Fooocus Inpainting | Text to Image | API Documentation

fal-ai/fooocus/inpaint

Default parameters with automated optimizations and quality improvements.

Inference

Commercial use

Schema

About

Inpaint

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. Please check the migration guide for more information.

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/fooocus/inpaint", {
  input: {
    inpaint_image_url: "https://raw.githubusercontent.com/CompVis/latent-diffusion/main/data/inpainting_examples/overture-creations-5sI6fQgYIuo.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);

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 case your app is running in an environment where you cannot set environment variables, you can set the API Key manually as a client configuration.

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/fooocus/inpaint", {
  input: {
    inpaint_image_url: "https://raw.githubusercontent.com/CompVis/latent-diffusion/main/data/inpainting_examples/overture-creations-5sI6fQgYIuo.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/fooocus/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/fooocus/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#

prompt string

The prompt to use for generating the image. Be as descriptive as possible for best results. Default value: ""

negative_prompt string

The 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: ""

styles list<Enum>

The style to use. Default value: Fooocus Enhance,Fooocus V2,Fooocus Sharp

performance PerformanceEnum

You can choose Speed or Quality Default value: "Extreme Speed"

Possible enum values: Speed, Quality, Extreme Speed, Lightning

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: 4

sharpness float

The sharpness of the generated image. Use it to control how sharp the generated image should be. Higher value means image and texture are sharper. Default value: 2

aspect_ratio string

The size of the generated image. You can choose between some presets or custom height and width that must be multiples of 8. Default value: "1024x1024"

num_images integer

Number of images to generate in one request Default value: 1

loras list<LoraWeight>

The LoRAs to use for the image generation. You can use up to 5 LoRAs and they will be merged together to generate the final image. Default value: [object Object]

refiner_model RefinerModelEnum

Refiner (SDXL or SD 1.5) Default value: "None"

Possible enum values: None, realisticVisionV60B1_v51VAE.safetensors

refiner_switch float

Use 0.4 for SD1.5 realistic models; 0.667 for SD1.5 anime models 0.8 for XL-refiners; or any value for switching two SDXL models. Default value: 0.8

output_format OutputFormatEnum

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

Possible enum values: png, jpeg, webp

sync_mode boolean

If set to true, the function will wait for the image to be generated and uploaded before returning the response. This will increase the latency of the function but it allows you to get the image directly in the response without going through the CDN.

seed integer

The same seed and the same prompt given to the same version of Stable Diffusion will output the same image every time.

inpaint_image_url string* required

The image to use as a reference for inpainting.

mask_image_url string

The image to use as a mask for the generated image.

inpaint_mode InpaintModeEnum

The mode to use for inpainting. Default value: "Inpaint or Outpaint (default)"

Possible enum values: Inpaint or Outpaint (default), Improve Detail (face, hand, eyes, etc.), Modify Content (add objects, change background, etc.)

inpaint_additional_prompt string

Describe what you want to inpaint. Default value: ""

outpaint_selections list<Enum>

The directions to outpaint. Default value: ``

override_inpaint_options boolean

If set to true, the advanced inpaint options ('inpaint_disable_initial_latent', 'inpaint_engine', 'inpaint_strength', 'inpaint_respective_field', 'inpaint_erode_or_dilate') will be overridden. Otherwise, the default values will be used.

inpaint_disable_initial_latent boolean

If set to true, the initial preprocessing will be disabled.

inpaint_engine InpaintEngineEnum

Version of Fooocus inpaint model Default value: "v2.6"

Possible enum values: None, v1, v2.5, v2.6

inpaint_strength float

Same as the denoising strength in A1111 inpaint. Only used in inpaint, not used in outpaint. (Outpaint always use 1.0) Default value: 1

inpaint_respective_field float

The area to inpaint. Value 0 is same as "Only Masked" in A1111. Value 1 is same as "Whole Image" in A1111. Only used in inpaint, not used in outpaint. (Outpaint always use 1.0) Default value: 0.618

inpaint_erode_or_dilate float

Positive value will make white area in the mask larger, negative value will make white area smaller. (default is 0, always process before any mask invert)

invert_mask boolean

If set to true, the mask will be inverted.

