Latent Consistency Models (v1.5/XL)

fal-ai/fast-lcm-diffusion

Inference

Commercial use

About

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

const result = await fal.subscribe("fal-ai/fast-lcm-diffusion", {
  input: {
    prompt: "Self-portrait oil painting, a beautiful cyborg with golden hair, 8k."
  },
  logs: true,
  onQueueUpdate: (update) => {
    if (update.status === "IN_PROGRESS") {
      update.logs.map((log) => log.message).forEach(console.log);
    }
  },
});

Real-time via WebSockets#

This model has a real-time mode via websockets, this is supported via the fal.realtime client.

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

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

connection.send({
  prompt: "Self-portrait oil painting, a beautiful cyborg with golden hair, 8k."
});

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

const { request_id } = await fal.queue.submit("fal-ai/fast-lcm-diffusion", {
  input: {
    prompt: "Self-portrait oil painting, a beautiful cyborg with golden hair, 8k."
  },
  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 * as fal from "@fal-ai/serverless-client";

const status = await fal.queue.status("fal-ai/fast-lcm-diffusion", {
  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 * as fal from "@fal-ai/serverless-client";

const result = await fal.queue.result("fal-ai/fast-lcm-diffusion", {
  requestId: "764cabcf-b745-4b3e-ae38-1200304cf45b"
});

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 * as fal from "@fal-ai/serverless-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#

model_name ModelNameEnum

The name of the model to use. Default value: "stabilityai/stable-diffusion-xl-base-1.0"

Possible enum values: stabilityai/stable-diffusion-xl-base-1.0, runwayml/stable-diffusion-v1-5

prompt string* required

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

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: ""

image_size ImageSize | Enum

The size of the generated image. Default value: square_hd

Possible enum values: square_hd, square, portrait_4_3, portrait_16_9, landscape_4_3, landscape_16_9

num_inference_steps integer

The number of inference steps to perform. Default value: 6

seed integer

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

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. Default value: 1.5

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. Default value: true

num_images integer

The number of images to generate. Default value: 1

enable_safety_checker boolean

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

safety_checker_version SafetyCheckerVersionEnum

The version of the safety checker to use. v1 is the default CompVis safety checker. v2 uses a custom ViT model. Default value: "v1"

Possible enum values: v1, v2

expand_prompt boolean

If set to true, the prompt will be expanded with additional prompts.

format FormatEnum

The format of the generated image. Default value: "jpeg"

Possible enum values: jpeg, png

guidance_rescale float

The rescale factor for the CFG.

request_id string

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

{
  "model_name": "stabilityai/stable-diffusion-xl-base-1.0",
  "prompt": "Self-portrait oil painting, a beautiful cyborg with golden hair, 8k.",
  "negative_prompt": "cartoon, illustration, animation. face. male, female",
  "image_size": "square_hd",
  "num_inference_steps": 6,
  "guidance_scale": 1.5,
  "sync_mode": true,
  "num_images": 1,
  "enable_safety_checker": true,
  "safety_checker_version": "v1",
  "format": "jpeg"
}

Output#

images list<Image>* required

The generated image files info.

timings Timings* required

seed integer* required

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.

has_nsfw_concepts list<boolean>* required

Whether the generated images contain NSFW concepts.

prompt string* required

The prompt used for generating the image.

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

Other types#

ImageSize#

width integer

The width of the generated image. Default value: 512

height integer

The height of the generated image. Default value: 512

LoraWeight#

path string* required

URL or the path to the LoRA weights. Or HF model name.

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

force boolean

If set to true, the embedding will be forced to be used.

Embedding#

path string* required

URL or the path to the embedding weights.

tokens list<string>

The list of tokens to use for the embedding. Default value: <s0>,<s1>

Image#

url string* required

width integer* required

height integer* required

content_type string

Default value: "image/jpeg"

Latent Consistency Models (v1.5/XL)

Table of contents

1. Calling the API

2. Authentication

3. Queue

4. Files

5. Schema

About

1. Calling the API#

Install the client#

Setup your API Key#

Submit a request#

Real-time via WebSockets#

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#

ImageSize#

LoraWeight#

Embedding#

Image#