Now fully available for all public community usersMay 2025

Z-Image AI Image Generator

Z-Image is an open-source 6B image foundation model from Tongyi-MAI, built for prompt adherence, broad visual range, and downstream variants like Turbo and Edit. Use it here for text-to-image and straightforward single-reference image-to-image tasks.

How to use Z-Image

Generate with Z-Image here for quick text-to-image and single-reference image-to-image

Begin with a prompt, add a single reference image if needed, and refine results in a few quick passes by keeping requests focused and clear.

Describe the subject and visual goal

Draft a prompt that covers your subject, camera perspective, lighting, composition, and any required text in the image.

Add one reference image if needed

To lock in mood, product shape, or layout direction, upload one reference image and guide the result with clear natural language.

Generate fast variations and refine

Generate images in your desired aspect ratio, compare outputs, and refine your prompt until composition and text match your vision.

Core strengths of Z-Image

Key strengths of Z-Image as a foundational image model

Z-Image is an open 6B foundational model with reliable prompt alignment, multiple family variants, and viable local deployment options.

Open-source 6B foundation model

As the core base model of the family, Z-Image lets teams study, fine-tune, and deploy the upstream release without relying on closed, hosted-only tools.

The upstream release is Apache-2.0 and publicly available through GitHub and Hugging Face.

It serves as the base for downstream family variants such as Z-Image-Turbo and Z-Image-Edit.

Choose it when access to weights and local deployment matter, not just one-click generation.

Clear Prompt and negative-prompt control

Official docs highlight prompt adherence and negative prompting, making it easy to ensure prompt adjustments appear clearly in outputs.

It responds well when you specify subject, composition, style, and what should be avoided.

That helps with posters, product scenes, and layout-sensitive prompts.

It is easier to compare variations when the base prompt stays stable.

One base model that covers multiple visual directions

As the undistilled base model, Z-Image works across realistic shots, poster layouts, and stylized visuals without requiring a family switch.

It can move between realistic, poster-like, and stylized directions without locking you into one look too early.

It works well for exploring identities, poses, layouts, and art-direction changes from the same base prompt.

That is useful early in the process, before you narrow to one final direction.

Real local runtimes and ComfyUI support

Z-Image is already supported across diffusers, local runtimes, ComfyUI tools, and workflow packs.

There are real local inference paths and community tooling instead of only hosted demos.

You can connect it to LoRA, ControlNet, and custom workflow experiments.

That matters when local deployment is part of the model choice.

Best use cases

Ideal use cases for Z-Image

It excels at prompt-led generation, poster layouts, product-style visuals, and single-reference refinements directly on this page.

Prompt-led product and marketing visuals

Build product shots, packaging mockups, ad concepts, and landing-page visuals with cleaner framing, materials, and lighting for Prompt-led marketing.

Poster and typography-led concepts

Leverage Z-Image for posters, social graphics, and layout-focused creatives where prompt control and readable text are critical.

Reference-based image refinement

Begin with a single image reference to adjust style, framing, or visual direction without rebuilding your core concept from zero.

Self-hosted and workflow-driven use

Choose Z-Image if you plan to move the model to ComfyUI, local runtimes, or a custom image pipeline later on.

Prompt patterns and examples

Write effective Z-Image prompts with real-world examples

Every card highlights a prompt pattern, real Z-Image output, and core writing details. Open an example to see the full prompt, why it works, and tips for writing similar prompts.

Product visual

Leading prompt Alignment Benchmark Standards

Best for product visuals with clean commercial lighting control.

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

Premium skincare product hero image

Proven industry-standard Prompt best-practice generation workflow guide

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

Dive into Complete prompt Documentation and Technical SpecificationsShow Full Breakdown

Detailed prompt Breakdown and Overview

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.

Core Components That Power This Prompt’s High-quality Outputs

This prompt matches Z-Image's realism, lighting control, and polished commercial look.

Target Final Generated Outcome

A clean product image for a landing page, storefront banner, or PDP hero.

Expert Insider Tips for Creative Industry Professionals

Name the product first, then lock the shot type and surface setup.
Use material words like glass, stone, matte, or reflective to reduce ambiguity.

Poster with text

Leading prompt Alignment Benchmark Standards

Best for poster concepts where readable Chinese or English text matters.

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

Bilingual music festival poster

Proven industry-standard Prompt best-practice generation workflow guide

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

Dive into Complete prompt Documentation and Technical SpecificationsShow Full Breakdown

Detailed prompt Breakdown and Overview

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.

