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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/nemotron-diffusion-vlm", {
input: {
image_url: "https://storage.googleapis.com/falserverless/example_inputs/dog.png",
prompt: "Describe the image in one short sentence."
},
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/nemotron-diffusion-vlm", {
input: {
image_url: "https://storage.googleapis.com/falserverless/example_inputs/dog.png",
prompt: "Describe the image in one short sentence."
},
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/nemotron-diffusion-vlm", {
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/nemotron-diffusion-vlm", {
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_url string* requiredURL of the image to be processed.
prompt string* requiredPrompt to answer about the image.
max_tokens integerMaximum number of tokens to generate. Default value: 512
num_inference_steps integerNumber of diffusion decoding steps. Defaults to 256, rounded up only when omitted and required by the upstream block schedule. Explicit values must be at least max_tokens / block_length and divisible by max_tokens / block_length. Default value: 256
block_length integerBlock length used by diffusion decoding. Default value: 32
threshold floatConfidence threshold used by diffusion decoding. Default value: 0.9
enable_safety_checker booleanAuthorized callers may set false to request relaxed screening. Default value: true
{
"image_url": "https://storage.googleapis.com/falserverless/example_inputs/dog.png",
"prompt": "Describe the image in one short sentence.",
"max_tokens": 512,
"num_inference_steps": 256,
"block_length": 32,
"threshold": 0.9,
"enable_safety_checker": true
}output string* requiredGenerated answer.
Token and diffusion decoding usage information.
Request timing breakdown in seconds.
{
"output": "The image shows a dog sitting outdoors.",
"usage": {},
"timings": {}
}total_time float* requiredTotal request time in seconds.
image_validation_time float* requiredTime spent validating and loading the input image.
preprocessing_time float* requiredTime spent expanding the chat template and preprocessing tensors.
gpu_transfer_time float* requiredTime spent moving tensors to the GPU.
generation_time float* requiredTime spent inside model generation.
decode_time float* requiredTime spent decoding output token IDs to text.
prompt_tokens integer* requiredNumber of prompt tokens sent to the model.
completion_tokens integer* requiredNumber of generated completion tokens.
num_function_evals integer* requiredNumber of model function evaluations used for generation.