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Fixes race condition in training when VAE is unloaded 

set_current_image can attempt to use the VAE when it is unloaded to
the CPU while training
This commit is contained in:
Fampai 2022-11-04 04:50:22 -04:00
parent f2b69709ea
commit 39541d7725
2 changed files with 9 additions and 0 deletions
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4
modules/hypernetworks/hypernetwork.py
View File

@ -390,7 +390,10 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log
with torch.autocast("cuda"): 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) 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)
old_parallel_processing_allowed = shared.parallel_processing_allowed
if unload: if unload:
shared.parallel_processing_allowed = False
shared.sd_model.cond_stage_model.to(devices.cpu) shared.sd_model.cond_stage_model.to(devices.cpu)
shared.sd_model.first_stage_model.to(devices.cpu) shared.sd_model.first_stage_model.to(devices.cpu)
@ -531,6 +534,7 @@ Last saved image: {html.escape(last_saved_image)}<br/>
filename = os.path.join(shared.cmd_opts.hypernetwork_dir, f'{hypernetwork_name}.pt') filename = os.path.join(shared.cmd_opts.hypernetwork_dir, f'{hypernetwork_name}.pt')
save_hypernetwork(hypernetwork, checkpoint, hypernetwork_name, filename) save_hypernetwork(hypernetwork, checkpoint, hypernetwork_name, filename)
shared.parallel_processing_allowed = old_parallel_processing_allowed
return hypernetwork, filename return hypernetwork, filename

5
modules/textual_inversion/textual_inversion.py
View File

@ -273,7 +273,11 @@ def train_embedding(embedding_name, learn_rate, batch_size, data_root, log_direc
shared.state.textinfo = f"Preparing dataset from {html.escape(data_root)}..." shared.state.textinfo = f"Preparing dataset from {html.escape(data_root)}..."
with torch.autocast("cuda"): 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=embedding_name, model=shared.sd_model, device=devices.device, template_file=template_file, batch_size=batch_size) 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=embedding_name, model=shared.sd_model, device=devices.device, template_file=template_file, batch_size=batch_size)
old_parallel_processing_allowed = shared.parallel_processing_allowed
if unload: if unload:
shared.parallel_processing_allowed = False
shared.sd_model.first_stage_model.to(devices.cpu) shared.sd_model.first_stage_model.to(devices.cpu)
embedding.vec.requires_grad = True embedding.vec.requires_grad = True
@ -410,6 +414,7 @@ Last saved image: {html.escape(last_saved_image)}<br/>
filename = os.path.join(shared.cmd_opts.embeddings_dir, f'{embedding_name}.pt') filename = os.path.join(shared.cmd_opts.embeddings_dir, f'{embedding_name}.pt')
save_embedding(embedding, checkpoint, embedding_name, filename, remove_cached_checksum=True) save_embedding(embedding, checkpoint, embedding_name, filename, remove_cached_checksum=True)
shared.sd_model.first_stage_model.to(devices.device) shared.sd_model.first_stage_model.to(devices.device)
shared.parallel_processing_allowed = old_parallel_processing_allowed
return embedding, filename return embedding, filename