# Flux Kontext Lora > 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. ## Overview - **Endpoint**: `https://fal.run/fal-ai/flux-kontext-lora/inpaint` - **Model ID**: `fal-ai/flux-kontext-lora/inpaint` - **Category**: image-to-image - **Kind**: inference **Tags**: image-editing, image-inpainting, image-to-image ## Pricing - **Price**: $0.035 per megapixels For more details, see [fal.ai pricing](https://fal.ai/pricing). ## API Information This model can be used via our HTTP API or more conveniently via our client libraries. See the input and output schema below, as well as the usage examples. ### Input Schema The API accepts the following input parameters: - **`image_url`** (`string`, _required_): The URL of the image to be inpainted. - Examples: "https://storage.googleapis.com/falserverless/example_inputs/image_kontext_inpaint.jpeg" - **`prompt`** (`string`, _required_): The prompt for the image to image task. - Examples: "A football lying on a field." - **`num_inference_steps`** (`integer`, _optional_): The number of inference steps to perform. Default value: `30` - Default: `30` - Range: `10` to `50` - **`seed`** (`integer`, _optional_): 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`, _optional_): 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` - Default: `2.5` - Range: `0` to `20` - **`sync_mode`** (`boolean`, _optional_): If `True`, the media will be returned as a data URI and the output data won't be available in the request history. - Default: `false` - **`num_images`** (`integer`, _optional_): The number of images to generate. Default value: `1` - Default: `1` - Range: `1` to `4` - **`enable_safety_checker`** (`boolean`, _optional_): If set to true, the safety checker will be enabled. Default value: `true` - Default: `true` - **`output_format`** (`OutputFormatEnum`, _optional_): The format of the generated image. Default value: `"png"` - Default: `"png"` - Options: `"jpeg"`, `"png"` - **`loras`** (`list`, _optional_): 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. - Default: `[]` - Array of LoraWeight - **`acceleration`** (`AccelerationEnum`, _optional_): The speed of the generation. The higher the speed, the faster the generation. Default value: `"none"` - Default: `"none"` - Options: `"none"`, `"regular"`, `"high"` - **`reference_image_url`** (`string`, _required_): The URL of the reference image for inpainting. - Examples: "https://storage.googleapis.com/falserverless/example_inputs/reference_kontext_inpaint.jpeg" - **`mask_url`** (`string`, _required_): The URL of the mask for inpainting. - Examples: "https://storage.googleapis.com/falserverless/example_inputs/mask_kontext_inpaint.png" - **`strength`** (`float`, _optional_): The strength of the initial image. Higher strength values are better for this model. Default value: `0.88` - Default: `0.88` - Range: `0.01` to `1` **Required Parameters Example**: ```json { "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" } ``` **Full Example**: ```json { "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 Schema The API returns the following output format: - **`images`** (`list`, _required_): The generated image files info. - Array of Image - Examples: [{"content_type":"image/jpeg","width":1248,"url":"https://storage.googleapis.com/falserverless/example_outputs/kontext_inpaint_output.png","height":832}] - **`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`, _required_): Whether the generated images contain NSFW concepts. - Array of boolean - **`prompt`** (`string`, _required_): The prompt used for generating the image. **Example Response**: ```json { "images": [ { "content_type": "image/jpeg", "width": 1248, "url": "https://storage.googleapis.com/falserverless/example_outputs/kontext_inpaint_output.png", "height": 832 } ], "prompt": "" } ``` ## Usage Examples ### cURL ```bash curl --request POST \ --url https://fal.run/fal-ai/flux-kontext-lora/inpaint \ --header "Authorization: Key $FAL_KEY" \ --header "Content-Type: application/json" \ --data '{ "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" }' ``` ### Python Ensure you have the Python client installed: ```bash pip install fal-client ``` Then use the API client to make requests: ```python import fal_client def on_queue_update(update): if isinstance(update, fal_client.InProgress): for log in update.logs: print(log["message"]) result = fal_client.subscribe( "fal-ai/flux-kontext-lora/inpaint", arguments={ "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" }, with_logs=True, on_queue_update=on_queue_update, ) print(result) ``` ### JavaScript Ensure you have the JavaScript client installed: ```bash npm install --save @fal-ai/client ``` Then use the API client to make requests: ```javascript 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); ``` ## Additional Resources ### Documentation - [Model Playground](https://fal.ai/models/fal-ai/flux-kontext-lora/inpaint) - [API Documentation](https://fal.ai/models/fal-ai/flux-kontext-lora/inpaint/api) - [OpenAPI Schema](https://fal.ai/api/openapi/queue/openapi.json?endpoint_id=fal-ai/flux-kontext-lora/inpaint) ### fal.ai Platform - [Platform Documentation](https://docs.fal.ai) - [Python Client](https://docs.fal.ai/clients/python) - [JavaScript Client](https://docs.fal.ai/clients/javascript)