bfl/flux-2/lora
Flux 2 Lora 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.
Example output — click Run to generate your own
Send your first request
OpenAI-compatible endpoint with unified authentication and usage tracking.
https://api.sandbase.ai/v1/runbfl/flux-2/loraInput Schema
7 parameters · 1 required · 6 optional
| Parameter | Type | Required | Description |
|---|---|---|---|
prompt | string | Required | The prompt to generate an image from. |
seed | integer | Optional | The seed to use for the generation. If not provided, a random seed will be used. |
loras | object[] | Optional | List of LoRA weights to apply (maximum 3). Each LoRA can be a URL, HuggingFace repo ID, or local path. · 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: "png" jpegpng |
guidance_scale | number | Optional | 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: 20 · Default: 2.5 |
num_inference_steps | integer | Optional | The number of inference steps to perform. · Min: 4 · Max: 50 · Default: 28 |
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-2/lora",
"loras": [],
"prompt": "Close shot a pianist plays in a luxurious room with tall windows overlooking a rainy metropolis. Shot with a 50mm lens at a side profile angle, soft tungsten light highlighting hands moving over keys. Capture detailed reflections in polished black piano surfaces, raindrops sliding down glass, and atmospheric warm/cool lighting contrast.",
"output_format": "png",
"guidance_scale": 2.5,
"num_inference_steps": 28
}'
# Step 2: Poll result (replace <id>)
curl https://api.sandbase.ai/v1/run/<id> \
-H "Authorization: Bearer YOUR_API_KEY"API README
Flux 2 Lora
Flux 2 Lora belongs at the adapted FLUX.2 visual production 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 teams can introduce a learned visual vocabulary while continuing to use familiar FLUX.2 planning and review practices. 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 2 Lora is repeatable brand aesthetics, licensed characters, specialized subject matter, and client-specific creative programs. 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
Learned-concept fidelity. Carries the trained subject or aesthetic into new prompts with recognizable characteristics.
Base-model quality retention. Preserves strong composition, materials, lighting, and typography while applying the adaptation.
Flexible concept recombination. Places the learned identity into new scenes, poses, and art directions.
Consistent creative series. Repeats a specialized visual language across multiple assets without retraining for each prompt. This configuration emphasizes the model family's characteristic balance of prompt accuracy, visual detail, and creative flexibility.
Pricing
| Billing unit | Price |
|---|---|
| Per request | $0.021 |
When to Use
| ✅ Good fit | ❌ Consider alternatives |
|---|---|
| The model's named workflow matches the source material and intended output | A different input modality or model route is required |
| A managed asynchronous result is suitable for the production pipeline | A synchronous, interactive editor is essential |
| The documented controls cover the required duration, framing, or format | The project needs controls outside this endpoint's schema |
| Creative iteration benefits from a repeatable request structure | Exact deterministic pixels, frames, geometry, or samples are mandatory |
| A finished downloadable media asset is the desired deliverable | Editable source layers or a native project file are required |
Prompt Guide
For generation, state the intended result first, then add the subject or source treatment, progression, style, and delivery constraints. Keep one creative variable per phrase, use the documented field names for controls, and change one setting at a time when comparing results.
{
"aspect_ratio": "21:9",
"output_format": "png",
"prompt": "Close shot a pianist plays in a luxurious room with tall windows overlooking a rainy metropolis. Shot with a 50mm lens at a side profile angle, soft tungsten light highlighting hands moving over keys. Capture detailed reflections in polished black piano surfaces, raindrops sliding down glass, and atmospheric warm/cool lighting contrast."
}
Technical Specs
| Spec | Value |
|---|---|
| Model ID | bfl/flux-2/lora |
| Inputs | aspect_ratio, guidance_scale, loras, num_inference_steps, output_format, prompt, seed |
| Required inputs | prompt |
| Output fields | content_type, url |
| Execution | Async (submit, then poll for result) |
| Aspect Ratio | 21:9 / 16:9 / 3:2 / 4:3 / 5:4 / 1:1 / 4:5 / 3:4 / 2:3 / 9:16 |
| Output Format | jpeg / png |
Related Models
bfl/flux-2/lora/edit— Compare this concrete local family route.bfl/flux-2/dev— Compare this concrete local family route.bfl/flux-2/dev/edit— Compare this concrete local family route.

