GPT Image 2

curl https://api.beansai.dev/v1/images/generations \
  -N \
  -H "Authorization: Bearer sk-beans-..." \
  -H "Accept: text/event-stream" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "gpt-image-2",
    "prompt": "A clean product photo of a green coffee bean mascot on a white background",
    "size": "1024x1024",
    "quality": "high",
    "n": 1,
    "response_format": "b64_json",
    "stream": true
  }'

IMAGE_B64="$(base64 -i reference.png | tr -d '\n')"

curl https://api.beansai.dev/v1/images/edits \
  -N \
  -H "Authorization: Bearer sk-beans-..." \
  -H "Accept: text/event-stream" \
  -H "Content-Type: application/json" \
  -d "{
    \"model\": \"gpt-image-2\",
    \"prompt\": \"Keep the same product, place it on a polished studio desk\",
    \"images\": [{ \"image_url\": \"data:image/png;base64,$IMAGE_B64\" }],
    \"size\": \"1024x1024\",
    \"quality\": \"high\",
    \"response_format\": \"b64_json\",
    \"stream\": true
  }"

curl https://api.beansai.dev/v1/images/generations \
  -N \
  -H "Authorization: Bearer sk-beans-..." \
  -H "Accept: text/event-stream" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "gpt-image-2",
    "prompt": "A cinematic poster for an AI API gateway called BeansAI",
    "size": "2016x1344",
    "quality": "high",
    "response_format": "b64_json",
    "stream": true
  }'

import base64
import json
import requests

res = requests.post(
    "https://api.beansai.dev/v1/images/generations",
    headers={
        "Authorization": "Bearer sk-beans-...",
        "Accept": "text/event-stream",
    },
    json={
        "model": "gpt-image-2",
        "prompt": "A minimal green coffee bean logo on a white background",
        "size": "1024x1024",
        "quality": "high",
        "response_format": "b64_json",
        "stream": True,
    },
    stream=True,
    timeout=600,
)
res.raise_for_status()

image_b64 = None
for line in res.iter_lines(decode_unicode=True):
    if not line or not line.startswith("data:"):
        continue
    payload = line[5:].strip()
    if payload == "[DONE]":
        break
    event = json.loads(payload)
    if str(event.get("type", "")).endswith(".completed"):
        image_b64 = event.get("b64_json")
        break

if not image_b64:
    raise RuntimeError("No completed image event returned")

with open("beansai-image.png", "wb") as f:
    f.write(base64.b64decode(image_b64))

import fs from "node:fs";

const res = await fetch("https://api.beansai.dev/v1/images/generations", {
  method: "POST",
  headers: {
    Authorization: "Bearer sk-beans-...",
    Accept: "text/event-stream",
    "Content-Type": "application/json",
  },
  body: JSON.stringify({
    model: "gpt-image-2",
    prompt: "A friendly AI dashboard illustration with green accents",
    size: "1024x1024",
    quality: "high",
    response_format: "b64_json",
    stream: true,
  }),
});

if (!res.ok) throw new Error(await res.text());
if (!res.body) throw new Error("No response body");

const decoder = new TextDecoder();
const reader = res.body.getReader();
let buffer = "";
let image = "";

function processBlock(block) {
  const data = block
    .split(/\r?\n/)
    .filter((line) => line.startsWith("data:"))
    .map((line) => line.slice(5).trimStart())
    .join("\n")
    .trim();
  if (!data || data === "[DONE]") return;
  const event = JSON.parse(data);
  if (String(event.type || "").endsWith(".completed")) {
    image = event.b64_json || "";
  }
}

while (!image) {
  const { done, value } = await reader.read();
  if (done) break;
  buffer += decoder.decode(value, { stream: true });
  const blocks = buffer.split(/\r?\n\r?\n/);
  buffer = blocks.pop() || "";
  for (const block of blocks) processBlock(block);
}

if (!image && buffer.trim()) processBlock(buffer);
if (!image) throw new Error("No completed image event returned");
fs.writeFileSync("beansai-image.png", Buffer.from(image, "base64"));