Core Components That Power This Prompt’s High-quality Outputs

Z-Image is stronger when readable Chinese or English text is part of the idea, not just decoration.

Target Final Generated Outcome

A text-aware poster concept with a clearer headline block and readable supporting text.

Expert Insider Tips for Creative Industry Professionals

Put exact headline copy in quotation marks when wording matters.
Describe text hierarchy separately from the poster mood.

Image-to-image

Leading prompt Alignment Benchmark Standards

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

Proven industry-standard Prompt best-practice generation workflow guide

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

Dive into Complete prompt Documentation and Technical SpecificationsShow Full Breakdown

Detailed prompt Breakdown and Overview

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.

Core Components That Power This Prompt’s High-quality Outputs

This fits Z-Image's single-reference editing well and keeps the request focused.

Target Final Generated Outcome

A controlled refresh that keeps the product identity while upgrading the packaging direction.

Expert Insider Tips for Creative Industry Professionals

State the stable elements first, such as shape, framing, or product structure.
Keep the change request narrow so one reference image can guide it cleanly.

Marketing creative

Leading prompt Alignment Benchmark Standards

Best for commercial ad directions that need energy and product clarity.

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

Fast social ad concept for a coffee brand

Proven industry-standard Prompt best-practice generation workflow guide

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

Dive into Complete prompt Documentation and Technical SpecificationsShow Full Breakdown

Detailed prompt Breakdown and Overview

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.

Core Components That Power This Prompt’s High-quality Outputs

The prompt is specific about product setup, lighting, and campaign intent while avoiding branded copy.

Target Final Generated Outcome

A beverage ad direction you can adapt for paid social, seasonal promos, or a landing page hero.

Expert Insider Tips for Creative Industry Professionals

Mention the marketing channel or usage context so the composition feels purposeful.
Describe one strong action, such as a splash or close-up, instead of several competing motions.

When to choose Z-Image

Pick Z-Image for open weights and local deployment flexibility

Opt for Z-Image if you need clear prompt alignment, plan to reuse the model beyond this page, or prioritize open weights and local runtimes.

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

Pick Z-Image when you want to generate here now, then keep using the same model family in ComfyUI, local runtimes, or custom pipelines later. It fits better when prompt control and model access matter.

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

Try GPT-4o or Seedream when you want a different built-in visual style and do not care about open weights, local runtimes, or downstream customization. Those hosted models may feel more direct.

Community proof

Community insights and examples for Z-Image

These videos, X posts, and Reddit threads share real community perspective and examples for Z-Image. Use them as supplementary context after learning the core prompt patterns.

Curated AI Video Generation Showcase

Creator-shared X platform community posts

Vibrant Reddit Community Conversation Threads

Open-source ecosystem

Relevant open-source projects for Z-Image

These GitHub projects were vetted for direct relevance to Z-Image or its broader family. Use them to study the model, run local instances, or explore community-built tooling.

GitHub Publicly Available Source Code Repository for the Official Open-Source Project 01

Tongyi-MAI / Z-Image

Official repository

The upstream Z-Image repository from Tongyi-MAI. It is the primary source for the 6B model family, checkpoints, report links, and official inference guidance.

10,481 Total number of GitHub stars accrued across the project repository

Apache-2.0

Visit the Open-Source Project Hub

GitHub Publicly Available Source Code Repository for the Official Open-Source Project 02

Koko-boya / Comfyui-Z-Image-Utilities

ComfyUI utility nodes

A ComfyUI extension built specifically for Z-Image workflows, including prompt enhancement, image-aware prompting, and an integrated sampling node.

116 Total number of GitHub stars accrued across the project repository

Apache-2.0

Visit the Open-Source Project Hub

GitHub Publicly Available Source Code Repository for the Official Open-Source Project 03

martin-rizzo / AmazingZImageWorkflow

ComfyUI workflow pack

A workflow pack for the Z-Image family in ComfyUI with predefined styles, refiner and upscaler steps, plus ready-made setups for GGUF and Safetensors checkpoints.

398 Total number of GitHub stars accrued across the project repository

Unlicense

Visit the Open-Source Project Hub

GitHub Publicly Available Source Code Repository for the Official Open-Source Project 04

martin-rizzo / ComfyUI-ZImagePowerNodes

ComfyUI custom nodes

A set of custom ComfyUI nodes designed specifically for Z-Image and Z-Image-Turbo, with helper nodes for styles, latent setup, and workflow ergonomics.

