AnimateDiff Video-to-Video Evolved | Video to Video | API Documentation

fal-ai/animatediff-v2v

Re-animate your videos with evolved consistency!

Inference

Commercial use

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-v2v", {
  input: {
    video_url: "https://storage.googleapis.com/falserverless/model_tests/animatediff_v2v/rocket.mp4",
    prompt: "masterpiece, best quality, rocket in space, galaxies in the background"
  },
  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-v2v", {
  input: {
    video_url: "https://storage.googleapis.com/falserverless/model_tests/animatediff_v2v/rocket.mp4",
    prompt: "masterpiece, best quality, rocket in space, galaxies in the background"
  },
  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-v2v", {
  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-v2v", {
  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#

video_url string* required

URL of the video.

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 address details that you don't want in the image. This could be colors, objects, scenery and even the small details (e.g. moustache, blurry, low resolution). Default value: "(bad quality, worst quality:1.2), ugly faces, bad anime"

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: 25

guidance_scale float

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: 7

base_model BaseModelEnum

Base model to use for animation generation. Default value: "cardosAnimev20"

Possible enum values: darkSushiMixMix_colorful, cardosAnimev20

loras list<LoraWeight>

The list of LoRA weights to use. Default value: ``

select_every_nth_frame integer

Select every Nth frame from the video. This can be used to reduce the number of frames to process, which can reduce the time and the cost. However, it can also reduce the quality of the final video. Default value: 2

seed integer

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

{
  "video_url": "https://storage.googleapis.com/falserverless/model_tests/animatediff_v2v/rocket.mp4",
  "prompt": "masterpiece, best quality, rocket in space, galaxies in the background",
  "negative_prompt": "(bad quality, worst quality:1.2), ugly faces, bad anime",
  "num_inference_steps": 25,
  "guidance_scale": 7,
  "base_model": "cardosAnimev20",
  "loras": [],
  "select_every_nth_frame": 2,
  "seed": 537306
}

Output#

video File* required

Generated video file.

seed integer* required

Seed used for generating the video.

timings Timings* required

{
  "video": {
    "content_type": "video/mp4",
    "url": "https://storage.googleapis.com/falserverless/model_tests/animatediff_v2v/turbo-rocket-output.mp4"
  }
}

Other types#

LoraWeight#

path string* required

URL or the path to the LoRA weights. Or HF model name.

scale float

The scale of the LoRA weight. This is used to scale the LoRA weight before merging it with the base model. Default value: 1

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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