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/v1/schemas/fal/image/fal-ai/flux-general

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kind inputactivity imagehash faf893be…extracted Sep 9, 2026version currentsource upstream spec ↗

field	recorded source
schema	Source linked above. upstream-spec Source trace `454bf4ad4b43ec36d421b5afbfec499dcf9b91ee156f0a527cd3fc887818d0e4` { "fetchedAt": 1788930053, "extractorVersion": "1", "verifiedAt": null }

Request body

33 properties · 1 required · 9 $defs

property	type	description	constraints
prompt*	string	The prompt to generate an image from.
loras	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: []
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: true
nag_tau	number	The 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.	n > 0 · default: 2.5
reference_end	number	The percentage of the total timesteps when the reference guidance is to be ended.	0 ≤ n ≤ 1 · default: 1
max_shift	number	Max shift for the scheduled timesteps	0.01 ≤ n ≤ 5 · default: 1.15
nag_end	number	The 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.	0 < n ≤ 1 · default: 0.25
controlnet_unions	ControlNetUnion[]	The controlnet unions to use for the image generation. Only one controlnet is supported at the moment.	default: []
use_real_cfg	boolean	Uses classical CFG as in SD1.5, SDXL, etc. Increases generation times and price when set to be true. If using XLabs IP-Adapter v1, this will be turned on!.	default: false
nag_alpha	number	The 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 lo…	0 < n ≤ 1 · default: 0.25
ip_adapters	IPAdapter[]	IP-Adapter to use for image generation.	default: []
image_size	ImageSize \| enum	The size of the generated image.
negative_prompt	string	Negative prompt to steer the image generation away from unwanted features. By default, we will be using NAG for processing the negative prompt.	default: ""
num_images	integer	The number of images to generate. This is always set to 1 for streaming output.	1 ≤ n ≤ 10 · default: 1
control_loras	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.	default: []
use_beta_schedule	boolean	Specifies whether beta sigmas ought to be used.	default: false
sigma_schedule	const	Sigmas schedule for the denoising process.	"sgm_uniform" · nullable
output_format	enum	The format of the generated image.	jpeg \| png · default: "png"
use_cfg_zero	boolean	Uses CFG-zero init sampling as in https://arxiv.org/abs/2503.18886.	default: false
real_cfg_scale	number	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.	0 ≤ n ≤ 5 · default: 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.	default: false
fill_image	ImageFillInput	Use an image input to influence the generation. Can be used to fill images in masked areas.	nullable
controlnets	ControlNet[]	The controlnets to use for the image generation. Only one controlnet is supported at the moment.	default: []
num_inference_steps	integer	The number of inference steps to perform.	1 ≤ n ≤ 50 · default: 28
reference_strength	number	Strength of reference_only generation. Only used if a reference image is provided.	-3 ≤ n ≤ 3 · default: 0.65
base_shift	number	Base shift for the scheduled timesteps	0.01 ≤ n ≤ 5 · default: 0.5
nag_scale	number	The scale for NAG. Higher values will result in a image that is more distant to the negative prompt.	1 < n ≤ 10 · default: 3
seed	integer	The same seed and the same prompt given to the same version of the model will output the same image every time.	nullable
guidance_scale	number	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.	0 ≤ n ≤ 20 · default: 3.5
scheduler	enum	Scheduler for the denoising process.	euler \| dpmpp_2m · default: "euler"
easycontrols	EasyControlWeight[]	EasyControl Inputs to use for image generation.	default: []
reference_start	number	The percentage of the total timesteps when the reference guidance is to bestarted.	0 ≤ n ≤ 1 · default: 0
reference_image_url	string	URL of Image for Reference-Only

Types

9 $defs

ControlLoraWeightobject

scale	Per-layer LoRA scales \| number	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: 1
control_image_url*	string	URL of the image to be used as the control image.
path*	string	URL or the path to the LoRA weights.
preprocess	enum	Type of preprocessing to apply to the input image.	canny \| depth \| None · default: "None"

