Flux 2 Flex Text To Image API Documentation

Flux 2 Flex Text To Image API Documentation

Playground

Try it on WaveSpeedAI!

FLUX.2 [flex] from Black Forest Labs delivers fast, flexible text-to-image generation with enhanced realism, sharper text rendering, and built-in editing for rapid iteration: a ready-to-use REST inference API, best performance, no cold starts, and affordable pricing.

Features

FLUX.2 [flex] is the creative workhorse of the FLUX.2 family: a configurable, style-forward text-to-image model that delivers professional visuals while leaving plenty of room for experimentation. It is designed for teams who want more control over aesthetics and behaviour than a strictly “locked” production model.


Where FLUX.2 [flex] fits best

  • Style-driven exploration and concept art
  • High-volume generation where visual diversity is important
  • Brand and product imagery that needs frequent refinements
  • Fine-tuning experiments for domain- or brand-specific looks

Creative-first generation with control knobs

Rather than fixing all sampling behaviour, FLUX.2 [flex] keeps the lean FLUX.2 core but exposes more room to steer style, strength, and interpretation. You get production-usable images at good speed, while being able to push colour, mood, and composition further than with purely “set-and-forget” pipelines.


What you can get from this?

• Wide stylistic latitude

Produces a broad range of looks and moods—from clean product shots to heavily stylised illustration—so a single prompt can be explored in multiple creative directions.

• Tunable quality–speed trade-off

Supports configuration of inference settings, letting you run quick drafts cheaply and then dial up quality for shortlisted ideas or final renders.

• Open, extensible foundation

Built on open FLUX.2 tooling and community contributions, making it straightforward to inspect, adapt, and embed flex deeply into custom stacks.

• Friendly to LoRA and custom training

Works well as a base for LoRA adapters or other lightweight fine-tuning, so you can lock in house styles, specific subjects, or niche domains without retraining a heavyweight model.

• Resource-conscious for large runs

The streamlined architecture keeps GPU usage moderate, which is ideal for batch jobs, internal tools, and cost-sensitive creative pipelines.

• Consistent, repeatable results

Seed control and stable behaviour make it easy to recreate favourite generations or generate controlled variations for A/B tests and iterative design work.


Pricing

Simple per-image billing:

  • $0.06 per generated image

FLUX.2 family on WaveSpeedAI

Combine FLUX.2 [flex] with the rest of the FLUX.2 lineup for a complete creation and editing workflow:


More Image Tools on WaveSpeedAI

  • Nano Banana Pro – Google’s Gemini-based text-to-image model for sharp, coherent, prompt-faithful visuals that work great for ads, keyframes, and product shots.
  • Seedream V4 – ’s style-consistent, multi-image generator ideal for posters, campaigns, and large batches of on-brand illustrations.
  • Qwen Edit Plus – an enhanced Qwen-based image editor for precise inpainting, cleanup, and local style changes while preserving overall composition.

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-flex/text-to-image" \
  -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

Retry temporary errors only while polling, never the submit request. Errors, retries and timeouts

Parameters

Task Submission Parameters

Request Parameters

ParameterTypeRequiredDefaultRangeDescription
promptstringYes-The positive prompt for the generation.
sizestringNo1024*1024256 ~ 2048The size of the generated media in pixels (width*height).
seedintegerNo--The random seed to use for the generation. -1 means a random seed will be used.

Every model also accepts the global request options enable_sync_mode and enable_base64_output; see their pages for details.

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.statusstringTask status. completed is successful; failed, cancelled, timeout, and deleted are failure terminal statuses.
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.statusstringStatus: completed is successful; failed, cancelled, timeout, and deleted are failure terminal statuses
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
© 2026 WaveSpeedAI. All rights reserved.