Images

AI image generation, editing, variations, upscaling, style transfer, and background removal. Use SeaLink /v1/images/* and choose model capabilities and parameters from the model detail page; first probes should use n=1 and a base size.

`POST /v1/images/generations`

AI image generation. The example model comes from the current public callable catalog; use /v1/models and the model detail page for available models, sizes, and reference-image support.

For image editing and reference-image models, production probes are most reliable with data:image/...;base64,... inputs. If you pass a plain URL, it must be directly downloadable by the upstream provider, and both image dimensions must be greater than 10 px.

Parameters

Parameter	Type	Required	Description
model	string	Yes	gpt-image-2
prompt	string	Yes*	Image description; *no if using template_id
template_id	string	No	Prompt template ID
variables	object	No	Prompt template variables; values must be strings
n	integer	No	1 - 10 (default 1)
size	string	No	256x256, 512x512, 768x768, 1024x1024, 1328x1328, 1472x1104, 1104x1472, 1664x928, 928x1664, 1792x1024, 1024x1792
response_format	string	No	url, b64_json (default url)
quality	string	No	standard, hd
style	string	No	vivid, natural
image / image_url	string	No	Single reference image URL or base64 for models that support reference input
images / image_urls / reference_images	string[]	No	Reference image array for models that support multiple references; use images: [url] when the selected model supports it
reference_url / reference_urls	string / string[]	No	Reference image aliases; use only when supported by the selected model

cURL

curl https://test.sealink.io/v1/images/generations \
  -H "Authorization: Bearer $SEALINK_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "gpt-image-2",
    "prompt": "A serene Japanese garden with cherry blossoms, soft morning light",
    "n": 1,
    "size": "1024x1024",
    "response_format": "url"
  }'

Python

import os
from openai import OpenAI

client = OpenAI(
    base_url="https://test.sealink.io/v1",
    api_key=os.environ["SEALINK_API_KEY"],
)

response = client.images.generate(
    model="gpt-image-2",
    prompt="A serene Japanese garden with cherry blossoms, soft morning light",
    n=1,
    size="1024x1024",
)
print(response.data[0].url)

Node.js

import OpenAI from "openai";

const client = new OpenAI({
  baseURL: "https://test.sealink.io/v1",
  apiKey: process.env.SEALINK_API_KEY || "",
});

const response = await client.images.generate({
  model: "gpt-image-2",
  prompt: "A serene Japanese garden with cherry blossoms, soft morning light",
  n: 1,
  size: "1024x1024",
});
console.log(response.data[0].url);

cURL - reference image

# Reference-image generation uses /v1/images/generations with images[]
IMG_B64="$(base64 -w0 photo.png)"
curl https://test.sealink.io/v1/images/generations \
  -H "Authorization: Bearer $SEALINK_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "<image-reference-model-from-/v1/models>",
    "prompt": "Turn this product photo into a clean catalog render",
    "images": ["data:image/png;base64,'"$IMG_B64"'"],
    "n": 1,
    "size": "1024x1024"
  }'

`POST /v1/images/edits`

Image editing. Choose a model whose detail page lists image editing support, then send it to /v1/images/edits. JSON and multipart/form-data are both supported; multipart files are limited to 10 MB.

Parameters

Parameter	Type	Required	Description
model	string	Yes	<image-edit-model-from-/v1/models>
prompt	string	Yes	Edit instruction
image / image_url	string	Yes	Source image base64 or URL; multipart mode uses image file field
mask / mask_url	string	No	Mask base64 or URL
n	integer	No	1 - 10
size	string	No	256x256, 512x512, 1024x1024
response_format	string	No	url, b64_json

cURL

# JSON mode — use a data URL for private or hard-to-fetch images
IMG_B64="$(base64 -w0 photo.png)"
curl https://test.sealink.io/v1/images/edits \
  -H "Authorization: Bearer $SEALINK_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "<image-edit-model-from-/v1/models>",
    "image_url": "data:image/png;base64,'"$IMG_B64"'",
    "prompt": "Add a rainbow in the sky",
    "n": 1,
    "size": "1024x1024"
  }'

Python

import os
from openai import OpenAI

client = OpenAI(
    base_url="https://test.sealink.io/v1",
    api_key=os.environ["SEALINK_API_KEY"],
)

response = client.images.edit(
    model="<image-edit-model-from-/v1/models>",
    image=open("photo.png", "rb"),
    prompt="Add a rainbow in the sky",
    n=1,
    size="1024x1024",
)
print(response.data[0].url)

Node.js

import OpenAI from "openai";
import fs from "fs";

const client = new OpenAI({
  baseURL: "https://test.sealink.io/v1",
  apiKey: process.env.SEALINK_API_KEY || "",
});

const response = await client.images.edit({
  model: "<image-edit-model-from-/v1/models>",
  image: fs.createReadStream("photo.png"),
  prompt: "Add a rainbow in the sky",
  n: 1,
  size: "1024x1024",
});
console.log(response.data[0].url);

`POST /v1/images/variations`

Image variations. Generate variations of an input image with similar style but different content. Multipart/form-data only.

