This model is no longer supported.
This model is no longer supported.
Train
The client provides a convenient way to interact with the model API.
npm install --save @fal-ai/clientThe @fal-ai/serverless-client package has been deprecated in favor of @fal-ai/client. Install the new package and update your imports — see client setup.
Set FAL_KEY as an environment variable in your runtime.
export FAL_KEY="YOUR_API_KEY"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/qwen-image-layered-trainer", {
input: {
image_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);The API uses an API Key for authentication. It is recommended you set the FAL_KEY environment variable in your runtime when possible.
import { fal } from "@fal-ai/client";
fal.config({
credentials: "YOUR_FAL_KEY"
});When running code on the client-side (e.g. in a browser, mobile app or GUI applications), make sure to not expose your FAL_KEY. Instead, use a server-side proxy to make requests to the API. For more information, check out our server-side integration guide.
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/qwen-image-layered-trainer", {
input: {
image_data_url: ""
},
webhookUrl: "https://optional.webhook.url/for/results",
});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/qwen-image-layered-trainer", {
requestId: "764cabcf-b745-4b3e-ae38-1200304cf45b",
logs: true,
});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/qwen-image-layered-trainer", {
requestId: "764cabcf-b745-4b3e-ae38-1200304cf45b"
});
console.log(result.data);
console.log(result.requestId);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.
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.
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.
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);The client will auto-upload the file for you if you pass a binary object (e.g. File, Data).
Read more about file handling in our file upload guide.
image_data_url string* requiredURL to the input data zip archive.
The zip should contain groups of images. The images should be named:
ROOT_start.EXT, ROOT_end.EXT, ROOT_end2.EXT, ..., ROOT_endN.EXT For example: photo_start.png, photo_end.png, photo_end2.png, ..., photo_endN.png
The start image is the base image that will be decomposed into layers. The end images are the layers that will be added to the base image. ROOT_end.EXT is the first layer, ROOT_end2.EXT is the second layer, and so on. You can have up to 8 layers. All image groups must have the same number of output layers.
The end images can contain transparent regions. Only PNG and WebP images are supported since these are the only formats that support transparency.
The zip can also contain a text file for each image group. The text file should be named: ROOT.txt For example: photo.txt
This text file can be used to specify a description of the base image.
If no text file is provided, the default_caption will be used.
If no default_caption is provided, the training will fail.
learning_rate floatLearning rate for LoRA parameters. Default value: 0.0001
steps integerNumber of steps to train for Default value: 1000
default_caption stringDefault caption to use when caption files are missing. If None, missing captions will cause an error.
{
"image_data_url": "",
"learning_rate": 0.0001,
"steps": 1000
}URL to the trained diffusers lora weights.
URL to the configuration file for the trained model.
{
"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
}
}url string* requiredThe URL where the file can be downloaded from.
content_type stringThe mime type of the file.
file_name stringThe name of the file. It will be auto-generated if not provided.
file_size integerThe size of the file in bytes.