image_prompt_1 ImagePrompt

image_prompt_2 ImagePrompt

image_prompt_3 ImagePrompt

image_prompt_4 ImagePrompt

mixing_image_prompt_and_inpaint boolean

Mixing Image Prompt and Inpaint

enable_safety_checker boolean

If set to false, the safety checker will be disabled. Default value: true

{
  "prompt": "a cat on a bench, realistic, highly detailed, 8k",
  "negative_prompt": "(worst quality, low quality, normal quality, lowres, low details, oversaturated, undersaturated, overexposed, underexposed, grayscale, bw, bad photo, bad photography, bad art:1.4), (watermark, signature, text font, username, error, logo, words, letters, digits, autograph, trademark, name:1.2), (blur, blurry, grainy), morbid, ugly, asymmetrical, mutated malformed, mutilated, poorly lit, bad shadow, draft, cropped, out of frame, cut off, censored, jpeg artifacts, out of focus, glitch, duplicate, (airbrushed, cartoon, anime, semi-realistic, cgi, render, blender, digital art, manga, amateur:1.3), (3D ,3D Game, 3D Game Scene, 3D Character:1.1), (bad hands, bad anatomy, bad body, bad face, bad teeth, bad arms, bad legs, deformities:1.3)",
  "styles": [
    "Fooocus Enhance",
    "Fooocus V2",
    "Fooocus Sharp"
  ],
  "performance": "Extreme Speed",
  "guidance_scale": 4,
  "sharpness": 2,
  "aspect_ratio": "1024x1024",
  "num_images": 1,
  "loras": [
    {
      "path": "https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0/resolve/main/sd_xl_offset_example-lora_1.0.safetensors",
      "scale": 0.1
    }
  ],
  "refiner_model": "None",
  "refiner_switch": 0.8,
  "output_format": "jpeg",
  "seed": 176400,
  "inpaint_image_url": "https://raw.githubusercontent.com/CompVis/latent-diffusion/main/data/inpainting_examples/overture-creations-5sI6fQgYIuo.png",
  "mask_image_url": "https://raw.githubusercontent.com/CompVis/latent-diffusion/main/data/inpainting_examples/overture-creations-5sI6fQgYIuo_mask.png",
  "inpaint_mode": "Inpaint or Outpaint (default)",
  "outpaint_selections": [],
  "inpaint_engine": "v2.6",
  "inpaint_strength": 1,
  "inpaint_respective_field": 0.618,
  "enable_safety_checker": true
}

Output#

images list<Image>* required

The generated image file info.

timings Timings* required

The time taken for the generation process.

has_nsfw_concepts list<boolean>* required

Whether the generated images contain NSFW concepts.

{
  "images": [
    {
      "url": "",
      "content_type": "image/png",
      "file_name": "z9RV14K95DvU.png",
      "file_size": 4404019,
      "width": 1024,
      "height": 1024
    }
  ]
}

Other types#

Image#

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.

file_data string

File data

width integer

The width of the image in pixels.

height integer

The height of the image in pixels.

ImagePrompt#

type TypeEnum

Default value: "ImagePrompt"

Possible enum values: ImagePrompt, PyraCanny, CPDS, FaceSwap

image_url string

stop_at float

Default value: 0.5

weight float

Default value: 1

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: 0.1

Related Models

fal-ai/stable-diffusion-v35-large

text-to-image

Stable Diffusion 3.5 Large is a Multimodal Diffusion Transformer (MMDiT) text-to-image model that features improved performance in image quality, typography, complex prompt understanding, and resource-efficiency.

diffusion

typography

style

fal-ai/stable-diffusion-v3-medium

text-to-image

Stable Diffusion 3 Medium (Text to Image) is a Multimodal Diffusion Transformer (MMDiT) model that improves image quality, typography, prompt understanding, and efficiency.

Default parameters with automated optimizations and quality improvements.

stylized

Fooocus Inpainting Text to Image

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#

Image#

ImagePrompt#

LoraWeight#

Related Models