Fast inpainting endpoint for the FLUX.1 Kontext [dev] model with LoRA support, enabling rapid and high-quality image inpainting with reference images, while using pre-trained LoRA adaptations for specific styles, brand identities, and product-specific outputs.

fal-ai/flux-kontext-lora/inpaint

Inference

Commercial use

Streaming

Schema

LLMs

Playground API

fal-ai/flux-kontext-lora/inpaint

Schema

fal-ai/flux-kontext-lora/inpaint

Inference

Commercial use

Streaming

Schema

LLMs

Playground API

1. Calling the API

2. Authentication

API Key

3. Queue

4. Files

5. Schema

About

FLUX.1 Kontext, perform inpainting operations with the FLUX.1 Kontext model.

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/flux-kontext-lora/inpaint", {
  input: {
    image_url: "https://storage.googleapis.com/falserverless/example_inputs/image_kontext_inpaint.jpeg",
    prompt: "A football lying on a field.",
    reference_image_url: "https://storage.googleapis.com/falserverless/example_inputs/reference_kontext_inpaint.jpeg",
    mask_url: "https://storage.googleapis.com/falserverless/example_inputs/mask_kontext_inpaint.png"
  },
  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);

Streaming#

This model supports streaming requests. You can stream data directly to the model and get the result in real-time.

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

const stream = await fal.stream("fal-ai/flux-kontext-lora/inpaint", {
  input: {
    image_url: "https://storage.googleapis.com/falserverless/example_inputs/image_kontext_inpaint.jpeg",
    prompt: "A football lying on a field.",
    reference_image_url: "https://storage.googleapis.com/falserverless/example_inputs/reference_kontext_inpaint.jpeg",
    mask_url: "https://storage.googleapis.com/falserverless/example_inputs/mask_kontext_inpaint.png"
  }
});

for await (const event of stream) {
  console.log(event);
}

const result = await stream.done();

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/flux-kontext-lora/inpaint", {
  input: {
    image_url: "https://storage.googleapis.com/falserverless/example_inputs/image_kontext_inpaint.jpeg",
    prompt: "A football lying on a field.",
    reference_image_url: "https://storage.googleapis.com/falserverless/example_inputs/reference_kontext_inpaint.jpeg",
    mask_url: "https://storage.googleapis.com/falserverless/example_inputs/mask_kontext_inpaint.png"
  },
  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/flux-kontext-lora/inpaint", {
  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/flux-kontext-lora/inpaint", {
  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#

image_url string* required

The URL of the image to be inpainted.

prompt string* required

The prompt for the image to image task.

num_inference_steps integer

The number of inference steps to perform. Default value: 30

seed integer

The same seed and the same prompt given to the same version of the model 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: 2.5

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. 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"

Possible enum values: jpeg, png

loras list<LoraWeight>

The LoRAs to use for the image generation. You can use any number of LoRAs and they will be merged together to generate the final image.

acceleration AccelerationEnum

The speed of the generation. The higher the speed, the faster the generation. Default value: "none"

Possible enum values: none, regular, high

reference_image_url string* required

The URL of the reference image for inpainting.

mask_url string* required

The URL of the mask for inpainting.

strength float

The strength of the initial image. Higher strength values are better for this model. Default value: 0.88

{
  "image_url": "https://storage.googleapis.com/falserverless/example_inputs/image_kontext_inpaint.jpeg",
  "prompt": "A football lying on a field.",
  "num_inference_steps": 30,
  "guidance_scale": 2.5,
  "num_images": 1,
  "enable_safety_checker": true,
  "output_format": "png",
  "acceleration": "none",
  "reference_image_url": "https://storage.googleapis.com/falserverless/example_inputs/reference_kontext_inpaint.jpeg",
  "mask_url": "https://storage.googleapis.com/falserverless/example_inputs/mask_kontext_inpaint.png",
  "strength": 0.88
}

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": [
    {
      "height": 832,
      "url": "https://storage.googleapis.com/falserverless/example_outputs/kontext_inpaint_output.png",
      "content_type": "image/jpeg",
      "width": 1248
    }
  ],
  "prompt": ""
}

Other types#

LoraWeight#

path string* required

URL or the path to the LoRA weights.

