Wan VACE 14B Video to Video API Docs | fal

VACE is a video generation model that uses a source image, mask, and video to create prompted videos with controllable sources.

fal-ai/wan-vace-14b/inpainting

VACE is a video generation model that uses a source image, mask, and video to create prompted videos with controllable sources.

Inference

Commercial use

Schema

LLMs

Playground API

About

Endpoint for inpainting a video from a source video and mask.

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. Install the new package and update your imports — see client setup.

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/wan-vace-14b/inpainting", {
  input: {
    prompt: "The video shows a man riding a horse on a vast grassland. He has long lavender hair and wears a traditional dress of a white top and black pants. The animation style makes him look like he is doing some kind of outdoor activity or performing. The background is a spectacular mountain range and cloud sky, giving a sense of tranquility and vastness. The entire video is shot from a fixed angle, focusing on the rider and his horse.",
    video_url: "https://storage.googleapis.com/falserverless/vace/src_video.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 an environment where you cannot set environment variables, you can configure the API key manually on the client.

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/wan-vace-14b/inpainting", {
  input: {
    prompt: "The video shows a man riding a horse on a vast grassland. He has long lavender hair and wears a traditional dress of a white top and black pants. The animation style makes him look like he is doing some kind of outdoor activity or performing. The background is a spectacular mountain range and cloud sky, giving a sense of tranquility and vastness. The entire video is shot from a fixed angle, focusing on the rider and his horse.",
    video_url: "https://storage.googleapis.com/falserverless/vace/src_video.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/wan-vace-14b/inpainting", {
  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/wan-vace-14b/inpainting", {
  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 to guide video generation.

negative_prompt string

Negative prompt for video generation. Default value: "letterboxing, borders, black bars, bright colors, overexposed, static, blurred details, subtitles, style, artwork, painting, picture, still, overall gray, worst quality, low quality, JPEG compression residue, ugly, incomplete, extra fingers, poorly drawn hands, poorly drawn faces, deformed, disfigured, malformed limbs, fused fingers, still picture, cluttered background, three legs, many people in the background, walking backwards"

match_input_num_frames boolean

If true, the number of frames in the generated video will match the number of frames in the input video. If false, the number of frames will be determined by the num_frames parameter.

num_frames integer

Number of frames to generate. Must be between 81 to 241 (inclusive). Default value: 81

match_input_frames_per_second boolean

If true, the frames per second of the generated video will match the input video. If false, the frames per second will be determined by the frames_per_second parameter.

frames_per_second integer

Frames per second of the generated video. Must be between 5 to 30. Ignored if match_input_frames_per_second is true. Default value: 16

seed integer

Random seed for reproducibility. If None, a random seed is chosen.

resolution ResolutionEnum

Resolution of the generated video. Default value: "auto"

Possible enum values: auto, 240p, 360p, 480p, 580p, 720p

aspect_ratio AspectRatioEnum

Aspect ratio of the generated video. Default value: "auto"

Possible enum values: auto, 16:9, 1:1, 9:16

num_inference_steps integer

Number of inference steps for sampling. Higher values give better quality but take longer. Default value: 30

guidance_scale float

Guidance scale for classifier-free guidance. Higher values encourage the model to generate images closely related to the text prompt. Default value: 5

sampler SamplerEnum

Sampler to use for video generation. Default value: "unipc"

Possible enum values: unipc, dpm++, euler

shift float

Shift parameter for video generation. Default value: 5

video_url string* required

URL to the source video file. Required for inpainting.

mask_video_url string

URL to the source mask file. Required for inpainting.

mask_image_url string

URL to the guiding mask file. If provided, the model will use this mask as a reference to create masked video using salient mask tracking. Will be ignored if mask_video_url is provided.

ref_image_urls list<string>

Urls to source reference image. If provided, the model will use this image as reference.

first_frame_url string

URL to the first frame of the video. If provided, the model will use this frame as a reference.

