fal-ai/cosmos-predict-2.5/text-to-video

Generate video from text using NVIDIA's 2B Cosmos Post-Trained Model

Inference

Commercial use

Schema

LLMs

Playground API

About

Generate a video from a text prompt using Cosmos Predict 2.5 2B (post-trained).

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

Migrate to @fal-ai/client

The @fal-ai/serverless-client package has been deprecated in favor of @fal-ai/client. Please check the migration guide for more information.

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/cosmos-predict-2.5/text-to-video", {
  input: {
    prompt: "A static, locked-off camera frames an industrial conveyor belt steadily transporting rough rocks through a dimly lit quarry processing facility. The belt runs horizontally across the center of the frame, its thick black rubber surface textured with dust and fine gravel. Jagged gray and brown stones of varying sizes tumble forward in a slow, continuous motion, their sharp edges catching the light. Subtle vibrations ripple through the belt’s surface as small pebbles bounce and shift. In the background, blurred steel beams, pipes, and muted industrial machinery create depth without distraction. Cool, diffused overhead lighting casts soft shadows and highlights the gritty textures, emphasizing dust particles in the air and the raw, rugged surfaces of the rocks."
  },
  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"
});

Protect your API 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.

3. Queue#

Long-running requests

For long-running requests, such as training jobs or models with slower inference times, it is recommended to check the Queue status and rely on Webhooks instead of blocking while waiting for the result.

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/cosmos-predict-2.5/text-to-video", {
  input: {
    prompt: "A static, locked-off camera frames an industrial conveyor belt steadily transporting rough rocks through a dimly lit quarry processing facility. The belt runs horizontally across the center of the frame, its thick black rubber surface textured with dust and fine gravel. Jagged gray and brown stones of varying sizes tumble forward in a slow, continuous motion, their sharp edges catching the light. Subtle vibrations ripple through the belt’s surface as small pebbles bounce and shift. In the background, blurred steel beams, pipes, and muted industrial machinery create depth without distraction. Cool, diffused overhead lighting casts soft shadows and highlights the gritty textures, emphasizing dust particles in the air and the raw, rugged surfaces of the rocks."
  },
  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/cosmos-predict-2.5/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/cosmos-predict-2.5/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);

Auto uploads

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.

5. Schema#

Input#

prompt string* required

The text prompt describing the video to generate.

negative_prompt string

A negative prompt to guide generation away from undesired content. Default value: "The video captures a series of frames showing ugly scenes, static with no motion, motion blur, over-saturation, shaky footage, low resolution, grainy texture, pixelated images, poorly lit areas, underexposed and overexposed scenes, poor color balance, washed out colors, choppy sequences, jerky movements, low frame rate, artifacting, color banding, unnatural transitions, outdated special effects, fake elements, unconvincing visuals, poorly edited content, jump cuts, visual noise, and flickering. Overall, the video is of poor quality."

num_frames integer

Number of frames to generate. Must be between 9 and 93. Default value: 93

num_inference_steps integer

Number of denoising steps. More steps yield higher quality but take longer. Default value: 35

guidance_scale float

Classifier-free guidance scale. Higher values increase prompt adherence. Default value: 7

seed integer

Random seed for reproducible generation.

sync_mode boolean

If True, the media will be returned as a data URI and the output data won't be available in the request history.

video_output_type VideoOutputTypeEnum

The format of the output video. Default value: "X264 (.mp4)"

Possible enum values: X264 (.mp4), VP9 (.webm), PRORES4444 (.mov), GIF (.gif)

video_quality VideoQualityEnum

The quality of the output video. Default value: "high"

Possible enum values: low, medium, high, maximum

{
  "prompt": "A static, locked-off camera frames an industrial conveyor belt steadily transporting rough rocks through a dimly lit quarry processing facility. The belt runs horizontally across the center of the frame, its thick black rubber surface textured with dust and fine gravel. Jagged gray and brown stones of varying sizes tumble forward in a slow, continuous motion, their sharp edges catching the light. Subtle vibrations ripple through the belt’s surface as small pebbles bounce and shift. In the background, blurred steel beams, pipes, and muted industrial machinery create depth without distraction. Cool, diffused overhead lighting casts soft shadows and highlights the gritty textures, emphasizing dust particles in the air and the raw, rugged surfaces of the rocks.",
  "negative_prompt": "The video captures a series of frames showing ugly scenes, static with no motion, motion blur, over-saturation, shaky footage, low resolution, grainy texture, pixelated images, poorly lit areas, underexposed and overexposed scenes, poor color balance, washed out colors, choppy sequences, jerky movements, low frame rate, artifacting, color banding, unnatural transitions, outdated special effects, fake elements, unconvincing visuals, poorly edited content, jump cuts, visual noise, and flickering. Overall, the video is of poor quality.",
  "num_frames": 93,
  "num_inference_steps": 35,
  "guidance_scale": 7,
  "video_output_type": "X264 (.mp4)",
  "video_quality": "high"
}

Output#

video VideoFile* required

The generated video file.

seed integer* required

The random seed used for generation.

{
  "video": {
    "content_type": "video/mp4",
    "url": "https://v3b.fal.media/files/b/0a8fabc5/qAi19s0dSuQHDZ3O7D_HV_FkSwbls1.mp4"
  }
}

Other types#

VideoFile#

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.

width integer

The width of the video

height integer

The height of the video

fps float

The FPS of the video

duration float

The duration of the video

num_frames integer

The number of frames in the video

fal-ai/cosmos-predict-2.5/text-to-video

Table of contents

1. Calling the API

2. Authentication

3. Queue

4. Files

5. Schema

About

1. Calling the API#

Install the client#

Migrate to @fal-ai/client

Setup your API Key#

Submit a request#

2. Authentication#

API Key#

Protect your API Key

3. Queue#

Long-running requests

Submit a request#

Fetch request status#

Get the result#

4. Files#

Data URI (base64)#

Hosted files (URL)#

Uploading files#

Auto uploads

5. Schema#

Input#

Output#

Other types#

VideoFile#

Related Models