DocsBlogPricingEnterpriseCareersResearch Grants
Log-inSign-up

Train Flux LoRA (legacy) Training

fal-ai/flux-lora-general-training
General Purpose LORA Training for Flux.
Training
Commercial use
PlaygroundAPI

About

Fine Tune

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-lora-general-training", {
  input: {
    images_data_url: ""
  },
  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-lora-general-training", {
  input: {
    images_data_url: ""
  },
  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-lora-general-training", {
  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-lora-general-training", {
  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#

images_data_url string* required

URL to zip archive with images of a consistent style. Try to use at least 10 images, although more is better.

In addition to images the archive can contain text files with captions. Each text file should have the same name as the image file it corresponds to.

The captions can include a special string [trigger]. If a trigger_word is specified, it will replace [trigger] in the captions.

data_archive_format string

File format to archive training artifacts

captions_file_url string

URL to a jsonl file with captions. Each line should contain a json object with a 'file_name' field that matches a file name in the images_data_url archive. It should also have a 'text' field with the caption.

{"file_name": "image1.jpg", "text": "In the style of TRIGGER_WORDS A picture of a cat."} {"file_name": "image2.jpg", "text": "In the style of TRIGGER_WORDS A picture of a dog."}

The captions can include a special string [trigger]. If a trigger_word is specified, it will replace [trigger] in the captions.

When captioning, in general, caption the elements you do NOT want to always associated with the trigger words. Every you do NOT caption should be unconditionally associated with the trigger words.

steps integer

Total number of training steps to perform. Default is 1000. Default value: 1000

trigger_word string

Trigger word to be used in the captions. If None, a trigger word will not be used.

If no captions are provide the trigger_work will be used instead of captions. If captions are provided, the trigger word will replace the [trigger] string in the captions.

rank integer

Rank of the model. Default is 16. Default value: 16

learning_rate float

Initial learning rate for the unet. Default is 4e-4 Default value: 0.0004

caption_dropout_rate float

Dropout rate for captions. Default is 0.05 Default value: 0.05

high_resolution_mode boolean

If true, will only train with the 1024 resolution bucket. Default is False. If True increases the price by 20% (price multiplied by 1.2).

experimental_optimizers ExperimentalOptimizersEnum

Experimental. Could change in the future. Default is 'adamw8bit'. Default value: "adamw8bit"

Possible enum values: adamw8bit, prodigy, adafactor

experimental_multi_checkpoints_count integer

Experimental. Could change in the future. Number of checkpoints to save. Default is 1. Checkpoints are only saved if the interval is set. Default value: 1

experimental_multi_checkpoints_interval integer

Experimental. Could change in the future. Interval between saving checkpoints. Default is None. If not None must be greater than 250.

{
  "images_data_url": "",
  "steps": 1000,
  "rank": 16,
  "learning_rate": 0.0004,
  "caption_dropout_rate": 0.05,
  "experimental_optimizers": "adamw8bit",
  "experimental_multi_checkpoints_count": 1
}

Output#

diffusers_lora_file File* required

URL to the trained diffusers lora weights.

config_file File* required

URL to the training configuration file.

debug_caption_files File

URL to the tar.gz file containing the caption files.

experimental_multi_checkpoints list<File>

URLs to the saved checkpoints.

{
  "diffusers_lora_file": {
    "url": "",
    "content_type": "image/png",
    "file_name": "z9RV14K95DvU.png",
    "file_size": 4404019
  },
  "config_file": {
    "url": "",
    "content_type": "image/png",
    "file_name": "z9RV14K95DvU.png",
    "file_size": 4404019
  },
  "experimental_multi_checkpoints": [
    {
      "url": "",
      "content_type": "image/png",
      "file_name": "z9RV14K95DvU.png",
      "file_size": 4404019
    }
  ]
}

Other types#

File#

url string* required

The URL where the file can be downloaded from.

content_type string

The mime type of the file.

file_name string

The name of the file. It will be auto-generated if not provided.

file_size integer

The size of the file in bytes.

file_data string

File data