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TEXT → IMAGE • CURATED • UPDATED JAN 1, 2026

Z-Image

Ultra-fast photorealistic image generation with bilingual text rendering

Generates high-quality photorealistic images from text prompts using Tongyi-MAI's Z-Image model with Single-Stream Diffusion Transformer (S3-DiT) architecture. Produces images in seconds with exceptional detail, lighting, and texture control. Features three variants: Z-Image-Turbo for ultra-fast generation, Z-Image-Base for community fine-tuning, and Z-Image-Edit for precise image editing. Excels at bilingual text rendering, accurately generating both Chinese and English text within images with commercial-grade quality.

Pricing Freemium
Platforms web, api
API Yes
Open Source Yes
Modalities Text → Image
Best For Best for Speed
Date Added 2026-01-01

Ultra-fast photorealistic generation with superior bilingual text rendering, making it ideal for designs requiring text-in-image accuracy.

Visit Z-Image website or access through API. Choose the appropriate variant: Z-Image-Turbo for speed, Z-Image-Base for customization, or Z-Image-Edit for image editing. Enter your text prompt, specifying any text you want rendered in the image. Generate images with fast iteration. For local deployment, download model weights from GitHub and set up the generation environment. The model is optimized for efficiency and can run on consumer GPUs.

Artificial Analysis Image Arena ·
1100 Elo
Rank #67 · 2026-07-23
1 Specify text content clearly in prompts for best bilingual rendering
2 Use Z-Image-Turbo for fastest generation when speed is priority
3 Leverage Z-Image-Edit for precise image modifications
4 Experiment with different aspect ratios for varied compositions
5 Use detailed prompts for photorealistic results
Website Model card GitHub Demo
GPT-Image-2 ComfyUI OpenArt Invoke Gemini Omni

Fast Photorealistic Image Generation

Generate high-quality photorealistic images quickly for design projects.

STEPS:
  1. Choose Z-Image-Turbo for fastest generation
  2. Write detailed prompt describing the scene and style
  3. Specify any text content you want in the image
  4. Generate and review results
  5. Iterate with refined prompts if needed
  6. Export final images for your project

Bilingual Text-in-Image Designs

Create designs with accurate Chinese and English text rendering.

STEPS:
  1. Include both Chinese and English text in your prompt
  2. Specify text placement and style preferences
  3. Generate images with text rendering
  4. Review text accuracy and readability
  5. Refine prompts for better text placement
  6. Export designs with accurate bilingual text
Freemium Free tier available

Free tier includes limited features. Paid plans unlock full access, higher usage limits, and commercial usage rights.

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Q

Is Z-Image free?

A

Z-Image offers a free tier with optional paid upgrades.

Q

Does Z-Image have an API?

A

Yes, Z-Image offers an API for programmatic integration.

Q

What is Z-Image best for?

A

Z-Image is best for Speed. Generates high-quality photorealistic images from text prompts using Tongyi-MAI's Z-Image model with Single-Stream Diffusion Transformer (S3-DiT) architecture. Ultra-fast photorealistic generation with superior bilingual text rendering, making it ideal for designs requiring text-in-image accuracy.

Q

What platforms does Z-Image support?

A

Z-Image supports web, api.

Q

Is Z-Image open source?

A

Yes, Z-Image is open source. You can access the source code on GitHub at https://github.com/Tongyi-MAI/Z-Image.

Q

How do I get started with Z-Image?

A

Visit Z-Image website or access through API. Choose the appropriate variant: Z-Image-Turbo for speed, Z-Image-Base for customization, or Z-Image-Edit for image editing. Enter your text prompt, specifying any text you want rendered in the image. Generate images with fast iteration. For local deployme...

Q

How do I generate images with Z-Image?

A

Z-Image creates images from text descriptions. Enter detailed prompts describing the image you want, including style, composition, colors, and subject matter. It excels at ultra-fast generation with minimal inference steps (8 steps).

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