Flux 2 Klein 4b Text To Image LoRA API Documentation

Flux 2 Klein 4b Text To Image LoRA API Documentation

Playground

FLUX.2 [klein] 4B with LoRA support is a compact 4-billion-parameter text-to-image model that delivers fast generation with quality results and LoRA customization. Ready-to-use REST inference API, best performance, no cold starts, affordable pricing.

Features

FLUX.2 Klein 4B Text-to-Image LoRA is a lightweight yet powerful text-to-image generation model with full LoRA support. Describe your vision in text, optionally apply custom LoRA adapters for personalized styles — all with fast generation and affordable pricing.

Why Choose This?

• High-quality generation Generate detailed, high-fidelity images from text descriptions with strong prompt adherence.

• 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.

• Lightweight and fast 4B parameter model optimized for quick turnaround at affordable pricing.

Parameters

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

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.
• Rapid Prototyping — Fast, affordable generation for creative iteration.
• Batch Generation — Low cost enables large-scale image production.

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.
• Processing speed is optimized for quick turnaround.

Related Models

• FLUX.2 Klein 4B Text-to-Image — Standard version without LoRA support.
• FLUX.2 Klein 4B Edit LoRA — Image editing with LoRA support.
• FLUX.2 Pro — Flagship-quality text-to-image generation.

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"
}
JSON
)

# 1. Submit the prediction once. Never retry this request automatically:
# a retry after an unclear failure can create a second, billed task.
CHECK='The task may already exist. Check History or the predictions list before submitting again.'
if ! SUBMIT_RESPONSE=$(curl --silent --show-error --fail-with-body \
  --connect-timeout 10 --max-time 60 \
  -X POST "https://api.wavespeed.ai/api/v3/wavespeed-ai/flux-2-klein-4b/text-to-image-lora" \
  -H "Authorization: Bearer ${WAVESPEED_API_KEY}" \
  -H "Content-Type: application/json" \
  -d "${REQUEST_BODY}"); then
  printf 'Submit failed. %s\n' "${CHECK}" >&2
  exit 1
fi

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 "${PREDICTION_ID}" ]; then
  printf 'Submission response did not contain a prediction id. %s\n' "${CHECK}" >&2
  exit 1
fi
RESULT_URL="https://api.wavespeed.ai/api/v3/predictions/${PREDICTION_ID}/result"

# 2. Poll until the prediction reaches a final status.
BODY_FILE=$(mktemp)
trap 'rm -f "${BODY_FILE}"' EXIT
while true; do
  # Transient errors (429, 5xx) are retried, honoring Retry-After.
  HTTP_CODE=$(curl --silent --show-error --fail-with-body \
    --connect-timeout 10 --max-time 60 --retry 5 --retry-delay 2 \
    --output "${BODY_FILE}" --write-out '%{http_code}' \
    "${RESULT_URL}" \
    -H "Authorization: Bearer ${WAVESPEED_API_KEY}" || true)
  case "${HTTP_CODE}" in
    2??) ;;
    000|429|5??)  # dropped connection or temporary error: keep polling
      printf 'Task %s: HTTP %s, polling again in 10s\n' "${PREDICTION_ID}" "${HTTP_CODE}" >&2
      sleep 10; continue ;;
    *) printf 'Task %s: HTTP %s\n' "${PREDICTION_ID}" "${HTTP_CODE}" >&2
      cat "${BODY_FILE}" >&2; exit 1 ;;
  esac
  RESULT=$(jq 'if type == "object" and has("data") then .data else . end' "${BODY_FILE}")
  STATUS=$(printf '%s' "${RESULT}" | jq -r '.status // empty')

  case "${STATUS}" in
    completed) printf '%s\n' "${RESULT}" | jq '.outputs'; break ;;
    failed|cancelled|timeout|deleted)
      printf 'Task %s ended with status %s\n' "${PREDICTION_ID}" "${STATUS}" >&2
      printf '%s\n' "${RESULT}" | jq . >&2; exit 1 ;;
    *) sleep 2 ;;
  esac
done

Parameters

Task Submission Parameters

Request Parameters

Response Parameters

Result Request Parameters

Result Response Parameters