Run LTX-2.3 video-to-video 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/ltx23-v2v-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/ltx23-v2v-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/ltx23-v2v-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/ltx23-v2v-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 adaptation. Higher values increase capacity but use more memory. Default value: "32"
Possible enum values: 8, 16, 32, 64, 128
number_of_steps integerThe number of training steps. Default value: 2000
learning_rate floatLearning rate for optimization. Higher values can lead to faster training but may cause overfitting. Default value: 0.0002
number_of_frames integerNumber of frames per training sample. Must satisfy frames % 8 == 1 (e.g., 1, 9, 17, 25, 33, 41, 49, 57, 65, 73, 81, 89, 97). Default value: 89
frame_rate integerTarget frames per second for the video. Default value: 25
resolution ResolutionEnumResolution to use for training. Higher resolutions require more memory. Default value: "medium"
Possible enum values: low, medium, high
aspect_ratio AspectRatioEnumAspect ratio to use for training. Default value: "1:1"
Possible enum values: 16:9, 1:1, 9:16
trigger_phrase stringA phrase that will trigger the LoRA style. Will be prepended to captions during training. 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. 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. Default value: 30
debug_dataset booleanWhen enabled, the trainer returns a downloadable archive of your preprocessed training data for manual inspection. Use this to verify that your videos, images, and captions were processed correctly before committing to a full training run.
first_frame_conditioning_p floatProbability of conditioning on the first frame during training. Lower values work better for video-to-video transformation. Default value: 0.1
A list of validation inputs with prompts and reference videos.
validation_negative_prompt stringA negative prompt to use for validation. Default value: "worst quality, inconsistent motion, blurry, jittery, distorted"
validation_number_of_frames integerThe number of frames in validation videos. Default value: 89
validation_frame_rate integerTarget frames per second for validation videos. Default value: 25
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
stg_scale floatSTG (Spatio-Temporal Guidance) scale. 0.0 disables STG. Recommended value is 1.0. Default value: 1
{
"training_data_url": "",
"rank": 32,
"number_of_steps": 2000,
"learning_rate": 0.0002,
"number_of_frames": 89,
"frame_rate": 25,
"resolution": "medium",
"aspect_ratio": "1:1",
"trigger_phrase": "",
"auto_scale_input": false,
"split_input_into_scenes": true,
"split_input_duration_threshold": 30,
"first_frame_conditioning_p": 0.1,
"validation": [],
"validation_negative_prompt": "worst quality, inconsistent motion, blurry, jittery, distorted",
"validation_number_of_frames": 89,
"validation_frame_rate": 25,
"validation_resolution": "high",
"validation_aspect_ratio": "1:1",
"stg_scale": 1
}The URL to the validation videos (with reference videos side-by-side), if any.
URL to the trained IC-LoRA weights (.safetensors).
Configuration used for setting up inference endpoints.
A downloadable archive containing the preprocessed training data, including decoded videos. Only present when debug_dataset is enabled in the input.
{
"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.
prompt string* requiredThe prompt to use for validation.
reference_video_url string* requiredURL to reference video for IC-LoRA validation. This is the input video that will be transformed.