This model is no longer supported.
This model is no longer supported.
Runs training.
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/ltx-video-trainer", {
input: {
training_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/ltx-video-trainer", {
input: {
training_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/ltx-video-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/ltx-video-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.
training_data_url string* requiredURL to zip archive with videos or images. Try to use at least 10 files, although more is better.
Supported video formats: .mp4, .mov, .avi, .mkv Supported image formats: .png, .jpg, .jpeg
Note: The dataset must contain ONLY videos OR ONLY images - mixed datasets are not supported.
The archive can also contain text files with captions. Each text file should have the same name as the media file it corresponds to.
rank RankEnumThe rank of the LoRA. Default value: "128"
Possible enum values: 8, 16, 32, 64, 128
number_of_steps integerThe number of steps to train for. Default value: 1000
number_of_frames integerThe number of frames to use for training. This is the number of frames per second multiplied by the number of seconds. Default value: 81
frame_rate integerThe target frames per second for the video. Default value: 25
resolution ResolutionEnumThe resolution to use for training. This is the resolution of the video. Default value: "medium"
Possible enum values: low, medium, high
aspect_ratio AspectRatioEnumThe aspect ratio to use for training. This is the aspect ratio of the video. Default value: "1:1"
Possible enum values: 16:9, 1:1, 9:16
learning_rate floatThe rate at which the model learns. Higher values can lead to faster training, but over-fitting. Default value: 0.0002
trigger_phrase stringThe phrase that will trigger the model to generate an image. Default value: ""
auto_scale_input booleanIf true, videos will be automatically scaled to the target frame count and fps. This option has no effect on image datasets.
split_input_into_scenes booleanIf true, videos above a certain duration threshold will be split into scenes. If you provide captions for a split video, the caption will be applied to each scene. If you do not provide captions, scenes will be auto-captioned. This option has no effect on image datasets. Default value: true
split_input_duration_threshold floatThe duration threshold in seconds. If a video is longer than this, it will be split into scenes. If you provide captions for a split video, the caption will be applied to each scene. If you do not provide captions, scenes will be auto-captioned. Default value: 30
A list of validation prompts to use during training. When providing an image, all validation inputs must have an image.
validation_negative_prompt stringA negative prompt to use for validation. Default value: "blurry, low quality, bad quality, out of focus"
validation_number_of_frames integerThe number of frames to use for validation. Default value: 81
validation_resolution ValidationResolutionEnumThe resolution to use for validation. Default value: "high"
Possible enum values: low, medium, high
validation_aspect_ratio ValidationAspectRatioEnumThe aspect ratio to use for validation. Default value: "1:1"
Possible enum values: 16:9, 1:1, 9:16
validation_reverse booleanIf true, the validation videos will be reversed. This is useful for effects that are learned in reverse and then applied in reverse.
{
"training_data_url": "",
"rank": 128,
"number_of_steps": 1000,
"number_of_frames": 81,
"frame_rate": 25,
"resolution": "medium",
"aspect_ratio": "1:1",
"learning_rate": 0.0002,
"trigger_phrase": "",
"auto_scale_input": false,
"split_input_into_scenes": true,
"split_input_duration_threshold": 30,
"validation": [],
"validation_negative_prompt": "blurry, low quality, bad quality, out of focus",
"validation_number_of_frames": 81,
"validation_resolution": "high",
"validation_aspect_ratio": "1:1"
}The URL to the validations video.
URL to the trained LoRA weights.
Configuration used for setting up the inference endpoints.
{
"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
}
}prompt string* requiredThe prompt to use for validation.
image_url stringAn image to use for image-to-video validation. If provided for one validation, all validation inputs must have an image.
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.