FLUX Control LoRA Canny is a high-performance endpoint that uses a control image using a Canny edge map to transfer structure to the generated image and another initial image to guide color.

fal-ai/flux-control-lora-canny/image-to-image

FLUX Control LoRA Canny is a high-performance endpoint that uses a control image using a Canny edge map to transfer structure to the generated image and another initial image to guide color.

Inference

Commercial use

This endpoint is deprecated

This model is no longer supported.

Schema

Playground API

fal-ai/flux-control-lora-canny/image-to-image

Schema

FLUX Control LoRA Canny is a high-performance endpoint that uses a control image using a Canny edge map to transfer structure to the generated image and another initial image to guide color.

fal-ai/flux-control-lora-canny/image-to-image

FLUX Control LoRA Canny is a high-performance endpoint that uses a control image using a Canny edge map to transfer structure to the generated image and another initial image to guide color.

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

FLUX.1 [dev], next generation text-to-image 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-control-lora-canny/image-to-image", {
  input: {
    prompt: "A photo of a lion sitting on a stone bench",
    image_url: "https://raw.githubusercontent.com/CompVis/latent-diffusion/main/data/inpainting_examples/overture-creations-5sI6fQgYIuo.png",
    control_lora_image_url: "https://storage.googleapis.com/falserverless/flux-lora/example-images/knight.jpeg"
  },
  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/flux-control-lora-canny/image-to-image", {
  input: {
    prompt: "A photo of a lion sitting on a stone bench",
    image_url: "https://raw.githubusercontent.com/CompVis/latent-diffusion/main/data/inpainting_examples/overture-creations-5sI6fQgYIuo.png",
    control_lora_image_url: "https://storage.googleapis.com/falserverless/flux-lora/example-images/knight.jpeg"
  },
  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-control-lora-canny/image-to-image", {
  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-control-lora-canny/image-to-image", {
  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 generate an image from.

image_url string* required

URL of image to use for inpainting. or img2img

image_size ImageSize | Enum

The size of the generated 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: 28

seed integer

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

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.

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: 3.5

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: "jpeg"

Possible enum values: jpeg, png

control_lora_image_url string* required

The image to use for control lora. This is used to control the style of the generated image.

control_lora_strength float

The strength of the control lora. Default value: 1

strength float

The strength to use for inpainting/image-to-image. Only used if the image_url is provided. 1.0 is completely remakes the image while 0.0 preserves the original. Default value: 0.85

{
  "prompt": "A photo of a lion sitting on a stone bench",
  "image_url": "https://raw.githubusercontent.com/CompVis/latent-diffusion/main/data/inpainting_examples/overture-creations-5sI6fQgYIuo.png",
  "num_inference_steps": 28,
  "guidance_scale": 3.5,
  "num_images": 1,
  "enable_safety_checker": true,
  "output_format": "jpeg",
  "control_lora_image_url": "https://storage.googleapis.com/falserverless/flux-lora/example-images/knight.jpeg",
  "control_lora_strength": 1,
  "strength": 0.85
}

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"

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

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

FLUX.1 [dev], next generation text-to-image 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-control-lora-canny/image-to-image", {
  input: {
    prompt: "A photo of a lion sitting on a stone bench",
    image_url: "https://raw.githubusercontent.com/CompVis/latent-diffusion/main/data/inpainting_examples/overture-creations-5sI6fQgYIuo.png",
    control_lora_image_url: "https://storage.googleapis.com/falserverless/flux-lora/example-images/knight.jpeg"
  },
  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/flux-control-lora-canny/image-to-image", {
  input: {
    prompt: "A photo of a lion sitting on a stone bench",
    image_url: "https://raw.githubusercontent.com/CompVis/latent-diffusion/main/data/inpainting_examples/overture-creations-5sI6fQgYIuo.png",
    control_lora_image_url: "https://storage.googleapis.com/falserverless/flux-lora/example-images/knight.jpeg"
  },
  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-control-lora-canny/image-to-image", {
  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-control-lora-canny/image-to-image", {
  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 generate an image from.

image_url string* required

URL of image to use for inpainting. or img2img

image_size ImageSize | Enum

The size of the generated 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: 28

seed integer

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

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.

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: 3.5

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: "jpeg"

Possible enum values: jpeg, png

control_lora_image_url string* required

The image to use for control lora. This is used to control the style of the generated image.

control_lora_strength float

The strength of the control lora. Default value: 1

strength float

The strength to use for inpainting/image-to-image. Only used if the image_url is provided. 1.0 is completely remakes the image while 0.0 preserves the original. Default value: 0.85

{
  "prompt": "A photo of a lion sitting on a stone bench",
  "image_url": "https://raw.githubusercontent.com/CompVis/latent-diffusion/main/data/inpainting_examples/overture-creations-5sI6fQgYIuo.png",
  "num_inference_steps": 28,
  "guidance_scale": 3.5,
  "num_images": 1,
  "enable_safety_checker": true,
  "output_format": "jpeg",
  "control_lora_image_url": "https://storage.googleapis.com/falserverless/flux-lora/example-images/knight.jpeg",
  "control_lora_strength": 1,
  "strength": 0.85
}

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"

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

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

FLUX.1 [dev] Control LoRA Canny Image to Image API Docs | fal

fal-ai/flux-control-lora-canny/image-to-image

This endpoint is deprecated

Table of contents

Table of contents

fal-ai/flux-control-lora-canny/image-to-image

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#

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#

LoraWeight#

ImageSize#

Related Models