fal-ai/goal-force

Physics-based video generation with Goal Force. Point where you want objects to move, set force direction and strength, get physically plausible results.

Inference

Commercial use

Schema

LLMs

Playground API

About

Generate

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/goal-force", {
  input: {},
  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/goal-force", {
  input: {},
  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/goal-force", {
  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/goal-force", {
  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

Text description of the scene and the desired physics interaction. Default value: "A hand pushes the hanging bulb, causing it to sway."

image_url string

URL of the input image (first frame). The image will be resized to 832x480. Default value: "https://raw.githubusercontent.com/brown-palm/goal-force/2d0209dc27333bffdb3f06df657ae05b11407615/datasets/examples/human-object-interaction/images/_bulb_tool.png"

goal_force ForceParams

Indirect goal force applied to a target object. The model generates physics-plausible interactions to move the target in the specified direction. Specify either this, projectile_force, or both.

projectile_force ForceParams

Direct force applied to a projectile object, causing it to move along the force direction. Specify either this, goal_force, or both.

seed integer

Random seed for reproducibility.

num_inference_steps integer

Number of denoising steps. Higher values produce better quality but take longer. Default value: 50

guidance_scale float

Classifier-free guidance scale. Default value: 5

negative_prompt string

Negative prompt for generation. Default value: "色调艳丽，过曝，静态，细节模糊不清，字幕，风格，作品，画作，画面，静止，整体发灰，最差质量，低质量，JPEG压缩残留，丑陋的，残缺的，多余的手指，画得不好的手部，画得不好的脸部，畸形的，毁容的，形态畸形的肢体，手指融合，静止不动的画面，杂乱的背景，三条腿，背景人很多，倒着走"

enable_safety_checker boolean

Enable safety checker for generated content. Default value: true

{
  "prompt": "A hand pushes the hanging bulb, causing it to sway.",
  "image_url": "https://raw.githubusercontent.com/brown-palm/goal-force/2d0209dc27333bffdb3f06df657ae05b11407615/datasets/examples/human-object-interaction/images/_bulb_tool.png",
  "goal_force": {
    "y": 0.18,
    "mass": -1,
    "angle": 0,
    "magnitude": 350,
    "x": 0.22
  },
  "projectile_force": {
    "y": 0.18,
    "mass": -1,
    "angle": 0,
    "magnitude": 350,
    "x": 0.22
  },
  "num_inference_steps": 50,
  "guidance_scale": 5,
  "negative_prompt": "色调艳丽，过曝，静态，细节模糊不清，字幕，风格，作品，画作，画面，静止，整体发灰，最差质量，低质量，JPEG压缩残留，丑陋的，残缺的，多余的手指，画得不好的手部，画得不好的脸部，畸形的，毁容的，形态畸形的肢体，手指融合，静止不动的画面，杂乱的背景，三条腿，背景人很多，倒着走",
  "enable_safety_checker": true
}

Output#

video VideoFile* required

The generated video.

seed integer* required

The seed used for generation.

timings Timings* required

Timing information.

{
  "video": {
    "file_size": 502438,
    "file_name": "tmpbrjvor4y.mp4",
    "content_type": "application/octet-stream",
    "url": "https://v3b.fal.media/files/b/0a91e900/S4Q1EK9vvZW_HATdXnbBo_tmpbrjvor4y.mp4"
  },
  "seed": 5,
  "timings": {
    "inference": 234.79
  }
}

Other types#

ForceParams#

x float* required

Normalized x position of the force application point (0 = left, 1 = right).

y float* required

Normalized y position of the force application point (0 = top, 1 = bottom).

angle float* required

Direction of force in degrees. 0 = right, 90 = up, 180 = left, 270 = down.

magnitude float* required

Strength of the force (30 = gentle push, 400 = strong impact).

mass float

Mass of the object (1.0 to 4.0). Use -1 to let the model infer mass automatically. Default value: -1

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/goal-force

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#

ForceParams#

VideoFile#