last_frame_url string

URL to the last frame of the video. If provided, the model will use this frame as a reference.

enable_safety_checker boolean

If set to true, the safety checker will be enabled. Disabling it requires account authorization; unauthorized requests are always checked.

enable_prompt_expansion boolean

Whether to enable prompt expansion.

preprocess boolean

Whether to preprocess the input video.

acceleration Enum

Acceleration to use for inference. Options are 'none' or 'regular'. Accelerated inference will very slightly affect output, but will be significantly faster. Default value: regular

Possible enum values: none, low, regular

video_quality VideoQualityEnum

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

Possible enum values: low, medium, high, maximum

video_write_mode VideoWriteModeEnum

The write mode of the generated video. Default value: "balanced"

Possible enum values: fast, balanced, small

num_interpolated_frames integer

Number of frames to interpolate between the original frames. A value of 0 means no interpolation.

temporal_downsample_factor integer

Temporal downsample factor for the video. This is an integer value that determines how many frames to skip in the video. A value of 0 means no downsampling. For each downsample factor, one upsample factor will automatically be applied.

enable_auto_downsample boolean

If true, the model will automatically temporally downsample the video to an appropriate frame length for the model, then will interpolate it back to the original frame length.

auto_downsample_min_fps float

The minimum frames per second to downsample the video to. This is used to help determine the auto downsample factor to try and find the lowest detail-preserving downsample factor. The default value is appropriate for most videos, if you are using a video with very fast motion, you may need to increase this value. If your video has a very low amount of motion, you could decrease this value to allow for higher downsampling and thus longer sequences. Default value: 15

interpolator_model InterpolatorModelEnum

The model to use for frame interpolation. Options are 'rife' or 'film'. Default value: "film"

Possible enum values: rife, film

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.

transparency_mode TransparencyModeEnum

The transparency mode to apply to the first and last frames. This controls how the transparent areas of the first and last frames are filled. Default value: "content_aware"

Possible enum values: content_aware, white, black

return_frames_zip boolean

If true, also return a ZIP file containing all generated frames.

{
  "prompt": "The video shows a man riding a horse on a vast grassland. He has long lavender hair and wears a traditional dress of a white top and black pants. The animation style makes him look like he is doing some kind of outdoor activity or performing. The background is a spectacular mountain range and cloud sky, giving a sense of tranquility and vastness. The entire video is shot from a fixed angle, focusing on the rider and his horse.",
  "negative_prompt": "letterboxing, borders, black bars, bright colors, overexposed, static, blurred details, subtitles, style, artwork, painting, picture, still, overall gray, worst quality, low quality, JPEG compression residue, ugly, incomplete, extra fingers, poorly drawn hands, poorly drawn faces, deformed, disfigured, malformed limbs, fused fingers, still picture, cluttered background, three legs, many people in the background, walking backwards",
  "match_input_num_frames": false,
  "num_frames": 81,
  "match_input_frames_per_second": false,
  "frames_per_second": 16,
  "resolution": "auto",
  "aspect_ratio": "auto",
  "num_inference_steps": 30,
  "guidance_scale": 5,
  "sampler": "unipc",
  "shift": 5,
  "video_url": "https://storage.googleapis.com/falserverless/vace/src_video.mp4",
  "mask_video_url": "https://storage.googleapis.com/falserverless/vace/src_mask.mp4",
  "ref_image_urls": [
    "https://storage.googleapis.com/falserverless/vace/src_ref_image_1.png"
  ],
  "enable_safety_checker": true,
  "enable_prompt_expansion": false,
  "preprocess": false,
  "acceleration": "regular",
  "video_quality": "high",
  "video_write_mode": "balanced",
  "num_interpolated_frames": 0,
  "temporal_downsample_factor": 0,
  "enable_auto_downsample": false,
  "auto_downsample_min_fps": 15,
  "interpolator_model": "film",
  "sync_mode": false,
  "transparency_mode": "content_aware",
  "return_frames_zip": false
}

Output#

video VideoFile* required

The generated inpainting video file.

prompt string* required

The prompt used for generation.

seed integer* required

The seed used for generation.

frames_zip File

ZIP archive of all video frames if requested.