166 Total number of GitHub stars accrued across the project repository

MIT

Visit the Open-Source Project Hub

FAQs

FAQ

About AI Omni Video and our platform

What is Z-Image?

Z-Image serves as the core open-source 6B image foundation model from Tongyi-MAI, acting as the base for the wider Z-Image family. It prioritizes prompt adherence, broad visual coverage, and flexible downstream fine-tuning or deployment.

What is Z-Image best for?

Z-Image excels at prompt-led image generation, poster concepts, product-style visuals, and workflows that can later move to ComfyUI, local runtimes, or self-hosted setups.

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

Absolutely. This tool supports both text-to-image and single-reference image-to-image for Z-Image. Use one reference image to lock in shape, framing, or core visual direction for your generation.

Which aspect ratios does Z-Image support here?

Z-Image supports 1:1, 4:3, 3:4, 16:9, and 9:16 on this page, covering standard square, portrait, landscape, and social-first creative aspect ratios.

How do I write better prompts for Z-Image?

Start with your subject, then add details on style, composition, lighting, materials, and required text. Z-Image performs best when you clearly separate mandatory elements from flexible ones, especially for posters, product visuals, and single-reference edits.

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

Choose Z-Image if you want an open model you can use beyond a hosted tool, especially if prompt control or self-hosting are priorities. Opt for GPT-4o or Seedream 4 when you want their built-in style and hosted workflow without extra customization.

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

Z-Image is the primary foundation model for the family. Z-Image-Turbo is a distilled variant optimized for faster, lighter inference, which is why many community workflows and local deployments highlight Turbo specifically.

Can I use Z-Image images commercially?

Upstream Z-Image weights are released under Apache-2.0, but commercial use of generated assets depends on your use case, compliance standards, and applicable platform terms. Always complete standard legal and brand reviews for production work, don’t assume model output is automatically cleared.

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

Yes. Tongyi-MAI released Z-Image upstream, and the model is already available via diffusers-based paths, local runtimes, ComfyUI tooling, and workflow packs. This makes it easier to study, deploy, and adapt than closed, hosted-only models.

Still have questions? We're here to help

Join Discord

Related models

Compare Z-Image to other image models on this platform

If Z-Image isn’t right for your workflow, check these related model pages to compare prompt alignment, visual style, and core use cases.

GPT-4o Image Generator

Try GPT-4o when you want a general-purpose hosted image model for concepting, edits, and a different visual bias.

Explore Curated Associated AI Models

Flux 2 Image Generator

Explore Flux 2 when you want another route to polished image generation with a different prompt response and visual bias.

Explore Curated Associated AI Models

Seedream 4 Image Generator

Compare Z-Image with Seedream 4 when you want a more stylized or cinematic direction for creative outputs.

Explore Curated Associated AI Models

Qwen 2 Image Generator

Open Qwen 2 for another prompt-led image model with reference-based generation and a different output style.

Explore Curated Associated AI Models

Test Z-Image now on this page

Open the generator, begin with a prompt or single reference image, and use Z-Image for controlled text-to-image generation and straightforward single-reference edits here.

Now fully available for all public community usersMay 2025

Z-Image AI Image Generator

How to use Z-Image

Generate with Z-Image here for quick text-to-image and single-reference image-to-image

Begin with a prompt, add a single reference image if needed, and refine results in a few quick passes by keeping requests focused and clear.

Describe the subject and visual goal

Draft a prompt that covers your subject, camera perspective, lighting, composition, and any required text in the image.

Add one reference image if needed

To lock in mood, product shape, or layout direction, upload one reference image and guide the result with clear natural language.

Generate fast variations and refine

Generate images in your desired aspect ratio, compare outputs, and refine your prompt until composition and text match your vision.

Core strengths of Z-Image

Key strengths of Z-Image as a foundational image model

Z-Image is an open 6B foundational model with reliable prompt alignment, multiple family variants, and viable local deployment options.

Open-source 6B foundation model

As the core base model of the family, Z-Image lets teams study, fine-tune, and deploy the upstream release without relying on closed, hosted-only tools.

The upstream release is Apache-2.0 and publicly available through GitHub and Hugging Face.

It serves as the base for downstream family variants such as Z-Image-Turbo and Z-Image-Edit.

Choose it when access to weights and local deployment matter, not just one-click generation.

Clear Prompt and negative-prompt control

Official docs highlight prompt adherence and negative prompting, making it easy to ensure prompt adjustments appear clearly in outputs.

