GPT Image 2 API 接入完整教程：Python / Node.js / Curl 全流程

pip install openai pillow

npm install openai
# 或
pnpm add openai

OPENAI_API_KEY=sk-proj-your-key-here

import os
from openai import OpenAI
import base64

client = OpenAI(api_key=os.environ["OPENAI_API_KEY"])

result = client.images.generate(
    model="gpt-image-2",
    prompt="一支白色不鏽鋼保溫杯立在淺米色亞麻桌布上，高級產品攝影，1:1 方圖。",
    size="1024x1024",
    quality="medium",
    n=1,
)

# 圖片以 base64 返回
image_b64 = result.data[0].b64_json
with open("output.png", "wb") as f:
    f.write(base64.b64decode(image_b64))

print("已儲存到 output.png")

import OpenAI from 'openai';
import fs from 'fs';

const client = new OpenAI({ apiKey: process.env.OPENAI_API_KEY });

const result = await client.images.generate({
  model: 'gpt-image-2',
  prompt:
    '一支白色不鏽鋼保溫杯立在淺米色亞麻桌布上，高級產品攝影，1:1 方圖。',
  size: '1024x1024',
  quality: 'medium',
  n: 1,
});

const imageB64 = result.data[0].b64_json;
fs.writeFileSync('output.png', Buffer.from(imageB64, 'base64'));
console.log('已儲存到 output.png');

curl https://api.openai.com/v1/images/generations \
  -H "Authorization: Bearer $OPENAI_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "gpt-image-2",
    "prompt": "一支白色不鏽鋼保溫杯立在淺米色亞麻桌布上。",
    "size": "1024x1024",
    "quality": "medium",
    "n": 1
  }'

result = client.images.edit(
    model="gpt-image-2",
    image=open("original.png", "rb"),
    prompt="把杯子的顏色從白色改成藏青色，其他全部保持不變。",
    size="1024x1024",
    quality="medium",
)

with open("edited.png", "wb") as f:
    f.write(base64.b64decode(result.data[0].b64_json))

import { toFile } from 'openai';

const result = await client.images.edit({
  model: 'gpt-image-2',
  image: await toFile(fs.createReadStream('original.png'), 'original.png'),
  prompt: '把杯子的顏色從白色改成藏青色，其他全部保持不變。',
  size: '1024x1024',
  quality: 'medium',
});

import asyncio
from openai import AsyncOpenAI

client = AsyncOpenAI(api_key=os.environ["OPENAI_API_KEY"])

async def generate_one(prompt: str, semaphore: asyncio.Semaphore):
    async with semaphore:
        result = await client.images.generate(
            model="gpt-image-2",
            prompt=prompt,
            size="1024x1024",
            quality="medium",
        )
        return result.data[0].b64_json

async def batch_generate(prompts: list[str], concurrency: int = 5):
    semaphore = asyncio.Semaphore(concurrency)
    tasks = [generate_one(p, semaphore) for p in prompts]
    return await asyncio.gather(*tasks)

results = asyncio.run(batch_generate(["prompt 1", "prompt 2", ...]))

import pLimit from 'p-limit';

const limit = pLimit(5); // 同時最多 5 個請求

const tasks = prompts.map((prompt) =>
  limit(async () => {
    const result = await client.images.generate({
      model: 'gpt-image-2',
      prompt,
      size: '1024x1024',
      quality: 'medium',
    });
    return result.data[0].b64_json;
  })
);

const results = await Promise.all(tasks);

from openai import OpenAI, APIError, RateLimitError, APIConnectionError, BadRequestError
import time

client = OpenAI(api_key=os.environ["OPENAI_API_KEY"])

def safe_generate(prompt: str, max_retries: int = 3):
    for attempt in range(max_retries):
        try:
            result = client.images.generate(
                model="gpt-image-2",
                prompt=prompt,
                size="1024x1024",
                quality="medium",
            )
            return result.data[0].b64_json

        except RateLimitError:
            # 429——退避後重試
            wait = (2 ** attempt) * 5
            print(f"被限流；等待 {wait} 秒")
            time.sleep(wait)

        except BadRequestError as e:
            # 400——通常是內容策略違規，不要重試
            print(f"請求錯誤：{e}")
            return None

        except APIConnectionError:
            # 網路抖動——重試
            time.sleep(2 ** attempt)

        except APIError as e:
            # 5xx 伺服器端錯誤——退避重試
            time.sleep(2 ** attempt)

    return None

# 第一步：5 個 prompt 變體跑 low 檔（總共 ¥0.40）
candidates = [generate(p, quality="low") for p in variants]

# 第二步：選最好的，high 檔重出
best_prompt = pick_winner(candidates)
final = generate(best_prompt, quality="high")  # ¥1.20

let completed = 0;
const tasks = prompts.map((prompt) =>
  limit(async () => {
    const result = await client.images.generate({ /* ... */ });
    completed++;
    onProgress({ completed, total: prompts.length });
    return result.data[0].b64_json;
  })
);

# 1. 提交請求 → 拿到 task_id
response = client.images.generate(
    model="gpt-image-2",
    prompt="...",
    size="1024x1024",
    quality="high",
    callback_url="https://your-app.com/api/image-callback",  # 可選
)
task_id = response.id

# 2. 每隔幾秒輪詢任務狀態
while True:
    status = client.images.retrieve(task_id)
    if status.status == "completed":
        image_url = status.data[0].url
        break
    elif status.status == "failed":
        raise Exception(status.error)
    time.sleep(3)