bfl/flux-kontext-lora
Flux Kontext Lora is BFL's intelligent image editing model. Transform, retouch, and reimagine existing images using text prompts - from background replacement to artistic style conversion.
PNG, JPEG, WebP, or GIF · 20 MiB maximum
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-kontext-loraInput Schema
8 parameters · 2 required · 6 optional
| Parameter | Type | Required | Description |
|---|---|---|---|
image | string | Required | The URL of the image to edit. Max width: 14142px, Max height: 14142px, Timeout: 20s |
prompt | string | Required | The prompt to edit the image. |
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: [] |
output_format | string | Optional | The format of the generated image. · Options: jpeg, png · Default: "png" 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: 20 · Default: 2.5 |
resolution_mode | string | Optional |
Determines how the output resolution is set for image editing.
- `auto`: The model selects an optimal resolution from a predefined set that best matches the input image's aspect ratio. This is the recommended setting for most use cases as it's what the model was trained on.
- `match_input`: The model will attempt to use the same resolution as the input image. The resolution will be adjusted to be compatible with the model's requirements (e.g. dimensions must be multiples of 16 and within supported limits).
Apart from these, a few aspect ratios are also supported.
· Options: auto, match_input, 1:1, 16:9, 21:9, 3:2, 2:3, 4:5, 5:4, 3:4, 4:3, 9:16, 9:21 · Default: "match_input" automatch_input1:116:921:93:22:34:55:43:44:39:169:21 |
num_inference_steps | integer | Optional | The number of inference steps to perform. · Min: 10 · Max: 50 · Default: 30 |
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-kontext-lora",
"image": "https://static.sandbase.ai/examples/bfl/flux-kontext-lora/input_image_0.webp",
"loras": [],
"prompt": "change the setting to a day time, add a lot of people walking the sidewalk while maintaining the same style of the painting",
"output_format": "png",
"guidance_scale": 2.5,
"resolution_mode": "match_input",
"num_inference_steps": 30
}'
# Step 2: Poll result (replace <id>)
curl https://api.sandbase.ai/v1/run/<id> \
-H "Authorization: Bearer YOUR_API_KEY"API README
Flux Kontext Lora
Flux Kontext Lora belongs at the context-aware creation shaped by a learned visual identity 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 the project combines iterative visual understanding with a compact customization layer, helping a series remain recognizable across changing briefs. 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 Kontext Lora is branded content systems, recurring characters, specialized editorial styles, and client programs that mix new imagery with later revisions. 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
Semantic instruction following. Understands requested changes at the level of objects, attributes, relationships, and scene intent.
Identity retention. Keeps important people, products, and branded details recognizable through iterative revisions.
Context-aware integration. Matches new content to source lighting, perspective, materials, and composition.
Iterative edit stability. Supports successive creative changes without unnecessarily redesigning unaffected image content. Kontext-LoRA combines semantic image understanding with a learned concept, allowing both source-aware change and specialized identity.
Pricing
| Configuration | Billing unit | Price |
|---|---|---|
| Base generation | Per request | $0.035 |
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.
{
"image": "https://example.com/start-frame.png",
"loras": [],
"output_format": "jpeg",
"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-kontext-lora |
| Input fields | seed (integer)<br>image (string)<br>loras (array)<br>prompt (string)<br>output_format (string; jpeg, png)<br>guidance_scale (number; 0–20)<br>resolution_mode (string; auto, match_input, 1:1, 16:9, 21:9, 3:2, 2:3, 4:5, 5:4, 3:4, 4:3, 9:16, 9:21)<br>num_inference_steps (integer; 10–50) |
| Required input | prompt, image |
| Output fields | url, content_type |
| Execution | Asynchronous job |
Related Models
bfl/flux-kontext-lora/inpaint— Compare this concrete local family route.bfl/flux-kontext-lora/text-to-image— Compare this concrete local family route.bfl/flux-1.1/pro— Compare this concrete local family route.