Wan-2.2 text-to-video is a video model that generates high-quality videos with high visual quality and motion diversity from text prompts. This endpoint supports LoRAs made for Wan 2.2.

fal-ai/wan/v2.2-a14b/text-to-video/lora

Wan-2.2 text-to-video is a video model that generates high-quality videos with high visual quality and motion diversity from text prompts. This endpoint supports LoRAs made for Wan 2.2.

Inference

Commercial use

Schema

Playground API

SandboxOpen in Sandbox

fal-ai/wan/v2.2-a14b/text-to-video/lora

Schema

SandboxOpen in Sandbox

Wan-2.2 text-to-video is a video model that generates high-quality videos with high visual quality and motion diversity from text prompts. This endpoint supports LoRAs made for Wan 2.2.

fal-ai/wan/v2.2-a14b/text-to-video/lora

Wan-2.2 text-to-video is a video model that generates high-quality videos with high visual quality and motion diversity from text prompts. This endpoint supports LoRAs made for Wan 2.2.

Inference

Commercial use

Schema

Playground API

SandboxOpen in Sandbox

About

Endpoint for generating a video from text, with LoRA support.

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/v2.2-a14b/text-to-video/lora", {
  input: {
    prompt: "A close-up of a young woman smiling gently in the rain, raindrops glistening on her face and eyelashes. The video captures the delicate details of her expression and the water droplets, with soft light reflecting off her skin in the rainy atmosphere."
  },
  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/v2.2-a14b/text-to-video/lora", {
  input: {
    prompt: "A close-up of a young woman smiling gently in the rain, raindrops glistening on her face and eyelashes. The video captures the delicate details of her expression and the water droplets, with soft light reflecting off her skin in the rainy atmosphere."
  },
  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/v2.2-a14b/text-to-video/lora", {
  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/v2.2-a14b/text-to-video/lora", {
  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: ""

num_frames integer

Number of frames to generate. Must be between 17 to 161 (inclusive). Default value: 81

frames_per_second integer

Frames per second of the generated video. Must be between 4 to 60. When using interpolation and adjust_fps_for_interpolation is set to true (default true,) the final FPS will be multiplied by the number of interpolated frames plus one. For example, if the generated frames per second is 16 and the number of interpolated frames is 1, the final frames per second will be 32. If adjust_fps_for_interpolation is set to false, this value will be used as-is. Default value: 16

seed integer

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

resolution ResolutionEnum

Resolution of the generated video (480p, 580p, or 720p). Default value: "720p"

Possible enum values: 480p, 580p, 720p

aspect_ratio AspectRatioEnum

Aspect ratio of the generated video (16:9 or 9:16). Default value: "16:9"

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

num_inference_steps integer

Number of inference steps for sampling. Higher values give better quality but take longer. Default value: 27

enable_safety_checker boolean

If set to true, input data will be checked for safety before processing. Disabling it requires account authorization; unauthorized requests are always checked.

enable_output_safety_checker boolean

If set to true, output video will be checked for safety after generation.

enable_prompt_expansion boolean

Whether to enable prompt expansion. This will use a large language model to expand the prompt with additional details while maintaining the original meaning.

acceleration AccelerationEnum

Acceleration level to use. The more acceleration, the faster the generation, but with lower quality. The recommended value is 'regular'. Default value: "regular"

Possible enum values: none, regular

guidance_scale float

Classifier-free guidance scale. Higher values give better adherence to the prompt but may decrease quality. Default value: 3.5

guidance_scale_2 float

Guidance scale for the second stage of the model. This is used to control the adherence to the prompt in the second stage of the model. Default value: 4

shift float

Shift value for the video. Must be between 1.0 and 10.0. Default value: 5

interpolator_model InterpolatorModelEnum

The model to use for frame interpolation. If None, no interpolation is applied. Default value: "film"

Possible enum values: none, film, rife

num_interpolated_frames integer

Number of frames to interpolate between each pair of generated frames. Must be between 0 and 4. Default value: 1

adjust_fps_for_interpolation boolean

If true, the number of frames per second will be multiplied by the number of interpolated frames plus one. For example, if the generated frames per second is 16 and the number of interpolated frames is 1, the final frames per second will be 32. If false, the passed frames per second will be used as-is. Default value: true

video_quality VideoQualityEnum

The quality of the output video. Higher quality means better visual quality but larger file size. Default value: "high"

Possible enum values: low, medium, high, maximum

video_write_mode VideoWriteModeEnum

The write mode of the output video. Faster write mode means faster results but larger file size, balanced write mode is a good compromise between speed and quality, and small write mode is the slowest but produces the smallest file size. Default value: "balanced"

Possible enum values: fast, balanced, small

loras list<LoRAWeight>

LoRA weights to be used in the inference.

reverse_video boolean

If true, the video will be reversed.

{
  "prompt": "A close-up of a young woman smiling gently in the rain, raindrops glistening on her face and eyelashes. The video captures the delicate details of her expression and the water droplets, with soft light reflecting off her skin in the rainy atmosphere.",
  "num_frames": 81,
  "frames_per_second": 16,
  "resolution": "720p",
  "aspect_ratio": "16:9",
  "num_inference_steps": 27,
  "enable_safety_checker": true,
  "enable_output_safety_checker": false,
  "enable_prompt_expansion": false,
  "acceleration": "regular",
  "guidance_scale": 3.5,
  "guidance_scale_2": 4,
  "shift": 5,
  "interpolator_model": "film",
  "num_interpolated_frames": 1,
  "adjust_fps_for_interpolation": true,
  "video_quality": "high",
  "video_write_mode": "balanced",
  "loras": []
}

Output#

video File* required

The generated video file.

prompt string

The text prompt used for video generation. Default value: ""

seed integer* required

The seed used for generation.

