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ControlNeXt SVD Video to Video

fal-ai/controlnext
Animate a reference image with a driving video using ControlNeXt.
Inference
Commercial use
Schema
LLMs
PlaygroundAPI

About

Run

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/controlnext", {
  input: {
    image_url: "https://storage.googleapis.com/falserverless/model_tests/musepose/ref.png",
    video_url: "https://storage.googleapis.com/falserverless/model_tests/musepose/dance.mp4"
  },
  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/controlnext", {
  input: {
    image_url: "https://storage.googleapis.com/falserverless/model_tests/musepose/ref.png",
    video_url: "https://storage.googleapis.com/falserverless/model_tests/musepose/dance.mp4"
  },
  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/controlnext", {
  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/controlnext", {
  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#

image_url string* required

URL of the reference image.

video_url string* required

URL of the input video.

height integer

Height of the output video. Default value: 1024

width integer

Width of the output video. Default value: 576

guidance_scale float

Guidance scale for the diffusion process. Default value: 3

num_inference_steps integer

Number of inference steps. Default value: 25

max_frame_num integer

Maximum number of frames to process. Default value: 240

batch_frames integer

Number of frames to process in each batch. Default value: 24

overlap integer

Number of overlapping frames between batches. Default value: 6

sample_stride integer

Stride for sampling frames from the input video. Default value: 2

decode_chunk_size integer

Chunk size for decoding frames. Default value: 2

motion_bucket_id float

Motion bucket ID for the pipeline. Default value: 127

fps integer

Frames per second for the output video. Default value: 7

controlnext_cond_scale float

Condition scale for ControlNeXt. Default value: 1

{
  "image_url": "https://storage.googleapis.com/falserverless/model_tests/musepose/ref.png",
  "video_url": "https://storage.googleapis.com/falserverless/model_tests/musepose/dance.mp4",
  "height": 1024,
  "width": 576,
  "guidance_scale": 3,
  "num_inference_steps": 25,
  "max_frame_num": 240,
  "batch_frames": 24,
  "overlap": 6,
  "sample_stride": 2,
  "decode_chunk_size": 2,
  "motion_bucket_id": 127,
  "fps": 7,
  "controlnext_cond_scale": 1
}

Output#

video File* required

The generated video.

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

Other types#

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.