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https://github.com/AUTOMATIC1111/stable-diffusion-webui.git
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Added TI training optimizations
option to use xattention optimizations when training option to unload vae when training
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@ -256,11 +256,12 @@ options_templates.update(options_section(('system', "System"), {
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}))
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options_templates.update(options_section(('training', "Training"), {
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"unload_models_when_training": OptionInfo(False, "Move VAE and CLIP to RAM when training hypernetwork. Saves VRAM."),
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"unload_models_when_training": OptionInfo(False, "Move VAE and CLIP to RAM when training if possible. Saves VRAM."),
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"dataset_filename_word_regex": OptionInfo("", "Filename word regex"),
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"dataset_filename_join_string": OptionInfo(" ", "Filename join string"),
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"training_image_repeats_per_epoch": OptionInfo(1, "Number of repeats for a single input image per epoch; used only for displaying epoch number", gr.Number, {"precision": 0}),
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"training_write_csv_every": OptionInfo(500, "Save an csv containing the loss to log directory every N steps, 0 to disable"),
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"training_xattention_optimizations": OptionInfo(False, "Use cross attention optimizations while training"),
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}))
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options_templates.update(options_section(('sd', "Stable Diffusion"), {
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@ -214,6 +214,7 @@ def train_embedding(embedding_name, learn_rate, batch_size, data_root, log_direc
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filename = os.path.join(shared.cmd_opts.embeddings_dir, f'{embedding_name}.pt')
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log_directory = os.path.join(log_directory, datetime.datetime.now().strftime("%Y-%m-%d"), embedding_name)
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unload = shared.opts.unload_models_when_training
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if save_embedding_every > 0:
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embedding_dir = os.path.join(log_directory, "embeddings")
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@ -238,6 +239,8 @@ 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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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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if unload:
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shared.sd_model.first_stage_model.to(devices.cpu)
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hijack = sd_hijack.model_hijack
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@ -303,6 +306,9 @@ def train_embedding(embedding_name, learn_rate, batch_size, data_root, log_direc
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if images_dir is not None and steps_done % create_image_every == 0:
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forced_filename = f'{embedding_name}-{steps_done}'
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last_saved_image = os.path.join(images_dir, forced_filename)
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shared.sd_model.first_stage_model.to(devices.device)
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p = processing.StableDiffusionProcessingTxt2Img(
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sd_model=shared.sd_model,
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do_not_save_grid=True,
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@ -330,6 +336,9 @@ def train_embedding(embedding_name, learn_rate, batch_size, data_root, log_direc
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processed = processing.process_images(p)
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image = processed.images[0]
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if unload:
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shared.sd_model.first_stage_model.to(devices.cpu)
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shared.state.current_image = image
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if save_image_with_stored_embedding and os.path.exists(last_saved_file) and embedding_yet_to_be_embedded:
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@ -25,8 +25,10 @@ def train_embedding(*args):
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assert not shared.cmd_opts.lowvram, 'Training models with lowvram not possible'
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apply_optimizations = shared.opts.training_xattention_optimizations
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try:
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sd_hijack.undo_optimizations()
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if not apply_optimizations:
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sd_hijack.undo_optimizations()
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embedding, filename = modules.textual_inversion.textual_inversion.train_embedding(*args)
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@ -38,5 +40,6 @@ Embedding saved to {html.escape(filename)}
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except Exception:
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raise
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finally:
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sd_hijack.apply_optimizations()
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if not apply_optimizations:
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sd_hijack.apply_optimizations()
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