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

Image#

url string* required

width integer* required

height integer* required

content_type string

Default value: "image/jpeg"

Related Models

1. Calling the API

2. Authentication

API Key

3. Queue

4. Files

5. Schema

About

FLUX.1 Kontext, perform inpainting operations with the FLUX.1 Kontext model.

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/flux-kontext-lora/inpaint", {
  input: {
    image_url: "https://storage.googleapis.com/falserverless/example_inputs/image_kontext_inpaint.jpeg",
    prompt: "A football lying on a field.",
    reference_image_url: "https://storage.googleapis.com/falserverless/example_inputs/reference_kontext_inpaint.jpeg",
    mask_url: "https://storage.googleapis.com/falserverless/example_inputs/mask_kontext_inpaint.png"
  },
  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);

Streaming#

This model supports streaming requests. You can stream data directly to the model and get the result in real-time.

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

const stream = await fal.stream("fal-ai/flux-kontext-lora/inpaint", {
  input: {
    image_url: "https://storage.googleapis.com/falserverless/example_inputs/image_kontext_inpaint.jpeg",
    prompt: "A football lying on a field.",
    reference_image_url: "https://storage.googleapis.com/falserverless/example_inputs/reference_kontext_inpaint.jpeg",
    mask_url: "https://storage.googleapis.com/falserverless/example_inputs/mask_kontext_inpaint.png"
  }
});

for await (const event of stream) {
  console.log(event);
}

const result = await stream.done();

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/flux-kontext-lora/inpaint", {
  input: {
    image_url: "https://storage.googleapis.com/falserverless/example_inputs/image_kontext_inpaint.jpeg",
    prompt: "A football lying on a field.",
    reference_image_url: "https://storage.googleapis.com/falserverless/example_inputs/reference_kontext_inpaint.jpeg",
    mask_url: "https://storage.googleapis.com/falserverless/example_inputs/mask_kontext_inpaint.png"
  },
  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/flux-kontext-lora/inpaint", {
  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/flux-kontext-lora/inpaint", {
  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#

image_url string* required

The URL of the image to be inpainted.

prompt string* required

The prompt for the image to image task.

num_inference_steps integer

The number of inference steps to perform. Default value: 30

seed integer

The same seed and the same prompt given to the same version of the model 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: 2.5

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

output_format OutputFormatEnum

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

Possible enum values: jpeg, png

loras list<LoraWeight>

The LoRAs to use for the image generation. You can use any number of LoRAs and they will be merged together to generate the final image.

acceleration AccelerationEnum

The speed of the generation. The higher the speed, the faster the generation. Default value: "none"

Possible enum values: none, regular, high

reference_image_url string* required

The URL of the reference image for inpainting.

mask_url string* required

The URL of the mask for inpainting.

strength float

The strength of the initial image. Higher strength values are better for this model. Default value: 0.88

{
  "image_url": "https://storage.googleapis.com/falserverless/example_inputs/image_kontext_inpaint.jpeg",
  "prompt": "A football lying on a field.",
  "num_inference_steps": 30,
  "guidance_scale": 2.5,
  "num_images": 1,
  "enable_safety_checker": true,
  "output_format": "png",
  "acceleration": "none",
  "reference_image_url": "https://storage.googleapis.com/falserverless/example_inputs/reference_kontext_inpaint.jpeg",
  "mask_url": "https://storage.googleapis.com/falserverless/example_inputs/mask_kontext_inpaint.png",
  "strength": 0.88
}

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": [
    {
      "height": 832,
      "url": "https://storage.googleapis.com/falserverless/example_outputs/kontext_inpaint_output.png",
      "content_type": "image/jpeg",
      "width": 1248
    }
  ],
  "prompt": ""
}

Other types#

LoraWeight#

path string* required

URL or the path to the LoRA weights.

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

Image#

url string* required

width integer* required

height integer* required

content_type string

Default value: "image/jpeg"

fal-ai/flux-kontext-lora/inpaint

Table of contents

Table of contents

fal-ai/flux-kontext-lora/inpaint

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#

Streaming#

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#

LoraWeight#

Image#

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#

Streaming#

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#

LoraWeight#

Image#

Related Models