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https://github.com/openvinotoolkit/stable-diffusion-webui.git
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Merge branch 'master' into master
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commit
7f8670c4ef
@ -244,7 +244,7 @@ def stack_conds(conds):
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return torch.stack(conds)
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def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log_directory, steps, create_image_every, save_hypernetwork_every, template_file, preview_from_txt2img, preview_prompt, preview_negative_prompt, preview_steps, preview_sampler_index, preview_cfg_scale, preview_seed, preview_width, preview_height):
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def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log_directory, training_width, training_height, steps, create_image_every, save_hypernetwork_every, template_file, preview_from_txt2img, preview_prompt, preview_negative_prompt, preview_steps, preview_sampler_index, preview_cfg_scale, preview_seed, preview_width, preview_height):
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assert hypernetwork_name, 'hypernetwork not selected'
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path = shared.hypernetworks.get(hypernetwork_name, None)
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@ -273,8 +273,7 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log
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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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ds = modules.textual_inversion.dataset.PersonalizedBase(data_root=data_root, width=512, height=512, 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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assert ds.length > 1, "Dataset should contain more than 1 images"
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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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if unload:
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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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@ -1341,6 +1341,8 @@ def create_ui(wrap_gradio_gpu_call):
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batch_size,
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dataset_directory,
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log_directory,
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training_width,
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training_height,
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steps,
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create_image_every,
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save_embedding_every,
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