint = sd_models.select_checkpoint()		checkpoint = sd_models.select_checkpoint()

+90 −95

Original line number	Original line	Diff line number	Diff line
	@@ -283,8 +283,6 @@ def train_embedding(embedding_name, learn_rate, batch_size, data_root, log_direc
	embedding_yet_to_be_embedded = False		embedding_yet_to_be_embedded = False

	pbar = tqdm.tqdm(enumerate(ds), total=steps-ititial_step)		pbar = tqdm.tqdm(enumerate(ds), total=steps-ititial_step)

	try:
	for i, entries in pbar:		for i, entries in pbar:
	embedding.step = i + ititial_step		embedding.step = i + ititial_step

	@@ -398,9 +396,6 @@ Last saved embedding: {html.escape(last_saved_file)}<br/>
	Last saved image: {html.escape(last_saved_image)}<br/>		Last saved image: {html.escape(last_saved_image)}<br/>
	</p>		</p>
	"""		"""
	finally:
	if embedding and embedding.vec is not None:
	embedding.vec.requires_grad = False

	checkpoint = sd_models.select_checkpoint()		checkpoint = sd_models.select_checkpoint()