Image Generation

const API_KEY = '<YOUR_API_KEY>'
const BASE_URL = 'https://developer.photogptai.com/api'

const headers = {
  Authorization: `Bearer ${API_KEY}`,
  'API-Version': '1',
}

const payload = {
  modelID: '<MODEL_ID>',
  prompt: 'professional studio headshot, charcoal blazer, soft key light',
  width: 1024,
  height: 1024,
  numImages: 1,
  steps: 30,
  seed: 0,
  enhanceFace: true,
}

const response = await fetch(`${BASE_URL}/images/generation`, {
  method: 'POST',
  headers: {
    ...headers,
    'Content-Type': 'application/json',
  },
  body: JSON.stringify(payload),
})

if (!response.ok) {
  throw new Error(await response.text())
}

const body = await response.json()
const jobId = body.result.jobId
console.log('Generation queued:', jobId)

import requests

API_KEY = "<YOUR_API_KEY>"
BASE_URL = "https://developer.photogptai.com/api"

headers = {
    "Authorization": f"Bearer {API_KEY}",
    "API-Version": "1",
}

payload = {
    "modelID": "<MODEL_ID>",
    "prompt": "professional studio headshot, charcoal blazer, soft key light",
    "width": 1024,
    "height": 1024,
    "numImages": 1,
    "steps": 30,
    "seed": 0,
    "enhanceFace": True,
}

response = requests.post(f"{BASE_URL}/images/generation", headers=headers, json=payload)
response.raise_for_status()

job_id = response.json()["result"]["jobId"]
print("Generation queued:", job_id)

const referenceFile = document.querySelector('input[type="file"]').files?.[0]

if (!referenceFile) {
  throw new Error('Select a reference image before uploading.')
}

const uploadData = {
  modelID: '<MODEL_ID>',
  type: 'auxiliaryType',
}

const formData = new FormData()
formData.append('file', referenceFile)
formData.append('data', JSON.stringify(uploadData))

const uploadResponse = await fetch(`${BASE_URL}/images/upload`, {
  method: 'POST',
  headers,
  body: formData,
})

if (!uploadResponse.ok) {
  throw new Error(await uploadResponse.text())
}

const uploadedImage = await uploadResponse.json()
const inputImageId = uploadedImage.result.id

const payload = {
  modelID: '<MODEL_ID>',
  prompt: 'cinematic portrait, warm window light',
  inputImageId,
  styleStrength: 0.35,
  numImages: 1,
  width: 1024,
  height: 1024,
}

const response = await fetch(`${BASE_URL}/images/generation`, {
  method: 'POST',
  headers: {
    ...headers,
    'Content-Type': 'application/json',
  },
  body: JSON.stringify(payload),
})

if (!response.ok) {
  throw new Error(await response.text())
}

const body = await response.json()
const jobId = body.result.jobId

import json
import requests

upload_data = {
    "modelID": "<MODEL_ID>",
    "type": "auxiliaryType",
}

with open("reference.jpg", "rb") as image_file:
    files = {
        "file": image_file,
        "data": (None, json.dumps(upload_data), "application/json"),
    }
    upload_response = requests.post(
        f"{BASE_URL}/images/upload",
        headers=headers,
        files=files,
    )

upload_response.raise_for_status()
input_image_id = upload_response.json()["result"]["id"]

payload = {
    "modelID": "<MODEL_ID>",
    "prompt": "cinematic portrait, warm window light",
    "inputImageId": input_image_id,
    "styleStrength": 0.35,
    "numImages": 1,
    "width": 1024,
    "height": 1024,
}

response = requests.post(f"{BASE_URL}/images/generation", headers=headers, json=payload)
response.raise_for_status()

job_id = response.json()["result"]["jobId"]

const payload = {
  modelID: 'gpt-image-2',
  prompt: 'minimal product photo of a matte black water bottle on marble',
  preset: 'editorial',
  aspectRatio: '1:1',
  numImages: 1,
  referenceImageURLs: ['https://example.com/reference-product.png'],
  options: {
    openai: {
      imageSize: '1K',
      quality: 'high',
      outputFormat: 'jpeg',
    },
  },
}

const response = await fetch(`${BASE_URL}/images/generation`, {
  method: 'POST',
  headers: {
    ...headers,
    'Content-Type': 'application/json',
  },
  body: JSON.stringify(payload),
})

if (!response.ok) {
  throw new Error(await response.text())
}

const body = await response.json()
const jobId = body.result.jobId

payload = {
    "modelID": "gpt-image-2",
    "prompt": "minimal product photo of a matte black water bottle on marble",
    "preset": "editorial",
    "aspectRatio": "1:1",
    "numImages": 1,
    "referenceImageURLs": [
        "https://example.com/reference-product.png",
    ],
    "options": {
        "openai": {
            "imageSize": "1K",
            "quality": "high",
            "outputFormat": "jpeg",
        },
    },
}

response = requests.post(f"{BASE_URL}/images/generation", headers=headers, json=payload)
response.raise_for_status()

job_id = response.json()["result"]["jobId"]

async function waitForJob(jobId) {
  const runningStatuses = new Set(['created', 'queued', 'running'])

  while (true) {
    const response = await fetch(`${BASE_URL}/jobs/${jobId}`, {
      headers,
    })

    if (!response.ok) {
      throw new Error(await response.text())
    }

    const body = await response.json()
    const job = body.result
    const status = String(job.status ?? '').toLowerCase()

    if (!runningStatuses.has(status)) {
      return job
    }

    await new Promise((resolve) => setTimeout(resolve, 5000))
  }
}

const job = await waitForJob(jobId)
const status = String(job.status ?? '').toLowerCase()

if (status === 'success') {
  for (const image of job.images ?? []) {
    console.log(image.id, image.url)
  }
} else {
  throw new Error(job.error ?? `Generation failed: ${job.status}`)
}

import time

def wait_for_job(job_id: str):
    running_statuses = {"created", "queued", "running"}

    while True:
        response = requests.get(f"{BASE_URL}/jobs/{job_id}", headers=headers)
        response.raise_for_status()

        job = response.json()["result"]
        status = job.get("status", "").lower()

        if status not in running_statuses:
            return job

        time.sleep(5)


job = wait_for_job(job_id)

if job.get("status", "").lower() == "success":
    for image in job.get("images", []):
        print(image["id"], image["url"])
else:
    raise RuntimeError(job.get("error") or f"Generation failed: {job.get('status')}")

const imageId = '<IMAGE_ID>'

const response = await fetch(`${BASE_URL}/images/${imageId}`, {
  headers,
})

if (!response.ok) {
  throw new Error(await response.text())
}

const body = await response.json()
const image = body.result
console.log(image.url)

image_id = "<IMAGE_ID>"

response = requests.get(f"{BASE_URL}/images/{image_id}", headers=headers)
response.raise_for_status()

image = response.json()["result"]
print(image["url"])

const params = new URLSearchParams({
  imageType: 'output',
  cursor: '',
  pageSize: '20',
})

const response = await fetch(`${BASE_URL}/models/<MODEL_ID>/images?${params}`, {
  headers,
})

if (!response.ok) {
  throw new Error(await response.text())
}

const body = await response.json()
const images = body.result
const nextCursor = body.hasMore ? body.nextCursor : null

response = requests.get(
    f"{BASE_URL}/models/<MODEL_ID>/images",
    headers=headers,
    params={"imageType": "output", "cursor": "", "pageSize": 20},
)
response.raise_for_status()

body = response.json()
images = body["result"]
next_cursor = body["nextCursor"] if body["hasMore"] else None