Flux Dev LoRA API Documentation

Flux Dev LoRA API Documentation

Playground

Try it on WaveSpeedAI!

FLUX.1 [dev] endpoint with LoRA support for fast, high-quality image generation and simple personalization via pre-trained LoRA adapters. Ready-to-use REST inference API, best performance, no coldstarts, affordable pricing.

Features

FLUX.1 [dev] is a 12B parameter rectified flow transformer for advanced text-to-image generation. It supports prompt-only generation as well as image inpainting and LoRA customization, making it a flexible tool for both research and creative workflows.


Why it looks great

  • High-quality output: Cutting-edge visual fidelity, second only to FLUX.1 [pro].
  • Prompt alignment: Strong competitive prompt following, rivaling closed-source alternatives.
  • Efficient training: Trained with guidance distillation for better speed-performance balance.
  • Flexible editing: Supports image + mask editing, LoRA fine-tuning, and custom strength control.
  • Open weights: Enables research, experimentation, and innovative creative pipelines.

Limits and Performance

  • Max resolution: up to 1536 × 1536 pixels

  • Optional inputs:

  • image (for img2img)

  • mask_image (for inpainting)

  • LoRA support: add multiple .safetensors with adjustable scale

  • Inference controls:

  • num_inference_steps (default ~28)

  • guidance_scale (default ~3.5)

  • strength (the strength of transform the reference image)

  • Output format: JPEG / PNG / WEBP

  • Seed: reproducibility (-1 = random)


Pricing

Just $0.015 per image !!


How to Use

  1. Write a prompt — detailed scene + style (lighting, realism, mood).
  2. (Optional) Upload an image to guide generation.
  3. (Optional) Add a mask image for inpainting.
  4. Adjust parameters:
  • Strength (the strength of transform the reference image).
  • LoRAs (add path/URL + scale).
  • Size (width & height, up to 1024×1024).
  • Inference steps and guidance scale.
  1. Set num_images (default 1).
  2. (Optional) Fix seed for reproducibility.
  3. Choose output format and run.

Pro tips

  • Use higher inference steps for more detail, lower for speed.
  • Adjust guidance scale to balance prompt strength vs. creativity (3–7 recommended).
  • Apply mask + strength for clean local edits (inpainting).
  • Blend multiple LoRAs for hybrid style outputs.
  • Use consistent seeds when testing parameter changes for controlled comparison.

Notes

  • The image URL must be valid and accessible; otherwise, the job may fail.
  • For mask_image, do not upload the original or unprocessed image directly — ensure the mask is correctly prepared.
  • LoRA files must be uploaded from trusted platforms and set to public access to be usable.
  • Parameters such as num_inference_steps (and others) directly affect runtime: the larger the value, the longer the generation will take.

Reference

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'
{
  "prompt": "A cinematic ocean wave at sunrise, highly detailed",
  "strength": 0.8,
  "size": "1024*1024",
  "num_inference_steps": 28,
  "guidance_scale": 3.5,
  "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-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
promptstringYes-The positive prompt for the generation.
imagestringNo--
mask_imagestringNo-The mask image tells the model where to generate new pixels (white) and where to preserve the original image (black). It acts as a stencil or guide for targeted image editing.
strengthnumberNo0.80 ~ 1Strength indicates extent to transform the reference image.
lorasarray<object>No0 ~ 3 itemsList of LoRAs to apply (max 3).
sizestringNo1024*1024-The size of the generated media in pixels (width*height).
num_inference_stepsintegerNo281 ~ 50The number of inference steps to perform.
guidance_scalenumberNo3.50 ~ 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.
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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