gpt-image-2
图像生成 API

import base64
import os
from pathlib import Path

import requests

endpoint = "https://new.fluapi.com/v1/images/generations"
api_key = os.environ["FLU_API_KEY"]

payload = {
    "model": "gpt-image-2",
    "prompt": "一台悬浮在纯白摄影棚中的未来感精密设备，黑色金属材质，青色光线，商业产品摄影",
    "n": 1,
    "size": "2048x2048",
    "quality": "high",
    "output_format": "png",
    "background": "opaque",
}

response = requests.post(
    endpoint,
    headers={
        "Authorization": f"Bearer {api_key}",
        "Content-Type": "application/json",
    },
    json=payload,
    timeout=600,
)
response.raise_for_status()

result = response.json()
image_bytes = base64.b64decode(result["data"][0]["b64_json"])
Path("gpt-image-2.png").write_bytes(image_bytes)

import { writeFile } from "node:fs/promises";

const endpoint =
  "https://new.fluapi.com/v1/images/generations";

const response = await fetch(endpoint, {
  method: "POST",
  headers: {
    Authorization: `Bearer ${process.env.FLU_API_KEY}`,
    "Content-Type": "application/json",
  },
  body: JSON.stringify({
    model: "gpt-image-2",
    prompt:
      "一台悬浮在纯白摄影棚中的未来感精密设备，黑色金属材质，青色光线，商业产品摄影",
    n: 1,
    size: "2048x2048",
    quality: "high",
    output_format: "png",
    background: "opaque",
  }),
  signal: AbortSignal.timeout(600_000),
});

if (!response.ok) {
  throw new Error(`${response.status}: ${await response.text()}`);
}

const result = await response.json();
const image = Buffer.from(result.data[0].b64_json, "base64");
await writeFile("gpt-image-2.png", image);