FLUX.1 Kontext [max] is a text-to-image model with max performance and greatly improved prompt adherence for accurate results. Ready-to-use REST inference API, best performance, no coldstarts, affordable pricing.
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$0.08cho mỗi lần chạy·~12 / $1

The camera begins in a dimly lit interrogation room, focusing closely on the eyes of a white woman in her early 30s. It slowly pulls back to reveal her full face—platinum blonde hair slightly disheveled, her expression calm but betraying flickers of anxiety. She sits upright in a metal chair, tense wrists resting on the cold tabletop. A harsh overhead light casts sharp shadows across her face, creating stark contrast and strong shadow lines.

Anime racer girl on flaming hoverboard, graffiti skyline, dynamic angle

Van Gogh-style starry night ocean, thick brushstrokes, swirling waves

Vintage bicycle leaning against brick wall with flower basket, cobblestone street, dusk blue hour ambiance

A cute lego doll with a garden background

A vibrant young superhero in a brightly colored costume, striking a dynamic pose atop a city skyline, with a lively urban landscape in the background. Bold lines, vivid colors, American comic book/Japanese anime style.

An elderly woman in elaborate Victorian attire, seated in a velvet-upholstered armchair, holding an open book with a serene expression, against the backdrop of a dimly lit classical study. Classical oil painting texture, soft Rembrandt lighting, rich and deep colors.

An 8-bit style adventurer character, holding a pixelated sword and shield, standing in a simple forest scene with blocky trees and bushes in the background. Clear pixel blocks, limited color palette, nostalgic retro game style.

A human silhouette with its head replaced by a floating geometric shape, situated in a distorted, dreamlike landscape, with broken clocks and floating islands in the sky. Non-linear perspective, bizarre juxtapositions, unexpected elements, surrealist art.