ControlNetobject

variant	string	The optional variant if a Hugging Face repo key is used.	nullable
end_percentage	number	The percentage of the image to end applying the controlnet in terms of the total timesteps.	0 ≤ n ≤ 1 · default: 1
control_image_url*	string	URL of the image to be used as the control image.
config_url	string	optional URL to the controlnet config.json file.	nullable
path*	string	URL or the path to the control net weights.
conditioning_scale	number	The scale of the control net weight. This is used to scale the control net weight before merging it with the base model.	0 ≤ n ≤ 2 · default: 1
start_percentage	number	The percentage of the image to start applying the controlnet in terms of the total timesteps.	0 ≤ n ≤ 1 · default: 0
mask_image_url	string	URL of the mask for the control image.
mask_threshold	number	Threshold for mask.	0.01 ≤ n ≤ 0.99 · default: 0.5

ControlNetUnionobject

controls*	ControlNetUnionInput[]	The control images and modes to use for the control net.
path*	string	URL or the path to the control net weights.
config_url	string	optional URL to the controlnet config.json file.	nullable
variant	string	The optional variant if a Hugging Face repo key is used.	nullable

ControlNetUnionInputobject

mask_image_url	string	URL of the mask for the control image.
end_percentage	number	The percentage of the image to end applying the controlnet in terms of the total timesteps.	0 ≤ n ≤ 1 · default: 1
control_image_url*	string	URL of the image to be used as the control image.
conditioning_scale	number	The scale of the control net weight. This is used to scale the control net weight before merging it with the base model.	0 ≤ n ≤ 2 · default: 1
control_mode*	enum	Control Mode for Flux Controlnet Union. Supported values are: - canny: Uses the edges for guided generation. - tile: Uses the tiles for guided generation. - depth: Utilizes a grayscale depth map for guided generation. - blur: Adds a blur to the image. - pose: …	canny \| tile \| depth \| blur \| pose \| gray · +1 more
start_percentage	number	The percentage of the image to start applying the controlnet in terms of the total timesteps.	0 ≤ n ≤ 1 · default: 0
mask_threshold	number	Threshold for mask.	0.01 ≤ n ≤ 0.99 · default: 0.5

EasyControlWeightobject

image_control_type*	enum	Control type of the image. Must be one of `spatial` or `subject`.	subject \| spatial
scale	number	Scale for the control method.	0 ≤ n ≤ 2 · default: 1
control_method_url*	string	URL to safetensor weights of control method to be applied. Can also be one of `canny`, `depth`, `hedsketch`, `inpainting`, `pose`, `seg`, `subject`, `ghibli`
image_url*	string	URL of an image to use as a control

ImageFillInputobject

fill_image_url

string | string[]

URLs of images to be filled for redux prompting

default: []

ImageSizeobject

width	integer	The width of the generated image.	0 < n ≤ 14142 · default: 512
height	integer	The height of the generated image.	0 < n ≤ 14142 · default: 512

IPAdapterobject

subfolder	string	Subfolder in which the ip_adapter weights exist	nullable
scale*	number	Scale for ip adapter.
path*	string	Hugging Face path to the IP-Adapter
image_encoder_path*	string	Path to the Image Encoder for the IP-Adapter, for example 'openai/clip-vit-large-patch14'
image_encoder_subfolder	string	Subfolder in which the image encoder weights exist.	nullable
image_encoder_weight_name	string	Name of the image encoder.	nullable
image_url*	string	URL of Image for IP-Adapter conditioning.
mask_image_url	string	URL of the mask for the control image.	nullable
weight_name	string	Name of the safetensors file containing the ip-adapter weights	nullable
mask_threshold	number	Threshold for mask.	0.01 ≤ n ≤ 0.99 · default: 0.5

LoraWeightobject

scale	Per-layer LoRA scales \| number	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: 1
path*	string	URL or the path to the LoRA weights.

Validate a payload

before you spend tokens

shell

$ curl -X POST https://modelschemas.com/v1/validate \
    -d '{"provider":"fal","endpointId":"fal-ai/flux-general","payload":{…}}'
→ {"valid": false, "errors": [{"path": "#", "keyword": "required", …}]}