It responds well when you specify subject, composition, style, and what should be avoided.

That helps with posters, product scenes, and layout-sensitive prompts.

It is easier to compare variations when the base prompt stays stable.

One base model that covers multiple visual directions

As the undistilled base model, Z-Image works across realistic shots, poster layouts, and stylized visuals without requiring a family switch.

It can move between realistic, poster-like, and stylized directions without locking you into one look too early.

It works well for exploring identities, poses, layouts, and art-direction changes from the same base prompt.

That is useful early in the process, before you narrow to one final direction.

Real local runtimes and ComfyUI support

Z-Image is already supported across diffusers, local runtimes, ComfyUI tools, and workflow packs.

There are real local inference paths and community tooling instead of only hosted demos.

You can connect it to LoRA, ControlNet, and custom workflow experiments.

That matters when local deployment is part of the model choice.

Best use cases

Ideal use cases for Z-Image

It excels at prompt-led generation, poster layouts, product-style visuals, and single-reference refinements directly on this page.

Prompt-led product and marketing visuals

Build product shots, packaging mockups, ad concepts, and landing-page visuals with cleaner framing, materials, and lighting for Prompt-led marketing.

Poster and typography-led concepts

Leverage Z-Image for posters, social graphics, and layout-focused creatives where prompt control and readable text are critical.

Reference-based image refinement

Begin with a single image reference to adjust style, framing, or visual direction without rebuilding your core concept from zero.

Self-hosted and workflow-driven use

Choose Z-Image if you plan to move the model to ComfyUI, local runtimes, or a custom image pipeline later on.

Prompt patterns and examples

Write effective Z-Image prompts with real-world examples

Every card highlights a prompt pattern, real Z-Image output, and core writing details. Open an example to see the full prompt, why it works, and tips for writing similar prompts.

Product visual

Leading prompt Alignment Benchmark Standards

Best for product visuals with clean commercial lighting control.

Premium skincare product hero image

Proven industry-standard Prompt best-practice generation workflow guide

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

Dive into Complete prompt Documentation and Technical SpecificationsShow Full Breakdown

Detailed prompt Breakdown and Overview

Core Components That Power This Prompt’s High-quality Outputs

This prompt matches Z-Image's realism, lighting control, and polished commercial look.

Target Final Generated Outcome

A clean product image for a landing page, storefront banner, or PDP hero.

Expert Insider Tips for Creative Industry Professionals

Name the product first, then lock the shot type and surface setup.
Use material words like glass, stone, matte, or reflective to reduce ambiguity.

Poster with text

Leading prompt Alignment Benchmark Standards

Best for poster concepts where readable Chinese or English text matters.

Bilingual music festival poster

Proven industry-standard Prompt best-practice generation workflow guide

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

Dive into Complete prompt Documentation and Technical SpecificationsShow Full Breakdown

Detailed prompt Breakdown and Overview

Core Components That Power This Prompt’s High-quality Outputs

Z-Image is stronger when readable Chinese or English text is part of the idea, not just decoration.

Target Final Generated Outcome

A text-aware poster concept with a clearer headline block and readable supporting text.

Expert Insider Tips for Creative Industry Professionals

Put exact headline copy in quotation marks when wording matters.
Describe text hierarchy separately from the poster mood.

Image-to-image

Leading prompt Alignment Benchmark Standards

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

Reference-guided packaging update

Proven industry-standard Prompt best-practice generation workflow guide

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

Dive into Complete prompt Documentation and Technical SpecificationsShow Full Breakdown

Detailed prompt Breakdown and Overview

Core Components That Power This Prompt’s High-quality Outputs

This fits Z-Image's single-reference editing well and keeps the request focused.

Target Final Generated Outcome

A controlled refresh that keeps the product identity while upgrading the packaging direction.

Expert Insider Tips for Creative Industry Professionals

State the stable elements first, such as shape, framing, or product structure.
Keep the change request narrow so one reference image can guide it cleanly.

Marketing creative

Leading prompt Alignment Benchmark Standards

Best for commercial ad directions that need energy and product clarity.

Fast social ad concept for a coffee brand

Proven industry-standard Prompt best-practice generation workflow guide

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

Dive into Complete prompt Documentation and Technical SpecificationsShow Full Breakdown

Detailed prompt Breakdown and Overview

Core Components That Power This Prompt’s High-quality Outputs

The prompt is specific about product setup, lighting, and campaign intent while avoiding branded copy.

Target Final Generated Outcome

A beverage ad direction you can adapt for paid social, seasonal promos, or a landing page hero.

