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Inpainting
The client provides a convenient way to interact with the model API.
npm install --save @fal-ai/clientThe @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.
Set FAL_KEY as an environment variable in your runtime.
export FAL_KEY="YOUR_API_KEY"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/playground-v25/inpainting", {
input: {
image_url: "https://raw.githubusercontent.com/CompVis/latent-diffusion/main/data/inpainting_examples/overture-creations-5sI6fQgYIuo.png",
mask_url: "https://raw.githubusercontent.com/CompVis/latent-diffusion/main/data/inpainting_examples/overture-creations-5sI6fQgYIuo_mask.png",
prompt: "a tiger sitting on a park bench"
},
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);The API uses an API Key for authentication. It is recommended you set the FAL_KEY environment variable in your runtime when possible.
import { fal } from "@fal-ai/client";
fal.config({
credentials: "YOUR_FAL_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.
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/playground-v25/inpainting", {
input: {
image_url: "https://raw.githubusercontent.com/CompVis/latent-diffusion/main/data/inpainting_examples/overture-creations-5sI6fQgYIuo.png",
mask_url: "https://raw.githubusercontent.com/CompVis/latent-diffusion/main/data/inpainting_examples/overture-creations-5sI6fQgYIuo_mask.png",
prompt: "a tiger sitting on a park bench"
},
webhookUrl: "https://optional.webhook.url/for/results",
});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/playground-v25/inpainting", {
requestId: "764cabcf-b745-4b3e-ae38-1200304cf45b",
logs: true,
});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/playground-v25/inpainting", {
requestId: "764cabcf-b745-4b3e-ae38-1200304cf45b"
});
console.log(result.data);
console.log(result.requestId);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.
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.
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.
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);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.
image_url string* requiredThe URL of the image to use as a starting point for the generation.
mask_url string* requiredThe URL of the mask to use for inpainting.
prompt string* requiredThe prompt to use for generating the image. Be as descriptive as possible for best results.
negative_prompt stringThe negative prompt to use.Use it to address details that you don't want
in the image. This could be colors, objects, scenery and even the small details
(e.g. moustache, blurry, low resolution). Default value: ""
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_inference_steps integerThe number of inference steps to perform. Default value: 25
guidance_scale floatThe 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: 3
strength floatdetermines how much the generated image resembles the initial image Default value: 0.95
seed integerThe same seed and the same prompt given to the same version of Stable Diffusion will output the same image every time.
num_images integerThe number of images to generate. Default value: 1
The list of embeddings to use.
enable_safety_checker booleanIf set to true, the safety checker will be enabled. Default value: true
safety_checker_version SafetyCheckerVersionEnumThe version of the safety checker to use. v1 is the default CompVis safety checker. v2 uses a custom ViT model. Default value: "v1"
Possible enum values: v1, v2
expand_prompt booleanIf set to true, the prompt will be expanded with additional prompts.
format FormatEnumThe format of the generated image. Default value: "jpeg"
Possible enum values: jpeg, png
request_id stringAn id bound to a request, can be used with response to identify the request
itself. Default value: ""
{
"image_url": "https://raw.githubusercontent.com/CompVis/latent-diffusion/main/data/inpainting_examples/overture-creations-5sI6fQgYIuo.png",
"mask_url": "https://raw.githubusercontent.com/CompVis/latent-diffusion/main/data/inpainting_examples/overture-creations-5sI6fQgYIuo_mask.png",
"prompt": "a tiger sitting on a park bench",
"negative_prompt": "cartoon, illustration, animation. face. male, female",
"image_size": "square_hd",
"num_inference_steps": 25,
"guidance_scale": 3,
"strength": 0.95,
"num_images": 1,
"embeddings": [],
"enable_safety_checker": true,
"safety_checker_version": "v1",
"format": "jpeg"
}The generated image files info.
seed integer* requiredSeed 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.
Whether the generated images contain NSFW concepts.
prompt string* requiredThe prompt used for generating the image.
{
"images": [
{
"url": "",
"content_type": "image/jpeg"
}
],
"prompt": ""
}url string* requiredwidth integer* requiredheight integer* requiredcontent_type stringDefault value: "image/jpeg"
width integerThe width of the generated image. Default value: 512
height integerThe height of the generated image. Default value: 512
path string* requiredURL or the path to the embedding weights.
The list of tokens to use for the embedding. When omitted, tokens are generated as <s0>, <s1>, ... to match the number of vectors in the embedding file.