命令：

python main.py --all-in-fp16 --always-gpu --host=0.0.0.0 --disable-image-log

--host=0.0.0.0 可以使用局域网访问

--disable-image-log 防止出现两份相同输出的图像数据

批量操作代码实例

import requests
import json
import time
import pandas as pd
'''
启动命令：python main.py --all-in-fp16 --always-gpu --host=0.0.0.0 --disable-image-log
'''
host = "http://127.0.0.1:7865"
# 图生文
def text2img(params: dict) -> dict:
    """
    文生图
    """
    result = requests.post(url=f"{host}/v1/generation/text-to-image",
                           data=json.dumps(params),
                           headers={"Content-Type": "application/json"})
    # print(result.text)
    return result.json()


# 查询指定任务完成情况
def taskResult(task_id: str) -> dict:
    # 获取任务状态
    task_status = requests.get(url=f"{host}/v1/generation/query-job",
                               params={"job_id": task_id,
                                       "require_step_preivew": False},
                               timeout=30)
    print(task_status.text)
    return task_status.json()

# 刷新模型
def refresh() -> dict:
    """
    refresh-models
    """
    response = requests.post(url=f"{host}/v1/engines/refresh-models",
                        timeout=30)
    return response.json()

# 查询可用模型（基础模型和lora模型）
def all_models() -> dict:
    """
    all-models
    """
    response = requests.get(url=f"{host}/v1/engines/all-models",
                        timeout=30)
    return response.json()

# 查询可用样式
def styles() -> dict:
    """
    styles
    """
    response = requests.get(url=f"{host}/v1/engines/styles",
                        timeout=30)
    return response.json()

# 查询处理进度
def job_queue() -> dict:
    """
    job-queue
    """
    response = requests.get(url=f"{host}/v1/generation/job-queue",
                        timeout=30)
    return response.json()

# 读取excel指定列的数据

def read_data(ex_path, lie):
    # 读取excel文件
    df = pd.read_excel(ex_path)

    # 获取sen1_trans列数据
    sen1_trans_data = df[lie].tolist()

    return sen1_trans_data


if __name__ == '__main__':

    # prompts = ["This part of the story paints a picture of the thick grass and the flames of war."]
    # 读取excel中的批量prompts
    prompts = read_data(r"C:\Users\pc\Downloads\ll_test.xlsx","sen1_trans")
    for i in prompts:
        result =text2img({
            "prompt": i,
            "negative_prompt": "low quality",
            "style_selections": ["Fooocus V2", "Fooocus Masterpiece","Fooocus Negative"],
            "aspect_ratios_selection":"1024*1024",
            "base_model_name":"骊歌_Linger_（SDXL真实系大模型）_2.0.safetensors",
            "guidance_scale":8,
            "image_number":2,
            "image_seed":-1,
            "loras":[{"model_name": "大自然的鬼匠神工SDXL_v1.0.safetensors", "weight": 0.6},
                     {"model_name": "中式古建筑_V1.safetensors", "weight": 0.4},
                     {"model_name": "WDR｜江南烟雨_v1.0.safetensors", "weight": 0.8}],
            "async_process": True})
        print(result)
        time.sleep(30)
    # print(taskResult('a88a356b-a408-45e8-a107-169f76214250'))

# 文生图参数
'''
prompt:描述词
negative_prompt: 反向描述词
style_selections: [] 风格列表, 需要是受支持的风格, 可以通过 样式接口 获取所有支持的样式
performance_selection: 性能选择, Speed, Quality, Extreme Speed 中的一个, 默认 Speed
aspect_ratios_selection: 分辨率, 默认 '1152*896'
image_number: 生成图片数量, 默认 1 , 最大32, 注: 非并行接口
image_seed: 默认 -1, 即随机生成
sharpness: 锐度, 默认 2.0 , 0-30
guidance_scale: 引导比例, 默认 4.0 , 1-30
base_model_name: 基础模型
refiner_model_name: 优化模型
refiner_switch: 优化模型切换时机, 默认 0.5
loras: lora 模型列表
advanced_params:高级参数
require_base64: 是否返回base64编码, 默认 False
async_process: 是否异步处理, 默认 False
'''

测试效果：

正常运行。

批量输出结果..

 可以挂一晚上，第二天早上查看结果了