Parameters

Parameter	Type	Required	Description
image	file	Yes	PNG image file, max 10MB, square
model	string	No	<image-edit-model-from-/v1/models>
n	integer	No	1 - 10
size	string	No	256x256, 512x512, 1024x1024

cURL

# Multipart mode — upload local image
curl https://test.sealink.io/v1/images/variations \
  -H "Authorization: Bearer $SEALINK_API_KEY" \
  -F "image=@photo.png" \
  -F "model=<image-edit-model-from-/v1/models>" \
  -F "n=1" \
  -F "size=1024x1024"

Python

import os
from openai import OpenAI

client = OpenAI(
    base_url="https://test.sealink.io/v1",
    api_key=os.environ["SEALINK_API_KEY"],
)

response = client.images.create_variation(
    model="<image-edit-model-from-/v1/models>",
    image=open("photo.png", "rb"),
    n=1,
    size="1024x1024",
)
print(response.data[0].url)

Node.js

import OpenAI from "openai";
import fs from "fs";

const client = new OpenAI({
  baseURL: "https://test.sealink.io/v1",
  apiKey: process.env.SEALINK_API_KEY || "",
});

const response = await client.images.createVariation({
  model: "<image-edit-model-from-/v1/models>",
  image: fs.createReadStream("photo.png"),
  n: 1,
  size: "1024x1024",
});
console.log(response.data[0].url);

`POST /v1/images/upscale`

Image upscaling. Enlarge low-resolution images by 2x or 4x with enhanced clarity and detail.

Parameters

Parameter	Type	Required	Description
model	string	Yes	<image-upscale-model-from-/v1/models>
image	string	Yes	Source image base64 or URL
scale	integer	No	2 or 4
response_format	string	No	url, b64_json

cURL

curl https://test.sealink.io/v1/images/upscale \
  -H "Authorization: Bearer $SEALINK_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "<image-upscale-model-from-/v1/models>",
    "image": "https://example.com/low-res.jpg",
    "scale": 4,
    "response_format": "url"
  }'

Python

import os
import requests

res = requests.post(
    "https://test.sealink.io/v1/images/upscale",
    headers={"Authorization": f"Bearer {os.environ['SEALINK_API_KEY']}"},
    json={
        "model": "<image-upscale-model-from-/v1/models>",
        "image": "https://example.com/low-res.jpg",
        "scale": 4,
        "response_format": "url",
    },
)
print(res.json()["data"][0]["url"])

Node.js

const res = await fetch("https://test.sealink.io/v1/images/upscale", {
  method: "POST",
  headers: {
    Authorization: `Bearer ${process.env.SEALINK_API_KEY || ""}`,
    "Content-Type": "application/json",
  },
  body: JSON.stringify({
    model: "<image-upscale-model-from-/v1/models>",
    image: "https://example.com/low-res.jpg",
    scale: 4,
    response_format: "url",
  }),
});
const data = await res.json();
console.log(data.data[0].url);

`POST /v1/images/style-transfer`

Style transfer. Apply the artistic style of one image to the content of another.

Parameters

Parameter	Type	Required	Description
model	string	Yes	<image-style-transfer-model-from-/v1/models>
image	string	Yes	Content image base64 or URL
style_reference	string	Yes	Style reference image base64 or URL
strength	number	No	0 - 1
response_format	string	No	url, b64_json

cURL

curl https://test.sealink.io/v1/images/style-transfer \
  -H "Authorization: Bearer $SEALINK_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "<image-style-transfer-model-from-/v1/models>",
    "image": "https://example.com/content.jpg",
    "style_reference": "https://example.com/style.jpg",
    "strength": 0.7,
    "response_format": "url"
  }'

Python

import os
import requests

res = requests.post(
    "https://test.sealink.io/v1/images/style-transfer",
    headers={"Authorization": f"Bearer {os.environ['SEALINK_API_KEY']}"},
    json={
        "model": "<image-style-transfer-model-from-/v1/models>",
        "image": "https://example.com/content.jpg",
        "style_reference": "https://example.com/style.jpg",
        "strength": 0.7,
        "response_format": "url",
    },
)
print(res.json()["data"][0]["url"])

Node.js

const res = await fetch("https://test.sealink.io/v1/images/style-transfer", {
  method: "POST",
  headers: {
    Authorization: `Bearer ${process.env.SEALINK_API_KEY || ""}`,
    "Content-Type": "application/json",
  },
  body: JSON.stringify({
    model: "<image-style-transfer-model-from-/v1/models>",
    image: "https://example.com/content.jpg",
    style_reference: "https://example.com/style.jpg",
    strength: 0.7,
    response_format: "url",
  }),
});
const data = await res.json();
console.log(data.data[0].url);

`POST /v1/images/background-remove`

Background removal. Automatically detect and remove image backgrounds, returning a transparent PNG.

Parameters

Parameter	Type	Required	Description
model	string	Yes	<image-background-remove-model-from-/v1/models>
image	string	Yes	Source image base64 or URL
response_format	string	No	url, b64_json

cURL

curl https://test.sealink.io/v1/images/background-remove \
  -H "Authorization: Bearer $SEALINK_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "<image-background-remove-model-from-/v1/models>",
    "image": "https://example.com/product.jpg",
    "response_format": "url"
  }'

Python

import os
import requests

res = requests.post(
    "https://test.sealink.io/v1/images/background-remove",
    headers={"Authorization": f"Bearer {os.environ['SEALINK_API_KEY']}"},
    json={
        "model": "<image-background-remove-model-from-/v1/models>",
        "image": "https://example.com/product.jpg",
        "response_format": "url",
    },
)
print(res.json()["data"][0]["url"])

Node.js

const res = await fetch("https://test.sealink.io/v1/images/background-remove", {
  method: "POST",
  headers: {
    Authorization: `Bearer ${process.env.SEALINK_API_KEY || ""}`,
    "Content-Type": "application/json",
  },
  body: JSON.stringify({
    model: "<image-background-remove-model-from-/v1/models>",
    image: "https://example.com/product.jpg",
    response_format: "url",
  }),
});
const data = await res.json();
console.log(data.data[0].url);