Allow variable img size (448b9ced) · Commits · github_fork / Stable Diffusion Webui

modules/textual_inversion/dataset.py

+11 −7

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):
		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):
		self.filename = filename
		self.filename_text = filename_text
		self.latent_dist = latent_dist
		@@ -25,6 +25,7 @@ class DatasetEntry:
		self.cond = cond
		self.cond_text = cond_text
		self.pixel_values = pixel_values
		self.img_shape = img_shape


		class PersonalizedBase(Dataset):
		@@ -33,8 +34,6 @@ class PersonalizedBase(Dataset):

		self.placeholder_token = placeholder_token

		self.width = width
		self.height = height
		self.flip = transforms.RandomHorizontalFlip(p=flip_p)

		self.dataset = []
		@@ -59,7 +58,11 @@ class PersonalizedBase(Dataset):
		if shared.state.interrupted:
		raise Exception("interrupted")
		try:
		image = Image.open(path).convert('RGB').resize((self.width, self.height), PIL.Image.BICUBIC)
		image = Image.open(path).convert('RGB')
		if width < 2000:
		image = image.resize((width, height), PIL.Image.BICUBIC)
		else:
		assert batch_size == 1, 'variable img size must have batch size 1'
		except Exception:
		continue

		@@ -88,14 +91,14 @@ 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)
		entry = DatasetEntry(filename=path, filename_text=filename_text, latent_sample=latent_sample, img_shape=image.size)
		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)
		entry = DatasetEntry(filename=path, filename_text=filename_text, latent_sample=latent_sample, img_shape=image.size)
		elif latent_sampling_method == "random":
		entry = DatasetEntry(filename=path, filename_text=filename_text, latent_dist=latent_dist)
		entry = DatasetEntry(filename=path, filename_text=filename_text, latent_dist=latent_dist, img_shape=image.size)

		if not (self.tag_drop_out != 0 or self.shuffle_tags):
		entry.cond_text = self.create_text(filename_text)
		@@ -151,6 +154,7 @@ 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
		@@ -451,8 +451,8 @@ def train_embedding(embedding_name, learn_rate, batch_size, gradient_step, data_
		else:
		p.prompt = batch.cond_text[0]
		p.steps = 20
		p.width = training_width
		p.height = training_height
		p.width = batch.img_shape[0][0]
		p.height = batch.img_shape[0][1]

		preview_text = p.prompt

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