FLUX.2 [klein] Base 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.
निष्क्रिय

$0.02प्रति रन·~50 / $1

A cinematic suspense still of an old apartment hallway at night, one door left slightly open, warm light spilling onto the dark floor, peeling wallpaper, long shadows, quiet tension, realistic film photography, dramatic atmosphere, ultra-detailed

A cinematic drama still of an adult sitting alone in a quiet hospital corridor at night, vending machine light, empty chairs, paper cup in hand, long fluorescent hallway, tired expression, emotional uncertainty, realistic film photography, muted colors, shallow depth of field, ultra-detailed

A cinematic romantic drama still of two adults standing apart on a rooftop at sunset, city skyline glowing behind them, one person looking away while the other waits silently, soft wind, warm golden light, emotional distance, realistic film photography, shallow depth of field, ultra-detailed
WaveSpeed AI FLUX.2 Klein Base 9B Text-to-Image LoRA is a high-quality text-to-image generation model with full LoRA support. Built on a 9B-parameter architecture, it delivers stronger detail, better prompt understanding, and more reliable visual fidelity than the 4B variant, while remaining fast and cost-effective for production workflows.
Higher-quality generation The 9B model produces richer detail, stronger prompt adherence, and more refined outputs than the 4B variant.
Full LoRA support Apply custom LoRA adapters for personalized styles, characters, aesthetics, or branded visual directions.
Flexible image sizing
Set custom width and height directly for the exact output dimensions you need.
Prompt Enhancer Built-in prompt enhancement can help improve image quality and prompt clarity.
Balanced speed and quality More capable than the 4B version while still remaining practical for fast creative iteration.
Production-ready workflow Suitable for custom character work, visual branding, style control, and other high-quality generation tasks.
| 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 during generation. |
| seed | No | Random seed for reproducibility. Use -1 for random generation. |
Each item in the loras array supports:
| Field | Required | Description |
|---|---|---|
| path | Yes | URL to the LoRA weights file. |
| scale | No | LoRA weight multiplier. Default: 1. |
width and height for your desired output dimensions.1 and fine-tune if needed.-1 for random generation, or a fixed value for reproducible results.Generate a cinematic fantasy portrait using a custom character LoRA and a painterly style LoRA, with dramatic lighting and highly detailed textures.
| Item | Cost |
|---|---|
| Per image | $0.02 |
width, height, seed, and the number of LoRAs do not affect pricingscale = 1 and adjust based on how strongly you want the adapter to influence the result.seed when comparing different prompts or LoRA combinations.1024 × 1024, then adjust once the concept is working well.prompt is required.width and height default to 1024.seed = -1 means random generation.Grab a WaveSpeedAI API key, then call POST https://api.wavespeed.ai/api/v3/wavespeed-ai/flux-2-klein-base-9b/text-to-image-lora with your input as JSON. The endpoint returns a prediction id. Start polling the result endpoint around every 2 seconds, increase the interval for long-running tasks, and stop on any terminal status. On completed, read output values from data.outputs. Examples for Flux 2 Klein Base 9b Text To Image Lora below.
set -euo pipefail
: "${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-base-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-base-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-base-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 Base 9b Text To Image Lora is a WaveSpeedAI model for AI inference, exposed as a REST API on WaveSpeedAI. FLUX.2 [klein] Base 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-base-9b-text-to-image-lora.
Flux 2 Klein Base 9b Text To Image Lora starts at $0.020 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-base-9b-text-to-image-lora.
Median end-to-end generation time on WaveSpeedAI is around 37 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.