A mechanic with goggles and gear embellishments, wearing a leather apron, repairing intricate machinery in a workshop filled with steam and contraptions, with gleaming metal and pipes in the background. Retro-futurism, metallic sheen, intricately detailed mechanical parts, industrial steampunk aesthetic.
FLUX Kontext Max is a high-quality text-to-image model built for prompt-faithful generation with strong composition control and cinematic detail. Write a clear scene description (subject, setting, action, camera, lighting, mood), and it produces polished, production-ready images—great for story frames, concept art, marketing visuals, and realistic or stylized illustration work.
$0.08 per image.
Total cost = num_images × $0.08 Example: num_images = 4 → $0.32
A reliable prompt structure:
Example pattern: A [shot type] of [subject] [action] in [scene]. [Lighting + mood]. [Camera/framing]. [Style cues].
Grab a WaveSpeedAI API key, then call POST https://api.wavespeed.ai/api/v3/wavespeed-ai/flux-kontext-max/text-to-image with your input as JSON. The endpoint returns a prediction id. Start polling the result endpoint around every 2 seconds, increase the interval for long-running tasks, and stop on any terminal status. On completed, read output values from data.outputs. Examples for Flux Kontext Max Text To Image below.
set -euo pipefail
: "${WAVESPEED_API_KEY:?Set WAVESPEED_API_KEY}"
REQUEST_BODY=$(cat <<'JSON'
{
"prompt": "A cinematic shot of a city at sunset, soft golden light",
"guidance_scale": 3.5,
"aspect_ratio": "1:1"
}
JSON
)
# 1. Submit the prediction.
SUBMIT_RESPONSE=$(curl --silent --show-error --fail-with-body \
-X POST "https://api.wavespeed.ai/api/v3/wavespeed-ai/flux-kontext-max/text-to-image" \
-H "Content-Type: application/json" \
-H "Authorization: Bearer $WAVESPEED_API_KEY" \
-d "$REQUEST_BODY")
TASK=$(printf '%s' "$SUBMIT_RESPONSE" | jq 'if has("data") then .data else . end')
PREDICTION_ID=$(printf '%s' "$TASK" | jq -r '.id')
if [ -z "$PREDICTION_ID" ] || [ "$PREDICTION_ID" = "null" ]; 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' "$RESPONSE" | jq 'if has("data") then .data else . end')
STATUS=$(printf '%s' "$RESULT" | jq -r '.status')
case "$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
doneconst submitUrl = "https://api.wavespeed.ai/api/v3/wavespeed-ai/flux-kontext-max/text-to-image";
const apiKey = process.env.WAVESPEED_API_KEY;
if (!apiKey) throw new Error('Set WAVESPEED_API_KEY');
async function requestJson(url, options = {}) {
const response = await fetch(url, options);
if (!response.ok) throw new Error(await response.text());
return response.json();
}
// 1. Submit the prediction.
const body = await requestJson(submitUrl, {
method: "POST",
headers: {
"Authorization": `Bearer ${apiKey}`,
"Content-Type": "application/json",
},
body: JSON.stringify({
"prompt": "A cinematic shot of a city at sunset, soft golden light",
"guidance_scale": 3.5,
"aspect_ratio": "1:1"
}),
});
const task = body.data ?? body;
if (!task.id) throw new Error("Submission response did not contain a prediction id");
const resultUrl = task.urls?.get ||
`https://api.wavespeed.ai/api/v3/predictions/${task.id}/result`;
// 2. Poll until the prediction finishes.
while (true) {
const resultBody = await requestJson(resultUrl, {
headers: { "Authorization": `Bearer ${apiKey}` },
});
const result = resultBody.data ?? resultBody;
if (result.status === "completed") {
console.log(result.outputs);
break;
}
if (["failed", "cancelled", "timeout"].includes(result.status)) throw new Error(JSON.stringify(result));
if (!["created", "processing"].includes(result.status)) throw new Error("Unexpected status: " + result.status);
await new Promise(resolve => setTimeout(resolve, 2000));
}import json
import os
import time
from urllib.request import Request, urlopen
api_key = os.environ["WAVESPEED_API_KEY"]
headers = {"Authorization": f"Bearer {api_key}", "Content-Type": "application/json"}
payload = {
"prompt": "A cinematic shot of a city at sunset, soft golden light",
"guidance_scale": 3.5,
"aspect_ratio": "1:1"
}
def request_json(url, data=None):
request = Request(url, data=data, headers=headers, method="POST" if data else "GET")
with urlopen(request) as response:
return json.load(response)
# 1. Submit the prediction.
body = request_json("https://api.wavespeed.ai/api/v3/wavespeed-ai/flux-kontext-max/text-to-image", json.dumps(payload).encode())
task = body.get("data", body)
if not task.get("id"):
raise RuntimeError("Submission response did not contain a prediction id")
result_url = task.get("urls", {}).get("get") or f"https://api.wavespeed.ai/api/v3/predictions/{task['id']}/result"
# 2. Poll until the prediction finishes.
while True:
result_body = request_json(result_url)
result = result_body.get("data", result_body)
status = result.get("status")
if status == "completed":
print(result.get("outputs", []))
break
if status in {"failed", "cancelled", "timeout"}:
raise RuntimeError(result)
if status not in {"created", "processing"}:
raise RuntimeError(f"Unexpected status: {status}")
time.sleep(2)Flux Kontext Max Text To Image is a WaveSpeedAI model for image generation, exposed as a REST API on WaveSpeedAI. FLUX.1 Kontext [max] is a text-to-image model with max performance and greatly improved prompt adherence for accurate results. Ready-to-use REST inference API, best performance, no coldstarts, affordable pricing. You can call it programmatically or try it from the playground above.
POST your input parameters to the model's REST endpoint (shown in the API tab of this playground) with your WaveSpeedAI API key in the Authorization header. Submission returns a prediction ID. Poll the result endpoint starting around every 2 seconds, increase the interval for long-running tasks, and stop on any terminal status. The playground generates production-oriented Python, JavaScript, and cURL examples with timeouts, transient-error handling, and safe GET retries. Full request/response shape is documented at https://wavespeed.ai/docs/docs-api/wavespeed-ai/flux-kontext-max-text-to-image.
Flux Kontext Max Text To Image starts at $0.080 per run. That figure is the base price — the final charge scales with the parameters you set in the form (output size, length, count, references, or whatever knobs this model exposes), so a higher-quality or larger output costs more than a minimal one. The exact cost for your current input is shown live next to the Generate button before you submit, and the actual per-call charge is recorded on the prediction afterwards.
Key inputs: `prompt`, `aspect_ratio`, `seed`, `guidance_scale`, `enable_sync_mode`. The full JSON schema (types, defaults, allowed values) is rendered above the Generate button and mirrored in the API reference at https://wavespeed.ai/docs/docs-api/wavespeed-ai/flux-kontext-max-text-to-image.
Median end-to-end generation time on WaveSpeedAI is around 13 seconds per request, based on recent successful runs. Queue time varies with global demand; live status is visible in the prediction record.
Commercial usage rights depend on the model's license, set by its provider (WaveSpeedAI). The license summary appears on the model card above; see WaveSpeedAI's Terms of Service for platform-level conditions.