remove/simplify some changes from #6481 (43bb5190) · Commits · github_fork / Stable Diffusion Webui

modules/textual_inversion/dataset.py

+5 −9

Original line number	Diff line number	Diff line
		@@ -17,7 +17,7 @@ re_numbers_at_start = re.compile(r"^[-\d]+\s*")


		class DatasetEntry:
		def __init__(self, filename=None, filename_text=None, latent_dist=None, latent_sample=None, cond=None, cond_text=None, pixel_values=None, img_shape=None):
		def __init__(self, filename=None, filename_text=None, latent_dist=None, latent_sample=None, cond=None, cond_text=None, pixel_values=None):
		self.filename = filename
		self.filename_text = filename_text
		self.latent_dist = latent_dist
		@@ -25,7 +25,6 @@ class DatasetEntry:
		self.cond = cond
		self.cond_text = cond_text
		self.pixel_values = pixel_values
		self.img_shape = img_shape


		class PersonalizedBase(Dataset):
		@@ -46,12 +45,10 @@ class PersonalizedBase(Dataset):
		assert data_root, 'dataset directory not specified'
		assert os.path.isdir(data_root), "Dataset directory doesn't exist"
		assert os.listdir(data_root), "Dataset directory is empty"
		if varsize:
		assert batch_size == 1, 'variable img size must have batch size 1'
		assert batch_size == 1 or not varsize, 'variable img size must have batch size 1'

		self.image_paths = [os.path.join(data_root, file_path) for file_path in os.listdir(data_root)]


		self.shuffle_tags = shuffle_tags
		self.tag_drop_out = tag_drop_out

		@@ -91,14 +88,14 @@ class PersonalizedBase(Dataset):
		if latent_sampling_method == "once" or (latent_sampling_method == "deterministic" and not isinstance(latent_dist, DiagonalGaussianDistribution)):
		latent_sample = model.get_first_stage_encoding(latent_dist).squeeze().to(devices.cpu)
		latent_sampling_method = "once"
		entry = DatasetEntry(filename=path, filename_text=filename_text, latent_sample=latent_sample, img_shape=image.size)
		entry = DatasetEntry(filename=path, filename_text=filename_text, latent_sample=latent_sample)
		elif latent_sampling_method == "deterministic":
		# Works only for DiagonalGaussianDistribution
		latent_dist.std = 0
		latent_sample = model.get_first_stage_encoding(latent_dist).squeeze().to(devices.cpu)
		entry = DatasetEntry(filename=path, filename_text=filename_text, latent_sample=latent_sample, img_shape=image.size)
		entry = DatasetEntry(filename=path, filename_text=filename_text, latent_sample=latent_sample)
		elif latent_sampling_method == "random":
		entry = DatasetEntry(filename=path, filename_text=filename_text, latent_dist=latent_dist, img_shape=image.size)
		entry = DatasetEntry(filename=path, filename_text=filename_text, latent_dist=latent_dist)

		if not (self.tag_drop_out != 0 or self.shuffle_tags):
		entry.cond_text = self.create_text(filename_text)
		@@ -154,7 +151,6 @@ class BatchLoader:
		self.cond_text = [entry.cond_text for entry in data]
		self.cond = [entry.cond for entry in data]
		self.latent_sample = torch.stack([entry.latent_sample for entry in data]).squeeze(1)
		self.img_shape = [entry.img_shape for entry in data]
		#self.emb_index = [entry.emb_index for entry in data]
		#print(self.latent_sample.device)

modules/textual_inversion/textual_inversion.py

+2 −2

Original line number	Diff line number	Diff line
		@@ -492,8 +492,8 @@ def train_embedding(embedding_name, learn_rate, batch_size, gradient_step, data_
		else:
		p.prompt = batch.cond_text[0]
		p.steps = 20
		p.width = batch.img_shape[0][0]
		p.height = batch.img_shape[0][1]
		p.width = training_width
		p.height = training_height

		preview_text = p.prompt

modules/ui.py

+1 −1

Original line number	Diff line number	Diff line
		@@ -1348,7 +1348,7 @@ def create_ui():
		template_file = gr.Textbox(label='Prompt template file', value=os.path.join(script_path, "textual_inversion_templates", "style_filewords.txt"), elem_id="train_template_file")
		training_width = gr.Slider(minimum=64, maximum=2048, step=8, label="Width", value=512, elem_id="train_training_width")
		training_height = gr.Slider(minimum=64, maximum=2048, step=8, label="Height", value=512, elem_id="train_training_height")
		varsize = gr.Checkbox(label="Ignore dimension settings and do not resize images", value=False, elem_id="train_varsize")
		varsize = gr.Checkbox(label="Do not resize images", value=False, elem_id="train_varsize")
		steps = gr.Number(label='Max steps', value=100000, precision=0, elem_id="train_steps")

		with FormRow():

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