Qwen-Image LoRA is a 20B MMDiT next-gen text-to-image model with LoRA support for fast customization and refined image generation. Ready-to-use REST inference API, best performance, no coldstarts, affordable pricing.
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$0.025cho mỗi lần chạy·~40 / $1

Valentin in a natural daylight selfie at a cafe entrance. He looks seriously into the camera, wearing a black coat or jacket and wireless earbud. Background includes wooden frames, warm pendant lights, and urban cafe details. With text "WaveSpeedAI"

realism, a female inventor with auburn hair in an intricate updo and goggles on her head, her eyes full of intellect. She wears a leather corset and a multi-layered skirt, standing in her workshop. The room is filled with brass gears, complex clockwork devices, and glowing vacuum tubes. Warm light from gas lamps illuminates the scene. Steampunk style, highly detailed, retro-futurism, masterpiece.

A glamorous woman with a sharp bob haircut and dark lipstick. She is dressed in a stunning black and gold sequined flapper dress with long pearls. She leans against a gilded Art Deco bar, with a jazz band softly blurred in the background. Sophisticated, low-key lighting creates a luxurious and intimate mood, Great Gatsby era, glamorous, geometric patterns.

A majestic Afrofuturist queen with magnificent braided hair adorned with golden rings and cybernetic circuits. She wears a vibrant robe that merges traditional Kente patterns with glowing energy lines. The background is a futuristic African metropolis with unique architecture and flying vehicles. Vibrant, vivid colors, sci-fi art, character portrait.

A close-up portrait of a stylish woman with wavy, dark brown long hair and a warm smile, wearing a beige cashmere sweater. The background is a blurred city street with a soft bokeh effect. Natural afternoon light, cinematic, photorealistic, high detail, 8K.

realism, a young woman sitting alone in a laundromat at midnight, wearing headphones, staring at the rotating dryer drum, neon reflections on the glass, a subtle expression of nostalgia on her face

realism, a woman with healthy tanned skin and natural long curly hair, with a few wildflowers woven into it. She wears a fringed, off-white linen dress and sits barefoot in a golden field at sunset, holding a guitar. The lighting is warm and soft, creating a free-spirited and romantic atmosphere, photorealistic, golden hour lighting.

real life anime, a woman with curly hair tied loosely, wearing a paint-stained oversized white shirt, barefoot, standing in a spacious industrial loft with large windows and exposed brick walls. She’s holding a large brush, working on a colorful abstract canvas. Natural light pouring in, art supplies scattered around, expressive, richly detailed scene.

realism, a woman like a mermaid, with flowing, long, blue hair and shimmering scales. She swims gracefully in clear tropical waters filled with coral and strange marine life. Sunlight penetrates the water's surface, creating moving beams of light that illuminate the entire scene—dreamy, vibrant, light and shadow effects, underwater photography, highly detailed.

realism, a young scholar with glasses, wearing a tweed blazer, sits in a grand, ancient library. Sunlight streams through a massive arched window, illuminating dust motes dancing in the air. An open book rests on her lap as she looks up thoughtfully. Warm and cozy atmosphere, light academia aesthetic, narrative lighting, photorealistic.

A resilient female survivor with wind-swept short hair and a determined gaze. She wears patched-up leather gear and tactical equipment, holding a modified staff. She stands on a hill overlooking the ruins of a city at dusk, against a dramatic orange sky. Cinematic, post-apocalyptic style, realism, atmospheric lighting, wide-angle shot.
Qwen-Image-LoRA extends the base 20B MMDiT text-to-image model by allowing users to plug in custom LoRA weights (.safetensors) for fine-tuned control over style, characters, or artistic domains. This makes it a versatile tool for creators who want both world-class text rendering and personalized generation.
.safetensors LoRA weights and control blending strength via scale.<owner>/<model-name> or external .safetensors URL.safetensors file.0.5 for subtle effect, 1.0 for full strength).-1 = random).Grab a WaveSpeedAI API key, then call POST https://api.wavespeed.ai/api/v3/wavespeed-ai/qwen-image/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 Qwen Image 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,
"output_format": "jpeg"
}
JSON
)
# 1. Submit the prediction.
SUBMIT_RESPONSE=$(curl --silent --show-error --fail-with-body \
-X POST "https://api.wavespeed.ai/api/v3/wavespeed-ai/qwen-image/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/qwen-image/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 ${apiKey}`,
"Content-Type": "application/json",
},
body: JSON.stringify({
"prompt": "A cinematic shot of a city at sunset, soft golden light",
"size": "1024*1024",
"seed": -1,
"output_format": "jpeg"
}),
});
const task = body.data ?? body;
if (!task.id) throw new Error("Submission response did not contain a prediction id");
const resultUrl = task.urls?.get ||
`https://api.wavespeed.ai/api/v3/predictions/${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,
"output_format": "jpeg"
}
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/qwen-image/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)Qwen Image Text To Image Lora is a WaveSpeedAI model for AI inference, exposed as a REST API on WaveSpeedAI. Qwen-Image LoRA is a 20B MMDiT next-gen text-to-image model with LoRA support for fast customization and refined image generation. 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/qwen-image-text-to-image-lora.
Qwen Image Text To Image Lora starts at $0.025 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/qwen-image-text-to-image-lora.
Median end-to-end generation time on WaveSpeedAI is around 28 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.