New Release: S3-DiT Architecture

Z-Image-Turbo

Lightweight · Efficient · Practical

A text-to-image model with only 6B parameters. Through Single-Stream Diffusion Transformer architecture and distillation technology, it only needs 8 sampling steps to generate high-quality images.

Lightweight Architecture

Only about 6 billion (6B) parameters, much smaller than traditional large models, optimized for consumer-grade graphics cards.

8-Step Ultra-Fast Sampling

Through distillation optimization, only 8 inference steps are needed to generate high-quality images with sub-second response time.

Open Source Friendly

Apache-2.0 license, fully open weights, suitable for personal, team, and commercial production environments.

⚙️ Core Technical Features

S3-DiT Architecture

Adopts Single-Stream Diffusion Transformer, compared to traditional U-Net structure, significantly reduces computational and memory requirements while maintaining generation quality.

Bilingual & Text Rendering

Native support for Chinese + English Prompts. Excellent at accurately rendering text onto posters, UI mockups, or product packaging, solving the "garbled text" pain point of mainstream models.

Wide Hardware Compatibility

Achieves sub-second inference on H800/H100; runs smoothly on 16GB consumer graphics cards; supports Apple Silicon (M-series chips) quantized deployment.

ARCH: S3-DiT
STEPS: 8
CFG: BAKED-IN

Photorealistic Output Quality

Precise control of lighting and materials
Realistic photorealistic style
Excellent composition of characters, scenes, and UI

🎯 Applicable Scenarios

High Quality + High Efficiency + Bilingual Support

Advertising & Marketing

Generate product visuals, banners, support Chinese and English copy rendering, suitable for cross-language markets.

Game/Concept Art

Quickly iterate character sketches, scene settings, prop previews, low-cost production.

UI & Graphic Design

Create posters, social media graphics, UI mockups, perfect fusion of text and images.

Research & Education

Low resource requirements, suitable for individual developers, small teams, and academic experimental use.

👍 Core Advantages

High Efficiency: Few-step sampling (8 Steps) suitable for quick preview and iteration.
Low Threshold: 16GB VRAM or even less can run, no expensive clusters needed.
Bilingual Friendly: Native support for Chinese Prompts, better understanding of Chinese context.
Business Friendly: Apache-2.0 license, allows commercial use.
Balanced Quality: Excellent balance between speed and quality.

⚠️ Limitations & Notes

Extreme Quality: For ultra-high resolution or extremely complex scenes, stability may not be as good as heavy large models.
Prompt Threshold: Requires structured Prompts to maximize advantages.
Hardware Trap: Insufficient VRAM may cause extremely slow inference or failure.
Ecosystem Integration: Advanced control features like ControlNet may not be as mature as SDXL ecosystem currently.

🧑‍💻 Best Practices Guide

config.json
## Recommended Parameter Settings
steps: 8
guidance_scale: 0 // Turbo has built-in guidance, setting to 0 works better
resolution: "1024x1024" // 16GB VRAM
// If VRAM < 12GB, recommend reducing to 768x768
## Prompt Structure Formula
Prompt = [Subject] + [Scene/Environment] + [Lighting/Time] + [Style] + [Text Content (Optional)]

Lock Seed for A/B Testing

When creating product images or multiple versions of ads, it is recommended to lock the Seed and only adjust color or prop words in the Prompt to maintain consistent composition.

Don't Rely on Negative Prompt

Due to the characteristics of distillation training, Z-Image-Turbo is not sensitive to negative prompts. It is recommended to focus on writing good positive Prompts.

Mac User Benefits

Apple Silicon users can use quantized versions or MPS acceleration support to achieve smooth inference locally.