Animatediff SparseCtrl LCM | Text to Video | API Documentation

fal-ai/animatediff-sparsectrl-lcm

Animate Your Drawings with Latent Consistency Models!

Inference

Partner

Commercial use

Schema

About

Generate Video

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/animatediff-sparsectrl-lcm", {
  input: {
    prompt: "Drone footage, futuristic city at night, synthwave, vaporware, neon lights, highly detailed, masterpeice, high quality"
  },
  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/animatediff-sparsectrl-lcm", {
  input: {
    prompt: "Drone footage, futuristic city at night, synthwave, vaporware, neon lights, highly detailed, masterpeice, high quality"
  },
  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/animatediff-sparsectrl-lcm", {
  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/animatediff-sparsectrl-lcm", {
  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 prompt to use for generating the image. Be as descriptive as possible for best results.

negative_prompt string

The negative prompt to use. Use it to specify what you don't want. Default value: ""

controlnet_type ControlnetTypeEnum

The type of controlnet to use for generating the video. The controlnet determines how the video will be animated. Default value: "scribble"

Possible enum values: scribble, rgb

num_inference_steps integer

Increasing the amount of steps tells Stable Diffusion that it should take more steps to generate your final result which can increase the amount of detail in your image. Default value: 4

guidance_scale integer

The 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: 1

seed integer

The same seed and the same prompt given to the same version of Stable Diffusion will output the same image every time.

keyframe_0_image_url string

The URL of the first keyframe to use for the generation.

keyframe_0_index integer

The frame index of the first keyframe to use for the generation.

keyframe_1_image_url string

The URL of the second keyframe to use for the generation.

keyframe_1_index integer

The frame index of the second keyframe to use for the generation.

keyframe_2_image_url string

The URL of the third keyframe to use for the generation.

keyframe_2_index integer

The frame index of the third keyframe to use for the generation.

{
  "prompt": "Drone footage, futuristic city at night, synthwave, vaporware, neon lights, highly detailed, masterpeice, high quality",
  "negative_prompt": "blurry, low resolution, bad, ugly, low quality, pixelated, interpolated, compression artifacts, noisey, grainy",
  "controlnet_type": "scribble",
  "num_inference_steps": 4,
  "guidance_scale": 1,
  "seed": 42,
  "keyframe_0_image_url": "https://storage.googleapis.com/falserverless/scribble2/scribble_2_1.png",
  "keyframe_0_index": 0,
  "keyframe_1_image_url": "https://storage.googleapis.com/falserverless/scribble2/scribble_2_2.png",
  "keyframe_1_index": 8,
  "keyframe_2_image_url": "https://storage.googleapis.com/falserverless/scribble2/scribble_2_3.png",
  "keyframe_2_index": 15
}

Output#

video File* required

Generated video file.

seed integer* required

The seed used to generate the video.

{
  "video": {
    "url": "https://url.to/generated/file/z9RV14K95DvU.png",
    "content_type": "image/png",
    "file_name": "z9RV14K95DvU.png",
    "file_size": 4404019
  }
}