Flux Kontext Dev LoRA API Documentation

Flux Kontext Dev LoRA API Documentation

Playground

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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
done

Parameters

Task Submission Parameters

Request Parameters

ParameterTypeRequiredDefaultRangeDescription
promptstringNo-The positive prompt for the generation.
imagestringNo-The image to generate an image from.
num_inference_stepsintegerNo281 ~ 50The number of inference steps to perform.
guidance_scalenumberNo2.50 ~ 20The guidance scale to use for the generation.
num_imagesintegerNo11 ~ 4The number of images to generate.
seedintegerNo-1-The random seed to use for the generation. -1 means a random seed will be used.
lorasarray<object>No0 ~ 3 itemsList of LoRAs to apply (max 3).
output_formatstringNojpegjpeg, png, webpThe format of the output image.
enable_base64_outputbooleanNofalse-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_modebooleanNofalse-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

ParameterTypeDescription
codeintegerHTTP status code (e.g., 200 for success)
messagestringStatus message (e.g., “success”)
data.idstringUnique identifier for the prediction, Task Id
data.modelstringModel ID used for the prediction
data.outputsarrayOutput values, usually URL strings; some models return text strings or structured result objects (empty when status is not completed)
data.urlsobjectObject containing related API endpoints
data.urls.getstringURL to retrieve the prediction result
data.statusstringStatus of the task: created, processing, completed, or failed
data.created_atstringISO timestamp of when the request was created (e.g., “2023-04-01T12:34:56.789Z”)
data.errorstringError message (empty if no error occurred)
data.timingsobjectObject containing timing details
data.timings.inferenceintegerInference time in milliseconds

Result Request Parameters

ParameterTypeRequiredDefaultDescription
idstringYes-Task ID

Result Response Parameters

ParameterTypeDescription
codeintegerHTTP status code (e.g., 200 for success)
messagestringStatus message (e.g., “success”)
dataobjectThe prediction data object containing all details
data.idstringUnique identifier for the prediction
data.modelstringModel ID used for the prediction
data.outputsarray<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.urlsobjectObject containing related API endpoints
data.urls.getstringURL to poll for the prediction result
data.statusstringStatus: created, processing, completed, or failed
data.created_atstringISO timestamp of when the request was created
data.errorstringError message (empty if no error occurred)
data.timingsobjectObject containing timing details
data.timings.inferenceintegerInference time in milliseconds
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