remove duplicated code (a8cbe50c) · Commits · github_fork / Stable Diffusion Webui

extensions-builtin/Lora/networks.py

+2 −29

Original line number	Diff line number	Diff line
		@@ -15,7 +15,7 @@ import torch
		from typing import Union

		from modules import shared, devices, sd_models, errors, scripts, sd_hijack
		from modules.textual_inversion.textual_inversion import Embedding
		import modules.textual_inversion.textual_inversion as textual_inversion

		from lora_logger import logger

		@@ -210,34 +210,7 @@ def load_network(name, network_on_disk):

		embeddings = {}
		for emb_name, data in bundle_embeddings.items():
		# textual inversion embeddings
		if 'string_to_param' in data:
		param_dict = data['string_to_param']
		param_dict = getattr(param_dict, '_parameters', param_dict) # fix for torch 1.12.1 loading saved file from torch 1.11
		assert len(param_dict) == 1, 'embedding file has multiple terms in it'
		emb = next(iter(param_dict.items()))[1]
		vec = emb.detach().to(devices.device, dtype=torch.float32)
		shape = vec.shape[-1]
		vectors = vec.shape[0]
		elif type(data) == dict and 'clip_g' in data and 'clip_l' in data: # SDXL embedding
		vec = {k: v.detach().to(devices.device, dtype=torch.float32) for k, v in data.items()}
		shape = data['clip_g'].shape[-1] + data['clip_l'].shape[-1]
		vectors = data['clip_g'].shape[0]
		elif type(data) == dict and type(next(iter(data.values()))) == torch.Tensor: # diffuser concepts
		assert len(data.keys()) == 1, 'embedding file has multiple terms in it'

		emb = next(iter(data.values()))
		if len(emb.shape) == 1:
		emb = emb.unsqueeze(0)
		vec = emb.detach().to(devices.device, dtype=torch.float32)
		shape = vec.shape[-1]
		vectors = vec.shape[0]
		else:
		raise Exception(f"Couldn't identify {emb_name} in lora: {name} as neither textual inversion embedding nor diffuser concept.")

		embedding = Embedding(vec, emb_name)
		embedding.vectors = vectors
		embedding.shape = shape
		embedding = textual_inversion.create_embedding_from_data(data, emb_name, filename=network_on_disk.filename + "/" + emb_name)
		embedding.loaded = None
		embeddings[emb_name] = embedding

modules/textual_inversion/textual_inversion.py

+40 −34

Original line number	Diff line number	Diff line
		@@ -181,40 +181,7 @@ class EmbeddingDatabase:
		else:
		return


		# textual inversion embeddings
		if 'string_to_param' in data:
		param_dict = data['string_to_param']
		param_dict = getattr(param_dict, '_parameters', param_dict) # fix for torch 1.12.1 loading saved file from torch 1.11
		assert len(param_dict) == 1, 'embedding file has multiple terms in it'
		emb = next(iter(param_dict.items()))[1]
		vec = emb.detach().to(devices.device, dtype=torch.float32)
		shape = vec.shape[-1]
		vectors = vec.shape[0]
		elif type(data) == dict and 'clip_g' in data and 'clip_l' in data: # SDXL embedding
		vec = {k: v.detach().to(devices.device, dtype=torch.float32) for k, v in data.items()}
		shape = data['clip_g'].shape[-1] + data['clip_l'].shape[-1]
		vectors = data['clip_g'].shape[0]
		elif type(data) == dict and type(next(iter(data.values()))) == torch.Tensor: # diffuser concepts
		assert len(data.keys()) == 1, 'embedding file has multiple terms in it'

		emb = next(iter(data.values()))
		if len(emb.shape) == 1:
		emb = emb.unsqueeze(0)
		vec = emb.detach().to(devices.device, dtype=torch.float32)
		shape = vec.shape[-1]
		vectors = vec.shape[0]
		else:
		raise Exception(f"Couldn't identify {filename} as neither textual inversion embedding nor diffuser concept.")

		embedding = Embedding(vec, name)
		embedding.step = data.get('step', None)
		embedding.sd_checkpoint = data.get('sd_checkpoint', None)
		embedding.sd_checkpoint_name = data.get('sd_checkpoint_name', None)
		embedding.vectors = vectors
		embedding.shape = shape
		embedding.filename = path
		embedding.set_hash(hashes.sha256(embedding.filename, "textual_inversion/" + name) or '')
		embedding = create_embedding_from_data(data, name, filename=filename, filepath=path)

		if self.expected_shape == -1 or self.expected_shape == embedding.shape:
		self.register_embedding(embedding, shared.sd_model)
		@@ -313,6 +280,45 @@ def create_embedding(name, num_vectors_per_token, overwrite_old, init_text='*'):
		return fn


		def create_embedding_from_data(data, name, filename='unknown embedding file', filepath=None):
		if 'string_to_param' in data: # textual inversion embeddings
		param_dict = data['string_to_param']
		param_dict = getattr(param_dict, '_parameters', param_dict) # fix for torch 1.12.1 loading saved file from torch 1.11
		assert len(param_dict) == 1, 'embedding file has multiple terms in it'
		emb = next(iter(param_dict.items()))[1]
		vec = emb.detach().to(devices.device, dtype=torch.float32)
		shape = vec.shape[-1]
		vectors = vec.shape[0]
		elif type(data) == dict and 'clip_g' in data and 'clip_l' in data: # SDXL embedding
		vec = {k: v.detach().to(devices.device, dtype=torch.float32) for k, v in data.items()}
		shape = data['clip_g'].shape[-1] + data['clip_l'].shape[-1]
		vectors = data['clip_g'].shape[0]
		elif type(data) == dict and type(next(iter(data.values()))) == torch.Tensor: # diffuser concepts
		assert len(data.keys()) == 1, 'embedding file has multiple terms in it'

		emb = next(iter(data.values()))
		if len(emb.shape) == 1:
		emb = emb.unsqueeze(0)
		vec = emb.detach().to(devices.device, dtype=torch.float32)
		shape = vec.shape[-1]
		vectors = vec.shape[0]
		else:
		raise Exception(f"Couldn't identify {filename} as neither textual inversion embedding nor diffuser concept.")

		embedding = Embedding(vec, name)
		embedding.step = data.get('step', None)
		embedding.sd_checkpoint = data.get('sd_checkpoint', None)
		embedding.sd_checkpoint_name = data.get('sd_checkpoint_name', None)
		embedding.vectors = vectors
		embedding.shape = shape

		if filepath:
		embedding.filename = filepath
		embedding.set_hash(hashes.sha256(filepath, "textual_inversion/" + name) or '')

		return embedding


		def write_loss(log_directory, filename, step, epoch_len, values):
		if shared.opts.training_write_csv_every == 0:
		return

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