码上生花：用API链接Qwen-Image系列及衍生LoRA生态模型

import requests
import time
import json
from PIL import Image
from io import BytesIO
base_url = 'https://api-inference.modelscope.cn/'
api_key = "<MODELSCOPE_SDK_TOKEN>"  # 请替换为您的 ModelScope Access Token
common_headers = {
    "Authorization": f"Bearer {api_key}",
    "Content-Type": "application/json",
}
# 提交图像生成任务
response = requests.post(
    f"{base_url}v1/images/generations",
    headers={**common_headers, "X-ModelScope-Async-Mode": "true"},
    data=json.dumps({
        "model": "Qwen/Qwen-Image",
        "prompt": "一只可爱的金色小猫在阳光下玩耍，毛发细节清晰，背景是温馨的家居环境，超清，4K，电影级构图",
    }, ensure_ascii=False).encode('utf-8')
)
response.raise_for_status()
task_id = response.json()["task_id"]
# 轮询任务状态
while True:
    result = requests.get(
        f"{base_url}v1/tasks/{task_id}",
        headers={**common_headers, "X-ModelScope-Task-Type": "image_generation"},
    )
    result.raise_for_status()
    data = result.json()
    if data["task_status"] == "SUCCEED":
        image = Image.open(BytesIO(requests.get(data["output_images"][0]).content))
        image.save("qwen_image_result.jpg")
        print("图像生成成功！")
        break
    elif data["task_status"] == "FAILED":
        print("图像生成失败")
        break
    time.sleep(5)

import requests
import time
import json
from PIL import Image
from io import BytesIO
base_url = 'https://api-inference.modelscope.cn/'
api_key='<MODELSCOPE_TOKEN>'# ModelScope Token
common_headers = {
    "Authorization": f"Bearer {api_key}",
    "Content-Type": "application/json",
}
response = requests.post(
    f"{base_url}v1/images/generations",
    headers={**common_headers, "X-ModelScope-Async-Mode": "true"},
    data=json.dumps({
        "model": 'Qwen/Qwen-Image-Edit-2509', # ModelScope Model-Id, required
        "prompt": "给图中的狗戴上一个生日帽",
        "image_url":
            [
            "https://modelscope.oss-cn-beijing.aliyuncs.com/Dog.png"
            ]
        # multiple-image edit can be done by passing in multiple images:
        # "prompt": "写实风格，生成一张图片，图一中的狗，去追图二中的飞盘",
        # "image_url":
            #[
            # "https://modelscope.oss-cn-beijing.aliyuncs.com/Dog.png",
            # "https://modelscope.oss-cn-beijing.aliyuncs.com/Frisbee.png"
            #]
    }, ensure_ascii=False).encode('utf-8')
)
response.raise_for_status()
task_id = response.json()["task_id"]
whileTrue:
    result = requests.get(
        f"{base_url}v1/tasks/{task_id}",
        headers={**common_headers, "X-ModelScope-Task-Type": "image_generation"},
    )
    result.raise_for_status()
    data = result.json()
    if data["task_status"] == "SUCCEED":
        image = Image.open(BytesIO(requests.get(data["output_images"][0]).content))
        image.save("result_image.jpg")
        break
    elif data["task_status"] == "FAILED":
        print("Image Generation Failed.")
        break
    time.sleep(5)

# 修改上述示例中的response部分即可
response = requests.post(
    f"{base_url}v1/images/generations",
    headers={**common_headers, "X-ModelScope-Async-Mode": "true"},
    data=json.dumps({
        "model": "MoYouuu/MYHuman-QWen", # LoRA ModelId on ModelScope 
        "prompt": "生成九宫格表情包，包含吐舌头、微笑、托腮、眨眼、大笑、比V手势等多种表情，保持人物身份一致，纯白背景，专业摄影棚光线"
    }, ensure_ascii=False).encode('utf-8')
)