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Minimal request

{
  "model": "gpt-image-1",
  "prompt": "A clean Api.Go product poster",
  "size": "1024x1024",
  "n": 1
}

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cURL example

curl https://mass.apigo.ai/v1/images/generations \
  -H "Authorization: Bearer $TIDEMIND_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "gpt-image-1",
    "prompt": "A clean Api.Go product poster",
    "size": "1024x1024",
    "n": 1
  }'

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Python example

from openai import OpenAI

client = OpenAI(
    base_url="https://mass.apigo.ai/v1",
    api_key="<TIDEMIND_API_KEY>",
)

response = client.images.generate(
    model="gpt-image-1",
    prompt="A clean Api.Go product poster",
    size="1024x1024",
    n=1,
)

print(response.data[0])

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Node.js example

import OpenAI from "openai";

const client = new OpenAI({
  baseURL: "https://mass.apigo.ai/v1",
  apiKey: process.env.TIDEMIND_API_KEY,
});

const response = await client.images.generate({
  model: "gpt-image-1",
  prompt: "A clean Api.Go product poster",
  size: "1024x1024",
  n: 1
});

console.log(response.data[0]);

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Best practices

  • Keep image generation and image editing on separate endpoints
  • For iterative editing, persist the source asset IDs or previous outputs server-side
  • Normalize URL and base64 handling in one backend asset layer