New Release

Unleash Sub-Second AI Image Generation

Name: Z-Image-Turbo: The Fastest AI Image Tool for Photorealistic Generation
Author: Z-Image-Turbo

Experience Z-Image-Turbo, the AI image tool by Tongyi-MAI, for photorealistic images generated in just 8 steps. Accessible on consumer GPUs.

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Runs on 16GB VRAM

Photorealistic Quality

Bilingual Text Rendering

Z-Image-Turbo showcasing photorealistic image generation.

Key Features of Z-Image-Turbo

Z-Image-Turbo delivers unmatched speed and quality in AI image generation.

Ultra-Fast Generation

Generate photorealistic images in just 8 NFEs, achieving sub-second inference latency on enterprise H800 GPUs. Experience unparalleled speed without sacrificing quality.

Consumer Hardware Compatible

Runs smoothly on consumer devices with 16GB VRAM, making professional-grade image generation accessible. No need for expensive data-center infrastructure.

Bilingual Text Rendering

Accurately render complex text in both English and Chinese within generated images, making it ideal for cross-market campaigns and multilingual content creation.

How Z-Image-Turbo Works

Generate high-quality images in a few simple steps.

Enter Your Prompt

Describe the image you want to generate in detail. Z-Image-Turbo uses prompt enhancement to understand your needs.

Generate in 8 Steps

Our model uses Scalable Single-Stream DiT architecture and Decoupled-DMD distillation to generate your image in just 8 NFEs.

Download & Use

Download your high-quality, photorealistic image and use it for your project. Enjoy professional-grade results with ease.

Frequently Asked Questions

Learn more about Z-Image-Turbo and its capabilities.

Z-Image-Turbo utilizes an ultra-efficient 8-step generation process (8 NFEs) combined with Decoupled-DMD distillation, allowing for sub-second inference on enterprise GPUs.

Yes! Z-Image-Turbo is designed to run comfortably on consumer devices with 16GB VRAM, making it accessible without data-center infrastructure.

Z-Image-Turbo achieves competitive performance against models like Nano Banana Pro and Seedream 4.0, while using significantly fewer parameters than models like FLUX.2 and Qwen-Image. It matches or exceeds Seedream 4.0 while using significantly fewer parameters and computational resources. This makes Z-Image-Turbo more practical for consumer hardware while maintaining comparable quality.

The guidance scale should be set to 0.0 for Z-Image-Turbo models as they are distilled specifically for this configuration.