Generate high-fidelity images from text with Krea 2 using a custom-trained LoRA. Apply your LoRA weights to carry a learned subject, character, or style into new generations, with aspect ratio, creativity, and seed controls.

fal-ai/krea-2/turbo/lora

Inference

Commercial use

Schema

LLMs

Playground API Examples

fal-ai/krea-2/turbo/lora

Schema

fal-ai/krea-2/turbo/lora

Inference

Commercial use

Schema

LLMs

Playground API Examples

1. Calling the API

2. Authentication

API Key

3. Queue

4. Files

5. Schema

About

Generate an image with the distilled Krea 2 turbo checkpoint and LoRA weights.

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. Install the new package and update your imports — see client setup.

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/krea-2/turbo/lora", {
  input: {
    prompt: "a vintage travel poster for Mars"
  },
  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 an environment where you cannot set environment variables, you can configure the API key manually on the client.

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/krea-2/turbo/lora", {
  input: {
    prompt: "a vintage travel poster for Mars"
  },
  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/krea-2/turbo/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/krea-2/turbo/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

Base text description of the image to generate.

seed integer

Random seed for reproducible generation. Image i uses seed + i.

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

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

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

num_images integer

The number of images to generate. Default value: 1

acceleration AccelerationEnum

The acceleration level to use for the image generation. Default value: "none"

Possible enum values: none, regular

enable_prompt_expansion boolean

If true, the prompt is expanded with an LLM into a more detailed image-generation prompt for higher quality results. On failure, the original prompt is used unchanged.

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.

enable_safety_checker boolean

If true, the output safety checker is enabled. Disabling it requires account authorization; unauthorized requests are always checked, and images flagged as unsafe are returned as black images. Default value: true

output_format OutputFormatEnum

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

Possible enum values: jpeg, png

loras list<LoRAInput>

List of LoRA weights to apply (maximum 3).

{
  "prompt": "a vintage travel poster for Mars",
  "image_size": "square_hd",
  "num_images": 1,
  "acceleration": "none",
  "enable_safety_checker": true,
  "output_format": "png",
  "loras": []
}

Output#

images list<ImageFile>* required

The generated images.

timings Timings* required

Timing information for the request, in seconds.

seed integer* required

The seed used to generate the image(s).

has_nsfw_concepts list<boolean>* required

Whether each generated image contains NSFW concepts.

prompt string* required

The prompt used for generation.

actual_prompt string

The final prompt after expansion, if prompt expansion was enabled.

{
  "images": [
    {
      "url": "",
      "content_type": "image/png",
      "file_name": "z9RV14K95DvU.png",
      "file_size": 4404019
    }
  ],
  "seed": 12345,
  "prompt": ""
}

Other types#

ImageFile#

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.

width integer

The width of the image

height integer

The height of the image

ImageSize#

width integer

The width of the generated image. Default value: 512

height integer

The height of the generated image. Default value: 512

LoRAInput#

path string* required

URL, HuggingFace repo ID (owner/repo), or local path to LoRA weights.

scale float

Scale factor for LoRA application (0.0 to 4.0). Default value: 1

Related Models

1. Calling the API

2. Authentication

API Key

3. Queue

4. Files

5. Schema

About

Generate an image with the distilled Krea 2 turbo checkpoint and LoRA weights.

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. Install the new package and update your imports — see client setup.

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/krea-2/turbo/lora", {
  input: {
    prompt: "a vintage travel poster for Mars"
  },
  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 an environment where you cannot set environment variables, you can configure the API key manually on the client.

import { fal } from "@fal-ai/client";

fal.config({
  credentials: "YOUR_FAL_KEY"
});

Protect your API Key

3. Queue#

Long-running requests

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/krea-2/turbo/lora", {
  input: {
    prompt: "a vintage travel poster for Mars"
  },
  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/krea-2/turbo/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/krea-2/turbo/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)#

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

Base text description of the image to generate.

seed integer

Random seed for reproducible generation. Image i uses seed + i.

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

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

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

num_images integer

The number of images to generate. Default value: 1

acceleration AccelerationEnum

The acceleration level to use for the image generation. Default value: "none"

Possible enum values: none, regular

enable_prompt_expansion boolean

If true, the prompt is expanded with an LLM into a more detailed image-generation prompt for higher quality results. On failure, the original prompt is used unchanged.

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.

enable_safety_checker boolean

output_format OutputFormatEnum

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

Possible enum values: jpeg, png

loras list<LoRAInput>

List of LoRA weights to apply (maximum 3).

{
  "prompt": "a vintage travel poster for Mars",
  "image_size": "square_hd",
  "num_images": 1,
  "acceleration": "none",
  "enable_safety_checker": true,
  "output_format": "png",
  "loras": []
}

Output#

images list<ImageFile>* required

The generated images.

timings Timings* required

Timing information for the request, in seconds.

seed integer* required

The seed used to generate the image(s).

has_nsfw_concepts list<boolean>* required

Whether each generated image contains NSFW concepts.

prompt string* required

The prompt used for generation.

actual_prompt string

The final prompt after expansion, if prompt expansion was enabled.

{
  "images": [
    {
      "url": "",
      "content_type": "image/png",
      "file_name": "z9RV14K95DvU.png",
      "file_size": 4404019
    }
  ],
  "seed": 12345,
  "prompt": ""
}

Other types#

ImageFile#

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.

width integer

The width of the image

height integer

The height of the image

ImageSize#

width integer

The width of the generated image. Default value: 512

height integer

The height of the generated image. Default value: 512

LoRAInput#

path string* required

URL, HuggingFace repo ID (owner/repo), or local path to LoRA weights.

scale float

Scale factor for LoRA application (0.0 to 4.0). Default value: 1

fal-ai/krea-2/turbo/lora

Table of contents

Table of contents

fal-ai/krea-2/turbo/lora

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#

ImageFile#

ImageSize#

LoRAInput#

Related Models

Table of contents

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#

ImageFile#

ImageSize#

LoRAInput#

Related Models