Flux Krea Dev LoRA API Documentation

Flux Krea Dev LoRA API Documentation

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FLUX.1 Krea [dev] is the open-weights Krea 1 model with LoRA support, delivering distinctive, realistic outputs for creative projects. Ready-to-use REST API, best performance, no coldstarts, affordable pricing.

Features

FLUX Krea Dev LoRA is a versatile text-to-image and image-to-image generation model with full LoRA support. Generate stunning images with custom styles, apply artistic effects, or transform existing images — all with fine-tuned control over the output.


Why It Stands Out

  • Dual mode support: Works as both text-to-image and image-to-image generator.
  • LoRA support: Apply custom LoRA models for specific styles, characters, or aesthetics.
  • Prompt Enhancer: Built-in AI-powered prompt optimization for better results.
  • Flexible resolution: Customize width and height independently for any aspect ratio.
  • Fine-tuned control: Adjust strength, guidance scale for precise results.
  • Multiple output formats: Export as JPEG or PNG based on your needs.
  • Affordable pricing: High-quality generation at just $0.02 per image.
  • Reproducibility: Use the seed parameter to recreate exact results.

Parameters

ParameterRequiredDescription
promptYesText description of the image you want to generate.
imageNoSource image for image-to-image transformation.
widthNoOutput width in pixels (default: 1024).
heightNoOutput height in pixels (default: 1024).
lorasNoLoRA models to apply for custom styles.
strengthNoTransformation intensity for I2I (0.0–1.0, default: 0.8).
guidance_scaleNoPrompt adherence strength (default: 3.5).
seedNoSet for reproducibility; -1 for random.
output_formatNoOutput format: jpeg or png (default: jpeg).
enable_base64_outputNoReturn base64 string instead of URL (API only).
enable_sync_modeNoWait for result before returning response (API only).

How to Use

Text-to-Image:

  1. Write a prompt describing the image you want. Use the Prompt Enhancer for AI-assisted optimization.
  2. Set dimensions — adjust width and height for your desired aspect ratio.
  3. Add LoRAs (optional) — apply custom style models.
  4. Click Run and download your image.

Image-to-Image:

  1. Upload a source image.
  2. Write a prompt describing the transformation.
  3. Adjust strength — lower values preserve more of the original.
  4. Add LoRAs (optional) for style transfer.
  5. Click Run and download your image.

Best Use Cases

  • Portrait Photography — Generate photorealistic portraits with custom styles.
  • Style Transfer — Apply LoRA styles to existing images.
  • Creative Projects — Generate unique images with specific aesthetics.
  • Character Consistency — Use LoRAs for consistent character appearances.
  • Product Visualization — Create stylized product concepts.

Pricing

OutputPrice
Per image$0.02

Pro Tips for Best Quality

  • Be detailed in your prompt — include subject, style, lighting, mood, and technical details.
  • Use LoRAs to apply consistent styles across multiple generations.
  • For image-to-image, use lower strength (0.3–0.5) to preserve more of the original.
  • Use higher strength (0.7–0.9) for more dramatic transformations.
  • Fix the seed when iterating to compare different LoRA or parameter combinations.

Notes

  • Ensure uploaded image URLs are publicly accessible.
  • Processing time varies based on resolution and current queue load.
  • Please ensure your prompts comply with content guidelines.

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",
  "size": "1024*1024",
  "strength": 0.8,
  "guidance_scale": 3.5,
  "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-krea-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--
sizestringNo1024*1024-The size of the generated media in pixels (width*height).
lorasarray<object>No0 ~ 3 itemsList of LoRAs to apply (max 3).
strengthnumberNo0.80 ~ 1Strength indicates extent to transform the reference image.
guidance_scalenumberNo3.50 ~ 20The guidance scale to use for the generation.
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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