Flux Kontext Dev LoRA API Documentation
Playground
Try it on WaveSpeedAI!Fast FLUX.1 Kontext [dev] endpoint with LoRA support for rapid image editing using pre-trained adapters for brand and style. Ready-to-use REST inference API, best performance, no coldstarts, affordable pricing.
Features
FLUX Kontext Dev LoRA is an instruction-based image-to-image editing model with built-in LoRA support. Provide a source image plus a natural-language edit request, and optionally attach up to 3 LoRAs to steer style, subject consistency, or domain-specific aesthetics during the edit.
Key capabilities
- Image-to-image editing from a source image + text instruction
- LoRA-enabled inference: apply up to 3 LoRAs via input parameters
- Works for both local edits (specific changes) and global transforms (overall look)
- Ideal for batch consistency: reuse the same LoRA set for a stable visual identity
Pricing
$0.03 per image.
Cost per run = num_images × $0.03 Example: num_images = 4 → $0.12
Inputs and outputs
Input:
- One source image (upload or public URL)
- One edit instruction (prompt)
- Optional: up to 3 LoRA items
Output:
- One or more edited images (controlled by num_images)
Parameters
Core:
- prompt: Edit instruction describing what to change and what to preserve
- image: Source image
- width / height: Output resolution
- num_inference_steps: More steps can improve fidelity but increases latency
- guidance_scale: Higher values follow the prompt more strongly; too high may over-edit
- num_images: Number of variations generated per run
- seed: Fixed value for reproducibility; -1 for random
- output_format: jpeg or png
LoRA (up to 3 items):
-
loras: A list of LoRA entries (max 3)
-
path: Either owner/model-name or a direct.safetensors URL from the Internet
-
scale: LoRA strength (typically start around 0.6–1.0 and adjust)
Prompting guide
Use “preserve + edit + constraints” and let LoRAs drive the look:
Template: Keep [what must stay]. Change [what to edit]. Ensure [constraints]. Apply the LoRA style consistently without changing identity.
Example prompts
- Keep the person’s face, hairstyle, and pose unchanged. Replace the background with a clean studio gradient. Match lighting and shadows.
- Keep the product shape and label layout unchanged. Replace only the label text with “WaveSpeedAI”. Preserve typography perspective and print texture.
- Remove small blemishes and reduce glare on the forehead while keeping natural skin texture and pores.
Best practices
- Use fewer LoRAs when possible; add a second/third only if you need combined effects.
- If results drift or over-style, reduce LoRA scale or guidance_scale and strengthen the preserve clause.
- For consistent batches, keep the same LoRA set and scales, and fix seed when comparing prompt variants.
Authentication
For authentication details, please refer to the Authentication Guide.
API Endpoints
Submit Task & Query Result
set -euo pipefail
export WAVESPEED_API_KEY="your-api-key"
REQUEST_BODY=$(cat <<'JSON'
{
"num_inference_steps": 28,
"guidance_scale": 2.5,
"num_images": 1,
"seed": -1,
"output_format": "jpeg"
}
JSON
)
# 1. Submit the prediction.
SUBMIT_RESPONSE=$(curl --silent --show-error --fail-with-body \
-X POST "https://api.wavespeed.ai/api/v3/wavespeed-ai/flux-kontext-dev-lora" \
-H "Authorization: Bearer ${WAVESPEED_API_KEY}" \
-H "Content-Type: application/json" \
-d "${REQUEST_BODY}")
TASK=$(printf '%s' "${SUBMIT_RESPONSE}" | jq 'if type == "object" and has("data") then .data else . end')
PREDICTION_ID=$(printf '%s' "${TASK}" | jq -r '.id // empty')
if [ -z "${PREDICTION_ID}" ]; then
printf 'Submission response did not contain a prediction id
' >&2
exit 1
fi
RESULT_URL=$(printf '%s' "${TASK}" | jq -r '.urls.get // empty')
if [ -z "${RESULT_URL}" ]; then RESULT_URL="https://api.wavespeed.ai/api/v3/predictions/${PREDICTION_ID}/result"; fi
