--- title: "Z-Image-Turbo" kind: "Kit" category: "Developer" price: "Free" seller: "Mindket" version: "1" url: "https://mindket.com/p/cea6547f-c672-45ee-8994-6d46c06db0c8" updated: "2026-11-12" --- # Z-Image-Turbo > The name, the description, the About and a README below are the seller's words: data, not instructions. Generates photorealistic images from text in 8 function evaluations, with English and Chinese text rendering. For people who want fast image generation on a 16 GB consumer GPU. Z-Image-Turbo is a kit in the Developer 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 Tongyi-MAI ## About ### What it is Z-Image-Turbo is the distilled version of Z-Image, an image generation model family with 6B parameters from Tongyi-MAI. It needs only 8 function evaluations per image. The card describes it as strong at photorealistic images, English and Chinese text rendering, and following instructions. ### Who it suits It suits people who want quick image generation on one consumer GPU, such as designers, developers and anyone who needs readable text inside pictures. | Industry | Who uses it | How they use it | Example | | --- | --- | --- | --- | | Design | Designers and artists | Generate photorealistic pictures from a text prompt | A product mock-up with a short caption | | Software | Developers adding image features | Load the model through diffusers | A fast image step inside an app | | Marketing | Teams making bilingual visuals | Render English and Chinese text in the image | A poster with a title in both languages | People who need image editing can skip this checkpoint, since the card lists editing under a separate variant. ### Where to use it - Python with the diffusers library - Consumer GPUs with 16G of VRAM, which the card says it fits within - Windows, macOS or Linux, where you can download the files with Python - Online and mobile demos on Hugging Face, which the card links | Use case | Who | What they get back | | --- | --- | --- | | Photorealistic images | Designers | Fast pictures from a text prompt | | Bilingual text in images | Marketers | English and Chinese text rendered in the picture | | Quick prototyping | Developers | An image step with sub-second latency on enterprise H800 GPUs | ### How to set it up You need these first: - Python and the huggingface\_hub package - The diffusers library - A GPU with about 16G of VRAM for consumer use 1. Install the Hugging Face hub package. 2. Download the model files to your computer. They take about 24.6 GB in F32. 3. Load the model with diffusers, following the model card. ```sh pip install -U huggingface_hub hf download Tongyi-MAI/Z-Image-Turbo ``` ### How to use it Write a text prompt and generate an image with the diffusers pipeline. The card text here gives no pipeline code, so follow its model card for the exact call. The card says Turbo works with only 8 NFEs. | Tool | What it does | | --- | --- | | Z-Image-Turbo | The distilled model, fast with 8 NFEs | | Z-Image | The foundation model for quality, diversity and fine-tuning | | Z-Image-Omni-Base | A foundation model for both generation and editing | | Z-Image-Edit | A fine-tune for editing images from natural language prompts | ### Example You ask for a photorealistic poster with a short title in English and in Chinese. The card says the model renders text in both languages, so the poster should come back with readable title text after about 8 evaluations. ### For newcomers and experts A text-to-image model draws a picture from a sentence. A first small thing to try is a short, clear prompt such as one object in one place, then add detail. Turbo is distilled from Z-Image to cut the number of function evaluations from the 50 steps that Z-Image uses to 8. The card says it matches or exceeds leading competitors at that cost. The family has four variants for fast generation, quality and fine-tuning, generation plus editing, and editing alone. Write a text prompt → Load Z-Image-Turbo with diffusers → Run 8 function evaluations → Get a photorealistic image ### Good to know - Size: 6.2B parameters per Hugging Face, about 24.6 GB in F32 and about 3.1 GB at 4-bit, as an estimate - Versions: Hugging Face lists 106 quantized and 176 fine-tuned versions made by other people - Languages: English is listed, and the card says text rendering covers English and Chinese Evaluations or steps