Flux API usage

For an overview of Flux fine-tuning, see Flux fine-tuning

• Unlike SD15 checkpoint training, Flux is trained as a LoRA model type. As such, inference is taking place a on a base line model such as Flux1.dev and prompt.text should specify the loaded lora such as <lora:123456:1> - will load lora with id=123456 and strength=1
• Flux1.Dev requires commercial licensing which Astria provides to its customers, and as such LoRA downloading is not available for API usage.

See LoRA docs on lora syntax

With Flux you cannot combine multiple LoRAs.

info

To avoid cloning inference details of different model types such as Flux LoRA vs SD1.5 checkpoint, please consider using the Packs API. Packs will help you abstract the inference logic so that you do not have to hard-code prompts and parameters such as w,h, cfg_scale in your backend. Moreover this will allow the creative department to launch packs, make modifications and even track likes, without needing to touch the backend code.

danger

If you are receiving 422 error model_type=lora is not supported. Use a checkpoint instead - Change the request URL to https://api.astria.ai/tunes/1504944/prompts with 1504944 as a hard-coded tune_id of Flux1.Dev from the gallery. See explanation above.

Step 1: Fine-tune a lora model

POST /tunes

cURL

# With images as multipart/form-data
# Hard coded tune id of Flux1.dev from the gallery - https://www.astria.ai/gallery/tunes 
# https://www.astria.ai/gallery/tunes/1504944/prompts
curl -X POST -H "Authorization: Bearer $API_KEY" https://api.astria.ai/tunes \
          -F tune[title]="John Doe - UUID - 1234-6789-1234-56789" \
          -F tune[name]=man \
          -F tune[callback]="https://optional-callback-url.com/webhooks/astria?user_id=1&tune_id=1" \
          -F tune[base_tune_id]=1504944 \
          -F tune[model_type]="lora" \
          -F "tune[images][][email protected]" \
          -F "tune[images][][email protected]" \
          -F "tune[images][][email protected]" \
          -F "tune[images][][email protected]"

# With image_urls as form-data
curl -X POST -H "Authorization: Bearer $API_KEY" https://api.astria.ai/tunes \
          -F tune[title]="Grumpy cat - UUID - 1234-6789-1234-56789" \
          -F tune[name]=cat \
          -F tune[callback]="https://optional-callback-url.com/to-your-service-when-ready?user_id=1&tune_id=1" \
          -F tune[base_tune_id]=1504944 \
          -F tune[model_type]="lora" \
          -F "tune[image_urls][]=https://i.imgur.com/HLHBnl9.jpeg" \
          -F "tune[image_urls][]=https://i.imgur.com/HLHBnl9.jpeg" \
          -F "tune[image_urls][]=https://i.imgur.com/HLHBnl9.jpeg" \
          -F "tune[image_urls][]=https://i.imgur.com/HLHBnl9.jpeg"
          
# As JSON
cat > data.json <<- EOM
{
  "tune": {
    "title": "Grumpy Cat - UUID - 1234-6789-1234-56789",
    "name": "cat",
    "base_tune_id": 1504944,
    "model_type": "lora",
    "callback": "https://optional-callback-url.com/to-your-service-when-ready?user_id=1&tune_id=1",
    "image_urls": [
      "https://i.imgur.com/HLHBnl9.jpeg",
      "https://i.imgur.com/HLHBnl9.jpeg",
      "https://i.imgur.com/HLHBnl9.jpeg",
      "https://i.imgur.com/HLHBnl9.jpeg"
    ]
  }
}
EOM

curl -X POST -H"Content-Type: application/json" -H "Authorization: Bearer $API_KEY" --data @data.json  https://api.astria.ai/tunes          

Node.js

// NodeJS 16
// With image_urls and fetch()
// For NodeJS 18 - do NOT import the below as it is built-in
import fetch from "node-fetch";

const API_KEY = 'sd_XXXXXX';
const DOMAIN = 'https://api.astria.ai';

function createTune() {
  let options = {
    method: 'POST',
    headers: { 'Authorization': 'Bearer ' + API_KEY, 'Content-Type': 'application/json' },
    body: JSON.stringify({
      tune: {
        "title": 'John Doe - UUID - 1234-6789-1234-56789',
        // Hard coded tune id of Flux1.dev from the gallery - https://www.astria.ai/gallery/tunes 
        // https://www.astria.ai/gallery/tunes/1504944/prompts
        "base_tune_id": 1504944,
        "model_type": "lora",
        "name": "cat",
        "image_urls": [
          "https://i.imgur.com/HLHBnl9.jpeg",
          "https://i.imgur.com/HLHBnl9.jpeg",
          "https://i.imgur.com/HLHBnl9.jpeg",
          "https://i.imgur.com/HLHBnl9.jpeg"
        ],
      }
    })
  };
  return fetch(DOMAIN + '/tunes', options)
    .then(r => r.json())
    .then(r => console.log(r))
}

createTune()


