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import yaml
import torch
from diffusers import StableDiffusionXLPipeline
from utils import PhotoMakerStableDiffusionXLPipeline
import os
def get_models_dict():
# 打开并读取YAML文件
with open('config/models.yaml', 'r') 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)
def load_models(model_info,device,photomaker_path):
path = model_info["path"]
single_files = model_info["single_files"]
use_safetensors = model_info["use_safetensors"]
model_type = model_info["model_type"]
if model_type == "original":
if single_files:
pipe = StableDiffusionXLPipeline.from_single_file(
path,
torch_dtype=torch.float16
)
else:
pipe = StableDiffusionXLPipeline.from_pretrained(path, torch_dtype=torch.float16, use_safetensors=use_safetensors)
pipe = pipe.to(device)
elif model_type == "Photomaker":
if single_files:
print("loading from a single_files")
pipe = PhotoMakerStableDiffusionXLPipeline.from_single_file(
path,
torch_dtype=torch.float16
)
else:
pipe = PhotoMakerStableDiffusionXLPipeline.from_pretrained(
path, torch_dtype=torch.float16, use_safetensors=use_safetensors)
pipe = pipe.to(device)
pipe.load_photomaker_adapter(
os.path.dirname(photomaker_path),
subfolder="",
weight_name=os.path.basename(photomaker_path),
trigger_word="img" # define the trigger word
)
pipe.fuse_lora()
else:
raise NotImplementedError("You should choice between original and Photomaker!",f"But you choice {model_type}")
return pipe |