Wan-2.1 Image-to-Video with LoRAs | Image to Video

fal-ai/wan-i2v-lora

Add custom LoRAs to Wan-2.1 is a image-to-video model that generates high-quality videos with high visual quality and motion diversity from images

Inference

Commercial use

Schema

Playground API

About

Inference

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/wan-i2v-lora", {
  input: {
    prompt: "Cars race in slow motion.",
    image_url: "https://storage.googleapis.com/falserverless/gallery/car_720p.png"
  },
  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/wan-i2v-lora", {
  input: {
    prompt: "Cars race in slow motion.",
    image_url: "https://storage.googleapis.com/falserverless/gallery/car_720p.png"
  },
  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-i2v-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-i2v-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: "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"

image_url string* required

URL of the input image. If the input image does not match the chosen aspect ratio, it is resized and center cropped.

num_frames integer

Number of frames to generate. Must be between 81 to 100 (inclusive). If the number of frames is greater than 81, the video will be generated with 1.25x more billing units. Default value: 81

frames_per_second integer

Frames per second of the generated video. Must be between 5 to 24. Default value: 16

seed integer

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

num_inference_steps integer

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

guide_scale float

Classifier-free guidance scale. Higher values give better adherence to the prompt but may decrease quality. Default value: 5

shift float

Shift parameter for video generation. Default value: 5

enable_safety_checker boolean

If set to true, the safety checker will be enabled.

enable_prompt_expansion boolean

Whether to enable prompt expansion.

aspect_ratio AspectRatioEnum

Aspect ratio of the output video. Default value: "16:9"

Possible enum values: 16:9, 9:16

loras list<LoraWeight>

LoRA weights to be used in the inference. Default value: ``

reverse_video boolean

If true, the video will be reversed.

turbo_mode boolean

If true, the video will be generated faster with no noticeable degradation in the visual quality. Default value: true

{
  "prompt": "Cars race in slow motion.",
  "negative_prompt": "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",
  "image_url": "https://storage.googleapis.com/falserverless/gallery/car_720p.png",
  "num_frames": 81,
  "frames_per_second": 16,
  "num_inference_steps": 30,
  "guide_scale": 5,
  "shift": 5,
  "enable_safety_checker": true,
  "enable_prompt_expansion": true,
  "aspect_ratio": "16:9",
  "loras": [],
  "turbo_mode": true
}

Output#

video File* required

The generated video file.

seed integer* required

The seed used for generation.

{
  "video": {
    "url": "https://storage.googleapis.com/falserverless/web-examples/wan/t2v.mp4"
  }
}

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

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

Wan-2.1 Image-to-Video with LoRAs Image to Video

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#

LoraWeight#

File#