{
  "video": {
    "url": "https://storage.googleapis.com/falserverless/example_outputs/wan-vace-inpainting-output.mp4"
  },
  "prompt": ""
}

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.

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

Related Models

Loading API documentation…

1. Calling the API

2. Authentication

API Key

3. Queue

4. Files

5. Schema

About

Endpoint for inpainting a video from a source video and mask.

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. Install the new package and update your imports — see client setup.

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/wan-vace-14b/inpainting", {
  input: {
    prompt: "The video shows a man riding a horse on a vast grassland. He has long lavender hair and wears a traditional dress of a white top and black pants. The animation style makes him look like he is doing some kind of outdoor activity or performing. The background is a spectacular mountain range and cloud sky, giving a sense of tranquility and vastness. The entire video is shot from a fixed angle, focusing on the rider and his horse.",
    video_url: "https://storage.googleapis.com/falserverless/vace/src_video.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 an environment where you cannot set environment variables, you can configure the API key manually on the client.

import { fal } from "@fal-ai/client";

fal.config({
  credentials: "YOUR_FAL_KEY"
});

Protect your API Key

3. Queue#

Long-running requests

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/wan-vace-14b/inpainting", {
  input: {
    prompt: "The video shows a man riding a horse on a vast grassland. He has long lavender hair and wears a traditional dress of a white top and black pants. The animation style makes him look like he is doing some kind of outdoor activity or performing. The background is a spectacular mountain range and cloud sky, giving a sense of tranquility and vastness. The entire video is shot from a fixed angle, focusing on the rider and his horse.",
    video_url: "https://storage.googleapis.com/falserverless/vace/src_video.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/wan-vace-14b/inpainting", {
  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/wan-vace-14b/inpainting", {
  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)#

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 to guide video generation.

negative_prompt string

match_input_num_frames boolean

If true, the number of frames in the generated video will match the number of frames in the input video. If false, the number of frames will be determined by the num_frames parameter.

num_frames integer

Number of frames to generate. Must be between 81 to 241 (inclusive). Default value: 81

match_input_frames_per_second boolean

If true, the frames per second of the generated video will match the input video. If false, the frames per second will be determined by the frames_per_second parameter.

frames_per_second integer

Frames per second of the generated video. Must be between 5 to 30. Ignored if match_input_frames_per_second is true. Default value: 16

seed integer

Random seed for reproducibility. If None, a random seed is chosen.

resolution ResolutionEnum

Resolution of the generated video. Default value: "auto"

Possible enum values: auto, 240p, 360p, 480p, 580p, 720p

aspect_ratio AspectRatioEnum

Aspect ratio of the generated video. Default value: "auto"

Possible enum values: auto, 16:9, 1:1, 9:16

num_inference_steps integer

Number of inference steps for sampling. Higher values give better quality but take longer. Default value: 30

guidance_scale float

Guidance scale for classifier-free guidance. Higher values encourage the model to generate images closely related to the text prompt. Default value: 5

sampler SamplerEnum

Sampler to use for video generation. Default value: "unipc"

Possible enum values: unipc, dpm++, euler

shift float

Shift parameter for video generation. Default value: 5

video_url string* required

URL to the source video file. Required for inpainting.

mask_video_url string

URL to the source mask file. Required for inpainting.

mask_image_url string

URL to the guiding mask file. If provided, the model will use this mask as a reference to create masked video using salient mask tracking. Will be ignored if mask_video_url is provided.

ref_image_urls list<string>

Urls to source reference image. If provided, the model will use this image as reference.

first_frame_url string

URL to the first frame of the video. If provided, the model will use this frame as a reference.

last_frame_url string

URL to the last frame of the video. If provided, the model will use this frame as a reference.

