Full Public ReleaseMay 2025

Z-Image AI Image Generator

Z-Image is an open-source 6B image foundation model developed by Tongyi-MAI. Engineered for consistent prompt adherence and broad creative versatility, it supports multiple production-ready downstream variants including Turbo and Edit. On this page, you can run both text-to-image and streamlined single-reference image-to-image workflows directly with no extra setup.

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Getting started with Z-Image

Run Z-Image right here for text-to-image and single-reference image-to-image

Begin by entering a prompt, optionally upload a reference image to guide your output, and refine your final result in just a few quick generations by keeping your request clear and focused.

Outline your core subject and creative goal

Draft your prompt including core subject, camera perspective, lighting style, composition layout, and any exact text you need included in the final image.

Add a reference image for creative guidance

If you need to retain an existing mood, product shape, or core layout direction, add your reference image and refine the output direction using natural language.

Generate variations and refine your final result

Generate multiple images in your preferred aspect ratio, compare different variations, and adjust your prompt until the composition and any included text match your creative vision.

Key strengths of Z-Image

What makes Z-Image stand out as a base image model

Z-Image is an open-source 6B foundation model, delivering reliable prompt responsiveness, multiple purpose-built family variants, and production-ready local deployment options.

Open-source 6B foundation model

Z-Image is the core base model of its family, allowing development teams to study, fine-tune, and deploy the public upstream release rather than being locked into a closed, hosted-only service.

The full upstream release is Apache-2.0 and is available publicly via both GitHub and Hugging Face.

It acts as the base model for all downstream family variants, including Z-Image-Turbo and Z-Image-Edit.

Select this model when access to full model weights and local deployment are priorities, beyond just quick one-click generation.

Prompt and negative-prompt control that delivers consistent results

The official model prioritizes strong prompt adherence and effective negative prompting, which ensures that prompt changes are clearly reflected in your final output.

The model responds consistently when you clearly specify your subject, composition, style, and any elements you want to exclude.

This reliability is especially useful for posters, product scenes, and layout-sensitive prompts.

Comparing output variations is simpler when the base prompt behavior remains stable across runs.

One base model that covers multiple visual directions

As the full undistilled base model, Z-Image can adapt to realistic photography, poster layouts, and a wide range of stylized creative directions without requiring you to switch to a different model family.

It can shift between realistic, graphic poster, and highly stylized creative directions without locking you into a single look early in your process.

It excels at exploring different character identities, poses, layouts, and art direction adjustments all starting from the same base prompt.

This flexibility is extremely valuable during early-stage creative work, before you narrow down to a single final direction.

Production local runtimes and ComfyUI support

Z-Image is already integrated across diffusers pipelines, local runtimes, ComfyUI utilities, and community-created workflow packs.

There are production-ready local inference paths and extensive community tooling available, not just limited hosted demos.

You can easily connect the model to LoRA, ControlNet, and custom experimental workflows.

This broad ecosystem support is a key advantage when local deployment is a core requirement for your project.

Top use cases

Use cases where Z-Image excels

This model is particularly well-suited for prompt-led generation, custom poster layouts, commercial product visuals, and single-reference image refinements when used on this page.

Prompt-led product and marketing visuals

Generate professional product shots, packaging mockups, ad concepts, and landing page hero visuals, with consistent control over framing, surface materials, and lighting.

Typography-led poster and graphic concepts

Use Z-Image to create event posters, social media graphics, and layout-focused creative projects where strong prompt control and readable text are core requirements.

Reference-based image refinement

Begin with an existing reference image and adjust its style, framing, or overall creative direction without having to rebuild your entire concept from scratch.

Self-hosted and workflow-driven use

Choose Z-Image for projects where you may eventually move the model into ComfyUI, local runtime environments, or a fully customized image generation pipeline.

Prompt best practices and examples

How to write better Z-Image prompts with real examples

Every example card below highlights a proven prompt structure, a real Z-Image output, and the specific writing choices that make it effective. Start by looking at the sample output, then expand the card to view the full prompt, learn why it works, and adapt the pattern to write your own prompts.

Product visual

Excellent prompt alignment

Ideal for product visuals that require precise control over clean, professional commercial lighting.

A premium skincare bottle photographed on a stone pedestal with soft studio light.

Premium skincare product hero image

Prompt composition

[product] + [camera angle] + [surface/background] + [lighting] + [commercial finish]

View full prompt analysisLoad More

Full prompt content

A premium glass skincare bottle on a light beige stone pedestal, soft directional studio lighting, subtle shadow, clean editorial composition, luxury e-commerce hero shot, minimal background, realistic reflections, high-end packaging photography.

