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Train LoRA Training

fal-ai/train-lora
Train LORAs for Stable Diffusion.
Inference
Commercial use
PlaygroundAPI

About

Train

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/train-lora", {
  input: {
    train_images_url: "https://storage.googleapis.com/falserverless/model_tests/lora/dog.zip",
    instance_prompt: "a photo of sks dog",
    model_name: "runwayml/stable-diffusion-v1-5"
  },
  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/train-lora", {
  input: {
    train_images_url: "https://storage.googleapis.com/falserverless/model_tests/lora/dog.zip",
    instance_prompt: "a photo of sks dog",
    model_name: "runwayml/stable-diffusion-v1-5"
  },
  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/train-lora", {
  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/train-lora", {
  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#

train_images_url CompressedFile* required

URL where the the ZIP-compressed training images file is located. Only the images with 'png', 'PNG', 'jpg', 'JPG', 'jpeg', and 'JPEG' will be used during the training. The training images also will be used during the inference process.

instance_prompt string* required

Description of the LORA being trained

model_name string* required

A base model identifier from huggingface.co/models

model_archicture ModelArchictureEnum

Architecture of the model to train Default value: "sd"

Possible enum values: sd, sdxl

max_training_steps integer

Total number of training steps to perform. Default value: 100

rank integer

The dimension of the LoRA update matrices Default value: 64

seed integer

A seed for reproducible training

{
  "train_images_url": "https://storage.googleapis.com/falserverless/model_tests/lora/dog.zip",
  "instance_prompt": "a photo of sks dog",
  "model_name": "runwayml/stable-diffusion-v1-5",
  "model_archicture": "sd",
  "max_training_steps": 100,
  "rank": 64
}

Output#

weights_file File* required

Generated weights

timings Timings* required
seed integer* required

The seed used for the training

{
  "weights_file": {
    "url": "",
    "content_type": "image/png",
    "file_name": "z9RV14K95DvU.png",
    "file_size": 4404019
  }
}

Other types#

CompressedFile#

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.

StorageOptions#

model_bucket string* required

The path to the trained model.

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.