Z-Image-Turbo LoRA (6B) enables ultra-fast text-to-image generation with external LoRA support. Generate photorealistic images in sub-second latency while applying up to 3 LoRAs for custom styles. Ready-to-use REST API, best performance, no coldstarts, affordable pricing.
就绪
$0.01每次运行·~100 / $1
示例查看全部
A dramatic cinematic movie poster with dark atmospheric lighting, central silhouette of a lone figure, smoky background, bold large title text “BEYOND THE SHADOWS”, smaller tagline “Every secret has a price”, credit block at bottom, lens flare, professional Hollywood poster style
A hyper-realistic close-up of a giant ramen bowl with a tiny glowing UFO slowly descending onto the noodles, steam rising, rich broth reflections, surreal but delicious-looking detail
An ultra-detailed robot sitting in a quiet garden carefully repairing a broken flower stem with tiny mechanical tools, soft sunlight, beautiful depth of field, metallic and organic textures blending naturally
A photorealistic macro shot of a fragile soap bubble floating in the air, but inside it is a vivid swirling galaxy with stars and nebulae, high detail, cosmic glow, razor-sharp reflections on bubble surface
a gigantic neon-colored snake coiled around a disco stage, mirror-ball scales reflecting rainbow lights, people dancing below, hyper-realistic glitter particles floating through the air
相关模型
README
Z-Image-Turbo LoRA — 6B-parameter, ultra-fast text-to-image with custom styles
Z-Image-Turbo LoRA is a personalised version of Tongyi-MAI’s 6B-parameter Z-Image-Turbo model. It keeps the same 8-step, ultra-fast sampler and low VRAM footprint, while letting you plug in up to three LoRA adapters to inject your own styles, characters, or brand identity into each generation.
Ultra-fast generation with LoRA personalisation
Where many diffusion models need dozens of steps, Z-Image-Turbo LoRA stays aggressively optimised around 8 sampling steps. On top of that, it adds LoRA hooks so you can steer the visual style without retraining the base model—perfect for interactive products, dashboards, and large-scale backends that still need a branded look.
Why it looks so good
• Photorealistic output at speed Generates high-fidelity, realistic images suitable for product photos, hero banners, and UI visuals—now with your own LoRA styles layered on top.
• Bilingual prompts and text Understands prompts in English and Chinese, and can render multilingual on-image text, ideal for cross-market campaigns and UI screenshots.
• LoRA-powered customisation Attach up to 3 LoRAs per request to add a specific art style, character look, or brand aesthetics without touching the base weights.
• Low-latency, low-step design Only 8 function evaluations per image deliver extremely low latency, ideal for chatbots, configuration tools, design assistants, and any “type → image” workflow.
• Friendly VRAM footprint Runs well in 16 GB VRAM environments, reducing hardware costs and making local or edge deployments more realistic—even with LoRAs enabled.
• Scales for bulk generation The efficient sampler keeps large jobs—catalogues, continuous feeds, or mass thumbnail generation—practical, even when every image uses one or more LoRAs.
• Reproducible generations A controllable seed parameter lets you recreate previous images or generate small, controlled variations for brand safety and experimentation.
How to use
-
prompt – natural-language description of the scene, style, and any on-image text (English or Chinese).
-
size (width / height) – choose the output resolution that fits your use case.
-
seed – set to -1 for random results, or use a fixed integer to make outputs reproducible.
-
loras – optional list of up to three LoRA adapters:
-
path – a LoRA identifier such as
<owner>/<model-name>or a direct .safetensors URL. -
scale – numeric strength for that LoRA; higher values apply a stronger stylistic effect.
You can click “Add Item” in the loras panel to add 1–3 LoRAs. They are combined during generation, so a single prompt can mix, for example, a character LoRA, a style LoRA, and a brand-colour LoRA.
For detailed, step-by-step guidance on finding, uploading, and using LoRAs on WaveSpeedAI, see our LoRA tutorials How to use LoRA.
