Merge pull request #4056 from MarkovInequality/TI_optimizations (f071a1d2) · Commits · github_fork / Stable Diffusion Webui

modules/shared.py

+2 −1

Original line number	Diff line number	Diff line
		@@ -288,11 +288,12 @@ options_templates.update(options_section(('system', "System"), {
		}))

		options_templates.update(options_section(('training', "Training"), {
		"unload_models_when_training": OptionInfo(False, "Move VAE and CLIP to RAM when training hypernetwork. Saves VRAM."),
		"unload_models_when_training": OptionInfo(False, "Move VAE and CLIP to RAM when training if possible. Saves VRAM."),
		"dataset_filename_word_regex": OptionInfo("", "Filename word regex"),
		"dataset_filename_join_string": OptionInfo(" ", "Filename join string"),
		"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}),
		"training_write_csv_every": OptionInfo(500, "Save an csv containing the loss to log directory every N steps, 0 to disable"),
		"training_xattention_optimizations": OptionInfo(False, "Use cross attention optimizations while training"),
		}))

		options_templates.update(options_section(('sd', "Stable Diffusion"), {

modules/textual_inversion/textual_inversion.py

+10 −0

Original line number	Diff line number	Diff line
		@@ -235,6 +235,7 @@ def train_embedding(embedding_name, learn_rate, batch_size, data_root, log_direc
		filename = os.path.join(shared.cmd_opts.embeddings_dir, f'{embedding_name}.pt')

		log_directory = os.path.join(log_directory, datetime.datetime.now().strftime("%Y-%m-%d"), embedding_name)
		unload = shared.opts.unload_models_when_training

		if save_embedding_every > 0:
		embedding_dir = os.path.join(log_directory, "embeddings")
		@@ -272,6 +273,8 @@ def train_embedding(embedding_name, learn_rate, batch_size, data_root, log_direc
		shared.state.textinfo = f"Preparing dataset from {html.escape(data_root)}..."
		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)
		if unload:
		shared.sd_model.first_stage_model.to(devices.cpu)

		embedding.vec.requires_grad = True
		optimizer = torch.optim.AdamW([embedding.vec], lr=scheduler.learn_rate)
		@@ -328,6 +331,9 @@ def train_embedding(embedding_name, learn_rate, batch_size, data_root, log_direc
		if images_dir is not None and steps_done % create_image_every == 0:
		forced_filename = f'{embedding_name}-{steps_done}'
		last_saved_image = os.path.join(images_dir, forced_filename)

		shared.sd_model.first_stage_model.to(devices.device)

		p = processing.StableDiffusionProcessingTxt2Img(
		sd_model=shared.sd_model,
		do_not_save_grid=True,
		@@ -355,6 +361,9 @@ def train_embedding(embedding_name, learn_rate, batch_size, data_root, log_direc
		processed = processing.process_images(p)
		image = processed.images[0]

		if unload:
		shared.sd_model.first_stage_model.to(devices.cpu)

		shared.state.current_image = image

		if save_image_with_stored_embedding and os.path.exists(last_saved_file) and embedding_yet_to_be_embedded:
		@@ -400,6 +409,7 @@ Last saved image: {html.escape(last_saved_image)}<br/>

		filename = os.path.join(shared.cmd_opts.embeddings_dir, f'{embedding_name}.pt')
		save_embedding(embedding, checkpoint, embedding_name, filename, remove_cached_checksum=True)
		shared.sd_model.first_stage_model.to(devices.device)

		return embedding, filename

modules/textual_inversion/ui.py

+5 −2

Original line number	Diff line number	Diff line
		@@ -25,7 +25,9 @@ def train_embedding(*args):

		assert not shared.cmd_opts.lowvram, 'Training models with lowvram not possible'

		apply_optimizations = shared.opts.training_xattention_optimizations
		try:
		if not apply_optimizations:
		sd_hijack.undo_optimizations()

		embedding, filename = modules.textual_inversion.textual_inversion.train_embedding(*args)
		@@ -38,5 +40,6 @@ Embedding saved to {html.escape(filename)}
		except Exception:
		raise
		finally:
		if not apply_optimizations:
		sd_hijack.apply_optimizations()

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