FLUX.1 [dev] with Controlnets and Loras

fal-ai/flux-general/differential-diffusion

Inference

Commercial use

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/serverless-client

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 * as fal from "@fal-ai/serverless-client";

const result = await fal.subscribe("fal-ai/flux-general/differential-diffusion", {
  input: {
    prompt: "Tree of life under the sea, ethereal, glittering, lens flares, cinematic lighting, artwork by Anna Dittmann & Carne Griffiths, 8k, unreal engine 5, hightly detailed, intricate detailed.",
    image_url: "https://fal.media/files/koala/h6a7KK2Ie_inuGbdartoX.jpeg",
    change_map_image_url: "https://fal.media/files/zebra/Wh4IYAiAAcVbuZ8M9ZMSn.jpeg"
  },
  logs: true,
  onQueueUpdate: (update) => {
    if (update.status === "IN_PROGRESS") {
      update.logs.map((log) => log.message).forEach(console.log);
    }
  },
});

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 * as fal from "@fal-ai/serverless-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 * as fal from "@fal-ai/serverless-client";

const { request_id } = await fal.queue.submit("fal-ai/flux-general/differential-diffusion", {
  input: {
    prompt: "Tree of life under the sea, ethereal, glittering, lens flares, cinematic lighting, artwork by Anna Dittmann & Carne Griffiths, 8k, unreal engine 5, hightly detailed, intricate detailed.",
    image_url: "https://fal.media/files/koala/h6a7KK2Ie_inuGbdartoX.jpeg",
    change_map_image_url: "https://fal.media/files/zebra/Wh4IYAiAAcVbuZ8M9ZMSn.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 * as fal from "@fal-ai/serverless-client";

const status = await fal.queue.status("fal-ai/flux-general/differential-diffusion", {
  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 * as fal from "@fal-ai/serverless-client";

const result = await fal.queue.result("fal-ai/flux-general/differential-diffusion", {
  requestId: "764cabcf-b745-4b3e-ae38-1200304cf45b"
});

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 * as fal from "@fal-ai/serverless-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_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. Default value: ``

controlnets list<ControlNet>

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

controlnet_unions list<ControlNetUnion>

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

ip_adapters list<IPAdapter>

IP-Adapter to use for image generation. Default value: ``

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

real_cfg_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_real_cfg boolean

Uses classical CFG as in SD1.5, SDXL, etc. Increases generation times and price when set to be true. If an IP-Adapter from Xlabs is used, this will be set to True!

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.

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. Default value: true

image_url string* required

URL of image to use as initial image.

change_map_image_url string* required

URL of change map.

strength float

The strength to use for differential diffusion. 1.0 is completely remakes the image while 0.0 preserves the original. Default value: 0.85

{
  "prompt": "Tree of life under the sea, ethereal, glittering, lens flares, cinematic lighting, artwork by Anna Dittmann & Carne Griffiths, 8k, unreal engine 5, hightly detailed, intricate detailed.",
  "image_size": {
    "width": 512,
    "height": 512
  },
  "num_inference_steps": 28,
  "controlnets": [],
  "controlnet_unions": [],
  "ip_adapters": [],
  "guidance_scale": 3.5,
  "real_cfg_scale": 3.5,
  "num_images": 1,
  "enable_safety_checker": true,
  "image_url": "https://fal.media/files/koala/h6a7KK2Ie_inuGbdartoX.jpeg",
  "change_map_image_url": "https://fal.media/files/zebra/Wh4IYAiAAcVbuZ8M9ZMSn.jpeg",
  "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#

LoraWeight#

path string* required

URL or the path to the LoRA weights.

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

width integer* required

height integer* required

content_type string

Default value: "image/jpeg"

IPAdapter#

path string* required

Hugging Face path to the IP-Adapter

image_encoder_path string* required

Hugging Face path to the Image Encoder for the IP-Adapter, for example 'openai/clip-vit-large-patch14'

image_url string* required

URL of Image for IP-Adapter conditioning.

mask_image_url string

URL of the mask for the control image.

mask_threshold float

Threshold for mask. Default value: 0.5

scale float* required

Scale for ip adapter.

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.

config_url string

optional URL to the controlnet config.json file.

variant string

The optional variant if a Hugging Face repo key is used.

controls list<ControlNetUnionInput>* required

The control images and modes to use for the control net.

ControlNet#

path string* required

URL or the path to the control net weights.

config_url string

optional URL to the controlnet config.json file.

variant string

The optional variant if a Hugging Face repo key is used.

control_image_url string* required

URL of the image to be used as the control image.

mask_image_url string

URL of the mask for the control image.

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.

end_percentage float

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

FLUX.1 [dev] with Controlnets and Loras

Table of contents

1. Calling the API

2. Authentication

3. Queue

4. Files

5. Schema

About

1. Calling the API#

Install the 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#

IPAdapter#

ImageSize#

ControlNetUnion#

ControlNet#