DocsBlogPricingEnterpriseCareersResearch Grants
Log-inSign-up

Train Flux LoRAs For Pro Models Training

fal-ai/flux-pro-trainer
FLUX LoRA for Pro endpoints.
Training
Commercial use
Schema
PlaygroundAPI

About

FLUX.1 Finetune [pro] API, next generation text-to-image model.

All usages of this model must comply with FLUX.1 PRO Terms of Service.

1. Calling the API#

Install the client#

The client provides a convenient way to interact with the model API.

npm install --save @fal-ai/client

Setup your API Key#

Set FAL_KEY as an environment variable in your runtime.

export FAL_KEY="YOUR_API_KEY"

Submit a request#

The client API handles the API submit protocol. It will handle the request status updates and return the result when the request is completed.

import { fal } from "@fal-ai/client";

const result = await fal.subscribe("fal-ai/flux-pro-trainer", {
  input: {
    data_url: "",
    finetune_comment: "test-1"
  },
  logs: true,
  onQueueUpdate: (update) => {
    if (update.status === "IN_PROGRESS") {
      update.logs.map((log) => log.message).forEach(console.log);
    }
  },
});
console.log(result.data);
console.log(result.requestId);

2. Authentication#

The API uses an API Key for authentication. It is recommended you set the FAL_KEY environment variable in your runtime when possible.

API Key#

In case your app is running in an environment where you cannot set environment variables, you can set the API Key manually as a client configuration.
import { fal } from "@fal-ai/client";

fal.config({
  credentials: "YOUR_FAL_KEY"
});

3. Queue#

Submit a request#

The client API provides a convenient way to submit requests to the model.

import { fal } from "@fal-ai/client";

const { request_id } = await fal.queue.submit("fal-ai/flux-pro-trainer", {
  input: {
    data_url: "",
    finetune_comment: "test-1"
  },
  webhookUrl: "https://optional.webhook.url/for/results",
});

Fetch request status#

You can fetch the status of a request to check if it is completed or still in progress.

import { fal } from "@fal-ai/client";

const status = await fal.queue.status("fal-ai/flux-pro-trainer", {
  requestId: "764cabcf-b745-4b3e-ae38-1200304cf45b",
  logs: true,
});

Get the result#

Once the request is completed, you can fetch the result. See the Output Schema for the expected result format.

import { fal } from "@fal-ai/client";

const result = await fal.queue.result("fal-ai/flux-pro-trainer", {
  requestId: "764cabcf-b745-4b3e-ae38-1200304cf45b"
});
console.log(result.data);
console.log(result.requestId);

4. Files#

Some attributes in the API accept file URLs as input. Whenever that's the case you can pass your own URL or a Base64 data URI.

Data URI (base64)#

You can pass a Base64 data URI as a file input. The API will handle the file decoding for you. Keep in mind that for large files, this alternative although convenient can impact the request performance.

Hosted files (URL)#

You can also pass your own URLs as long as they are publicly accessible. Be aware that some hosts might block cross-site requests, rate-limit, or consider the request as a bot.

Uploading files#

We provide a convenient file storage that allows you to upload files and use them in your requests. You can upload files using the client API and use the returned URL in your requests.

import { fal } from "@fal-ai/client";

const file = new File(["Hello, World!"], "hello.txt", { type: "text/plain" });
const url = await fal.storage.upload(file);

Read more about file handling in our file upload guide.

5. Schema#

Input#

data_url string* required

URL to the training data

mode ModeEnum

Determines the finetuning approach based on your concept Default value: "character"

Possible enum values: character, product, style, general

finetune_comment string* required

Descriptive note to identify your fine-tune since names are UUIDs. Will be displayed in finetune_details.

iterations integer

Defines training duration Default value: 300

learning_rate float

Learning rate for training. Lower values may be needed for certain scenarios. Default is 1e-5 for full and 1e-4 for LoRA.

priority PriorityEnum

The speed priority will improve training and inference speed Default value: "quality"

Possible enum values: speed, quality, high_res_only

captioning boolean

Enables/disables automatic image captioning Default value: true

trigger_word string

Unique word/phrase that will be used in the captions, to reference the newly introduced concepts Default value: "TOK"

lora_rank integer

Choose between 32 and 16. A lora_rank of 16 can increase training efficiency and decrease loading times. Default value: 32

finetune_type FinetuneTypeEnum

Choose between 'full' for a full finetuning + post hoc extraction of the trained weights into a LoRA or 'lora' for a raw LoRA training Default value: "full"

Possible enum values: full, lora

{
  "data_url": "",
  "mode": "character",
  "finetune_comment": "test-1",
  "iterations": 300,
  "priority": "quality",
  "captioning": true,
  "trigger_word": "TOK",
  "lora_rank": 32,
  "finetune_type": "full"
}

Output#

finetune_id string* required

References your specific model

{
  "finetune_id": ""
}

Other types#