FLUX.1 [dev] with Controlnets and Loras

bfl/flux-general/rf-inversion

Flux General Rf Inversion is BFL's intelligent image editing model. Transform, retouch, and reimagine existing images using text prompts - from background replacement to artistic style conversion.

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Base price: $0.0013USD / run
Execution: async
Model type: image
Input fields: 28

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Playground

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Input

Prompt(string)

The prompt to edit the image with

Image Url(string)

Upload

PNG, JPEG, WebP, or GIF · 20 MiB maximum

URL of image to be edited

This field is required.

Output Format(string)

The format of the generated image. Allowed values: jpeg, png.

Sigma Schedule(string)

Sigmas schedule for the denoising process.

Control Loras(array)

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.

Use Beta Schedule(boolean)

Specifies whether beta sigmas ought to be used.

Reference Image Url(string)

Upload

PNG, JPEG, WebP, or GIF · 20 MiB maximum

URL of Image for Reference-Only

Reverse Guidance End(integer)

Timestep to stop guidance during reverse process.

NAG scale(number)

The scale for NAG. Higher values will result in a image that is more distant to the negative prompt. Maximum: 10.

Controller Guidance Forward(number)

0.013

The controller guidance (gamma) used in the creation of structured noise. Range: 0.01 to 3.

Proportion of steps to apply NAG(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. Maximum: 1.

Reference Strength(number)

-33

Strength of reference_only generation. Only used if a reference image is provided. Range: -3 to 3.

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.

Reference Start(number)

The percentage of the total timesteps when the reference guidance is to bestarted. Range: 0 to 1.

Reverse Guidance Start(integer)

Timestep to start guidance during reverse process.

Reference End(number)

The percentage of the total timesteps when the reference guidance is to be ended. Range: 0 to 1.

Max Shift(number)

0.015

Max shift for the scheduled timesteps Range: 0.01 to 5.

Reverse Guidance Schedule(string)

Scheduler for applying reverse guidance. Allowed values: constant, linear_increase, linear_decrease.

Base Shift(number)

0.015

Base shift for the scheduled timesteps Range: 0.01 to 5.

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 lower values will result in considering the positive prompt guidance more. Maximum: 1.

Fill Image(string)

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

Easycontrols(array)

EasyControl Inputs to use for image generation.

Controller Guidance Reverse(number)

0.013

The controller guidance (eta) used in the denoising process.Using values closer to 1 will result in an image closer to input. Range: 0.01 to 3.

Aspect Ratio(string)

The aspect ratio of the generated image. Allowed values: 21:9, 16:9, 3:2, 4:3, 5:4, 1:1, 4:5, 3:4, 2:3, 9:16.

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(array)

Enter a JSON array.

Guidance Scale(number)

020

Range: 0 to 20.

Num Inference Steps(integer)

150

Range: 1 to 50.

OutputReady

Your output will appear here

Complete the inputs, then click Run.

Model card

Specifications

Pricing

Base price: $0.0013 / run

Context & modalities

Input: Schema-defined
Output: image

Capabilities

Chat: Not supported
Vision: Not supported
Reasoning: Not supported
Structured output: Not supported
Function calling: Not supported
Audio input: Not supported

Access

Provider: BFL
Model ID: bfl/flux-general/rf-inversion
Execution: async
Base URL: https://api.sandbase.ai
API: Unified Run API
Endpoint: /v1/run

API README

FLUX.1 [dev] with Controlnets and Loras

FLUX.1 [dev] with Controlnets and Loras is a image generation and editing workflow in the FLUX family. Flux General Rf Inversion is BFL's intelligent image editing model. Transform, retouch, and reimagine existing images using text prompts - from background replacement to artistic style conversion. It is designed to translate deliberate creative direction and supplied reference material into a coherent production asset, with attention to subject identity, composition, visual detail, and the specific transformation implied by this exact route.

Use this route when the project needs a controlled visual generation or revision. Describe the intended result, what must remain recognizable, and the required composition, lighting, materials, atmosphere, and finish; keep technical delivery choices in the schema fields below.

Highlights

Reversible image representation. Maps a source image into a model-compatible representation for controlled reconstruction and editing.
Source-faithful recovery. Retains recognizable composition and visual detail before applying the requested transformation.
Prompt-guided modification. Uses written direction to change style, background, objects, or atmosphere from the inverted source.
Controlled variation. Supports edits that remain more tightly connected to the original than unconstrained regeneration.

Pricing

Configuration	Billing unit	Price
Base generation	Per request	$0.00125

When to Use

✅ Good fit	❌ Consider alternatives
The project needs this exact FLUX capability	The intended task belongs to a different media workflow
Required source and control media are available	Necessary assets or usage rights are unavailable
Creative direction can state change and preservation goals	Output must be deterministic at pixel or frame level
Supported dimensions and formats match final delivery	Placement requires unsupported specifications
An asynchronous generated result fits production	A live frame-synchronous response is mandatory

Prompt Guide

Lead with the main subject and action, then specify composition, context, lighting, materials, style, typography, and atmosphere. For editing, identify each source and clearly separate the requested transformation from the subjects, regions, geometry, or identity that must remain unchanged.

