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Train Hunyuan LoRA Training

fal-ai/hunyuan-video-lora-training
Train Hunyuan Video lora on people, objects, characters and more!
Training
Commercial use
PlaygroundAPI

About

Hunyuan Video LoRA fine-tuning endpoint.

This endpoint fine-tunes a LoRA model on a dataset of images.

To provide your own captions, you can include a text file with the same name as the image file. For example, if you have an image photo.jpg, you can include a text file photo.txt with the caption.

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/hunyuan-video-lora-training", {
  input: {
    images_data_url: "",
    steps: 1000
  },
  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/hunyuan-video-lora-training", {
  input: {
    images_data_url: "",
    steps: 1000
  },
  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/hunyuan-video-lora-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/hunyuan-video-lora-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. Try to use at least 4 images in general the more the 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.

steps integer* required

Number of steps to train the LoRA on.

trigger_word string

The trigger word to use. Default value: ""

learning_rate float

Learning rate to use for training. Default value: 0.0001

do_caption boolean

Whether to generate captions for the images. Default value: true

data_archive_format string

The format of the archive. If not specified, the format will be inferred from the URL.

{
  "images_data_url": "",
  "steps": 1000,
  "learning_rate": 0.0001,
  "do_caption": true
}

Output#

diffusers_lora_file File* required

URL to the trained diffusers lora weights.

config_file File* required

URL to the lora configuration file.

{
  "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
  }
}

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.