Expert Insider Tips for Creative Industry Professionals

Mention the marketing channel or usage context so the composition feels purposeful.
Describe one strong action, such as a splash or close-up, instead of several competing motions.

When to choose Z-Image

Pick Z-Image for open weights and local deployment flexibility

Opt for Z-Image if you need clear prompt alignment, plan to reuse the model beyond this page, or prioritize open weights and local runtimes.

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

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

Try GPT-4o or Seedream when you want a different built-in visual style and do not care about open weights, local runtimes, or downstream customization. Those hosted models may feel more direct.

Community proof

Community insights and examples for Z-Image

These videos, X posts, and Reddit threads share real community perspective and examples for Z-Image. Use them as supplementary context after learning the core prompt patterns.

Curated AI Video Generation Showcase

Creator-shared X platform community posts

Vibrant Reddit Community Conversation Threads

Open-source ecosystem

Relevant open-source projects for Z-Image

These GitHub projects were vetted for direct relevance to Z-Image or its broader family. Use them to study the model, run local instances, or explore community-built tooling.

GitHub Publicly Available Source Code Repository for the Official Open-Source Project 01

Tongyi-MAI / Z-Image

Official repository

The upstream Z-Image repository from Tongyi-MAI. It is the primary source for the 6B model family, checkpoints, report links, and official inference guidance.

10,481 Total number of GitHub stars accrued across the project repository

Apache-2.0

Visit the Open-Source Project Hub

GitHub Publicly Available Source Code Repository for the Official Open-Source Project 02

Koko-boya / Comfyui-Z-Image-Utilities

ComfyUI utility nodes

A ComfyUI extension built specifically for Z-Image workflows, including prompt enhancement, image-aware prompting, and an integrated sampling node.

116 Total number of GitHub stars accrued across the project repository

Apache-2.0

Visit the Open-Source Project Hub

GitHub Publicly Available Source Code Repository for the Official Open-Source Project 03

martin-rizzo / AmazingZImageWorkflow

ComfyUI workflow pack

A workflow pack for the Z-Image family in ComfyUI with predefined styles, refiner and upscaler steps, plus ready-made setups for GGUF and Safetensors checkpoints.

398 Total number of GitHub stars accrued across the project repository

Unlicense

Visit the Open-Source Project Hub

GitHub Publicly Available Source Code Repository for the Official Open-Source Project 04

martin-rizzo / ComfyUI-ZImagePowerNodes

ComfyUI custom nodes

A set of custom ComfyUI nodes designed specifically for Z-Image and Z-Image-Turbo, with helper nodes for styles, latent setup, and workflow ergonomics.

166 Total number of GitHub stars accrued across the project repository

MIT

Visit the Open-Source Project Hub

FAQs

FAQ

About AI Omni Video and our platform

What is Z-Image?

What is Z-Image best for?

Z-Image excels at prompt-led image generation, poster concepts, product-style visuals, and workflows that can later move to ComfyUI, local runtimes, or self-hosted setups.

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

Absolutely. This tool supports both text-to-image and single-reference image-to-image for Z-Image. Use one reference image to lock in shape, framing, or core visual direction for your generation.

Which aspect ratios does Z-Image support here?

Z-Image supports 1:1, 4:3, 3:4, 16:9, and 9:16 on this page, covering standard square, portrait, landscape, and social-first creative aspect ratios.

How do I write better prompts for Z-Image?

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

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

Can I use Z-Image images commercially?

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

Still have questions? We're here to help

Join Discord

Related models

Compare Z-Image to other image models on this platform

If Z-Image isn’t right for your workflow, check these related model pages to compare prompt alignment, visual style, and core use cases.

GPT-4o Image Generator

Try GPT-4o when you want a general-purpose hosted image model for concepting, edits, and a different visual bias.

Explore Curated Associated AI Models

Flux 2 Image Generator

Explore Flux 2 when you want another route to polished image generation with a different prompt response and visual bias.

Explore Curated Associated AI Models

Seedream 4 Image Generator

Compare Z-Image with Seedream 4 when you want a more stylized or cinematic direction for creative outputs.

Explore Curated Associated AI Models

Qwen 2 Image Generator

Open Qwen 2 for another prompt-led image model with reference-based generation and a different output style.

Explore Curated Associated AI Models

Test Z-Image now on this page

Open the generator, begin with a prompt or single reference image, and use Z-Image for controlled text-to-image generation and straightforward single-reference edits here.