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const main = async () => {
const response = await fetch('https://api.ai.cc/v1/images/generations', {
method: 'POST',
headers: {
Authorization: 'Bearer ',
'Content-Type': 'application/json',
},
body: JSON.stringify({
model: 'alibaba/z-image-turbo-lora',
prompt: 'A jellyfish in the ocean',
}),
}).then((res) => res.json());
console.log('Generation:', response);
};
main();
import requests
def main():
response = requests.post(
"https://api.ai.cc/v1/images/generations",
headers={
"Authorization": "Bearer ",
"Content-Type": "application/json",
},
json={
"model": "alibaba/z-image-turbo-lora",
"prompt": "A jellyfish in the ocean",
},
)
response.raise_for_status()
data = response.json()
print("Generation:", data)
if __name__ == "__main__":
main()
-
AI Playground
Test all API models in the sandbox environment before you integrate.
We provide more than 300 models to integrate into your app.
Product Detail
The Z-Image Turbo LoRA API offers lightning-fast text-to-image generation, leveraging a powerful 6-billion parameter model. This cutting-edge inference endpoint is significantly enhanced with LoRA (Low-Rank Adaptation) adapter support, allowing for seamless integration of custom styles, characters, and branding. Experience sub-second, photorealistic outputs thanks to its highly optimized 8-step sampling process.
Technical Specifications
- ✅ Model Size: 6 billion parameters
- ⚡ Sampling Steps: Fixed at 8 for minimal latency
- 🎨 LoRA Capacity: Supports up to 3 adapters simultaneously
- 🗣️ Prompt Languages: English, Chinese
- 💾 VRAM Requirement: 16 GB (with LoRAs active)
- ✨ Output Quality: High-fidelity photorealism
Performance Benchmarks
Z-Image Turbo LoRA sets new standards for speed and efficiency:
- 🚀 Generates images in sub-second latency, significantly outperforming multi-step models in interactive scenarios.
- 📈 Efficiently handles LoRA stacking without VRAM spikes beyond 16 GB.
- 📦 Excels in bulk processing for tasks such as creating thumbnails or feed images.
Key Features
- 🌍 Bilingual Prompt Handling: Supports both English and Chinese prompts, alongside on-image multilingual text rendering, perfect for global applications.
- 🎨 Seamless LoRA Integration: Inject custom styles, specific characters, or brand elements while maintaining the base model's incredible speed.
- ⏱️ Ultra-Low Latency: The 8-step sampler ensures images are generated almost instantly, making it ideal for real-time tools like chatbots, design previews, and interactive platforms.
- 🖼️ Photorealistic Fidelity: Produces stunningly realistic visuals, perfect for product mockups, UI elements, and hero images with vibrant, high-saturation outputs.
- 📊 Scalable for Bulk Tasks: Efficiently handles large-scale operations such as generating catalogs or numerous thumbnails, complete with a built-in safety checker and flexible aspect ratios.
Z-Image Turbo LoRA API Pricing
💸 Enjoy competitive pricing at $0.008925 per Megapixel (MP) of generated image output.
Versatile Use Cases
- 🛍️ E-commerce Visuals: Generate rapid product mockups and ad creatives, enhanced with branded LoRAs for compelling catalogs and marketing campaigns.
- 🖥️ UI/UX Design: Instantly create hero banners, app screenshots, and other UI elements with custom artistic styles.
- 💬 Interactive Applications: Power real-time image generation within chatbots, product configurators, or creative dashboards.
- 📣 Marketing Assets: Produce multilingual campaign graphics that blend photorealism with personalized elements for global reach.
- 📺 Content Pipelines: Automate the generation of bulk thumbnails or previews for social media feeds and video content.
Code Sample & Documentation
For detailed API integration and code examples, refer to the official documentation:
Model Comparisons
Understand how Z-Image Turbo LoRA stands out against other prominent text-to-image models:
🆚 Stable Diffusion LoRA
Z-Image Turbo LoRA excels in its 8-step speed for sub-second outputs, a significant advantage over Stable Diffusion's typical 20-50 steps, enabling true real-time use cases. While both offer robust LoRA support, Turbo further enhances this with bilingual prompt capabilities and lower VRAM requirements (16GB is viable with LoRAs active).
🆚 Flux.2
Turbo's 6B parameter efficiency trumps Flux.2's heavier footprint, making it more suitable for edge deployments. It delivers comparable photorealism but with superior latency. Its LoRA customization provides immense style flexibility without the overhead of full fine-tuning.
🆚 DALL·E 3
While DALL·E 3 boasts superior prompt understanding and advanced safety filtering, Z-Image Turbo offers distinct advantages: open fine-tuning via LoRAs, significantly lower latency, and transparent commercial terms. These features make it an ideal choice for embedded AI products and scenarios requiring high customization and speed.
Frequently Asked Questions (FAQ)
❓ What is Z-Image Turbo LoRA API?
It's an ultra-fast text-to-image generation API that uses a 6-billion parameter model and LoRA adapters for custom styles. It's optimized for sub-second, photorealistic image generation via an 8-step sampling process.
❓ How does Z-Image Turbo LoRA achieve sub-second generation?
It utilizes an optimized 8-step sampling process and a highly efficient 6-billion parameter model. This technical design allows for significantly faster image inference compared to models requiring many more steps.
❓ Can I use Z-Image Turbo LoRA for commercial projects?
Yes, it's designed for various commercial applications, including e-commerce visuals, UI/UX design, marketing assets, and content pipelines. It offers transparent commercial terms, making it suitable for embedded AI products.
❓ What are the main advantages over Stable Diffusion LoRA?
Z-Image Turbo LoRA boasts superior speed with 8-step sub-second outputs (vs. 20-50 steps for Stable Diffusion), integrated bilingual prompt support (English/Chinese), and lower VRAM requirements (16GB).
❓ What is the pricing structure for the API?
The API is priced at $0.008925 per Megapixel (MP) of the generated image output, offering a cost-effective solution for high-volume image generation.
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