FLUX 3 Image API Reference

Authentication

Bearer token authentication

All requests to the LumozAI API require authentication using your secret API key. Pass your key in the standard Authorization HTTP header as a Bearer token.

cURL Authorization header quickstart

curl -X POST https://lumozai.com/api/v4/run   -H "Authorization: Bearer YOUR_API_KEY"   -H "Accept: application/json"   -d "model_id=flux-3-image"   -d "prompt=A futuristic cityscape illuminated by neon bioluminescence"

PHP Native cURL request

<?php
$apiKey = 'YOUR_API_KEY';

$curl = curl_init();
curl_setopt_array($curl, [
    CURLOPT_URL => 'https://lumozai.com/api/v4/run',
    CURLOPT_RETURNTRANSFER => true,
    CURLOPT_POST => true,
    CURLOPT_POSTFIELDS => [
        'model_id' => 'flux-3-image',
        'prompt' => 'A futuristic cityscape illuminated by neon bioluminescence'
    ],
    CURLOPT_HTTPHEADER => [
        'Accept: application/json',
        'Authorization: Bearer ' . $apiKey
    ]
]);

$response = curl_exec($curl);
curl_close($curl);

$result = json_decode($response, true);
print_r($result);

POST /api/v4/run

Execute FLUX 3 Image inference

Submit a synchronous or asynchronous generation job for FLUX 3 Image (flux-3-image). Fast-running image models return the generated asset directly; longer inference pipelines return a request_id for asynchronous polling.

Request parameters for FLUX 3 Image

10 total fields

Parameter	Type	Required	Default	Description / allowed values
`model_id`	string	Required	`flux-3-image`	Unique model identifier. Must be set to `flux-3-image`.
`prompt` Prompt	string	Required	—	Text description of the image to generate, or of the edit to apply to reference images.
`images` Reference Images	array of URLs	Optional	—	Up to 10 reference images the prompt edits or draws from. When present, runs as an edit and the first image drives 'auto' aspect ratio.
`aspect_ratio` Aspect Ratio	enum	Optional	`auto`	Aspect ratio of the generated image. 'auto' picks the ratio from prompt or first reference image. Options: auto 1:1 16:9 9:16 4:3 3:4 3:2 2:3 21:9 9:21 4:5 5:4 7:5 5:7 2:1 1:2
`resolution` Resolution	enum	Optional	`1k`	Resolution of the output image. Options: 768sq 1k 1.5k 2k 4k
`grounding` Research Grounding	enum	Optional	`true`	Let the model research the prompt with web and image search before generating. Options: true false
`output_format` Output Format	enum	Optional	`webp`	Format of the output image. Options: webp jpg png
`output_quality` Output Quality	number	Optional	`80`	Quality when saving output images (0 to 100). Range: `0` to `100` (step: `1`)
`safety_tolerance` Safety Tolerance	number	Optional	`2`	Moderation tolerance for inputs and outputs (0 strictest, 4 most permissive). Range: `0` to `4` (step: `1`)
`webhook`	string (URL)	Optional	—	Optional callback URL to receive an HTTP POST when async generation completes.

Model integration code

Production snippets configured with flux-3-image and tailored parameter inputs.

cURL POST /api/v4/run (flux-3-image)

curl -X POST https://lumozai.com/api/v4/run \
  -H "Authorization: Bearer YOUR_API_KEY" \
  -H "Accept: application/json" \
  -d "model_id=flux-3-image" \
  -d "prompt=A futuristic cybernetic metropolis illuminated by neon bioluminescence, 8k cinematic masterpiece" \
  -d "images[]=https://lumozai.com/samples/sample_input.jpg" \
  -d "aspect_ratio=auto" \
  -d "resolution=1k" \
  -d "grounding=true" \
  -d "output_format=webp" \
  -d "output_quality=80" \
  -d "safety_tolerance=2"

