Instantid | Unknown

fal-ai/instantid/lcm

Inference

Private

Schema

LLMs

Playground API

About

Generate Image Lcm

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/instantid/lcm", {
  input: {
    face_image_url: "https://storage.googleapis.com/falserverless/model_tests/instantid/post_malone.jpg",
    prompt: "man"
  },
  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/instantid/lcm", {
  input: {
    face_image_url: "https://storage.googleapis.com/falserverless/model_tests/instantid/post_malone.jpg",
    prompt: "man"
  },
  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/instantid/lcm", {
  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/instantid/lcm", {
  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#

face_image_url string* required

The image of the person you want to generate.

prompt string* required

The prompt to use for generating the image. Be as descriptive as possible for best results.

style StyleEnum

Default value: "Headshot"

Possible enum values: (No style), Headshot, Spring Festival, Watercolor, Film Noir, Neon, Jungle, Mars, Vibrant Color, Snow, Line art

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: ""

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. If using LCM scheduler, use a value in the range of 1 to 12 for best results. Default value: 5

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. If using LCM scheduler, use a value in the range of 0.1 to 2.0 for best results. Default value: 1.5

controlnet_selection ControlnetSelectionEnum

Type of the ControlNet. If not provided, ControlNets will not be used. Default value: "canny"

Possible enum values: pose, canny, depth

controlnet_conditioning_scale float

The scale of the controlnet conditioning. Default value: 0.4

ip_adapter_scale float

The scale of the IP adapter (increase to improve details). Default value: 0.7

identity_controlnet_conditioning_scale float

The scale of the controlnet conditioning (increase to preserve identity). This only affects the identity controlnet. Default value: 0.7

enhance_face_region boolean

Whether to enhance the face region or not. Default value: true

seed integer

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

model_name string

The link of the model to use for generating the image. If not provided, the default model will be used. Only SDXL models are supported.

{
  "face_image_url": "https://storage.googleapis.com/falserverless/model_tests/instantid/post_malone.jpg",
  "prompt": "man",
  "style": "Headshot",
  "negative_prompt": "nsfw, lowres, bad anatomy, bad hands, text, error, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality, normal quality, jpeg artifacts, signature, watermark, username, blurry",
  "num_inference_steps": 5,
  "guidance_scale": 1.5,
  "controlnet_selection": "canny",
  "controlnet_conditioning_scale": 0.4,
  "ip_adapter_scale": 0.7,
  "identity_controlnet_conditioning_scale": 0.7,
  "enhance_face_region": true,
  "seed": 42
}

Output#

image Image* required

The generated image

seed integer* required

The seed used to generate the image

{
  "image": {
    "height": 1280,
    "content_type": "image/png",
    "url": "https://storage.googleapis.com/falserverless/model_tests/instantid/post_malone_after_canny.png",
    "width": 960
  }
}

Other types#

Image#

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

width integer

The width of the image in pixels.

height integer

The height of the image in pixels.

Instantid Unknown

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#

Image#