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Wan-2.1 Text-to-Image Text to Image

fal-ai/wan-t2i
Generate images using Wan-2.1 14B
Inference
Private
PlaygroundAPI

About

Generate an image.

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/wan-t2i", {
  input: {
    prompt: ""
  },
  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/wan-t2i", {
  input: {
    prompt: ""
  },
  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-t2i", {
  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-t2i", {
  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#

prompt string* required

Prompt to generate the image from

negative_prompt string

Negative prompt to avoid in the generated image Default value: "low quality, bad anatomy, worst quality, lowres, jpeg artifacts, signature, watermark, blurry"

width integer

Width of the generated image Default value: 1024

height integer

Height of the generated image Default value: 1024

num_inference_steps integer

Number of steps to generate the image Default value: 28

num_images integer

Number of images to generate Default value: 1

guidance_scale float

Guidance scale for the generation. Default value: 5

seed integer

Seed for random number generation. If not provided, a random seed will be used.

loras list<LoRAWeight>

List of LoRA weights to use for generation. If not provided, no LoRA weights will be used.

output_format OutputFormatEnum

Output format of the image. Default is jpeg. Default value: "jpeg"

Possible enum values: jpeg, png

sync_mode boolean

If set to true, the function will wait for the image to be generated and uploaded before returning the response. This will increase the latency of the function but it allows you to get the image directly in the response without going through the CDN.

use_finetune boolean

If set to true, the function will use the finetuned model, otherwise it will use the base model.

{
  "prompt": "",
  "negative_prompt": "low quality, bad anatomy, worst quality, lowres, jpeg artifacts, signature, watermark, blurry",
  "width": 1024,
  "height": 1024,
  "num_inference_steps": 28,
  "num_images": 1,
  "guidance_scale": 5,
  "loras": [],
  "output_format": "jpeg",
  "sync_mode": false,
  "use_finetune": false
}

Output#

images list<Image>* required

List of generated images

seed integer* required

Seed used for random number generation

{
  "images": [
    {
      "url": "",
      "content_type": "image/jpeg"
    }
  ]
}

Other types#

LoRAWeight#

path string* required

Path to the LoRA weight file

weight_name string

Name of the weight in the LoRA file. Only used if path is a HuggingFace repository, and is only required if the repository contains multiple LoRA weights.

scale float

Scale of the LoRA weight. Default is 1.0. Default value: 1

Image#

url string* required
width integer* required
height integer* required
content_type string

Default value: "image/jpeg"