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Update load_models_utils.py

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update
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smthemex
2024-06-22 20:45:22 +08:00
committed by GitHub
parent 105cf9e084
commit 11205a8a30
1 changed files with 45 additions and 28 deletions
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utils/load_models_utils.py
+45 -28
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@@ -10,20 +10,38 @@ dif_version_int= int(dif_version.split(".")[1])
dir_path = os.path.dirname(os.path.abspath(__file__))
path_dir = os.path.dirname(dir_path)
file_path = os.path.dirname(path_dir)
yaml_path = os.path.join(path_dir ,'config','models.yaml')
original_config_file=os.path.join(path_dir,'config','sd_xl_base.yaml')
def get_instance_path(path):
instance_path = os.path.normpath(path)
if sys.platform == 'win32':
instance_path = path.replace('\\', "/")
return instance_path
yaml_path=get_instance_path(yaml_path)
original_config_file = get_instance_path(original_config_file)
#print(yaml_path,original_config_file)
yaml_path = get_instance_path(os.path.join(path_dir ,'config','models.yaml'))
original_config_file=get_instance_path(os.path.join(path_dir,'config','sd_xl_base.yaml'))
loras_path = get_instance_path(os.path.join(path_dir,"config","lora.yaml"))
def get_lora_dict():
# 打开并读取YAML文件
with open(loras_path, 'r', encoding="UTF-8") as stream:
try:
# 解析YAML文件内容
data = yaml.safe_load(stream)
# 此时 'data' 是一个Python字典，里面包含了YAML文件的所有数据
# print(data)
return data
except yaml.YAMLError as exc:
# 如果在解析过程中发生了错误，打印异常信息
print(exc)
datas = get_lora_dict()
lora_lightning_list = datas["lightning_xl_lora"]
#print(lora_lightning_list)
def get_models_dict():
# 打开并读取YAML文件
with open(yaml_path, 'r') as stream:
with open(yaml_path, 'r', encoding="UTF-8") as stream:
try:
# 解析YAML文件内容
data = yaml.safe_load(stream)
@@ -36,7 +54,7 @@ def get_models_dict():
# 如果在解析过程中发生了错误，打印异常信息
print(exc)
def load_models(model_info,_sd_type,device,photomaker_path,lora,trigger_words,lora_scale):
def load_models(model_info,_sd_type,device,photomaker_path,lora,lora_path,trigger_words,lora_scale):
path = model_info["path"]
single_files = model_info["single_files"]
use_safetensors = model_info["use_safetensors"]
@@ -54,19 +72,19 @@ def load_models(model_info,_sd_type,device,photomaker_path,lora,trigger_words,lo
)
else:
if _sd_type=="Playground_v2p5":
pipe = DiffusionPipeline.from_pretrained(
path, torch_dtype=torch.float16, use_safetensors=use_safetensors
)
else:
pipe = StableDiffusionXLPipeline.from_pretrained(
path, torch_dtype=torch.float16,use_safetensors=use_safetensors
)
pipe = StableDiffusionXLPipeline.from_pretrained(
path, torch_dtype=torch.float16,use_safetensors=use_safetensors
)
pipe = pipe.to(device)
if lora != "none":
pipe.load_lora_weights(lora, adapter_name=trigger_words)
pipe.fuse_lora(adapter_names=[trigger_words,], lora_scale=lora_scale)
if lora in lora_lightning_list:
pipe.load_lora_weights(lora_path)
pipe.fuse_lora()
else:
pipe.load_lora_weights(lora_path, adapter_name=trigger_words)
pipe.fuse_lora(adapter_names=[trigger_words, ], lora_scale=lora_scale)
elif model_type == "Photomaker":
if single_files:
#print("loading from a single_files")
@@ -78,25 +96,24 @@ def load_models(model_info,_sd_type,device,photomaker_path,lora,trigger_words,lo
pretrained_model_link_or_path=path, original_config_file=original_config_file, torch_dtype=torch.float16,use_safetensors=use_safetensors
)
else:
if _sd_type=="Playground_v2p5":
pipe = PhotoMakerStableDiffusionXLPipeline.from_pretrained(
path, torch_dtype=torch.float16, use_safetensors=use_safetensors
)
else:
pipe = PhotoMakerStableDiffusionXLPipeline.from_pretrained(
path, torch_dtype=torch.float16,use_safetensors=use_safetensors
)
pipe = PhotoMakerStableDiffusionXLPipeline.from_pretrained(
path, torch_dtype=torch.float16,use_safetensors=use_safetensors
)
pipe = pipe.to(device)
if lora != "none":
pipe.load_lora_weights(lora, adapter_name=trigger_words)
pipe.fuse_lora(adapter_names=[trigger_words,], lora_scale=lora_scale)
pipe.load_photomaker_adapter(
os.path.dirname(photomaker_path),
subfolder="",
weight_name=os.path.basename(photomaker_path),
trigger_word="img" # define the trigger word
)
if lora != "none":
if lora in lora_lightning_list:
pipe.load_lora_weights(lora_path)
pipe.fuse_lora()
else:
pipe.load_lora_weights(lora_path, adapter_name=trigger_words)
pipe.fuse_lora(adapter_names=[trigger_words, ], lora_scale=lora_scale)
else: