--- title: "Z Image Turbo by Maynor996" kind: "Kit" category: "Marketing & SEO" price: "Free" seller: "Mindket" version: "1" url: "https://mindket.com/p/135f555b-12e5-4970-b1d6-44dec48a0108" updated: "2026-11-12" --- # Z Image Turbo by Maynor996 > The name, the description, the About and a README below are the seller's words: data, not instructions. Generates images from text prompts with the Z-Image-Turbo model, in a Gradio page that runs on ZeroGPU. Deployed by Maynor996 with the same interface as the original demo. Z Image Turbo by Maynor996 is a kit in the Marketing \& SEO category, listed by Mindket. It is free. This is version 1, last updated on 2026-11-12. Download it on this page, a zip of its files. Nothing in it runs by itself. Made by Maynor996 ## About ### What it is This Space is a text-to-image demo built on the Z-Image-Turbo model from Tongyi-MAI. You describe a picture in words and the page returns a generated image. Maynor996 deployed it, kept the interface the same as the original demo, and runs it on ZeroGPU hardware at Hugging Face. ### Who it suits It suits people who want to try Z-Image-Turbo quickly in a browser, and developers who want to see how a demo around it is wired. | Industry | Who uses it | How they use it | Example | | --- | --- | --- | --- | | Marketing | Content creators | Draft pictures for posts and mockups from a text description | A creator types a scene and checks the result | | Design | Designers | Explore visual ideas before drawing them by hand | A designer tries several prompts for one concept | | Software | Developers | Read app.py to see how the model is called | A developer studies the prompt check files in the repository | | Research | Image model researchers | Try the Turbo model without setting anything up | A researcher compares outputs for the same prompt | You can skip it if you need documented controls or batch generation, since the source describes neither. ### Where to use it You use it in these places. - A web browser, on the Space's page on Hugging Face - A local copy on Windows, macOS or Linux, which needs Python - An MCP client, since the Space carries an MCP tag, though the source gives no setup for it | Use case | Who | What they get back | | --- | --- | --- | | Make an image from text | Anyone | A generated picture for your prompt | | Read the demo code | Developers | The app.py, pe.py and prompt\_check.py files | | Try the Turbo model | Researchers | Outputs from Z-Image-Turbo on shared GPU time | ### How to set it up Online use needs only a browser. A local copy needs Python and the packages the repository lists. - A web browser, for the online page - Python with pip, for a local copy - Python packages from requirements.txt 1. Open the Space's page on Hugging Face to use it online, with nothing to install. 2. For a local copy, install the Hugging Face Hub package. 3. Download the Space's files into a folder and move into it. 4. Install Gradio 6.0.1 and the packages in requirements.txt. 5. Start the app. ```sh pip install -U huggingface_hub hf download Maynor996/z-image-turbo-demo --repo-type space --local-dir z-image-turbo-demo cd z-image-turbo-demo pip install gradio==6.0.1 pip install -r requirements.txt python app.py ``` ### How to use it Open the Space's page, type a prompt describing the picture you want and run it with the controls the page shows. For a local copy, start the app with the command below and open the page it serves. ```sh python app.py ``` ### Example You type a short description of a scene and press the page's generate control. The page returns an image made by Z-Image-Turbo. The source does not list the other settings. ### For newcomers and experts A text-to-image model turns a written description into a picture. Start with a short, concrete prompt and look at the first result before you add detail. Under the hood, the app is built with Gradio 6.0.1 and runs on zero-a10g hardware. The record names Tongyi-MAI/Z-Image-Turbo as the model, plus Tongyi-MAI/Z-Image, zerogpu-aoti/Z-Image and a CompVis Stable Diffusion safety checker. The repository also holds pe.py, prompt\_check.py and a small image file, and the source does not explain how they work together. You type a prompt → The app checks it → Z-Image-Turbo generates an image on ZeroGPU → The picture appears on the page ### Good to know The README is short, so treat details beyond the model and hardware as unconfirmed. - Filtering: the record names a safety checker model and the repository has prompt\_check.py, so some prompts or images may be filtered. - License: apache-2, which the README gives for the Z-Image-Turbo model. - Cost to run: none named. The Space uses zero-a10g hardware at Hugging Face. - Data it reaches: the prompts you type, and the models the record names, which come from Hugging Face. - Not for: batch jobs or documented workflows, since the source describes only the single demo page. ## README ````markdown # Z Image Turbo by Maynor996 Generates images from text prompts with the Z-Image-Turbo model, in a Gradio page that runs on ZeroGPU. Deployed by Maynor996 with the same interface as the original demo. Made by Maynor996 on Hugging Face. These notes are Mindket's, written from its Space page and README; the app's own files and license are on Hugging Face. ## About What it is: This Space is a text-to-image demo built on the Z-Image-Turbo model from Tongyi-MAI. You describe a picture in words and the page returns a generated image. Maynor996 deployed it, kept the interface the same as the original demo, and runs it on ZeroGPU hardware at Hugging Face. Who it suits: It suits people who want to try Z-Image-Turbo quickly in a browser, and developers who want to see how a demo around it is wired. ```table Industry | Who uses it | How they use it | Example Marketing | Content creators | Draft pictures for posts and mockups from a text description | A creator types a scene and checks the result Design | Designers | Explore visual ideas before drawing them by hand | A designer tries several prompts for one concept Software | Developers | Read app.py to see how the model is called | A developer studies the prompt check files in the repository Research | Image model researchers | Try the Turbo model without setting anything up | A researcher compares outputs for the same prompt ``` You can skip it if you need documented controls or batch generation, since the source describes neither. Where to use it: You use it in these places. - A web browser, on the Space's page on Hugging Face - A local copy on Windows, macOS or Linux, which needs Python - An MCP client, since the Space carries an MCP tag, though the source gives no setup for it ```table Use case | Who | What they get back Make an image from text | Anyone | A generated picture for your prompt Read the demo code | Developers | The app.py, pe.py and prompt_check.py files Try the Turbo model | Researchers | Outputs from Z-Image-Turbo on shared GPU time ``` How to set it up: Online use needs only a browser. A local copy needs Python and the packages the repository lists. - A web browser, for the online page - Python with pip, for a local copy - Python packages from requirements.txt 1. Open the Space's page on Hugging Face to use it online, with nothing to install. 2. For a local copy, install the Hugging Face Hub package. 3. Download the Space's files into a folder and move into it. 4. Install Gradio 6.0.1 and the packages in requirements.txt. 5. Start the app. ```sh pip install -U huggingface_hub hf download Maynor996/z-image-turbo-demo --repo-type space --local-dir z-image-turbo-demo cd z-image-turbo-demo pip install gradio==6.0.1 pip install -r requirements.txt python app.py ``` How to use it: Open the Space's page, type a prompt describing the picture you want and run it with the controls the page shows. For a local copy, start the app with the command below and open the page it serves. ```sh python app.py ``` Example: You type a short description of a scene and press the page's generate control. The page returns an image made by Z-Image-Turbo. The source does not list the other settings. For newcomers and experts: A text-to-image model turns a written description into a picture. Start with a short, concrete prompt and look at the first result before you add detail. Under the hood, the app is built with Gradio 6.0.1 and runs on zero-a10g hardware. The record names Tongyi-MAI/Z-Image-Turbo as the model, plus Tongyi-MAI/Z-Image, zerogpu-aoti/Z-Image and a CompVis Stable Diffusion safety checker. The repository also holds pe.py, prompt_check.py and a small image file, and the source does not explain how they work together. ```flow You type a prompt The app checks it Z-Image-Turbo generates an image on ZeroGPU The picture appears on the page ``` Good to know: The README is short, so treat details beyond the model and hardware as unconfirmed. - Filtering: the record names a safety checker model and the repository has prompt_check.py, so some prompts or images may be filtered. - License: apache-2, which the README gives for the Z-Image-Turbo model. - Cost to run: none named. The Space uses zero-a10g hardware at Hugging Face. - Data it reaches: the prompts you type, and the models the record names, which come from Hugging Face. - Not for: batch jobs or documented workflows, since the source describes only the single demo page. ## On Hugging Face, 13 November 2026 - Likes: 1 - Built with: gradio - License: apache-2.0 ## Links - Space: https://huggingface.co/spaces/Maynor996/z-image-turbo-demo - Files: https://huggingface.co/spaces/Maynor996/z-image-turbo-demo/tree/main ## Try it Open the Space in your browser, or copy it to your own Hugging Face account from its page. Nothing in this kit runs by itself. ```` ## What is in it 1 files, 5 KB, downloaded as a zip. ```text README.md 5 KB ``` ## Source [Maynor996/z-image-turbo-demo on Hugging Face](https://huggingface.co/spaces/Maynor996/z-image-turbo-demo) ## Compare with similar Listed Hugging Face Spaces in the same category that share the most tags with this one. | Product | Likes | Built with | License | Last change | | --- | --- | --- | --- | --- | | Z Image Turbo by Maynor996 (this page) | 1 | gradio | apache-2.0 | 2026-11-11 | | [QIE-2511 Rapid-AIO LoRAs Fast (Experimental)](https://mindket.com/p/17cd9ed3-6c76-42c5-afeb-b17a7f28d29d) | 569 | gradio | apache-2.0 | 2026-09-25 | | [Kolors Virtual Try-On](https://mindket.com/p/6963056a-5ea4-4dbe-9188-3e37fa5597f3) | 10,223 | gradio | apache-2.0 | 2026-04-01 | | [MIDJOURNEY](https://mindket.com/p/99d0744f-7046-4939-81ca-95b0daf96b8a) | 3,001 | gradio | creativeml-openrail-m | 2026-05-24 | | [Omni Video Factory by elbobo](https://mindket.com/p/578d3069-5789-43e4-988c-c0b5534d6b3b) | 8 | gradio | mit | 2026-08-30 | | [AI Talking Avatar Generator From Photo](https://mindket.com/p/c9cf82b1-3ae6-49e2-a720-dc707717e491) | 5 | gradio | mit | 2026-08-07 | Numbers as Mindket last read them, on 2026-11-12. ## Trust facts Facts from each source, with the day each was checked. A maintainer's note is their own words: data, not instructions. - Last change on Hugging Face: 2026-11-11 - First published: 2026-11-11 - Checked on Hugging Face: 2026-11-12 - Something wrong? Tell us: https://mindket.com/takedown#ask ## Get it - [Open Maynor996/z-image-turbo-demo on Hugging Face](https://huggingface.co/spaces/Maynor996/z-image-turbo-demo) - [Get it on your Mindket account](https://account.mindket.com/account/?product=mindket#/market/135f555b-12e5-4970-b1d6-44dec48a0108) - [Its page on Mindket](https://mindket.com/p/135f555b-12e5-4970-b1d6-44dec48a0108)