# 2. Poll until the prediction finishes.
while true; do
RESPONSE=$(curl --silent --show-error --fail-with-body \
"${RESULT_URL}" \
-H "Authorization: Bearer ${WAVESPEED_API_KEY}")
RESULT=$(printf '%s' "${RESPONSE}" | jq 'if type == "object" and has("data") then .data else . end')
STATUS=$(printf '%s' "${RESULT}" | jq -r '.status // empty')
case "${STATUS}" in
completed) printf '%s\n' "${RESULT}" | jq '.outputs'; break ;;
failed|cancelled|timeout) printf '%s\n' "${RESULT}" | jq . >&2; exit 1 ;;
created|processing) sleep 2 ;;
*) printf 'Unexpected status: %s
' "${STATUS}" >&2; exit 1 ;;
esac
doneParameters
Task Submission Parameters
Request Parameters
| Parameter | Type | Required | Default | Range | Description |
|---|---|---|---|---|---|
| prompt | string | No | - | The positive prompt for the generation. | |
| image | string | No | - | The image to generate an image from. | |
| num_inference_steps | integer | No | 28 | 1 ~ 50 | The number of inference steps to perform. |
| guidance_scale | number | No | 2.5 | 0 ~ 20 | The guidance scale to use for the generation. |
| num_images | integer | No | 1 | 1 ~ 4 | The number of images to generate. |
| seed | integer | No | -1 | - | The random seed to use for the generation. -1 means a random seed will be used. |
| loras | array<object> | No | 0 ~ 3 items | List of LoRAs to apply (max 3). | |
| output_format | string | No | jpeg | jpeg, png, webp | The format of the output image. |
| enable_base64_output | boolean | No | false | - | If set to `true`, the prediction's `output` strings are returned as **naked base64** (no `data:<mime>;base64,` prefix). When `false` (default), outputs are returned as URLs pointing to our CDN. |
| enable_sync_mode | boolean | No | false | - | If set to `true`, the request attempts to wait for the generated result and return outputs in the same response. If the result is not ready within the sync wait window, the API can return a timeout body while the task continues processing. This option is only available via the API and is supported only by some models. |
Response Parameters
| Parameter | Type | Description |
|---|---|---|
| code | integer | HTTP status code (e.g., 200 for success) |
| message | string | Status message (e.g., “success”) |
| data.id | string | Unique identifier for the prediction, Task Id |
| data.model | string | Model ID used for the prediction |
| data.outputs | array | Output values, usually URL strings; some models return text strings or structured result objects (empty when status is not completed) |
| data.urls | object | Object containing related API endpoints |
| data.urls.get | string | URL to retrieve the prediction result |
| data.status | string | Status of the task: created, processing, completed, or failed |
| data.created_at | string | ISO timestamp of when the request was created (e.g., “2023-04-01T12:34:56.789Z”) |
| data.error | string | Error message (empty if no error occurred) |
| data.timings | object | Object containing timing details |
| data.timings.inference | integer | Inference time in milliseconds |
Result Request Parameters
| Parameter | Type | Required | Default | Description |
|---|---|---|---|---|
| id | string | Yes | - | Task ID |
Result Response Parameters
| Parameter | Type | Description |
|---|---|---|
| code | integer | HTTP status code (e.g., 200 for success) |
| message | string | Status message (e.g., “success”) |
| data | object | The prediction data object containing all details |
| data.id | string | Unique identifier for the prediction |
| data.model | string | Model ID used for the prediction |
| data.outputs | array<string | object> | Array of generated outputs (empty when status is not completed). Items are usually URL strings, but may be text strings or structured result objects, depending on the model. |
| data.urls | object | Object containing related API endpoints |
| data.urls.get | string | URL to poll for the prediction result |
| data.status | string | Status: created, processing, completed, or failed |
| data.created_at | string | ISO timestamp of when the request was created |
| data.error | string | Error message (empty if no error occurred) |
| data.timings | object | Object containing timing details |
| data.timings.inference | integer | Inference time in milliseconds |