/// With form-data, fetch() and nested prompts
// For NodeJS 18 - do NOT import the two below as they are built-in
import fetch from "node-fetch";
import FormData from 'form-data';
import fs from 'fs';

const API_KEY = 'sd_XXXX';
const DOMAIN = 'https://api.astria.ai';
function createTune() {
  let formData = new FormData();
  formData.append('tune[title]', 'John Doe - UUID - 1234-6789-1234-56789');
  // Hard coded tune id of Flux1.dev from the gallery - https://www.astria.ai/gallery/tunes 
  // https://www.astria.ai/gallery/tunes/1504944/prompts
  formData.append('tune[base_tune_id]', 1504944);
  formData.append('tune[model_type]', 'lora');
  formData.append('tune[name]', 'man');
  // Load all JPGs from ./samples directory and append to FormData
  let files = fs.readdirSync('./samples');
  files.forEach(file => {
    if(file.endsWith('.jpg')) {
      formData.append('tune[images][]', fs.createReadStream(`./samples/${file}`), file);
    }
  });
  formData.append('tune[callback]', 'https://optional-callback-url.com/to-your-service-when-ready?user_id=1&tune_id=1');

  let options = {
    method: 'POST',
    headers: {
      'Authorization': 'Bearer ' + API_KEY
    },
    body: formData
  };
  return fetch(DOMAIN + '/tunes', options)
    .then(r => r.json())
    .then(r => console.log(r));
}

createTune();

Python

import requests
headers = {'Authorization': f'Bearer {API_KEY}'}

def load_image(file_path):
  with open(file_path, "rb") as f:
    return f.read()

# Assuming `prompts` and `tune.images` are already defined in your context

image_data = load_image("assets/image.jpeg")

data = {
  "tune[title]": "John Doe - UUID - 1234-6789-1234-56789",
  "tune[name]": "man",
  "tune[base_tune_id]": 1504944,
  "tune[model_type]": "lora",
}
files = []

for image in tune.images:
  image_data = load_image(image)  # Assuming image is a file path
  files.append(("tune[images][]", image_data))

API_URL = 'https://api.astria.ai/tunes'
response = requests.post(API_URL, data=data, files=files, headers=headers)
response.raise_for_status()

Step 2: Generate images using the fine-tuned model

POST /tunes/:id/prompts

cURL

# Note the hard-coded 1504944 which is the tune_id of Flux1.dev from the gallery
curl -X POST -H "Authorization: Bearer $API_KEY" https://api.astria.ai/tunes/1504944/prompts \
          -F prompt[text]="<lora:tune_id:strength> a painting of ohwx man in the style of Van Gogh" \
          -F prompt[callback]="https://optional-callback-url.com/to-your-service-when-ready?prompt_id=1" 

Node.js

const fetch = require('node-fetch');
const FormData = require('form-data');

// Note the hard-coded 1504944 which is the tune_id of Flux1.dev from the gallery
const API_URL = 'https://api.astria.ai/tunes/1504944/prompts';
const API_KEY = 'YOUR_API_KEY'; // Replace with your actual API key
const headers = { Authorization: `Bearer ${API_KEY}` }

const form = new FormData();
form.append('prompt[text]', '<lora:tune_id:strength> a painting of ohwx man in the style of Van Gogh');
form.append('prompt[callback]', 'https://optional-callback-url.com/to-your-service-when-ready?prompt_id=1');

fetch(API_URL, {
  method: 'POST',
  headers: headers,
  body: form
}).then(response => response.json())

Python

import requests

# Note the hard-coded 1504944 which is the tune_id of Flux1.dev from the gallery
API_URL = 'https://api.astria.ai/tunes/1504944/prompts'
API_KEY = 'YOUR_API_KEY'  # Replace with your actual API key

headers = {
    'Authorization': f'Bearer {API_KEY}'
}

data = {
  'prompt[text]': '<lora:tune_id:strength> a painting of ohwx man in the style of Van Gogh',
  'prompt[callback]': 'https://optional-callback-url.com/to-your-service-when-ready?prompt_id=1'
}
files = []

response = requests.post(API_URL, headers=headers, data=data)