Flux Kontext Pro Text To Image
Playground
Try it on WaveSpeedAI!FLUX.1 Kontext [pro] is a premium text-to-image model with maximum performance and greatly improved prompt adherence for accurate outputs. Ready-to-use REST inference API, best performance, no coldstarts, affordable pricing.
Features
FLUX Kontext Pro is a fast, high-quality text-to-image model designed for reliable everyday generation. It delivers strong prompt adherence and clean composition with efficient latency—great for marketing creatives, story frames, concept art, and general-purpose visual production.
Key capabilities
- Prompt-faithful text-to-image generation with consistent visual quality
- Strong composition control via scene + camera + lighting language
- Produces clean, usable outputs quickly for iterative workflows
- Includes a configurable safety_tolerance setting for content filtering control
Pricing
$0.04 per image.
Total cost = num_images × $0.04 Example: num_images = 4 → $0.16
How to use
- Write a prompt describing the subject, scene, action, and style.
- Choose an aspect ratio that matches your layout (e.g., 16:9, 1:1, 9:16).
- Set guidance_scale for how strictly the image should follow your prompt.
- Optionally set seed for reproducible results, then generate.
Parameters
- prompt (required): Text description of what to generate
- aspect_ratio: Output aspect ratio (e.g., 16:9, 1:1, 9:16)
- seed: Fixed value for reproducibility; leave empty/random for variation
- guidance_scale: Prompt adherence strength (higher = stricter; too high may look rigid)
- safety_tolerance: Safety filtering level (higher typically allows more; use responsibly)
Prompting guide
A stable prompt structure:
- Subject: who/what is in frame
- Action: what is happening
- Scene: location + time + atmosphere
- Camera: framing, angle, lens vibe
- Lighting: softbox, sunset, neon, rim light, etc.
- Style: realistic, illustration, film still, etc.
Example pattern: A [shot type] of [subject] [action] in [scene]. [Lighting + mood]. [Camera/framing]. [Style cues].
Example prompts
- A close-up portrait of a child in a yellow raincoat hugging a golden retriever after the rain, wet grass and a faint rainbow behind, soft natural light, warm mood, shallow depth of field.
- Wide shot of a cozy café interior at night, warm tungsten lighting, rain on the windows, cinematic composition, film still look.
- Studio product photo of a minimal sneaker on a clean background, softbox lighting, crisp shadows, premium advertising style.
Best practices
- Keep the first sentence concrete, then add camera and lighting constraints.
- Use guidance_scale sparingly; moderate values often look more natural.
- For consistent iteration, fix seed and adjust only one variable at a time.
Authentication
For authentication details, please refer to the Authentication Guide.
API Endpoints
Submit Task & Query Result
set -euo pipefail
export WAVESPEED_API_KEY="your-api-key"
REQUEST_BODY=$(cat <<'JSON'
{
"prompt": "A cinematic ocean wave at sunrise, highly detailed",
"aspect_ratio": "1:1",
"guidance_scale": 3.5,
"safety_tolerance": "2"
}
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-kontext-pro/text-to-image" \
-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
doneParameters
Task Submission Parameters
Request Parameters
| Parameter | Type | Required | Default | Range | Description |
|---|---|---|---|---|---|
| prompt | string | Yes | - | The positive prompt for the generation. | |
| aspect_ratio | string | No | 1:1 | 21:9, 16:9, 4:3, 3:2, 1:1, 2:3, 3:4, 9:16, 9:21 | The aspect ratio of the generated media. |
| seed | integer | No | - | - | The random seed to use for the generation. |
| guidance_scale | number | No | 3.5 | 1 ~ 20 | The guidance scale to use for the generation. |
| safety_tolerance | string | No | 2 | 1, 2, 3, 4, 5 | The safety tolerance level for the generated image. 1 being the most strict and 5 being the most permissive. |
| enable_sync_mode | boolean | No | false | - | 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
| Parameter | Type | Description |
|---|---|---|
| code | integer | HTTP status code (e.g., 200 for success) |
| message | string | Status message (e.g., “success”) |
| data.id | string | Unique identifier for the prediction, Task Id |
| data.model | string | Model ID used for the prediction |
| data.outputs | array | Output values, usually URL strings; some models return text strings or structured result objects (empty when status is not completed) |
| data.urls | object | Object containing related API endpoints |
| data.urls.get | string | URL to retrieve the prediction result |
| data.status | string | Status of the task: created, processing, completed, or failed |
| data.created_at | string | ISO timestamp of when the request was created (e.g., “2023-04-01T12:34:56.789Z”) |
| data.error | string | Error message (empty if no error occurred) |
| data.timings | object | Object containing timing details |
| data.timings.inference | integer | Inference time in milliseconds |
Result Request Parameters
| Parameter | Type | Required | Default | Description |
|---|---|---|---|---|
| id | string | Yes | - | Task ID |
Result Response Parameters
| Parameter | Type | Description |
|---|---|---|
| code | integer | HTTP status code (e.g., 200 for success) |
| message | string | Status message (e.g., “success”) |
| data | object | The prediction data object containing all details |
| data.id | string | Unique identifier for the prediction |
| data.model | string | Model ID used for the prediction |
| data.outputs | array<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.urls | object | Object containing related API endpoints |
| data.urls.get | string | URL to poll for the prediction result |
| data.status | string | Status: created, processing, completed, or failed |
| data.created_at | string | ISO timestamp of when the request was created |
| data.error | string | Error message (empty if no error occurred) |
| data.timings | object | Object containing timing details |
| data.timings.inference | integer | Inference time in milliseconds |