Generate Images with ControlNet.

fal-ai/fast-sdxl-controlnet-canny/inpainting

Generate Images with ControlNet.

Inference

Commercial use

This endpoint is deprecated

This model is no longer supported.

Schema

Playground API

This endpoint is deprecated

This model is no longer supported.

About

Inpainting

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/fast-sdxl-controlnet-canny/inpainting", {
  input: {
    prompt: "Ice fortress, aurora skies, polar wildlife, twilight",
    control_image_url: "https://fal-cdn.batuhan-941.workers.dev/files/rabbit/MiN_j3St9B8esJleCZKMU.jpeg",
    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"
  },
  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/fast-sdxl-controlnet-canny/inpainting", {
  input: {
    prompt: "Ice fortress, aurora skies, polar wildlife, twilight",
    control_image_url: "https://fal-cdn.batuhan-941.workers.dev/files/rabbit/MiN_j3St9B8esJleCZKMU.jpeg",
    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"
  },
  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/fast-sdxl-controlnet-canny/inpainting", {
  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/fast-sdxl-controlnet-canny/inpainting", {
  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* required

The prompt to use for generating the image. Be as descriptive as possible for best results.

control_image_url string* required

The URL of the control image.

controlnet_conditioning_scale float

The scale of the controlnet conditioning. Default value: 0.5

image_url string* required

The URL of the image to use as a starting point for the generation.

mask_url string* required

The URL of the mask to use for inpainting.

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: ""

image_size ImageSize | Enum

The size of the generated image. Leave it none to automatically infer from the control image.

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 integer

The number of inference steps to perform. Default value: 35

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: 7.5

strength float

determines how much the generated image resembles the initial image Default value: 0.95

seed integer

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

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

loras list<LoraWeight>

The list of LoRA weights to use.

embeddings list<Embedding>

The list of embeddings to use.

enable_safety_checker boolean

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

safety_checker_version SafetyCheckerVersionEnum

The 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

enable_prompt_expansion boolean

If set to true, the prompt will be expanded with additional prompts.

format FormatEnum

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

Possible enum values: jpeg, png

request_id string

An id bound to a request, can be used with response to identify the request itself. Default value: ""

{
  "prompt": "Ice fortress, aurora skies, polar wildlife, twilight",
  "control_image_url": "https://fal-cdn.batuhan-941.workers.dev/files/rabbit/MiN_j3St9B8esJleCZKMU.jpeg",
  "controlnet_conditioning_scale": 0.5,
  "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",
  "negative_prompt": "cartoon, illustration, animation. face. male, female",
  "image_size": null,
  "num_inference_steps": 35,
  "guidance_scale": 7.5,
  "strength": 0.95,
  "num_images": 1,
  "loras": [],
  "embeddings": [],
  "enable_safety_checker": true,
  "safety_checker_version": "v1",
  "format": "jpeg"
}

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": [
    {
      "url": "",
      "content_type": "image/jpeg"
    }
  ],
  "prompt": ""
}

Other types#

Image#

url string* required

width integer* required

height integer* required

content_type string

Default value: "image/jpeg"

Embedding#

path string* required

URL or the path to the embedding weights.

tokens list<string>

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.

LoraWeight#

path string* required

URL or path to a LoRA safetensors file. Or HF model name that resolves to safetensors weights.

force boolean

If set to true, the embedding will be forced to be used.

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

ImageSize#

width integer

The width of the generated image. Default value: 512

height integer

The height of the generated image. Default value: 512

Related Models

This endpoint is deprecated

This model is no longer supported.

About

Inpainting

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/fast-sdxl-controlnet-canny/inpainting", {
  input: {
    prompt: "Ice fortress, aurora skies, polar wildlife, twilight",
    control_image_url: "https://fal-cdn.batuhan-941.workers.dev/files/rabbit/MiN_j3St9B8esJleCZKMU.jpeg",
    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"
  },
  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/fast-sdxl-controlnet-canny/inpainting", {
  input: {
    prompt: "Ice fortress, aurora skies, polar wildlife, twilight",
    control_image_url: "https://fal-cdn.batuhan-941.workers.dev/files/rabbit/MiN_j3St9B8esJleCZKMU.jpeg",
    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"
  },
  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/fast-sdxl-controlnet-canny/inpainting", {
  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/fast-sdxl-controlnet-canny/inpainting", {
  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#

prompt string* required

The prompt to use for generating the image. Be as descriptive as possible for best results.

control_image_url string* required

The URL of the control image.

controlnet_conditioning_scale float

The scale of the controlnet conditioning. Default value: 0.5

image_url string* required

The URL of the image to use as a starting point for the generation.

mask_url string* required

The URL of the mask to use for inpainting.

negative_prompt string

image_size ImageSize | Enum

The size of the generated image. Leave it none to automatically infer from the control image.

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 integer

The number of inference steps to perform. Default value: 35

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: 7.5

strength float

determines how much the generated image resembles the initial image Default value: 0.95

seed integer

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

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

loras list<LoraWeight>

The list of LoRA weights to use.

embeddings list<Embedding>

The list of embeddings to use.

enable_safety_checker boolean

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

safety_checker_version SafetyCheckerVersionEnum

The 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

enable_prompt_expansion boolean

If set to true, the prompt will be expanded with additional prompts.

format FormatEnum

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

Possible enum values: jpeg, png

request_id string

An id bound to a request, can be used with response to identify the request itself. Default value: ""

{
  "prompt": "Ice fortress, aurora skies, polar wildlife, twilight",
  "control_image_url": "https://fal-cdn.batuhan-941.workers.dev/files/rabbit/MiN_j3St9B8esJleCZKMU.jpeg",
  "controlnet_conditioning_scale": 0.5,
  "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",
  "negative_prompt": "cartoon, illustration, animation. face. male, female",
  "image_size": null,
  "num_inference_steps": 35,
  "guidance_scale": 7.5,
  "strength": 0.95,
  "num_images": 1,
  "loras": [],
  "embeddings": [],
  "enable_safety_checker": true,
  "safety_checker_version": "v1",
  "format": "jpeg"
}

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": [
    {
      "url": "",
      "content_type": "image/jpeg"
    }
  ],
  "prompt": ""
}

Other types#

Image#

url string* required

width integer* required

height integer* required

content_type string

Default value: "image/jpeg"

Embedding#

path string* required

URL or the path to the embedding weights.

tokens list<string>

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.

LoraWeight#

path string* required

URL or path to a LoRA safetensors file. Or HF model name that resolves to safetensors weights.

force boolean

If set to true, the embedding will be forced to be used.

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

ImageSize#

width integer

The width of the generated image. Default value: 512

height integer

The height of the generated image. Default value: 512

Related Models

ControlNet SDXL Image to Image API Docs | fal

fal-ai/fast-sdxl-controlnet-canny/inpainting

This endpoint is deprecated

Table of contents

Table of contents

fal-ai/fast-sdxl-controlnet-canny/inpainting

This endpoint is deprecated

This endpoint is deprecated

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#

Embedding#

LoraWeight#

ImageSize#

Related Models

Table of contents

This endpoint is deprecated

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#

Embedding#

LoraWeight#

ImageSize#

Related Models