Why this example works well

This prompt leans directly into Z-Image's strengths in realistic rendering, precise lighting control, and polished commercial output.

Desired output outcome

A polished product image ready for use as a landing page hero, storefront banner, or product detail page focal point.

Helpful usage tips

Lead with your product name, then define your shot type and background/surface setup.
Use specific material terms like glass, stone, matte, or reflective to cut down on output ambiguity.

Poster with text

Excellent prompt alignment

Ideal for poster concepts where clear, readable Chinese or English text is a core requirement.

A bilingual festival poster with a large Summer Pulse 2026 headline and bold Chinese text.

Bilingual music festival poster

Prompt composition

[poster subject] + [headline text] + [text language] + [layout hierarchy] + [background style]

View full prompt analysisLoad More

Full prompt content

Modern bilingual music festival poster, bold headline "Summer Pulse 2026", smaller Chinese subtitle "城市电子音乐节", black background with neon orange and cyan accents, clear visual hierarchy, centered headline block, dynamic but readable event poster design.

Why this example works well

Z-Image performs particularly well when readable Chinese or English text is a core part of your concept, not just an afterthought decoration.

Desired output outcome

A text-accurate poster concept with a well-defined headline block and easy-to-read supporting text.

Helpful usage tips

Wrap exact headline copy in quotation marks when the specific wording is critical to your design.
Describe your text layout hierarchy separately from the overall mood of the poster to reduce confusion.

Image-to-image

Excellent prompt alignment

Best for single-reference edits where the object identity should stay stable.

A matte white skincare pump bottle with sage green accents generated from a reference-driven packaging refresh prompt.

Reference-guided packaging update

Prompt composition

[what stays the same] + [what changes] + [new lighting/style/composition direction]

View full prompt analysisLoad More

Full prompt content

Keep the bottle shape, cap structure, and front-facing composition from the reference image. Change the packaging style to a modern matte white and sage green palette, softer studio light, cleaner premium skincare branding direction, more refined retail presentation.

Why this example works well

This approach aligns perfectly with Z-Image's single-reference editing capabilities and keeps your generation request focused and clear.

Desired output outcome

A controlled packaging update that retains your original product identity while shifting to an updated visual design direction.

Helpful usage tips

List the elements that need to stay stable first, such as object shape, framing, or product structure.
Keep your requested changes narrow and specific so your single reference image can guide the output cleanly.

Marketing creative

Excellent prompt alignment

Ideal for commercial ad concepts that require energetic composition and clear product focus.

An iced coffee ad visual with splashing cold brew on a sunny beach background.

Fast social ad concept for a coffee brand

Prompt composition

[subject] + [visual direction] + [composition] + [color / lighting] + [usage context]

View full prompt analysisLoad More

Full prompt content

Commercial iced coffee campaign visual, close-up cold brew cup with ice splash, premium coffee packaging beside the drink, bright summer daylight, beachside mood, energetic composition, crisp product photography, premium beverage advertising style, no logos, no brand names, clean packaging design.

Why this example works well

This prompt clearly specifies product setup, lighting style, and campaign intent, while avoiding unnecessary branded elements that would limit reuse.

Desired output outcome

A flexible beverage ad concept you can easily adapt for paid social campaigns, seasonal promotions, or a landing page hero section.

Helpful usage tips

Name your intended marketing channel or use case up front so the generated composition matches your needs.
Focus on one clear dynamic action, like a liquid splash or tight close-up, instead of including multiple conflicting motions.

When to choose Z-Image

Choose Z-Image when you want open weights and local deployment options

Choose Z-Image when you need prompt changes to be clearly reflected in your output, plan to reuse the model outside this page, or prioritize open model weights and local runtime support.

Choose Z-Image when you want one model you can keep using later

Pick Z-Image if you want to generate your images here today, then continue using the same model family in ComfyUI, local runtimes, or custom pipelines down the line. It is the better choice when prompt control and full model access are core priorities for your work.

Use another model when you want a hosted style out of the box

Try GPT-4o or Seedream if you want a unique pre-built visual style and do not need open weights, local deployment, or downstream customization. These hosted models often offer a more straightforward, out-of-the-box experience for quick generation.

Community examples

Community examples and outside discussion around Z-Image

Below you’ll find third-party videos, X posts, and Reddit discussions that share real community examples and independent perspective on Z-Image. These resources work best as supplementary reference after you’ve learned the core model capabilities and prompt patterns covered earlier on this page.

