gpt-image-2-chat

3840x2160 'your prompt'
auto 'your prompt'

curl https://api.aiearth.dev/v1/chat/completions \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer sk-your-api-key" \
  -d '{
    "model": "gpt-image-2-chat",
    "messages": [
      {
        "role": "user",
        "content": [
          {
            "type": "text",
            "text": "3840x2160 '参考这张图片，生成一张产品海报。保留主体风格，背景换成明亮的摄影棚。'"
          },
          {
            "type": "image_url",
            "image_url": {
              "url": "https://example.com/source-image.png"
            }
          }
        ]
      }
    ],
    "stream": false
  }'

import base64
import json
import mimetypes
from pathlib import Path
from typing import Any
from urllib.parse import urlparse
from urllib.request import Request, urlopen

from openai import OpenAI

API_KEY = "sk-your-api-key"
BASE_URL = "https://api.aiearth.dev/v1/"

IMAGE_REF = "https://example.com/source-image.png"
QUESTION = "3840x2160 '参考这张图片，生成一张产品海报。保留主体风格，背景换成明亮的摄影棚。'"
STREAM = False


def guess_image_mime(source: str, content_type: str | None = None) -> str:
    if content_type:
        mime_type = content_type.split(";", 1)[0].strip().lower()
        if mime_type.startswith("image/"):
            return mime_type

    parsed_path = urlparse(source).path or source
    mime_type, _ = mimetypes.guess_type(parsed_path)
    if mime_type and mime_type.startswith("image/"):
        return mime_type

    return "image/jpeg"


def read_image_bytes(source: str) -> tuple[bytes, str]:
    if source.startswith(("http://", "https://")):
        request = Request(source, headers={"User-Agent": "Mozilla/5.0"})
        with urlopen(request, timeout=120) as response:
            image_bytes = response.read()
            content_type = response.headers.get("Content-Type")
        return image_bytes, guess_image_mime(source, content_type)

    image_path = Path(source).expanduser()
    return image_path.read_bytes(), guess_image_mime(str(image_path))


def image_reference_to_data_url(source: str) -> str:
    if source.startswith("data:image"):
        return source

    image_bytes, mime_type = read_image_bytes(source)
    base64_image = base64.b64encode(image_bytes).decode("utf-8")
    return f"data:{mime_type};base64,{base64_image}"


def serialize_for_log(value: Any) -> Any:
    if hasattr(value, "model_dump"):
        return value.model_dump(mode="json")
    if isinstance(value, dict):
        return {str(key): serialize_for_log(item) for key, item in value.items()}
    if isinstance(value, (list, tuple)):
        return [serialize_for_log(item) for item in value]
    return value


messages = [
    {
        "role": "user",
        "content": [
            {"type": "text", "text": QUESTION},
            {
                "type": "image_url",
                "image_url": {"url": image_reference_to_data_url(IMAGE_REF)},
            },
        ],
    }
]

client = OpenAI(
    base_url=BASE_URL,
    api_key=API_KEY,
)

response = client.chat.completions.create(
    model="gpt-image-2-chat",
    messages=messages,
    stream=STREAM,
    metadata={
        "quality": "high",
    },
)

if not STREAM:
    response_data = serialize_for_log(response)
    print(json.dumps(response_data, ensure_ascii=False, indent=2))

    message = response_data["choices"][0]["message"]
    print(message.get("content", ""))
else:
    for chunk in response:
        print(chunk)

{
  "id": "chatcmpl-example",
  "object": "chat.completion",
  "created": 1741570283,
  "model": "gpt-image-2-chat",
  "choices": [
    {
      "index": 0,
      "message": {
        "role": "assistant",
        "content": "已生成图片：https://example.com/generated-image.png"
      },
      "finish_reason": "stop"
    }
  ],
  "usage": {
    "prompt_tokens": 1117,
    "completion_tokens": 46,
    "total_tokens": 1163
  }
}

curl https://api.aiearth.dev/v1/chat/completions \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer sk-your-api-key" \
  -d '{
    "model": "gpt-image-2-chat",
    "messages": [
      {
        "role": "user",
        "content": [
          {
            "type": "text",
            "text": "auto '参考这张图片，生成一张横版科技海报。'"
          },
          {
            "type": "image_url",
            "image_url": {
              "url": "https://example.com/source-image.png"
            }
          }
        ]
      }
    ],
    "stream": true
  }'