FLUX.1 [dev], next generation text-to-image model.
The client provides a convenient way to interact with the model API.
npm install --save @fal-ai/clientThe @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.
Set FAL_KEY as an environment variable in your runtime.
export FAL_KEY="YOUR_API_KEY"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);The API uses an API Key for authentication. It is recommended you set the FAL_KEY environment variable in your runtime when possible.
import { fal } from "@fal-ai/client";
fal.config({
credentials: "YOUR_FAL_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.
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",
});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,
});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);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.
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.
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.
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);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.
prompt string* requiredThe prompt to edit the image with
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 integerThe number of inference steps to perform. Default value: 28
seed integerThe same seed and the same prompt given to the same version of the model will output the same image every time.
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.
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.
The controlnets to use for the image generation. Only one controlnet is supported at the moment.
The controlnet unions to use for the image generation. Only one controlnet is supported at the moment.
EasyControl Inputs to use for image generation.
Use an image input to influence the generation. Can be used to fill images in masked areas.
guidance_scale floatThe 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 booleanUses CFG-zero init sampling as in https://arxiv.org/abs/2503.18886.
sync_mode booleanIf True, the media will be returned as a data URI and the output data won't be available in the request history.
num_images integerThe number of images to generate. This is always set to 1 for streaming output. Default value: 1
enable_safety_checker booleanIf 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
reference_image_url stringURL of Image for Reference-Only
reference_strength floatStrength of reference_only generation. Only used if a reference image is provided. Default value: 0.65
reference_start floatThe percentage of the total timesteps when the reference guidance is to bestarted.
reference_end floatThe percentage of the total timesteps when the reference guidance is to be ended. Default value: 1
base_shift floatBase shift for the scheduled timesteps Default value: 0.5
max_shift floatMax shift for the scheduled timesteps Default value: 1.15
output_format OutputFormatEnumThe format of the generated image. Default value: "png"
Possible enum values: jpeg, png
use_beta_schedule booleanSpecifies whether beta sigmas ought to be used.
sigma_schedule stringSigmas schedule for the denoising process.
scheduler SchedulerEnumScheduler for the denoising process. Default value: "euler"
Possible enum values: euler, dpmpp_2m
negative_prompt stringNegative prompt to steer the image generation away from unwanted features.
By default, we will be using NAG for processing the negative prompt. Default value: ""
nag_scale floatThe scale for NAG. Higher values will result in a image that is more distant
to the negative prompt. Default value: 3
nag_tau floatThe tau for NAG. Controls the normalization of the hidden state.
Higher values will result in a less aggressive normalization,
but may also lead to unexpected changes with respect to the original image.
Not recommended to change this value. Default value: 2.5
nag_alpha floatThe alpha value for NAG. This value is used as a final weighting
factor for steering the normalized guidance (positive and negative prompts)
in the direction of the positive prompt. Higher values will result in less
steering on the normalized guidance where lower values will result in
considering the positive prompt guidance more. Default value: 0.25
nag_end floatThe proportion of steps to apply NAG. After the specified proportion
of steps has been iterated, the remaining steps will use original
attention processors in FLUX. Default value: 0.25
image_url string* requiredURL of image to be edited
controller_guidance_forward floatThe controller guidance (gamma) used in the creation of structured noise. Default value: 0.6
controller_guidance_reverse floatThe 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 integerTimestep to start guidance during reverse process.
reverse_guidance_end integerTimestep to stop guidance during reverse process. Default value: 8
reverse_guidance_schedule ReverseGuidanceScheduleEnumScheduler 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",
"nag_scale": 3,
"nag_tau": 2.5,
"nag_alpha": 0.25,
"nag_end": 0.25,
"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"
}The generated image files info.
seed integer* requiredSeed 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.
Whether the generated images contain NSFW concepts.
prompt string* requiredThe prompt used for generating the image.
{
"images": [
{
"url": "",
"content_type": "image/jpeg"
}
],
"prompt": ""
}control_method_url string* requiredURL to safetensor weights of control method to be applied. Can also be one of canny, depth, hedsketch, inpainting, pose, seg, subject, ghibli
scale floatScale for the control method. Default value: 1
image_url string* requiredURL of an image to use as a control
image_control_type ImageControlTypeEnum* requiredControl type of the image. Must be one of spatial or subject.
Possible enum values: subject, spatial
width integerThe width of the generated image. Default value: 512
height integerThe height of the generated image. Default value: 512
path string* requiredURL or the path to the LoRA weights.
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
control_image_url string* requiredURL of the image to be used as the control image.
preprocess PreprocessEnumType of preprocessing to apply to the input image. Default value: "None"
Possible enum values: canny, depth, None
path string* requiredURL or the path to the control net weights.
config_url stringoptional URL to the controlnet config.json file.
variant stringThe optional variant if a Hugging Face repo key is used.
control_image_url string* requiredURL of the image to be used as the control image.
mask_image_url stringURL of the mask for the control image.
mask_threshold floatThreshold for mask. Default value: 0.5
conditioning_scale floatThe 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 floatThe percentage of the image to start applying the controlnet in terms of the total timesteps.
end_percentage floatThe percentage of the image to end applying the controlnet in terms of the total timesteps. Default value: 1
URLs of images to be filled for redux prompting
path string* requiredURL or the path to the LoRA weights.
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
path string* requiredURL or the path to the control net weights.
config_url stringoptional URL to the controlnet config.json file.
variant stringThe optional variant if a Hugging Face repo key is used.
The control images and modes to use for the control net.
url string* requiredwidth integer* requiredheight integer* requiredcontent_type stringDefault value: "image/jpeg"
control_image_url string* requiredURL of the image to be used as the control image.
mask_image_url stringURL of the mask for the control image.
control_mode ControlModeEnum* requiredControl Mode for Flux Controlnet Union. Supported values are:
Possible enum values: canny, tile, depth, blur, pose, gray, low-quality
conditioning_scale floatThe 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
mask_threshold floatThreshold for mask. Default value: 0.5
start_percentage floatThe percentage of the image to start applying the controlnet in terms of the total timesteps.
end_percentage floatThe percentage of the image to end applying the controlnet in terms of the total timesteps. Default value: 1