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The client provides a convenient way to interact with the model API.
npm install --save @fal-ai/clientSet 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/hidream-i1-full", {
input: {
prompt: "a cat holding a skateboard which has 'fal' written on it in red spray paint"
},
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);This model supports streaming requests. You can stream data directly to the model and get the result in real-time.
import { fal } from "@fal-ai/client";
const stream = await fal.stream("fal-ai/hidream-i1-full", {
input: {
prompt: "a cat holding a skateboard which has 'fal' written on it in red spray paint"
}
});
for await (const event of stream) {
console.log(event);
}
const result = await stream.done();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"
});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/hidream-i1-full", {
input: {
prompt: "a cat holding a skateboard which has 'fal' written on it in red spray paint"
},
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/hidream-i1-full", {
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/hidream-i1-full", {
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);Read more about file handling in our file upload guide.
prompt string* requiredThe prompt to generate an image from.
negative_prompt stringThe negative prompt to use. Use it to address details that you don't want
in the image. This could be colors, objects, scenery and even the small details
(e.g. moustache, blurry, low resolution). Default value: ""
The size of the generated image.
Possible enum values: square_hd, square, portrait_4_3, portrait_16_9, landscape_4_3, landscape_16_9
Note: For custom image sizes, you can pass the width and height as an object:
"image_size": {
"width": 1280,
"height": 720
}num_inference_steps integerThe number of inference steps to perform. Default value: 50
seed integerThe same seed and the same prompt given to the same version of the model will output the same image every time.
guidance_scale floatThe CFG (Classifier Free Guidance) scale is a measure of how close you want
the model to stick to your prompt when looking for a related image to show you. Default value: 5
sync_mode booleanIf True, the media will be returned as a data URI and the output data won't be available in the request history.
num_images integerThe number of images to generate. Default value: 1
enable_safety_checker booleanIf set to true, the safety checker will be enabled. Disabling it requires account authorization; unauthorized requests are always checked, and images flagged as unsafe are returned as black images. Default value: true
output_format OutputFormatEnumThe format of the generated image. Default value: "jpeg"
Possible enum values: jpeg, png
A list of LoRAs to apply to the model. Each LoRA specifies its path, scale, and optional weight name.
{
"prompt": "a cat holding a skateboard which has 'fal' written on it in red spray paint",
"negative_prompt": "",
"image_size": {
"width": 1024,
"height": 1024
},
"num_inference_steps": 50,
"guidance_scale": 5,
"num_images": 1,
"enable_safety_checker": true,
"output_format": "jpeg",
"loras": []
}The generated image files info.
seed integer* requiredSeed of the generated Image. It will be the same value of the one passed in the input or the randomly generated that was used in case none was passed.
Whether the generated images contain NSFW concepts.
prompt string* requiredThe prompt used for generating the image.
{
"images": [
{
"url": "",
"content_type": "image/jpeg"
}
],
"prompt": ""
}url string* requiredwidth integer* requiredheight integer* requiredcontent_type stringDefault value: "image/jpeg"
path string* requiredURL or the path to the LoRA weights.
weight_name stringName of the LoRA weight. Used only if path is a Hugging Face repository, and required only if you have more than 1 safetensors file in the repo.
scale floatThe scale of the LoRA weight. This is used to scale the LoRA weight
before merging it with the base model. Default value: 1
width integerThe width of the generated image. Default value: 512
height integerThe height of the generated image. Default value: 512