FLUX.1 [dev] with Controlnets and Loras | Image to Image

fal-ai/flux-general/rf-inversion

A general purpose endpoint for the FLUX.1 [dev] model, implementing the RF-Inversion pipeline. This can be used to edit a reference image based on a prompt.

Inference

Commercial use

Schema

Playground API

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. 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/flux-general/rf-inversion", {
  input: {
    prompt: "Wearing glasses",
    image_url: "https://storage.googleapis.com/falserverless/flux-general-tests/anime_style.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/flux-general/rf-inversion", {
  input: {
    prompt: "Wearing glasses",
    image_url: "https://storage.googleapis.com/falserverless/flux-general-tests/anime_style.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/flux-general/rf-inversion", {
  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-general/rf-inversion", {
  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 edit the image with Default value: undefined

image_size ImageSize | Enum

The size of the generated image. Default value: undefined

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. Default value: undefined

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.

control_loras list<ControlLoraWeight>

The LoRAs to use for the image generation which use a control image. You can use any number of LoRAs and they will be merged together to generate the final image.

controlnets list<ControlNet>

The controlnets to use for the image generation. Only one controlnet is supported at the moment.

controlnet_unions list<ControlNetUnion>

The controlnet unions to use for the image generation. Only one controlnet is supported at the moment.

easycontrols list<EasyControlWeight>

EasyControl Inputs to use for image generation.

fill_image ImageFillInput

Use an image input to influence the generation. Can be used to fill images in masked areas. Default value: undefined

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

use_cfg_zero boolean

Uses CFG-zero init sampling as in https://arxiv.org/abs/2503.18886. Default value: false

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. Default value: false

num_images integer

The number of images to generate. This is always set to 1 for streaming output. Default value: 1

enable_safety_checker boolean

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

reference_image_url string

URL of Image for Reference-Only Default value: undefined

reference_strength float

Strength of reference_only generation. Only used if a reference image is provided. Default value: 0.65

reference_start float

The percentage of the total timesteps when the reference guidance is to bestarted. Default value: 0

reference_end float

The percentage of the total timesteps when the reference guidance is to be ended. Default value: 1

base_shift float

Base shift for the scheduled timesteps Default value: 0.5

max_shift float

Max shift for the scheduled timesteps Default value: 1.15

output_format OutputFormatEnum

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

Possible enum values: jpeg, png

use_beta_schedule boolean

Specifies whether beta sigmas ought to be used. Default value: false

sigma_schedule SigmaScheduleEnum

Sigmas schedule for the denoising process. Default value: undefined

Possible enum values: sgm_uniform

scheduler SchedulerEnum

Scheduler for the denoising process. Default value: "euler"

Possible enum values: euler, dpmpp_2m

image_url string* required

URL of image to be edited Default value: undefined

controller_guidance_forward float

The controller guidance (gamma) used in the creation of structured noise. Default value: 0.6

controller_guidance_reverse float

The controller guidance (eta) used in the denoising process.Using values closer to 1 will result in an image closer to input. Default value: 0.75

reverse_guidance_start integer

Timestep to start guidance during reverse process. Default value: 0

reverse_guidance_end integer

Timestep to stop guidance during reverse process. Default value: 8

reverse_guidance_schedule ReverseGuidanceScheduleEnum

Scheduler for applying reverse guidance. Default value: "constant"

Possible enum values: constant, linear_increase, linear_decrease

{
  "prompt": "Wearing glasses",
  "num_inference_steps": 28,
  "controlnets": [],
  "controlnet_unions": [],
  "easycontrols": [],
  "guidance_scale": 3.5,
  "num_images": 1,
  "enable_safety_checker": true,
  "reference_strength": 0.65,
  "reference_end": 1,
  "base_shift": 0.5,
  "max_shift": 1.15,
  "output_format": "png",
  "scheduler": "euler",
  "image_url": "https://storage.googleapis.com/falserverless/flux-general-tests/anime_style.png",
  "controller_guidance_forward": 0.6,
  "controller_guidance_reverse": 0.75,
  "reverse_guidance_end": 8,
  "reverse_guidance_schedule": "constant"
}