Example generated video outputs

Community shares from X

Reddit public community threads

Open-source ecosystem

Curated open-source projects for Z-Image

All GitHub projects below have been manually curated for direct relevance to Z-Image and its broader model family. Use these resources to study the model, run it locally on your own hardware, or explore how the community is building custom tools around it.

Project Repository 01

Tongyi-MAI / Z-Image

Official repository

This is the official upstream Z-Image repository published by Tongyi-MAI. It is the primary source for the 6B model family, including official checkpoints, research report links, and official inference guidance.

10,481 starred users

Apache-2.0

Visit project page

Project Repository 02

Koko-boya / Comfyui-Z-Image-Utilities

ComfyUI utility nodes

This is a community-built ComfyUI extension built exclusively for Z-Image workflows, with features including prompt enhancement, image-aware prompting, and a pre-built integrated sampling node.

116 starred users

Apache-2.0

Visit project page

Project Repository 03

martin-rizzo / AmazingZImageWorkflow

ComfyUI workflow pack

This is a complete workflow pack for the Z-Image model family in ComfyUI, including predefined style presets, pre-configured refiner and upscaler steps, and ready-to-use setups for both GGUF and Safetensors checkpoints.

398 starred users

Unlicense

Visit project page

Project Repository 04

martin-rizzo / ComfyUI-ZImagePowerNodes

ComfyUI custom nodes

This collection of custom ComfyUI nodes is built specifically for Z-Image and Z-Image-Turbo, including helper nodes for style management, latent space setup, and improved workflow usability.

166 starred users

MIT

Visit project page

FAQs

FAQ

About Kling 4 and our platform

What is Z-Image?

Z-Image is the open-source 6B image foundation model from Tongyi-MAI, acting as the core base layer for the entire Z-Image model family. It is optimized for strong prompt adherence, wide visual coverage, and flexible downstream use for fine-tuning and custom deployment.

What is Z-Image best for?

Z-Image shines for prompt-led image creation, event marketing posters, polished commercial product shots, and any project where you plan to eventually move your work to ComfyUI, local runtime environments, or other self-hosted infrastructure.

Does Z-Image support image-to-image here?

Yes, full support is available directly on this page. Z-Image natively supports both text-to-image and single-reference image-to-image workflows. Upload a single reference image any time you need to preserve existing object shapes, composition framing, or the overall creative direction of your work.

Which aspect ratios does Z-Image support here?

Z-Image currently supports 1:1, 4:3, 3:4, 16:9, and 9:16 on this platform, covering all common use cases from square social posts to vertical portrait, horizontal landscape, and every other popular creative format for digital content.

How do I write better prompts for Z-Image?

Start by clearly stating your core subject, then add specific details about style, camera composition, lighting, surface materials, and any exact text that needs to appear in the final image. Z-Image delivers the most consistent results when you clearly separate required elements from flexible preferences, which works especially well for posters, product shots, and one-reference edits.

When should I use Z-Image instead of GPT-4o or Seedream 4?

Choose Z-Image if you want an open-weight model that you can continue using outside of this hosted interface, particularly when reliable prompt control or self-hosting capability are priorities for your project. Opt for GPT-4o or Seedream 4 when you’re primarily looking for their unique built-in visual styles and a streamlined hosted workflow.

What is the difference between Z-Image and Z-Image-Turbo?

Z-Image is the full, original undistilled 6B foundation model. Z-Image-Turbo is a distilled variant from the same model family, optimized for much faster, lower-resource inference. This speed and efficiency make it a popular pick for community workflows and local deployments, which is why it is often referenced separately.

Can I use Z-Image images commercially?

The upstream Z-Image model weights are published under the Apache-2.0 license, but commercial use of any generated content still depends on your specific use case, internal review standards, and the platform terms applicable here. For commercial production work, always follow your standard legal and brand review processes, and do not assume any model output is automatically cleared for unrestricted commercial use.

Is Z-Image open-source and can it be self-hosted?

Yes, it is both fully open-source and self-hostable. Tongyi-MAI publicly released the Z-Image model upstream, and it is already integrated into diffusers-based pipelines, local runtime environments, ComfyUI tooling, and shared community workflow packs. This makes it far easier to study, deploy, and customize than closed, hosted-only models.

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Seedream 4 Image Generator

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Qwen Image 2 Image Generator

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Try Z-Image here

Open the generator, get started with a prompt or a single reference image, and use Z-Image for highly controllable text-to-image generation and straightforward single-reference edits directly on this page.

Z-Image AI Image Generator

Community examples and outside discussion around Z-Image