Add warning when meet emb name conflicting (81e94de3) · Commits · github_fork / Stable Diffusion Webui

extensions-builtin/Lora/lora_logger.py

0 → 100644

+33 −0

Original line number	Diff line number	Diff line
		import sys
		import copy
		import logging


		class ColoredFormatter(logging.Formatter):
		COLORS = {
		"DEBUG": "\033[0;36m", # CYAN
		"INFO": "\033[0;32m", # GREEN
		"WARNING": "\033[0;33m", # YELLOW
		"ERROR": "\033[0;31m", # RED
		"CRITICAL": "\033[0;37;41m", # WHITE ON RED
		"RESET": "\033[0m", # RESET COLOR
		}

		def format(self, record):
		colored_record = copy.copy(record)
		levelname = colored_record.levelname
		seq = self.COLORS.get(levelname, self.COLORS["RESET"])
		colored_record.levelname = f"{seq}{levelname}{self.COLORS['RESET']}"
		return super().format(colored_record)


		logger = logging.getLogger("lora")
		logger.propagate = False


		if not logger.handlers:
		handler = logging.StreamHandler(sys.stdout)
		handler.setFormatter(
		ColoredFormatter("[%(name)s]-%(levelname)s: %(message)s")
		)
		logger.addHandler(handler)
		No newline at end of file

extensions-builtin/Lora/networks.py

+48 −32

Original line number	Diff line number	Diff line
		@@ -17,6 +17,8 @@ from typing import Union
		from modules import shared, devices, sd_models, errors, scripts, sd_hijack
		from modules.textual_inversion.textual_inversion import Embedding

		from lora_logger import logger

		module_types = [
		network_lora.ModuleTypeLora(),
		network_hada.ModuleTypeHada(),
		@@ -206,7 +208,40 @@ def load_network(name, network_on_disk):

		net.modules[key] = net_module

		net.bundle_embeddings = bundle_embeddings
		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.loaded = None
		embeddings[emb_name] = embedding

		net.bundle_embeddings = embeddings

		if keys_failed_to_match:
		logging.debug(f"Network {network_on_disk.filename} didn't match keys: {keys_failed_to_match}")
		@@ -229,7 +264,8 @@ def load_networks(names, te_multipliers=None, unet_multipliers=None, dyn_dims=No
		for net in loaded_networks:
		if net.name in names:
		already_loaded[net.name] = net
		for emb_name in net.bundle_embeddings:
		for emb_name, embedding in net.bundle_embeddings.items():
		if embedding.loaded:
		emb_db.register_embedding_by_name(None, shared.sd_model, emb_name)

		loaded_networks.clear()
		@@ -273,37 +309,17 @@ def load_networks(names, te_multipliers=None, unet_multipliers=None, dyn_dims=No
		net.dyn_dim = dyn_dims[i] if dyn_dims else 1.0
		loaded_networks.append(net)

		for emb_name, data in net.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
		for emb_name, embedding in net.bundle_embeddings.items():
		if embedding.loaded is None and emb_name in emb_db.word_embeddings:
		logger.warning(
		f'Skip bundle embedding: "{emb_name}"'
		' as it was already loaded from embeddings folder'
		)
		continue

		embedding.loaded = False
		if emb_db.expected_shape == -1 or emb_db.expected_shape == embedding.shape:
		embedding.loaded = True
		emb_db.register_embedding(embedding, shared.sd_model)
		else:
		emb_db.skipped_embeddings[name] = embedding

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