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.
ว่าง

$0.015ต่อครั้ง·~66 / $1

head made of stone of the big art deco sculpture buried in half in icelandic ash on the black desert, late sunset light, intrinsic, photorealistic

A full-body editorial fashion photoshoot featuring a white male model with green eyes, standing on a wide city avenue lined with tall urban buildings in winter, heavy snowfall dominating the scene, snow-covered streets and sidewalks, icy air visible in breath, overcast winter sky with soft diffused daylight, cool muted color palette, cinematic lighting with gentle rim light through falling snow, high-fashion winter wardrobe with structured silhouettes and flowing fabrics, wind-driven snow adding motion, elegant and dramatic mood, ultra-realistic photography, shallow depth of field, subtle bokeh background, Vogue-style editorial aesthetic, atmospheric urban winter setting

full body Animation Cartoon werewolf man with long white beard wearing black drop shoulder oversize Graphic T shirt and , satan demonic style , evil mode, white eyes, tattoo number 18 on neck , looking to the eyes the sky, concept artt, award of the year, wide view, insanely detailed, comic 3d Render, thick outline stroke , poster style,vivid background

a snowboarder carving through powder, leaving behind an arc of white on the pristine surface of deep blue snow. award-winning photography, in the style of realistic photography.

a small intelligent dog wearing glasses and a purple knit sweater sitting at a laptop, looking at the camera as if welcoming the viewer, soft warm lighting, background in pastel lilac tones, minimalist setup, cozy and photorealistic style, cinematic composition, shallow depth of field, style photographic
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.
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.
| Parameter | Required | Description |
|---|---|---|
| prompt | Yes | Text description of the image you want to generate |
| width | No | Output width in pixels (default: 1024) |
| height | No | Output height in pixels (default: 1024) |
| loras | No | List of LoRA adapters to apply |
| seed | No | Random seed for reproducibility (-1 for random) |
Each LoRA in the loras array has:
| Item | Cost |
|---|---|
| Per image | $0.015 |
Simple flat-rate pricing regardless of image size or LoRA count.
Grab a WaveSpeedAI API key, then call POST https://api.wavespeed.ai/api/v3/wavespeed-ai/flux-2-klein-9b/text-to-image-lora with your input as JSON. The endpoint returns a prediction id. Start polling the result endpoint around every 2 seconds, increase the interval for long-running tasks, and stop on any terminal status. On completed, read output values from data.outputs. Examples for Flux 2 Klein 9b Text To Image Lora below.
set -euo pipefail
: "${WAVESPEED_API_KEY:?Set WAVESPEED_API_KEY}"
REQUEST_BODY=$(cat <<'JSON'
{
"prompt": "A cinematic shot of a city at sunset, soft golden light",
"size": "1024*1024",
"seed": -1
}
JSON
)
# 1. Submit the prediction.
SUBMIT_RESPONSE=$(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 "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-2-klein-9b/text-to-image-lora";
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 ${apiKey}`,
"Content-Type": "application/json",
},
body: JSON.stringify({
"prompt": "A cinematic shot of a city at sunset, soft golden light",
"size": "1024*1024",
"seed": -1
}),
});
const task = body.data ?? body;
if (!task.id) throw new Error("Submission response did not contain a prediction id");
const resultUrl = task.urls?.get ||
`https://api.wavespeed.ai/api/v3/predictions/${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",
"size": "1024*1024",
"seed": -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-2-klein-9b/text-to-image-lora", 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 2 Klein 9b Text To Image Lora is a WaveSpeedAI model for AI inference, exposed as a REST API on WaveSpeedAI. 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. 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-2-klein-9b-text-to-image-lora.
Flux 2 Klein 9b Text To Image Lora starts at $0.015 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`, `size`, `seed`, `enable_base64_output`, `enable_sync_mode`, `loras`. 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-2-klein-9b-text-to-image-lora.
Median end-to-end generation time on WaveSpeedAI is around 8 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.