Output#

images list<Image>* required

The generated image files info. Default value: undefined

timings Timings* required

Default value: undefined

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. Default value: undefined

has_nsfw_concepts list<boolean>* required

Whether the generated images contain NSFW concepts. Default value: undefined

prompt string* required

The prompt used for generating the image. Default value: undefined

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

Other types#

LoraWeight#

path string* required

URL or the path to the LoRA weights. Default value: undefined

scale object | float

The scale of the LoRA weight. This is used to scale the LoRA weight before merging it with the base model. Providing a dictionary as {"layer_name":layer_scale} allows per-layer lora scale settings. Layers with no scale provided will have scale 1.0. Default value: 1

Image#

url string* required

Default value: undefined

width integer* required

Default value: undefined

height integer* required

Default value: undefined

content_type string

Default value: "image/jpeg"

EasyControlWeight#

control_method_url string* required

URL to safetensor weights of control method to be applied. Can also be one of canny, depth, hedsketch, inpainting, pose, seg, subject, ghibli Default value: undefined

scale float

Scale for the control method. Default value: 1

image_url string* required

URL of an image to use as a control Default value: undefined

image_control_type ImageControlTypeEnum* required

Control type of the image. Must be one of spatial or subject. Default value: undefined

Possible enum values: subject, spatial

IPAdapter#

path string* required

Hugging Face path to the IP-Adapter Default value: undefined

subfolder string

Subfolder in which the ip_adapter weights exist Default value: undefined

weight_name string

Name of the safetensors file containing the ip-adapter weights Default value: undefined

image_encoder_path string* required

Path to the Image Encoder for the IP-Adapter, for example 'openai/clip-vit-large-patch14' Default value: undefined

image_encoder_subfolder string

Subfolder in which the image encoder weights exist. Default value: undefined

image_encoder_weight_name string

Name of the image encoder. Default value: undefined

image_url string* required

URL of Image for IP-Adapter conditioning. Default value: undefined

mask_image_url string

URL of the mask for the control image. Default value: undefined

mask_threshold float

Threshold for mask. Default value: 0.5

scale float* required

Scale for ip adapter. Default value: undefined

ImageSize#

width integer

The width of the generated image. Default value: 512

height integer

The height of the generated image. Default value: 512

ControlNetUnion#

path string* required

URL or the path to the control net weights. Default value: undefined

config_url string

optional URL to the controlnet config.json file. Default value: undefined

variant string

The optional variant if a Hugging Face repo key is used. Default value: undefined

controls list<ControlNetUnionInput>* required

The control images and modes to use for the control net. Default value: undefined

ControlNet#

path string* required

URL or the path to the control net weights. Default value: undefined

config_url string

optional URL to the controlnet config.json file. Default value: undefined

variant string

The optional variant if a Hugging Face repo key is used. Default value: undefined

control_image_url string* required

URL of the image to be used as the control image. Default value: undefined

mask_image_url string

URL of the mask for the control image. Default value: undefined

mask_threshold float

Threshold for mask. Default value: 0.5

conditioning_scale float

The scale of the control net weight. This is used to scale the control net weight before merging it with the base model. Default value: 1

start_percentage float

The percentage of the image to start applying the controlnet in terms of the total timesteps. Default value: 0

end_percentage float

The percentage of the image to end applying the controlnet in terms of the total timesteps. Default value: 1

ControlLoraWeight#

path string* required

URL or the path to the LoRA weights. Default value: undefined

scale object | float

control_image_url string* required

URL of the image to be used as the control image. Default value: undefined

preprocess PreprocessEnum

Type of preprocessing to apply to the input image. Default value: "None"

Possible enum values: canny, depth, None

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#

LoraWeight#

Image#

EasyControlWeight#

IPAdapter#

ImageSize#

ControlNetUnion#

ControlNet#

ControlLoraWeight#

Related Models

FLUX.1 [dev] with Controlnets and Loras Image 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#

LoraWeight#

Image#

EasyControlWeight#

IPAdapter#

ImageSize#

ControlNetUnion#

ControlNet#

ControlLoraWeight#

Related Models