Flux 2 Klein 9b Text To Image LoRA

Flux 2 Klein 9b Text To Image LoRA

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FLUX.2 [klein] 9B with LoRA support is a high-quality text-to-image model with 9B parameters, offering enhanced realism, crisper text generation, and fast LoRA customization. Ready-to-use REST inference API, best performance, no cold starts, affordable pricing.

Features

FLUX.2 Klein 9B Text-to-Image LoRA is a powerful text-to-image generation model with full LoRA support. With 9B parameters, it delivers higher quality and detail compared to the 4B variant while maintaining fast generation. Apply custom LoRA adapters for personalized styles and characters.


Why Choose This?

  • Enhanced quality 9B parameter model delivers richer detail and better prompt understanding than the 4B variant.

  • LoRA support Apply custom LoRA adapters for personalized styles, characters, or visual aesthetics.

  • Flexible sizing Custom width and height controls for any aspect ratio.

  • Prompt Enhancer Built-in tool to automatically improve your prompts for better results.

  • Balanced performance More capable than 4B while remaining fast and affordable.


Parameters

ParameterRequiredDescription
promptYesText description of the image you want to generate
widthNoOutput width in pixels (default: 1024)
heightNoOutput height in pixels (default: 1024)
lorasNoList of LoRA adapters to apply
seedNoRandom seed for reproducibility (-1 for random)

LoRA Format

Each LoRA in the loras array has:

  • path (required) — URL to the LoRA weights file
  • scale (optional) — Weight multiplier, default 1

How to Use

  1. Write your prompt — describe the image including subject, style, lighting, and mood.
  2. Set size — adjust width and height for your desired dimensions.
  3. Add LoRAs (optional) — click ”+ Add Item” to include custom LoRA adapters.
  4. Set seed — use -1 for random, or specify a number for reproducibility.
  5. Run — submit and download the generated image.

Pricing

ItemCost
Per image$0.015

Simple flat-rate pricing regardless of image size or LoRA count.


Best Use Cases

  • Custom Character Generation — Apply character LoRAs for consistent identity.
  • Style Personalization — Use style LoRAs for unique visual aesthetics.
  • Brand Content — Create on-brand visuals with trained LoRA styles.
  • High-Quality Output — When 4B quality isn’t enough but full-size models are overkill.
  • Production Work — Balanced quality and cost for professional content.

Pro Tips

  • Be specific in your prompts — include subject, style, lighting, colors, and atmosphere.
  • Start with LoRA scale 1.0 and adjust based on results.
  • Combine multiple LoRAs for hybrid styles (e.g., character + art style).
  • Use the same seed to compare different prompts or LoRA combinations.
  • If your LoRA uses trigger words, include them in your prompt.

Notes

  • LoRAs can be stacked for combined effects.
  • For best results, use LoRAs trained on FLUX-compatible models.
  • 9B model offers better detail than 4B at slightly higher cost.

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",
  "seed": -1
}
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-2-klein-9b/text-to-image-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.
sizestringNo1024*1024-The size of the generated media in pixels (width*height).
lorasarray<object>No0 ~ 3 itemsList of LoRAs to apply (maximum 3).
seedintegerNo-1-The random seed to use for the generation. -1 means a random seed will be used.
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.
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.

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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