Python requests.post (flux-3-image)

import requests

url = "https://lumozai.com/api/v4/run"
headers = {
    "Authorization": "Bearer YOUR_API_KEY",
    "Accept": "application/json"
}
payload = {
    "model_id": "flux-3-image",
    "prompt": "A futuristic cybernetic metropolis illuminated by neon bioluminescence, 8k cinematic masterpiece",
    "images": ["https:\/\/lumozai.com\/samples\/sample_input.jpg"],
    "aspect_ratio": "auto",
    "resolution": "1k",
    "grounding": "true",
    "output_format": "webp",
    "output_quality": 80,
    "safety_tolerance": 2
}

response = requests.post(url, headers=headers, json=payload)
print(response.json())

PHP cURL POST (flux-3-image)

<?php
$apiKey = 'YOUR_API_KEY';

$curl = curl_init();
curl_setopt_array($curl, [
    CURLOPT_URL => 'https://lumozai.com/api/v4/run',
    CURLOPT_RETURNTRANSFER => true,
    CURLOPT_POST => true,
    CURLOPT_POSTFIELDS => [
        'model_id' => 'flux-3-image',
        'prompt' => 'A futuristic cybernetic metropolis illuminated by neon bioluminescence, 8k cinematic masterpiece',
        'images[]' => 'https://lumozai.com/samples/sample_input.jpg',
        'aspect_ratio' => 'auto',
        'resolution' => '1k',
        'grounding' => 'true',
        'output_format' => 'webp',
        'output_quality' => 80,
        'safety_tolerance' => 2
    ],
    CURLOPT_HTTPHEADER => [
        'Accept: application/json',
        'Authorization: Bearer ' . $apiKey
    ]
]);

$response = curl_exec($curl);
curl_close($curl);

$result = json_decode($response, true);
print_r($result);

JavaScript fetch async/await (flux-3-image)

const response = await fetch('https://lumozai.com/api/v4/run', {
  method: 'POST',
  headers: {
    'Authorization': 'Bearer YOUR_API_KEY',
    'Content-Type': 'application/json',
    'Accept': 'application/json'
  },
  body: JSON.stringify({
    "model_id": "flux-3-image",
    "prompt": "A futuristic cybernetic metropolis illuminated by neon bioluminescence, 8k cinematic masterpiece",
    "images": ["https:\/\/lumozai.com\/samples\/sample_input.jpg"],
    "aspect_ratio": "auto",
    "resolution": "1k",
    "grounding": "true",
    "output_format": "webp",
    "output_quality": 80,
    "safety_tolerance": 2
  })
});

const data = await response.json();
console.log(data);

Response structure (200 OK)

JSON Output asset payload

{
  "success": true,
  "data": {
    "request_id": 982348712938172,
    "output": "https://lumozai.com/storage/outputs/result_flux-3-image_9823.png",
    "output_type": "image"
  }
}

POST /api/v4/check

Poll asynchronous task status

Poll the current execution state of an asynchronous job using the returned request_id.

Request parameters

Parameter	Type	Required	Description
`request_id`	integer	Required	The unique job ID returned when calling `/api/v4/run`.

JSON Completed status (200 OK)

{
  "success": true,
  "data": {
    "request_id": 982348712938172,
    "status": "completed",
    "output": "https://lumozai.com/storage/outputs/video_8172.mp4",
    "output_type": "video"
  }
}

JSON In-progress status (200 OK)

{
  "success": true,
  "data": {
    "request_id": 982348712938172,
    "status": "processing",
    "progress": 68
  }
}

Error handling

Standard error codes

The API uses conventional HTTP response codes to indicate success or failure. In general, 2xx indicates success, 4xx indicates client input issues, and 5xx indicates server errors.

Status	Error code	Description
401	`authentication_failed`	Missing or invalid Authorization Bearer header.
403	`permission_denied`	Insufficient credits or account tier permissions.
404	`model_not_found`	The specified `model_id` does not exist or is deprecated.
422	`validation_error`	Missing required parameters or invalid parameter types.
429	`rate_limit_exceeded`	Request rate quota exceeded for your tier.
500	`internal_server_error`	Downstream GPU compute error or worker timeout.

