bfl/flux-control-lora-depth
Flux Control Lora Depth is BFL's advanced text-to-image AI model. Create photorealistic images, illustrations, and concept art from natural language descriptions with exceptional detail and prompt adherence.
PNG, JPEG, WebP, or GIF · 20 MiB maximum
PNG, JPEG, WebP, or GIF · 20 MiB maximum
Example output — click Run to generate your own
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OpenAI-compatible endpoint with unified authentication and usage tracking.
https://api.sandbase.ai/v1/runbfl/flux-control-lora-depthInput Schema
11 parameters · 2 required · 9 optional
| Parameter | Type | Required | Description |
|---|---|---|---|
image | string | Required | URL of image to use for image-to-image generation. |
prompt | string | Required | The prompt to generate an image from. |
seed | integer | Optional | The same seed and the same prompt given to the same version of the model will output the same image every time. |
loras | object[] | Optional | 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: [] |
aspect_ratio | string | Optional | The aspect ratio of the generated image. · Options: 21:9, 16:9, 3:2, 4:3, 5:4, 1:1, 4:5, 3:4, 2:3, 9:16 21:916:93:24:35:41:14:53:42:39:16 |
output_format | string | Optional | The format of the generated image. · Options: jpeg, png · Default: "jpeg" jpegpng |
guidance_scale | number | Optional | 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. · Min: 0 · Max: 35 · Default: 3.5 |
preprocess_depth | boolean | Optional | If set to true, the input image will be preprocessed to extract depth information. This is useful for generating depth maps from images. · Default: true |
num_inference_steps | integer | Optional | The number of inference steps to perform. · Min: 1 · Max: 50 · Default: 28 |
control_lora_strength | number | Optional | The strength of the control lora. · Min: 0 · Max: 2 · Default: 1 |
control_lora_image_url | string | Optional | The image to use for control lora. This is used to control the style of the generated image. |
Output Schema
| Field | Type | Description |
|---|---|---|
id | string | Unique identifier for the generation task |
status | string | Task status: pending, running, completed, failed, timeout |
model | string | Model used for the generation |
outputs | array | Array of output items |
outputs[].url | string | URL of the generated artifact |
outputs[].content_type | string | MIME type (e.g. image/png, video/mp4) |
error | object | null | Error details if failed, null on success |
error.type | string | Machine-readable error type code |
error.message | string | Human-readable error description |
Async Workflow
This model uses asynchronous execution. Submit a request and poll for the result.
- Submit — POST to /v1/run, receive an
id - Poll — GET /v1/run/{id} until status is
completed,failed, ortimeout - Retrieve — Read
outputsfrom the completed response
Code Examples
Ready-to-run snippets
# Step 1: Submit
curl -X POST https://api.sandbase.ai/v1/run \
-H "Content-Type: application/json" \
-H "Authorization: Bearer YOUR_API_KEY" \
-d '{
"model": "bfl/flux-control-lora-depth",
"loras": [],
"prompt": "Extreme close-up of a single tiger eye, direct frontal view. Detailed iris and pupil. Sharp focus on eye texture and color. Natural lighting to capture authentic eye shine and depth. The word \"FLUX\" is painted over it in big, white brush strokes with visible texture.",
"output_format": "jpeg",
"guidance_scale": 3.5,
"preprocess_depth": true,
"num_inference_steps": 28,
"control_lora_strength": 1,
"control_lora_image_url": "https://static.sandbase.ai/examples/bfl/flux-control-lora-depth/input_control_lora_image_url_0.jpg"
}'
# Step 2: Poll result (replace <id>)
curl https://api.sandbase.ai/v1/run/<id> \
-H "Authorization: Bearer YOUR_API_KEY"API README
Flux Control Lora Depth
Flux Control Lora Depth belongs at the spatially planned visual development with an adapted aesthetic stage of a visual workflow. Rather than treating the model as an isolated demonstration, teams can place it inside briefing, review, selection, and handoff practices where a scene’s distance organization remains the planning backbone while a learned treatment determines how the final image is presented. This positioning clarifies why the model earns a place in a real creative pipeline and what kind of decision it helps people make.
A practical use of Flux Control Lora Depth is interior visualization, environment concepts, product placement, and stylized scenes where foreground and background arrangement is already decided. Begin by agreeing on the creative objective and review criteria, prepare only the source material needed for that objective, and compare results against audience, brand, editorial, and production needs. Technical request choices remain documented below so the prose can stay focused on planning and creative value.
Highlights
Depth-faithful composition. Maintains foreground, middle-ground, and background ordering in the generated still image.
Volume-aware restyling. Changes materials and appearance while respecting the source scene's perceived geometry.
Viewpoint structure retention. Preserves spatial relationships and object scale across the generated still composition.
Prompt-and-depth fusion. Combines written art direction with depth conditioning for controlled still-image creation. Control-LoRA couples structural conditioning with a lightweight learned adapter so guidance remains strong without replacing the base generator.
Pricing
| Configuration | Billing unit | Price |
|---|---|---|
| Base generation | Per request | $0.04 |
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.
{
"aspect_ratio": "21:9",
"image": "https://example.com/start-frame.png",
"loras": [],
"prompt": "A cinematic, precisely composed result with a clear subject, controlled camera or viewpoint, realistic lighting, exact materials, and intentional atmosphere",
"seed": 1
}
Technical Specs
| Spec | Value |
|---|---|
| Model ID | bfl/flux-control-lora-depth |
| Input fields | seed (integer)<br>image (string)<br>loras (array)<br>prompt (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>output_format (string; jpeg, png)<br>guidance_scale (number; 0–35)<br>preprocess_depth (boolean)<br>num_inference_steps (integer; 1–50)<br>control_lora_strength (number; 0–2)<br>control_lora_image_url (string) |
| Required input | prompt, image |
| Output fields | url, content_type |
| Execution | Asynchronous job |
Related Models
bfl/flux-control-lora-depth/image-to-image— Compare this concrete local family route.bfl/flux-1.1/pro— Compare this concrete local family route.bfl/flux-1.1/pro-ultra— Compare this concrete local family route.