feat(image): add gpt-image-2 single and multi-image workflows
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zhangheng
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docs/usage.md
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docs/usage.md
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# 图片生成接口指南
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## 1. 环境准备
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Windows PowerShell：
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```powershell
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py -m pip install requests pillow
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```
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Mac / Linux Terminal：
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```bash
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python3 -m pip install requests pillow
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```
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## 2. 接口地址
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`POST https://api.toskaxy.xyz/v1/images/generations`
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## 3. 鉴权方式
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```http
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Authorization: Bearer pk_live_xxx
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Content-Type: application/json
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```
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## 4. 支持的图片模型
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- `gpt-image-1`
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- `gpt-image-1.5`
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- `gpt-image-2`
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## 5. 文生图最小字段
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- `model`：图片模型名称，例如 `gpt-image-2`
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- `prompt`：图片提示词
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- `size`：输出尺寸。推荐直接传像素尺寸，例如 `4096x4096`、`4096x2304`、`2304x4096`
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- `resolution`：兼容字段。使用比例写法时传 `1K`、`2K`、`4K`，网关会转换成实际像素尺寸
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## 6. `gpt-image-2` 的 4K 尺寸规则
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推荐写法：直接传像素。
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```json
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{ "model": "gpt-image-2", "prompt": "draw a clean product poster", "size": "4096x4096" }
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{ "model": "gpt-image-2", "prompt": "draw a clean product poster", "size": "4096x2304" }
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{ "model": "gpt-image-2", "prompt": "draw a clean product poster", "size": "2304x4096" }
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```
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兼容写法：比例 + `resolution`。
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```json
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{ "model": "gpt-image-2", "prompt": "draw a clean product poster", "size": "1:1", "resolution": "4K" }
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{ "model": "gpt-image-2", "prompt": "draw a clean product poster", "size": "16:9", "resolution": "4K" }
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{ "model": "gpt-image-2", "prompt": "draw a clean product poster", "size": "9:16", "resolution": "4K" }
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```
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## 7. 比例转换示例
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- `1:1` + `4K` -> `4096x4096`
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- `16:9` + `4K` -> `4096x2304`
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- `9:16` + `4K` -> `2304x4096`
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- `3:4` + `4K` -> `3072x4096`
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- `4:3` + `4K` -> `4096x3072`
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## 8. 旧图片模型的 4K 降级规则
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`gpt-image-1` 和 `gpt-image-1.5` 不支持真正 4K。如果用户传 `resolution: "4K"`，网关会自动降级成上游支持的最大尺寸。
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- 横图比例：`1536x1024`
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- 竖图比例：`1024x1536`
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- 方图比例：`1024x1024`
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## 9. 支持的比例
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`1:1`、`16:9`、`9:16`、`4:3`、`3:4`、`3:2`、`2:3`、`5:4`、`4:5`、`2:1`、`1:2`、`21:9`、`9:21`
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建议只通过 `size` 传比例，不要在 `prompt` 里重复写比例。
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## 10. 4K 文生图完整 Python 示例
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把 `API_KEY` 改成自己的密钥，保存为 `gpt_image2_generate.py` 后运行。
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```python
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import base64
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import requests
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API_KEY = "pk_live_xxx"
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BASE_URL = "https://api.toskaxy.xyz"
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payload = {
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"model": "gpt-image-2",
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"prompt": "高端香水产品海报，玻璃瓶，干净背景，商业摄影，细节清晰",
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"n": 1,
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"size": "4096x4096",
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"quality": "high",
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"response_format": "b64_json",
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}
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headers = {
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"Authorization": f"Bearer {API_KEY}",
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"Content-Type": "application/json",
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}
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resp = requests.post(
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f"{BASE_URL}/v1/images/generations",
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json=payload,
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headers=headers,
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timeout=240,
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)
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resp.raise_for_status()
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data = resp.json()
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img_b64 = data["data"][0]["b64_json"]
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with open("output.png", "wb") as f:
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f.write(base64.b64decode(img_b64))
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print("已保存 output.png")
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```
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## 11. 图生图：参考图为公开 URL
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```python
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import base64
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import requests
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API_KEY = "pk_live_xxx"
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BASE_URL = "https://api.toskaxy.xyz"
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payload = {
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"model": "gpt-image-2",
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"prompt": "把参考图变成水彩画风格，保留主体结构",
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"n": 1,
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"size": "2048x2048",
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"quality": "high",
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"response_format": "b64_json",
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"image_urls": [
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"https://example.com/reference.jpg"
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],
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}
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headers = {
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"Authorization": f"Bearer {API_KEY}",
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"Content-Type": "application/json",
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}
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resp = requests.post(
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f"{BASE_URL}/v1/images/generations",
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json=payload,
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headers=headers,
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timeout=240,
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)
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resp.raise_for_status()
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data = resp.json()
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img_b64 = data["data"][0]["b64_json"]
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with open("output.png", "wb") as f:
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f.write(base64.b64decode(img_b64))
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print("已保存 output.png")
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```
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## 12. 图生图：参考图为本地文件 base64
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把 `reference.png` 放到脚本同一个文件夹，脚本会把它转成 data URL 再提交。
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```python
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import base64
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import requests
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API_KEY = "pk_live_xxx"