{
  "prompt": "A cinematic, precisely composed result with a clear subject, controlled camera or viewpoint, realistic lighting, exact materials, and intentional atmosphere",
  "image": "https://example.com/reference.png",
  "seed": 1,
  "loras": [],
  "nag_end": 0.25
}

Technical Specs

Spec	Value
Model ID	`bfl/flux-general/rf-inversion`
Input fields	`seed` (integer)<br>`image` (string)<br>`loras` (array)<br>`prompt` (string)<br>`nag_end` (number; …–1)<br>`nag_tau` (number)<br>`max_shift` (number; 0.01–5)<br>`nag_alpha` (number; …–1)<br>`nag_scale` (number; …–10)<br>`base_shift` (number; 0.01–5)<br>`fill_image` (string)<br>`aspect_ratio` (string; `21:9`, `16:9`, `3:2`, `4:3`, `5:4`, `1:1`, `4:5`, `3:4`, `2:3`, `9:16`)<br>`easycontrols` (array)<br>`control_loras` (array)<br>`output_format` (string; `jpeg`, `png`)<br>`reference_end` (number; 0–1)<br>`guidance_scale` (number; 0–20)<br>`sigma_schedule` (string)<br>`reference_start` (number; 0–1)<br>`use_beta_schedule` (boolean)<br>`reference_strength` (number; -3–3)<br>`num_inference_steps` (integer; 1–50)<br>`reference_image_url` (string)<br>`reverse_guidance_end` (integer)<br>`reverse_guidance_start` (integer)<br>`reverse_guidance_schedule` (string; `constant`, `linear_increase`, `linear_decrease`)<br>`controller_guidance_forward` (number; 0.01–3)<br>`controller_guidance_reverse` (number; 0.01–3)
Required input	`prompt`, `image`
Output fields	`url`, `content_type`
Execution	Asynchronous job

Related Models

bfl/flux-1/dev/edit
bfl/flux-1/dev/image-to-image
bfl/flux-1/dev

Start building

Send your first request

OpenAI-compatible endpoint with unified authentication and usage tracking.

Production API

Unified Run API endpoint

https://api.sandbase.ai/v1/run

Model ID

bfl/flux-general/rf-inversion

# The quoted heredoc keeps Unicode and shell metacharacters unchanged.
result=$(curl --fail-with-body --silent \
  -X POST "https://api.sandbase.ai/v1/run" \
  -H "Authorization: Bearer $SANDBASE_API_KEY" \
  -H "Content-Type: application/json" \
  --data-binary @- <<'SANDBASE_JSON'
{
  "model": "bfl/flux-general/rf-inversion",
  "image": "https://static.sandbase.ai/examples/bfl/flux-general/rf-inversion/input_image_0.png",
  "loras": [],
  "prompt": "Wearing glasses",
  "nag_end": 0.25,
  "nag_tau": 2.5,
  "max_shift": 1.15,
  "nag_alpha": 0.25,
  "nag_scale": 3,
  "base_shift": 0.5,
  "easycontrols": [],
  "control_loras": [],
  "output_format": "png",
  "reference_end": 1,
  "guidance_scale": 3.5,
  "reference_start": 0,
  "use_beta_schedule": false,
  "reference_strength": 0.65,
  "num_inference_steps": 28,
  "reverse_guidance_end": 8,
  "reverse_guidance_start": 0,
  "reverse_guidance_schedule": "constant",
  "controller_guidance_forward": 0.6,
  "controller_guidance_reverse": 0.75
}
SANDBASE_JSON
)
run_id=$(printf '%s' "$result" | jq -r .id)
for attempt in $(seq 1 120); do
  status=$(printf '%s' "$result" | jq -r .status)
  case "$status" in completed|failed|timeout) break ;; esac
  sleep 2
  result=$(curl --fail-with-body --silent \
    -H "Authorization: Bearer $SANDBASE_API_KEY" \
    "https://api.sandbase.ai/v1/run/$run_id")
done
status=$(printf '%s' "$result" | jq -r .status)
[ "$status" = completed ] || { echo "Generation ended: $status" >&2; exit 1; }
printf '%s\n' "$result"

Choose your model

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Questions

FAQ

How do I call BFL FLUX.1 [dev] with Controlnets and Loras through SandBase?

Create a SandBase API key, then send requests with the model ID "bfl/flux-general/rf-inversion" to Unified Run API (/v1/run) at https://api.sandbase.ai. The request examples on this page show the exact payload.

Do I need a separate BFL account?

No. One SandBase API key and balance gives you access to BFL FLUX.1 [dev] with Controlnets and Loras and the other models in the catalog; you do not need to sign up with BFL separately.