Z-Image Turbo | Image to Image

fal-ai/z-image/turbo/controlnet/lora

Generate images from text and edge, depth or pose images using custom LoRA and Z-Image Turbo, Tongyi-MAI's super-fast 6B model.

Inference

Commercial use

Schema

LLMs

Playground API

About

Generate images using Z-Image Turbo with ControlNet and LoRAs at lightning speed.

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/z-image/turbo/controlnet/lora", {
  input: {
    prompt: "A single leopard, its spotted golden coat detailed with black rosettes, cautiously peeks its head through dense green foliage. The leopard’s eyes are alert and focused forward, ears perked, whiskers slightly visible. The bushes consist of thick, leafy shrubs with varying shades of green, some leaves partially obscuring the leopard’s muzzle and forehead. Soft natural daylight filters through the canopy above, casting dappled shadows across the animal’s fur and surrounding leaves. The composition is a medium close-up, centered on the leopard’s head emerging from the undergrowth, with shallow depth of field blurring the background vegetation.",
    image_url: "https://storage.googleapis.com/falserverless/example_inputs/z-image-turbo-controlnet-input.jpg"
  },
  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/z-image/turbo/controlnet/lora", {
  input: {
    prompt: "A single leopard, its spotted golden coat detailed with black rosettes, cautiously peeks its head through dense green foliage. The leopard’s eyes are alert and focused forward, ears perked, whiskers slightly visible. The bushes consist of thick, leafy shrubs with varying shades of green, some leaves partially obscuring the leopard’s muzzle and forehead. Soft natural daylight filters through the canopy above, casting dappled shadows across the animal’s fur and surrounding leaves. The composition is a medium close-up, centered on the leopard’s head emerging from the undergrowth, with shallow depth of field blurring the background vegetation.",
    image_url: "https://storage.googleapis.com/falserverless/example_inputs/z-image-turbo-controlnet-input.jpg"
  },
  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/z-image/turbo/controlnet/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/z-image/turbo/controlnet/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 prompt to generate an image from.

image_size ImageSize | Enum

The size of the generated image. Default value: auto

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

Note: For custom image sizes, you can pass the width and height as an object:

"image_size": {
  "width": 1280,
  "height": 720
}

num_inference_steps integer

The number of inference steps to perform. Default value: 8

seed integer

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

sync_mode boolean

If True, the media will be returned as a data URI and the output data won't be available in the request history.

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

enable_prompt_expansion boolean

Whether to enable prompt expansion. Note: this will increase the price by 0.0025 credits per request.

output_format OutputFormatEnum

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

Possible enum values: jpeg, png, webp

acceleration AccelerationEnum

The acceleration level to use. Default value: "none"

Possible enum values: none, regular, high

image_url string* required

URL of Image for ControlNet generation.

control_scale float

The scale of the controlnet conditioning. Default value: 0.9

control_start float

The start of the controlnet conditioning.

control_end float

The end of the controlnet conditioning. Default value: 0.4

preprocess Enum

What kind of preprocessing to apply to the image, if any. Default value: none

Possible enum values: none, canny, depth, pose

loras list<LoRAInput>

List of LoRA weights to apply (maximum 3).

{
  "prompt": "A single leopard, its spotted golden coat detailed with black rosettes, cautiously peeks its head through dense green foliage. The leopard’s eyes are alert and focused forward, ears perked, whiskers slightly visible. The bushes consist of thick, leafy shrubs with varying shades of green, some leaves partially obscuring the leopard’s muzzle and forehead. Soft natural daylight filters through the canopy above, casting dappled shadows across the animal’s fur and surrounding leaves. The composition is a medium close-up, centered on the leopard’s head emerging from the undergrowth, with shallow depth of field blurring the background vegetation.",
  "image_size": "auto",
  "num_inference_steps": 8,
  "num_images": 1,
  "enable_safety_checker": true,
  "output_format": "png",
  "acceleration": "none",
  "image_url": "https://storage.googleapis.com/falserverless/example_inputs/z-image-turbo-controlnet-input.jpg",
  "control_scale": 0.9,
  "control_end": 0.4,
  "preprocess": "none",
  "loras": []
}

Output#

images list<ImageFile-Output>* required

The generated image files info.

timings Timings* required

The timings of the generation process.

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": [
    {
      "height": 1024,
      "content_type": "image/png",
      "url": "https://storage.googleapis.com/falserverless/example_outputs/z-image-turbo-controlnet-output.jpg",
      "width": 1536
    }
  ],
  "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

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#

ImageSize#

Related Models

Z-Image Turbo Image to Image

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#

ImageSize#

Related Models