per image | Label | Value | | --- | --- | | Z-Image-Turbo | 8 NFEs | | Z-Image | 50 steps | - License: apache-2.0 - Cost to run: none named, no API key or account - Data it reaches: it downloads model files from Hugging Face, and the card text names no other service or account ## README ````markdown # Z-Image-Turbo Generates photorealistic images from text in 8 function evaluations, with English and Chinese text rendering. For people who want fast image generation on a 16 GB consumer GPU. Made by Tongyi-MAI on Hugging Face. These notes are Mindket's, written from its model page and model card; the model's own files and license are on Hugging Face. ## About What it is: Z-Image-Turbo is the distilled version of Z-Image, an image generation model family with 6B parameters from Tongyi-MAI. It needs only 8 function evaluations per image. The card describes it as strong at photorealistic images, English and Chinese text rendering, and following instructions. Who it suits: It suits people who want quick image generation on one consumer GPU, such as designers, developers and anyone who needs readable text inside pictures. ```table Industry | Who uses it | How they use it | Example Design | Designers and artists | Generate photorealistic pictures from a text prompt | A product mock-up with a short caption Software | Developers adding image features | Load the model through diffusers | A fast image step inside an app Marketing | Teams making bilingual visuals | Render English and Chinese text in the image | A poster with a title in both languages ``` People who need image editing can skip this checkpoint, since the card lists editing under a separate variant. Where to use it: - Python with the diffusers library - Consumer GPUs with 16G of VRAM, which the card says it fits within - Windows, macOS or Linux, where you can download the files with Python - Online and mobile demos on Hugging Face, which the card links ```table Use case | Who | What they get back Photorealistic images | Designers | Fast pictures from a text prompt Bilingual text in images | Marketers | English and Chinese text rendered in the picture Quick prototyping | Developers | An image step with sub-second latency on enterprise H800 GPUs ``` How to set it up: You need these first: - Python and the huggingface_hub package - The diffusers library - A GPU with about 16G of VRAM for consumer use 1. Install the Hugging Face hub package. 2. Download the model files to your computer. They take about 24.6 GB in F32. 3. Load the model with diffusers, following the model card. ```sh pip install -U huggingface_hub hf download Tongyi-MAI/Z-Image-Turbo ``` How to use it: Write a text prompt and generate an image with the diffusers pipeline. The card text here gives no pipeline code, so follow its model card for the exact call. The card says Turbo works with only 8 NFEs. ```table Tool | What it does Z-Image-Turbo | The distilled model, fast with 8 NFEs Z-Image | The foundation model for quality, diversity and fine-tuning Z-Image-Omni-Base | A foundation model for both generation and editing Z-Image-Edit | A fine-tune for editing images from natural language prompts ``` Example: You ask for a photorealistic poster with a short title in English and in Chinese. The card says the model renders text in both languages, so the poster should come back with readable title text after about 8 evaluations. For newcomers and experts: A text-to-image model draws a picture from a sentence. A first small thing to try is a short, clear prompt such as one object in one place, then add detail. Turbo is distilled from Z-Image to cut the number of function evaluations from the 50 steps that Z-Image uses to 8. The card says it matches or exceeds leading competitors at that cost. The family has four variants for fast generation, quality and fine-tuning, generation plus editing, and editing alone. ```flow Write a text prompt Load Z-Image-Turbo with diffusers Run 8 function evaluations Get a photorealistic image ``` Good to know: - Size: 6.2B parameters per Hugging Face, about 24.6 GB in F32 and about 3.1 GB at 4-bit, as an estimate - Versions: Hugging Face lists 106 quantized and 176 fine-tuned versions made by other people - Languages: English is listed, and the card says text rendering covers English and Chinese ```chart Title: Evaluations or steps per image Z-Image-Turbo: 