LTX Video Trainer Training
About
Runs training.
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
Migrate to @fal-ai/client
The @fal-ai/serverless-client
package has been deprecated in favor of @fal-ai/client
. Please check the migration guide for more information.
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/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);
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#
import { fal } from "@fal-ai/client";
fal.config({
credentials: "YOUR_FAL_KEY"
});
Protect your API 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.
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/ltx-video-trainer", {
input: {
training_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/ltx-video-trainer", {
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/ltx-video-trainer", {
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);
Auto uploads
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.
5. Schema#
Input#
training_data_url
string
* requiredURL to zip archive with images of a consistent style. Try to use at least 10 images and/or videos, 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/video file it corresponds to.
rank
RankEnum
The rank of the LoRA. Default value: "128"
Possible enum values: 8, 16, 32, 64, 128
number_of_steps
integer
The number of steps to train for. Default value: 1000
number_of_frames
integer
The 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
integer
The target frames per second for the video. Default value: 25
resolution
ResolutionEnum
The resolution to use for training. This is the resolution of the video. Default value: "medium"
Possible enum values: low, medium, high
aspect_ratio
AspectRatioEnum
The 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
float
The rate at which the model learns. Higher values can lead to faster training, but over-fitting. Default value: 0.0002
trigger_phrase
string
The phrase that will trigger the model to generate an image. Default value: ""
auto_scale_input
boolean
If true, the input will be automatically scale the video to DEFAULT_NUM_FRAMES frames at 16fps.
split_input_into_scenes
boolean
If true, input 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. Default value: true
split_input_duration_threshold
float
The 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. Default value: ``
validation_negative_prompt
string
A negative prompt to use for validation. Default value: "blurry, low quality, bad quality, out of focus"
validation_number_of_frames
integer
The number of frames to use for validation. Default value: 81
validation_resolution
ValidationResolutionEnum
The resolution to use for validation. Default value: "high"
Possible enum values: low, medium, high
validation_aspect_ratio
ValidationAspectRatioEnum
The aspect ratio to use for validation. Default value: "1:1"
Possible enum values: 16:9, 1:1, 9:16
validation_reverse
boolean
If 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"
}
Output#
The URL to the validations video.
URL to the trained LoRA weights.
Configuration used for setting up the inference endpoints.
{
"video": {
"url": "",
"content_type": "image/png",
"file_name": "z9RV14K95DvU.png",
"file_size": 4404019
},
"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#
Validation#
prompt
string
* requiredThe prompt to use for validation.
image_url
string
An image to use for image-to-video validation. If provided for one validation, all validation inputs must have an image.
File#
url
string
* requiredThe 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.