Pricing
Simple per-image billing:
- $0.01 per generated image
Try more models and compare
-
stability-ai/sdxl-lora – Stability AI’s SDXL LoRA hub, offering a wide range of ready-made styles and subjects for fast, lightweight customisation on top of the SDXL base model.
-
wavespeed-ai/qwen-image/edit-plus-lora – Qwen Image Edit Plus with LoRA support, combining strong semantic understanding with style-controllable, localised image editing.
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wavespeed-ai/flux-2-dev/edit-lora – FLUX.2 [dev] Edit enhanced with LoRA adapters, enabling prompt-based image editing that can also match specific art styles, characters, or brand looks.
Reference
Z Image Turbo Lora API — Quick start
Grab a WaveSpeedAI API key, then call POST https://api.wavespeed.ai/api/v3/wavespeed-ai/z-image/turbo-lora with your input as JSON. The endpoint returns a prediction id; poll the prediction endpoint until status flips to completed, then read the output URL from data.outputs[0]. Examples for Z Image Turbo Lora below.
HTTP example
# Submit the prediction
curl -X POST "https://api.wavespeed.ai/api/v3/wavespeed-ai/z-image/turbo-lora" \
-H "Content-Type: application/json" \
-H "Authorization: Bearer $WAVESPEED_API_KEY" \
-d '{
"prompt": "A cinematic shot of a city at sunset, soft golden light",
"size": "1024*1024",
"seed": -1,
"output_format": "jpeg",
"enable_sync_mode": false,
"enable_base64_output": false
}'
# Response includes a prediction id. Poll for the result:
curl -X GET "https://api.wavespeed.ai/api/v3/predictions/{request_id}/result" \
-H "Authorization: Bearer $WAVESPEED_API_KEY"
# When status is "completed", read the output from data.outputs[0].Node.js example
// npm install wavespeed
const WaveSpeed = require('wavespeed');
const client = new WaveSpeed(); // reads WAVESPEED_API_KEY from env
const result = await client.run("wavespeed-ai/z-image/turbo-lora", {
"prompt": "A cinematic shot of a city at sunset, soft golden light",
"size": "1024*1024",
"seed": -1,
"output_format": "jpeg",
"enable_sync_mode": false,
"enable_base64_output": false
});
console.log(result.outputs[0]); // → URL of the generated outputPython example
# pip install wavespeed
import wavespeed
output = wavespeed.run(
"wavespeed-ai/z-image/turbo-lora",
{
"prompt": "A cinematic shot of a city at sunset, soft golden light",
"size": "1024*1024",
"seed": -1,
"output_format": "jpeg",
"enable_sync_mode": false,
"enable_base64_output": false
}
)
print(output["outputs"][0]) # → URL of the generated outputZ Image Turbo Lora API — Frequently asked questions
What is the Z Image Turbo Lora API?
Z Image Turbo Lora is a WaveSpeedAI model for AI inference, exposed as a REST API on WaveSpeedAI. Z-Image-Turbo LoRA (6B) enables ultra-fast text-to-image generation with external LoRA support. Generate photorealistic images in sub-second latency while applying up to 3 LoRAs for custom styles. Ready-to-use REST API, best performance, no coldstarts, affordable pricing. You can call it programmatically or try it from the playground above.
How do I call the Z Image Turbo Lora API?
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 prediction endpoint until status flips to "completed", then read the output URL from the result. The playground generates a ready-to-paste code sample in Python, JavaScript, or cURL for whatever inputs you've set. Full request/response shape is documented at https://wavespeed.ai/docs/docs-api/wavespeed-ai/z-image-turbo-lora.
How much does Z Image Turbo Lora cost per run?
Z Image Turbo Lora starts at $0.010 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.
What inputs does Z Image Turbo Lora accept?
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/z-image-turbo-lora.
How long does Z Image Turbo Lora take to generate?
Average end-to-end generation time on WaveSpeedAI is around 9 seconds per request — measured across recent runs. Queue time scales with global demand; live status is visible in the prediction record.
Can I use Z Image Turbo Lora outputs commercially?
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.