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BASE_URL = "https://api.toskaxy.xyz"
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REFERENCE_FILE = "reference.png"
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with open(REFERENCE_FILE, "rb") as f:
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reference_b64 = base64.b64encode(f.read()).decode("utf-8")
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payload = {
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"model": "gpt-image-2",
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"prompt": "基于参考图生成一张高端产品海报，干净构图，中文界面可用",
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"n": 1,
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"size": "4096x2304",
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"quality": "high",
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"response_format": "b64_json",
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"image_urls": [
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f"data:image/png;base64,{reference_b64}"
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],
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}
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headers = {
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"Authorization": f"Bearer {API_KEY}",
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"Content-Type": "application/json",
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}
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resp = requests.post(
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f"{BASE_URL}/v1/images/generations",
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json=payload,
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headers=headers,
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timeout=240,
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)
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resp.raise_for_status()
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data = resp.json()
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img_b64 = data["data"][0]["b64_json"]
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with open("output.png", "wb") as f:
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f.write(base64.b64decode(img_b64))
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print("已保存 output.png")
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```
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## 13. 下载后校验是否为 4K
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```python
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from io import BytesIO
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from PIL import Image
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import requests
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image_url = "<接口返回的图片URL>"
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img_bytes = requests.get(image_url, timeout=120).content
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img = Image.open(BytesIO(img_bytes))
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print(img.size)
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width, height = img.size
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assert max(width, height) >= 3840, f"not 4K enough: {img.size}"
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```
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## 14. 同步返回与异步轮询兼容写法
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```python
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import base64
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import time
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import requests
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API_KEY = "pk_live_xxx"
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BASE_URL = "https://api.toskaxy.xyz"
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headers = {
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"Authorization": f"Bearer {API_KEY}",
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"Content-Type": "application/json",
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}
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payload = {
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"model": "gpt-image-2",
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"prompt": "a corgi astronaut on the moon, cinematic, 8k",
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"size": "16:9",
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"resolution": "4K",
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"n": 1,
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"response_format": "b64_json",
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}
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resp = requests.post(
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f"{BASE_URL}/v1/images/generations",
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json=payload,
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headers=headers,
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timeout=240,
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)
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resp.raise_for_status()
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data = resp.json()
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# 情况 1：同步返回 base64
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if isinstance(data, dict) and data.get("data") and data["data"][0].get("b64_json"):
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with open("output.png", "wb") as f:
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f.write(base64.b64decode(data["data"][0]["b64_json"]))
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print("已保存 output.png")
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# 情况 2：异步任务返回 task_id
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elif isinstance(data, dict) and isinstance(data.get("data"), dict) and data["data"].get("id"):
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task_id = data["data"]["id"]
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task_url = f"{BASE_URL}/v1/tasks/{task_id}"
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time.sleep(10)
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while True:
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task_resp = requests.get(task_url, headers=headers, timeout=60)
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task_resp.raise_for_status()
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task = task_resp.json()
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status = task["data"]["status"]
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if status == "completed":
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print(task["data"]["result"]["images"][0]["url"][0])
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break
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if status == "failed":
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raise RuntimeError(task["data"]["error"]["message"])
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time.sleep(5)
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else:
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raise RuntimeError(f"unexpected response: {data}")
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```
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## 15. 图片接口常见问题
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- 只传 `size: "16:9"` 不能保证一定是 4K；要 4K，推荐直接传 `4096x2304` 等像素尺寸。
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- `gpt-image-1` 和 `gpt-image-1.5` 不支持真正 4K，网关会自动降级。
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- `gpt-image-2` 的图生图不要把参考图塞进 `/v1/images/generations` 的 JSON。应改走 `/v1/images/edits`，并用 `multipart/form-data` 上传 `image` 或 `image[]`。
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- 单张图可能耗时较长，客户端 `timeout` 建议至少 120 秒；4K 建议 240 秒。
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- 如果返回 `b64_json`，说明当前是同步模式，不需要再轮询 `task_id`。
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## 16. 直连测试命令
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- 运行 `scripts/run_figma_direct_test.sh` 会先读取当前 `.env`，再临时清空本地代理并直连 `vip.auto-code.net`。
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- 它只强制 `IMAGE_REQUEST_STYLE=apicodex`、`IMAGE_SIZE=3840x2160` 和长超时，其余参数继续沿用 `.env` 和 `gpt_image2_generate.py` 的默认值。
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- 每次请求会落到 `outputs/image-demo-runs/<时间戳>/`。
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- 直接运行：`bash scripts/run_figma_direct_test.sh`
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- 只测 `https://api.aoixx.com/v1/images/generations` 时，运行 `bash scripts/run_aoixx_direct_test.sh`。
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- 如果要走 OpenAI-compatible 的 `gpt-image-2` 图生图，运行 `bash scripts/run_openai_gpt_image2_edit.sh`。
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- 如果要走多图参考生成多图，运行 `bash scripts/run_openai_gpt_image2_multi.sh`。
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- 图生图可通过环境变量传参考图：
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`REFERENCE_IMAGE=./input.png`，或 `REFERENCE_IMAGE_URL=https://example.com/input.png`。
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- 如需 mask，可额外传：
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`MASK_IMAGE=./mask.png`，或 `MASK_IMAGE_URL=https://example.com/mask.png`。
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- 多图参考推荐两种写法：
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`REFERENCE_IMAGE_1=./a.png`、`REFERENCE_IMAGE_2=./b.png`，或
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`REFERENCE_IMAGES='["./a.png","./b.png","./c.png"]'`。
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- 多图输出用 `IMAGE_N=3` 控制，未设置时默认等于参考图数量。
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- 示例：
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`REFERENCE_IMAGE=./input.png IMAGE_PROMPT="改成极简白底产品海报" bash scripts/run_openai_gpt_image2_edit.sh`
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