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Kandinsky5 Pro Text to Video

fal-ai/kandinsky5-pro/text-to-video
Kandinsky 5.0 Pro is a diffusion model for fast, high-quality text-to-video generation.
Inference
Commercial use
Schema
LLMs
PlaygroundAPI

About

Generate a video from text prompt.

1. Calling the API#

Install the client#

The client provides a convenient way to interact with the model API.

npm install --save @fal-ai/client

Setup your API Key#

Set FAL_KEY as an environment variable in your runtime.

export FAL_KEY="YOUR_API_KEY"

Submit a request#

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/kandinsky5-pro/text-to-video", {
  input: {
    prompt: "A medium shot establishes a modern, minimalist office setting: clean lines, muted grey walls, and polished wood surfaces. The focus shifts to a close-up on a woman in sharp, navy blue business attire. Her crisp white blouse contrasts with the deep blue of her tailored suit jacket. The subtle texture of the fabric is visible—a fine weave with a slight sheen. Her expression is serious, yet engaging, as she speaks to someone unseen just beyond the frame. Close-up on her eyes, showing the intensity of her gaze and the fine lines around them that hint at experience and focus. Her lips are slightly parted, as if mid-sentence. The light catches the subtle highlights in her auburn hair, meticulously styled. Note the slight catch of light on the silver band of her watch. High resolution 4k"
  },
  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);

2. Authentication#

The API uses an API Key for authentication. It is recommended you set the FAL_KEY environment variable in your runtime when possible.

API Key#

In case your app is running in an environment where you cannot set environment variables, you can set the API Key manually as a client configuration.
import { fal } from "@fal-ai/client";

fal.config({
  credentials: "YOUR_FAL_KEY"
});

3. Queue#

Submit a request#

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/kandinsky5-pro/text-to-video", {
  input: {
    prompt: "A medium shot establishes a modern, minimalist office setting: clean lines, muted grey walls, and polished wood surfaces. The focus shifts to a close-up on a woman in sharp, navy blue business attire. Her crisp white blouse contrasts with the deep blue of her tailored suit jacket. The subtle texture of the fabric is visible—a fine weave with a slight sheen. Her expression is serious, yet engaging, as she speaks to someone unseen just beyond the frame. Close-up on her eyes, showing the intensity of her gaze and the fine lines around them that hint at experience and focus. Her lips are slightly parted, as if mid-sentence. The light catches the subtle highlights in her auburn hair, meticulously styled. Note the slight catch of light on the silver band of her watch. High resolution 4k"
  },
  webhookUrl: "https://optional.webhook.url/for/results",
});

Fetch request status#

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/kandinsky5-pro/text-to-video", {
  requestId: "764cabcf-b745-4b3e-ae38-1200304cf45b",
  logs: true,
});

Get the result#

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/kandinsky5-pro/text-to-video", {
  requestId: "764cabcf-b745-4b3e-ae38-1200304cf45b"
});
console.log(result.data);
console.log(result.requestId);

4. Files#

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.

Data URI (base64)#

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.

Hosted files (URL)#

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.

Uploading files#

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.

5. Schema#

Input#

prompt string* required

The text prompt to guide video generation.

resolution ResolutionEnum

Video resolution: 512p or 1024p. Default value: "512P"

Possible enum values: 512P, 1024P

aspect_ratio AspectRatioEnum

Aspect ratio of the generated video. One of (3:2, 1:1, 2:3). Default value: "3:2"

Possible enum values: 3:2, 1:1, 2:3

duration DurationEnum

The length of the video to generate (5s or 10s) Default value: "5s"

Possible enum values: 5s

num_inference_steps integer

The number of inference steps. Default value: 28

acceleration AccelerationEnum

Acceleration level for faster generation. Default value: "regular"

Possible enum values: none, regular

{
  "prompt": "A medium shot establishes a modern, minimalist office setting: clean lines, muted grey walls, and polished wood surfaces. The focus shifts to a close-up on a woman in sharp, navy blue business attire. Her crisp white blouse contrasts with the deep blue of her tailored suit jacket. The subtle texture of the fabric is visible—a fine weave with a slight sheen. Her expression is serious, yet engaging, as she speaks to someone unseen just beyond the frame. Close-up on her eyes, showing the intensity of her gaze and the fine lines around them that hint at experience and focus. Her lips are slightly parted, as if mid-sentence. The light catches the subtle highlights in her auburn hair, meticulously styled. Note the slight catch of light on the silver band of her watch. High resolution 4k",
  "resolution": "512P",
  "aspect_ratio": "3:2",
  "duration": "5s",
  "num_inference_steps": 28,
  "acceleration": "regular"
}

Output#

video File

The generated video file.

{
  "video": {
    "file_size": 14530500,
    "file_name": "output.mp4",
    "content_type": "application/octet-stream",
    "url": "https://v3b.fal.media/files/b/0a87754e/o5FWdz83KTXzq0FB7aG5Q_output.mp4"
  }
}

Other types#

KandinskyI2VRequest#

prompt string* required

The prompt to generate the video from.

image_url string* required

The URL of the image to use as a reference for the video generation.

resolution ResolutionEnum

Video resolution: 512p or 1024p. Default value: "512P"

Possible enum values: 512P, 1024P

duration DurationEnum

Video duration. Default value: "5s"

Possible enum values: 5s

num_inference_steps integer

Default value: 28

acceleration AccelerationEnum

Acceleration level for faster generation. Default value: "regular"

Possible enum values: none, regular

KandinskyI2VResponse#

video File

The generated video file.

File#

url string* required

The URL where the file can be downloaded from.

content_type string

The mime type of the file.

file_name string

The name of the file. It will be auto-generated if not provided.

file_size integer

The size of the file in bytes.

file_data string

File data

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