JSON Validation error example

{
  "success": false,
  "error": {
    "code": "validation_error",
    "message": "The model_id field is required.",
    "details": {
      "model_id": ["The model_id field is required."]
    }
  }
}

Rate limits

Quota & throughput limits

Rate limits are enforced per API key to guarantee fair access and consistent low latency across GPU workers. Every API response includes informative headers:

Header	Description
`X-RateLimit-Limit`	Maximum number of requests permitted per rolling hour.
`X-RateLimit-Remaining`	Number of remaining requests in the active window.
`X-RateLimit-Reset`	Unix epoch timestamp when the current window refreshes.

Handling 429 status codes

When rate-limited, wait for the number of seconds until X-RateLimit-Reset or implement exponential backoff retries. For custom high-concurrency limits, contact support.

RAG API /api/v1/memory/*

Serverless vector memory & semantic search

Store, index, and retrieve vector embeddings with sub-50ms latency. Unlike traditional vector databases that charge monthly cluster upkeep fees, Lumoz includes 5,000 vector memory slots and 10,000 semantic queries with any active balance.

Core endpoints

Method	Endpoint	Description
GET	`/api/v1/memory/usage`	Inspect active tier, vector storage meter, and monthly search counters.
POST	`/api/v1/memory/projects`	Create an isolated project/namespace (e.g. `support-bot`).
POST	`/api/v1/memory/store`	Embed and index text documents or knowledge chunks.
POST	`/api/v1/memory/search`	Cosine similarity search with hybrid keyword weighting.
DELETE	`/api/v1/memory/{id}`	Delete an indexed memory item.

Code examples

cURL Store knowledge & semantic search

# 1. Ingest text into an isolated namespace
curl -X POST "https://lumozai.com/api/v1/memory/store"   -H "Authorization: Bearer YOUR_LUMOZ_API_KEY"   -H "Content-Type: application/json"   -d '{
    "project": "support-bot",
    "text": "Refunds are processed within 48 hours to original payment method.",
    "metadata": {"category": "billing"}
  }'

# 2. Query vector memory via cosine similarity
curl -X POST "https://lumozai.com/api/v1/memory/search"   -H "Authorization: Bearer YOUR_LUMOZ_API_KEY"   -H "Content-Type: application/json"   -d '{
    "project": "support-bot",
    "query": "How long do refunds take?",
    "top_k": 3
  }'

Python Vector store & search with requests

import requests

headers = {
    "Authorization": "Bearer YOUR_LUMOZ_API_KEY",
    "Content-Type": "application/json"
}

# Store Knowledge
requests.post("https://lumozai.com/api/v1/memory/store", headers=headers, json={
    "project": "support-bot",
    "text": "Enterprise SLA guarantees 99.99% uptime with dedicated GPU routing.",
    "metadata": {"tier": "enterprise"}
})

# Semantic Search
res = requests.post("https://lumozai.com/api/v1/memory/search", headers=headers, json={
    "project": "support-bot",
    "query": "What is the uptime SLA?",
    "top_k": 2
})
print("Top Matches:", res.json()["results"])

Zero-code RAG with Lumoz LLM

Pass "memory": {"project": "..."} directly to /api/v1/chat/completions. Lumoz automatically injects relevant context without custom client-side vector code:

Node.js Automatic memory context injection

const response = await fetch("https://lumozai.com/api/v1/chat/completions", {
  method: "POST",
  headers: {
    "Authorization": "Bearer YOUR_LUMOZ_API_KEY",
    "Content-Type": "application/json"
  },
  body: JSON.stringify({
    model: "lumoz-llm-1.0",
    messages: [
      { role: "user", content: "What is your refund policy window?" }
    ],
    memory: {
      project: "support-bot",
      top_k: 3
    }
  })
});

const data = await response.json();
console.log(data.choices[0].message.content);