enable_safety_checker boolean

If set to true, the safety checker will be enabled. Disabling it requires account authorization; unauthorized requests are always checked.

enable_prompt_expansion boolean

Whether to enable prompt expansion.

preprocess boolean

Whether to preprocess the input video.

acceleration Enum

Acceleration to use for inference. Options are 'none' or 'regular'. Accelerated inference will very slightly affect output, but will be significantly faster. Default value: regular

Possible enum values: none, low, regular

video_quality VideoQualityEnum

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

Possible enum values: low, medium, high, maximum

video_write_mode VideoWriteModeEnum

The write mode of the generated video. Default value: "balanced"

Possible enum values: fast, balanced, small

num_interpolated_frames integer

Number of frames to interpolate between the original frames. A value of 0 means no interpolation.

temporal_downsample_factor integer

enable_auto_downsample boolean

If true, the model will automatically temporally downsample the video to an appropriate frame length for the model, then will interpolate it back to the original frame length.

auto_downsample_min_fps float

interpolator_model InterpolatorModelEnum

The model to use for frame interpolation. Options are 'rife' or 'film'. Default value: "film"

Possible enum values: rife, film

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.

transparency_mode TransparencyModeEnum

The transparency mode to apply to the first and last frames. This controls how the transparent areas of the first and last frames are filled. Default value: "content_aware"

Possible enum values: content_aware, white, black

return_frames_zip boolean

If true, also return a ZIP file containing all generated frames.

{
  "prompt": "The video shows a man riding a horse on a vast grassland. He has long lavender hair and wears a traditional dress of a white top and black pants. The animation style makes him look like he is doing some kind of outdoor activity or performing. The background is a spectacular mountain range and cloud sky, giving a sense of tranquility and vastness. The entire video is shot from a fixed angle, focusing on the rider and his horse.",
  "negative_prompt": "letterboxing, borders, black bars, bright colors, overexposed, static, blurred details, subtitles, style, artwork, painting, picture, still, overall gray, worst quality, low quality, JPEG compression residue, ugly, incomplete, extra fingers, poorly drawn hands, poorly drawn faces, deformed, disfigured, malformed limbs, fused fingers, still picture, cluttered background, three legs, many people in the background, walking backwards",
  "match_input_num_frames": false,
  "num_frames": 81,
  "match_input_frames_per_second": false,
  "frames_per_second": 16,
  "resolution": "auto",
  "aspect_ratio": "auto",
  "num_inference_steps": 30,
  "guidance_scale": 5,
  "sampler": "unipc",
  "shift": 5,
  "video_url": "https://storage.googleapis.com/falserverless/vace/src_video.mp4",
  "mask_video_url": "https://storage.googleapis.com/falserverless/vace/src_mask.mp4",
  "ref_image_urls": [
    "https://storage.googleapis.com/falserverless/vace/src_ref_image_1.png"
  ],
  "enable_safety_checker": true,
  "enable_prompt_expansion": false,
  "preprocess": false,
  "acceleration": "regular",
  "video_quality": "high",
  "video_write_mode": "balanced",
  "num_interpolated_frames": 0,
  "temporal_downsample_factor": 0,
  "enable_auto_downsample": false,
  "auto_downsample_min_fps": 15,
  "interpolator_model": "film",
  "sync_mode": false,
  "transparency_mode": "content_aware",
  "return_frames_zip": false
}

Output#

video VideoFile* required

The generated inpainting video file.

prompt string* required

The prompt used for generation.

seed integer* required

The seed used for generation.

frames_zip File

ZIP archive of all video frames if requested.

{
  "video": {
    "url": "https://storage.googleapis.com/falserverless/example_outputs/wan-vace-inpainting-output.mp4"
  },
  "prompt": ""
}

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.

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/wan-vace-14b/inpainting

fal-ai/wan-vace-14b/inpainting

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#

File#

VideoFile#

Related Models

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#

File#

VideoFile#

Related Models