Realtime image edit (base model, no 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"This model has a real-time mode via WebSockets, supported by fal.realtime.connect.
import { fal } from "@fal-ai/client";
const connection = fal.realtime.connect("fal-ai/flux-2/klein/realtime", {
onResult: (result) => {
console.log(result);
},
onError: (error) => {
console.error(error);
},
// Fetch short-lived JWT token from your backend
tokenProvider: async (app) => {
const response = await fetch("/api/fal/realtime-token", {
method: "POST",
headers: { "Content-Type": "application/json" },
body: JSON.stringify({ app }),
});
return response.text();
},
tokenExpirationSeconds: 10,
});
connection.send({
image_url: ""
});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.
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.
prompt stringThe prompt to guide image editing. Default value: "Turn this into "Living oil painting, melting gold and sapphire""
seed integerRandom seed for reproducibility. Default value: 35
output_feedback_strength floatOutput feedback loop. 1.0 = pure noise (no feedback), 0.9 = 90% noise + 10% previous output latent. Default value: 1
num_inference_steps integerDefault value: 3
enable_interpolation booleanEnable RIFE frame interpolation between consecutive frames (doubles output frames).
image_url string* requiredBase64-encoded image data URI for editing. CDN URLs are not supported for realtime. For optimal performance, use 704x704 JPEG images with 50% quality. Other sizes will be resized automatically.
schedule_mu floatSchedule mu for time shift. 2.3=default, lower=more even denoising, 0.3=nearly linear. Default value: 2.3
image_size ImageSizeEnumThe size of the generated image. square=768x768, square_hd=1024x1024. Default value: "square"
Possible enum values: square, square_hd
{
"prompt": "Turn this into a watercolor painting",
"seed": 35,
"output_feedback_strength": 1,
"num_inference_steps": 3,
"image_url": "",
"schedule_mu": 2.3,
"image_size": "square"
}seed integer* requiredSeed used for generation.
Generated images as raw bytes. When interpolation is enabled, returns [interpolated_frame, current_frame] in chronological order. Otherwise returns [current_frame].
{
"images": [
{
"content_type": "image/jpeg",
"content": ""
}
]
}height integer* requiredcontent_type stringDefault value: "image/jpeg"
content string* requiredwidth integer* required