modelscope流水线如何加载lora合并权重

以下为例

其中的
pipe.scheduler = LCMScheduler.from_config(pipe.scheduler.config)
和
pipe.load_lora_weights(adapter_id)
pipe.fuse_lora()

如何写？

import torch
from diffusers import LCMScheduler, AutoPipelineForText2Image

model_id = "stabilityai/stable-diffusion-xl-base-1.0"
adapter_id = "latent-consistency/lcm-lora-sdxl"

pipe = AutoPipelineForText2Image.from_pretrained(model_id, torch_dtype=torch.float16, variant="fp16")
pipe.scheduler = LCMScheduler.from_config(pipe.scheduler.config)
pipe.to("cuda")

# load and fuse lcm lora
pipe.load_lora_weights(adapter_id)
pipe.fuse_lora()


prompt = "Self-portrait oil painting, a beautiful cyborg with golden hair, 8k"

# disable guidance_scale by passing 0
image = pipe(prompt=prompt, num_inference_steps=4, guidance_scale=0).images[0]

另外一个sdxl的写法没有问题也没有找到合并lora权重的方法

from modelscope.utils.constant import Tasks
from modelscope.pipelines import pipeline
import cv2

pipe = pipeline(task=Tasks.text_to_image_synthesis, 
                model='AI-ModelScope/stable-diffusion-xl-base-1.0',
                use_safetensors=True,
                model_revision='v1.0.0')

prompt = 'a dog'
output = pipe({'text': prompt})
cv2.imwrite('result.png', output['output_imgs'][0])