Train a custom LoRA on your own images to teach Krea 2 a new subject, character, or style. Provide a set of training images (and an optional trigger word), and the trainer outputs LoRA weights you can use for inference with the Krea 2 LoRA endpoint.

fal-ai/krea-2-trainer

Training

Commercial use

Schema

LLMs

Playground API

fal-ai/krea-2-trainer

Schema

fal-ai/krea-2-trainer

Training

Commercial use

Schema

LLMs

Playground API

1. Calling the API

2. Authentication

API Key

3. Queue

4. Files

5. Schema

About

Train

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-trainer", {
  input: {
    images_data_url: ""
  },
  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-trainer", {
  input: {
    images_data_url: ""
  },
  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-trainer", {
  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-trainer", {
  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#

images_data_url string* required

URL to a .zip archive of training images (PNG/JPG/JPEG/WebP). Each image may carry a same-stem caption: an image named ROOT.EXT (e.g. 001.jpg) pairs with a caption file named ROOT.txt (e.g. 001.txt). Images with no caption file fall back to trigger_phrase, so each image must have either a caption file or a non-empty trigger_phrase — otherwise the request is rejected (422).

trigger_phrase string

Instance phrase used as the caption for any image that ships no .txt caption file (DreamBooth-style); also seeds the default validation prompts. Required when some images have no caption file and auto_captioning is Off. Default value: ""

auto_captioning AutoCaptioningEnum

Optional automatic captioning mode. Off preserves the existing behavior: every image must include a same-stem .txt caption or fall back to trigger_phrase. Object/Character, Style, and Custom use dataset-captioner to generate captions with google/gemma-4-31B-it; trigger_phrase is included in the captioning prompt and normalized in the final sidecars using the selected LoRA captioning convention. Cost of captioning will be passed through directly. Default value: "Off"

Possible enum values: Off, Object/Character, Style, Custom

steps integer

Number of LoRA training steps. Default value: 100

learning_rate float

Learning rate for the LoRA B factor (and for A when lora_a_lr_multiplier=1). Krea recommends 3e-4–7e-4 (constant schedule). For muon this is the match_rms_adamw-scaled (AdamW-equivalent) rate. Default value: 0.0005

resolution ResolutionEnum

Training resolution (images are cover-resized + center-cropped to a square). 768 trains ~1.7x faster at lower VRAM with near-identical style quality; 1024 maximizes fine detail. Default value: "768"

Possible enum values: 768, 1024

debug_dataset boolean

When enabled, returns a downloadable archive of the exact dataset used for training after caption backfill or auto-captioning.

{
  "images_data_url": "",
  "auto_captioning": "Off",
  "steps": 100,
  "learning_rate": 0.0005,
  "resolution": 768
}

Output#

lora_file File* required

URL to the trained Krea-2 LoRA weights (safetensors).

config_file File* required

URL to the training/LoRA configuration JSON.

prompt_enhancement_system_prompt string

System prompt for writing or enhancing inference prompts so they match the captioning convention used during auto-captioned training. Returned when auto_captioning is enabled.

debug_dataset File

Downloadable archive of the prepared training data, including auto-generated captions, when debug_dataset is enabled.

{
  "lora_file": {
    "url": "",
    "content_type": "image/png",
    "file_name": "z9RV14K95DvU.png",
    "file_size": 4404019
  },
  "config_file": {
    "url": "",
    "content_type": "image/png",
    "file_name": "z9RV14K95DvU.png",
    "file_size": 4404019
  }
}

Other types#

File#

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.

Related Models

1. Calling the API

2. Authentication

API Key

3. Queue

4. Files

5. Schema

About

Train

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-trainer", {
  input: {
    images_data_url: ""
  },
  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-trainer", {
  input: {
    images_data_url: ""
  },
  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-trainer", {
  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-trainer", {
  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#

images_data_url string* required

trigger_phrase string

auto_captioning AutoCaptioningEnum

Possible enum values: Off, Object/Character, Style, Custom

steps integer

Number of LoRA training steps. Default value: 100

learning_rate float

resolution ResolutionEnum

Possible enum values: 768, 1024

debug_dataset boolean

When enabled, returns a downloadable archive of the exact dataset used for training after caption backfill or auto-captioning.

{
  "images_data_url": "",
  "auto_captioning": "Off",
  "steps": 100,
  "learning_rate": 0.0005,
  "resolution": 768
}

Output#

lora_file File* required

URL to the trained Krea-2 LoRA weights (safetensors).

config_file File* required

URL to the training/LoRA configuration JSON.

prompt_enhancement_system_prompt string

System prompt for writing or enhancing inference prompts so they match the captioning convention used during auto-captioned training. Returned when auto_captioning is enabled.

debug_dataset File

Downloadable archive of the prepared training data, including auto-generated captions, when debug_dataset is enabled.

{
  "lora_file": {
    "url": "",
    "content_type": "image/png",
    "file_name": "z9RV14K95DvU.png",
    "file_size": 4404019
  },
  "config_file": {
    "url": "",
    "content_type": "image/png",
    "file_name": "z9RV14K95DvU.png",
    "file_size": 4404019
  }
}

Other types#

File#

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.

fal-ai/krea-2-trainer

Table of contents

Table of contents

fal-ai/krea-2-trainer

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#

File#

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#

File#

Related Models