Lora
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/trellis-2-lora-trainer", {
input: {
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/trellis-2-lora-trainer", {
input: {
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/trellis-2-lora-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/trellis-2-lora-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.
data_url string* requiredURL to a .zip archive containing raw 3D assets or a TRELLIS.2 preprocessed dataset zip returned by the data_preprocessing endpoint.
TRELLIS.2 denoisers to fine-tune. One LoRA adapter is trained per entry, and all selected denoisers are trained in parallel from the same preprocessed dataset. Provide 1-3 unique values out of sparse_structure, geometry, and texture.
resolution ResolutionEnumTraining resolution. Must match the resolution the dataset is preprocessed at. Sparse-structure training uses the same config at both resolutions; geometry and texture have dedicated 512/1024 configs. Default value: "512"
Possible enum values: 512, 1024
rank RankEnumLoRA rank. Higher values increase adapter capacity. Default value: "32"
Possible enum values: 16, 32, 64
learning_rate floatLearning rate for LoRA optimization. Default value: 0.0001
training_steps integerNumber of training steps. Default value: 1000
{
"data_url": "",
"resolution": 512,
"rank": 32,
"learning_rate": 0.0001,
"training_steps": 1000
}Trained LoRA adapters, one per denoiser that completed successfully. Use each adapter's file in the matching trellis-2-lora inference field.
Denoisers whose training did not complete. Empty when every requested denoiser succeeded.
Reusable TRELLIS.2 preprocessed dataset zip used for training.
{
"adapters": [
{
"denoiser": "sparse_structure",
"resolution": 512,
"lora_file": {
"url": "",
"content_type": "image/png",
"file_name": "z9RV14K95DvU.png",
"file_size": 4404019
},
"rank": 16
}
],
"failed": [
{
"denoiser": "sparse_structure",
"error": ""
}
],
"preprocessed_data_file": {
"url": "",
"content_type": "image/png",
"file_name": "z9RV14K95DvU.png",
"file_size": 4404019
}
}denoiser DenoiserEnum* requiredTRELLIS.2 denoiser this LoRA adapter was trained for: sparse_structure, geometry, or texture.
Possible enum values: sparse_structure, geometry, texture
resolution ResolutionEnum* requiredResolution this LoRA adapter was trained at. Use the same resolution in trellis-2-lora inference.
Possible enum values: 512, 1024
Trained TRELLIS.2 LoRA adapter.
rank RankEnum* requiredLoRA rank used for training.
Possible enum values: 16, 32, 64
training_steps integer* requiredNumber of training steps run.
learning_rate float* requiredLearning rate used for LoRA optimization.
denoiser DenoiserEnum* requiredTRELLIS.2 denoiser whose training did not complete.
Possible enum values: sparse_structure, geometry, texture
error string* requiredReason the training did not complete.
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.