Optimized Latent Consistency (SDv1.5)

fal-ai/lcm-sd15-i2i

This API is available for commercial use.

API documentation

Generate Image

Real-time support

This API supports real-time inference. You can use the fal clients to create real-time experiences in your own applications.

import * as fal from "@fal-ai/serverless-client";

const connection = fal.realtime.connect("fal-ai/lcm-sd15-i2i", {
  onResult: (result) => {
    console.log(result);
  },
  onError: (error) => {
    console.error(error);
  }
});

connection.send({
  prompt: "masterpiece, colorful, photo of a beach in hawaii, sun",
  image_url: "https://storage.googleapis.com/falserverless/model_tests/lcm/beach.png"
});

Calling the API

The API follows common HTTP semantics and should work with the language of your preference. Below you will find the API endpoint and some code snippets to help you get started:

https://fal.run/fal-ai/lcm-sd15-i2i

import * as fal from "@fal-ai/serverless-client";

const result = await fal.subscribe("fal-ai/lcm-sd15-i2i", {
  input: {
    prompt: "masterpiece, colorful, photo of a beach in hawaii, sun",
    image_url: "https://storage.googleapis.com/falserverless/model_tests/lcm/beach.png"
  },
  logs: true,
  onQueueUpdate: (update) => {
    if (update.status === "IN_PROGRESS") {
      update.logs.map((log) => log.message).forEach(console.log);
    }
  },
});

For requests that take longer than several seconds, checkout our queue and webhook features. Our JS and Python clients use the queue behind the scenes. Head to our client page in our documenation site to read more about our Python and Javascript clients.

Authentication guidelines

The snippets above assume your have the FAL_KEY variable set in your environment. Make sure you check out our docs to find out more about our API and how to manage authentication, long-running requests and more.

Input

prompt*string

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

Examples:

"masterpiece, colorful, photo of a beach in hawaii, sun"

image_url*string

The image to use as a base.

Examples:

"https://storage.googleapis.com/falserverless/model_tests/lcm/beach.png"

strengthfloat

The strength of the image. Default value: 0.8

negative_promptstring

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: ""

Examples:

"cartoon, illustration, animation. face. male, female"
"ugly, deformed"

seedinteger

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

guidance_scalefloat

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. Default value: 1

num_inference_stepsinteger

The number of inference steps to use for generating the image. The more steps the better the image will be but it will also take longer to generate. Default value: 4

sync_modeboolean

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.

num_imagesinteger

The number of images to generate. The function will return a list of images with the same prompt and negative prompt but different seeds. Default value: 1

enable_safety_checksboolean

If set to true, the resulting image will be checked whether it includes any potentially unsafe content. If it does, it will be replaced with a black image. Default value: true

request_idstring

An id bound to a request, can be used with response to identify the request itself. Default value: ""

Input sample:

{
  "prompt": "masterpiece, colorful, photo of a beach in hawaii, sun",
  "image_url": "https://storage.googleapis.com/falserverless/model_tests/lcm/beach.png",
  "strength": 0.8,
  "negative_prompt": "cartoon, illustration, animation. face. male, female",
  "seed": 42,
  "guidance_scale": 1,
  "num_inference_steps": 4,
  "num_images": 1,
  "enable_safety_checks": true
}

Output

images*list<Image>

The generated image files info.

timings*Timings

seed*integer

Seed of the generated Image. It will be the same value of the one passed in the input or the randomly generated that was used in case none was passed.

num_inference_stepsinteger

Number of inference steps used to generate the image. It will be the same value of the one passed in the input or the default one in case none was passed. Default value: 4

request_idstring

An id bound to a request, can be used with response to identify the request itself. Default value: ""

nsfw_content_detected*list<boolean>

A list of booleans indicating whether the generated image contains any potentially unsafe content. If the safety check is disabled, this field will have a false for each generated image.

Output sample:

{
  "images": [
    {
      "url": "",
      "content_type": "image/jpeg"
    }
  ],
  "num_inference_steps": 4
}

Error types

Some errors may also contain a JSON payload with additional details. In this section you can find the possible error types and their properties.

`ValidationError`

When input validation fails, the API will response with a 422 status and an array of ValidationError object in the response body. The ValidationError object will contain a list of errors, each with a loc (location) attribute that indicates the path to the invalid input, and a msg (message) attribute that describes the error.

loc*list<string | integer>

msg*string

type*string

`HTTPValidationError`

detaillist<ValidationError>

Types

`Image`

url*string

width*integer

height*integer

content_typestring

Default value: "image/jpeg"