{
  "video": {
    "url": "https://storage.googleapis.com/falserverless/model_tests/wan/v2.2-woman-output.mp4"
  },
  "prompt": "A close-up of a young woman smiling gently in the rain, raindrops glistening on her face and eyelashes. The video captures the delicate details of her expression and the water droplets, with soft light reflecting off her skin in the rainy atmosphere."
}

Other types#

LoRAWeight#

path string* required

URL or the path to the LoRA weights.

weight_name string

Name of the LoRA weight. Used only if path is a Hugging Face repository, and required only if you have more than 1 safetensors file in the repo.

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

transformer TransformerEnum

Specifies the transformer to load the lora weight into. 'high' loads into the high-noise transformer, 'low' loads it into the low-noise transformer, while 'both' loads the LoRA into both transformers. Default value: "high"

Possible enum values: high, low, both

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.

Related Models

About

Endpoint for generating a video from text, with LoRA support.

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/v2.2-a14b/text-to-video/lora", {
  input: {
    prompt: "A close-up of a young woman smiling gently in the rain, raindrops glistening on her face and eyelashes. The video captures the delicate details of her expression and the water droplets, with soft light reflecting off her skin in the rainy atmosphere."
  },
  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/v2.2-a14b/text-to-video/lora", {
  input: {
    prompt: "A close-up of a young woman smiling gently in the rain, raindrops glistening on her face and eyelashes. The video captures the delicate details of her expression and the water droplets, with soft light reflecting off her skin in the rainy atmosphere."
  },
  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/v2.2-a14b/text-to-video/lora", {
  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/v2.2-a14b/text-to-video/lora", {
  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

Negative prompt for video generation. Default value: ""

num_frames integer

Number of frames to generate. Must be between 17 to 161 (inclusive). Default value: 81

frames_per_second integer

seed integer

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

resolution ResolutionEnum

Resolution of the generated video (480p, 580p, or 720p). Default value: "720p"

Possible enum values: 480p, 580p, 720p

aspect_ratio AspectRatioEnum

Aspect ratio of the generated video (16:9 or 9:16). Default value: "16:9"

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

num_inference_steps integer

Number of inference steps for sampling. Higher values give better quality but take longer. Default value: 27

enable_safety_checker boolean

If set to true, input data will be checked for safety before processing. Disabling it requires account authorization; unauthorized requests are always checked.

enable_output_safety_checker boolean

If set to true, output video will be checked for safety after generation.

enable_prompt_expansion boolean

Whether to enable prompt expansion. This will use a large language model to expand the prompt with additional details while maintaining the original meaning.

acceleration AccelerationEnum

Acceleration level to use. The more acceleration, the faster the generation, but with lower quality. The recommended value is 'regular'. Default value: "regular"

Possible enum values: none, regular

guidance_scale float

Classifier-free guidance scale. Higher values give better adherence to the prompt but may decrease quality. Default value: 3.5

guidance_scale_2 float

Guidance scale for the second stage of the model. This is used to control the adherence to the prompt in the second stage of the model. Default value: 4

shift float

Shift value for the video. Must be between 1.0 and 10.0. Default value: 5

interpolator_model InterpolatorModelEnum

The model to use for frame interpolation. If None, no interpolation is applied. Default value: "film"

Possible enum values: none, film, rife

num_interpolated_frames integer

Number of frames to interpolate between each pair of generated frames. Must be between 0 and 4. Default value: 1

adjust_fps_for_interpolation boolean

video_quality VideoQualityEnum

The quality of the output video. Higher quality means better visual quality but larger file size. Default value: "high"

Possible enum values: low, medium, high, maximum

video_write_mode VideoWriteModeEnum

Possible enum values: fast, balanced, small

loras list<LoRAWeight>

LoRA weights to be used in the inference.

reverse_video boolean

If true, the video will be reversed.

{
  "prompt": "A close-up of a young woman smiling gently in the rain, raindrops glistening on her face and eyelashes. The video captures the delicate details of her expression and the water droplets, with soft light reflecting off her skin in the rainy atmosphere.",
  "num_frames": 81,
  "frames_per_second": 16,
  "resolution": "720p",
  "aspect_ratio": "16:9",
  "num_inference_steps": 27,
  "enable_safety_checker": true,
  "enable_output_safety_checker": false,
  "enable_prompt_expansion": false,
  "acceleration": "regular",
  "guidance_scale": 3.5,
  "guidance_scale_2": 4,
  "shift": 5,
  "interpolator_model": "film",
  "num_interpolated_frames": 1,
  "adjust_fps_for_interpolation": true,
  "video_quality": "high",
  "video_write_mode": "balanced",
  "loras": []
}

Output#

video File* required

The generated video file.

prompt string

The text prompt used for video generation. Default value: ""

seed integer* required

The seed used for generation.

{
  "video": {
    "url": "https://storage.googleapis.com/falserverless/model_tests/wan/v2.2-woman-output.mp4"
  },
  "prompt": "A close-up of a young woman smiling gently in the rain, raindrops glistening on her face and eyelashes. The video captures the delicate details of her expression and the water droplets, with soft light reflecting off her skin in the rainy atmosphere."
}

Other types#

LoRAWeight#

path string* required

URL or the path to the LoRA weights.

weight_name string

Name of the LoRA weight. Used only if path is a Hugging Face repository, and required only if you have more than 1 safetensors file in the repo.

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

transformer TransformerEnum

Possible enum values: high, low, both

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.

fal-ai/wan/v2.2-a14b/text-to-video/lora

Table of contents

Table of contents

fal-ai/wan/v2.2-a14b/text-to-video/lora

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#

LoRAWeight#

File#

Related Models

Table of contents

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#

LoRAWeight#

File#

Related Models