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https://github.com/openvinotoolkit/stable-diffusion-webui.git
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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
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@ -390,7 +390,10 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log
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with torch.autocast("cuda"):
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with torch.autocast("cuda"):
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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)
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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)
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old_parallel_processing_allowed = shared.parallel_processing_allowed
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if unload:
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if unload:
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shared.parallel_processing_allowed = False
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shared.sd_model.cond_stage_model.to(devices.cpu)
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shared.sd_model.cond_stage_model.to(devices.cpu)
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shared.sd_model.first_stage_model.to(devices.cpu)
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shared.sd_model.first_stage_model.to(devices.cpu)
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@ -531,6 +534,7 @@ Last saved image: {html.escape(last_saved_image)}<br/>
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filename = os.path.join(shared.cmd_opts.hypernetwork_dir, f'{hypernetwork_name}.pt')
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filename = os.path.join(shared.cmd_opts.hypernetwork_dir, f'{hypernetwork_name}.pt')
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save_hypernetwork(hypernetwork, checkpoint, hypernetwork_name, filename)
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save_hypernetwork(hypernetwork, checkpoint, hypernetwork_name, filename)
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shared.parallel_processing_allowed = old_parallel_processing_allowed
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return hypernetwork, filename
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return hypernetwork, filename
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@ -273,7 +273,11 @@ def train_embedding(embedding_name, learn_rate, batch_size, data_root, log_direc
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shared.state.textinfo = f"Preparing dataset from {html.escape(data_root)}..."
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shared.state.textinfo = f"Preparing dataset from {html.escape(data_root)}..."
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with torch.autocast("cuda"):
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with torch.autocast("cuda"):
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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)
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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)
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old_parallel_processing_allowed = shared.parallel_processing_allowed
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if unload:
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if unload:
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shared.parallel_processing_allowed = False
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shared.sd_model.first_stage_model.to(devices.cpu)
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shared.sd_model.first_stage_model.to(devices.cpu)
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embedding.vec.requires_grad = True
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embedding.vec.requires_grad = True
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@ -410,6 +414,7 @@ Last saved image: {html.escape(last_saved_image)}<br/>
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filename = os.path.join(shared.cmd_opts.embeddings_dir, f'{embedding_name}.pt')
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filename = os.path.join(shared.cmd_opts.embeddings_dir, f'{embedding_name}.pt')
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save_embedding(embedding, checkpoint, embedding_name, filename, remove_cached_checksum=True)
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save_embedding(embedding, checkpoint, embedding_name, filename, remove_cached_checksum=True)
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shared.sd_model.first_stage_model.to(devices.device)
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shared.sd_model.first_stage_model.to(devices.device)
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shared.parallel_processing_allowed = old_parallel_processing_allowed
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return embedding, filename
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return embedding, filename
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