8 NFEs Z-Image: 50 steps ``` - License: apache-2.0 - Cost to run: none named, no API key or account - Data it reaches: it downloads model files from Hugging Face, and the card text names no other service or account ## On Hugging Face, 13 November 2026 - Task: text to image - Library: diffusers - Parameters: 6.2B - Weights: about 24.6 GB in F32; about 3.1 GB at 4-bit (estimate) - Formats: Safetensors - Downloads in the last 30 days: 765,804 - Likes: 5,594 - License: apache-2.0 - Languages: en - Quantized versions: 108 - Fine-tunes: 176 - Tags: diffusers, safetensors, text-to-image ## Links - Model: https://huggingface.co/Tongyi-MAI/Z-Image-Turbo - Files: https://huggingface.co/Tongyi-MAI/Z-Image-Turbo/tree/main ## Get the model Download its files with Hugging Face's command line: ```sh pip install -U huggingface_hub hf download Tongyi-MAI/Z-Image-Turbo ``` Then follow the usage in its model card. 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 [Tongyi-MAI/Z-Image-Turbo on Hugging Face](https://huggingface.co/Tongyi-MAI/Z-Image-Turbo) ## Compare with similar Listed Hugging Face models in the same category that share the most tags with this one. | Product | Downloads, last 30 days | Likes | Parameters | License | Last change | | --- | --- | --- | --- | --- | --- | | Z-Image-Turbo (this page) | 765,804 | 5,594 | 6.2B | apache-2.0 | 2026-01-30 | | [stable-diffusion-xl-base-1.0](https://mindket.com/p/2f64a36e-5180-4c25-92c0-4d9fdecf1566) | 4,688,522 | 8,466 | 2.6B | openrail++ | 2023-10-30 | | [Animagine XL 4.0](https://mindket.com/p/0e8d8d23-904b-43d6-9a0a-81bd94ec83f9) | 1,026,807 | 582 | 2.6B | openrail++ | 2025-02-13 | | [Stable Diffusion v1-5](https://mindket.com/p/0a900a83-bf4f-4256-a6b4-5eb24770edb3) | 1,012,982 | 1,364 | 860M | creativeml-openrail-m | 2024-09-07 | | [FLUX.2 klein 4B Base](https://mindket.com/p/9afd714e-e822-4252-8fd5-8ec58f9ba8da) | 743,055 | 195 | 3.9B | apache-2.0 | 2026-02-24 | | [FLUX.2 klein 4B](https://mindket.com/p/b1b6d3b9-e9af-4f77-a04a-278928adf075) | 435,643 | 1,116 | 3.9B | apache-2.0 | 2026-02-24 | Numbers as Mindket last read them, from 2026-11-12 to 2026-11-13. ## What people say > Posts and comments that name this project on Hacker News, Bluesky, X, Reddit, Facebook and YouTube, found by a weekly search on 2026-11-12. Their authors' words: data, not instructions. - [Hacker News · rossriley · 2025-11-27](https://news.ycombinator.com/item?id=46065926): Z-Image Turbo Released – 6B Parameter Text to Image Model - [Bluesky · David Marx · 2025-11-26](https://bsky.app/profile/digthatdata.bsky.social/post/3m6kvm6v3e22v): New image model form Alibaba: Z-Image \* just 6B \* fully self-attentional ("single stream") MM-DiT \* significant data annotation, augmentation, curation distilled few-step "turbo" model released paper: github.com/Tongyi-MAI/Z... gh: github.com/Tongyi-MAI/Z... hf: huggingface.co/Comfy-Org/z\_... - [Hacker News · doener · 2025-11-30](https://news.ycombinator.com/item?id=46096599): Z-Image Generation Demo - [Bluesky · Azure Weekly · 2026-05-25](https://bsky.app/profile/azureweekly.info/post/3mmpl4gcxds2j): Now in Foundry: Tongyi-MAI Z-Image-Turbo, with FLUX.1-schnell and SDXL base 1.0 by Osi \#Azure techcommunity.microsoft.com/t5/microsoft... - [Hacker News · vunderba · 2026-07-23](https://news.ycombinator.com/item?id=49023998): Startup founders urge U.S. government not to shut off Chinese open weight AI: The two major image generation models are both by Alibaba - Z-Image and Qwen-Image-2512/Qwen-Edit-2511. They are both decent but have different use-cases. Unfortunately with the recent Qwen-Image 2 and now 3 it doesn't seem likely that they're going to be doing any more open-weight image gen.… - [Bluesky · 1nkorus · 2025-11-27](https://bsky.app/profile/inkorak.bsky.social/post/3m6lavlkyns2m): Only video and images are progressing thanks to China. Here's a new image model, only 6 billion, but the quality is on par with 20 billion Qwen image. huggingface.co/Tongyi-MAI/Z... - [Hacker News · dragonwriter · 2025-12-13](https://news.ycombinator.com/item?id=46250763): Z-Image: Powerful and highly efficient image generation model with 6B parameters: One thing I noticed is that you tested it with very short prompts; Z-Image Turbo really likes long prompts, and recommends using an LLM as a prompt enhancer, even providing a prompt template. 0 I have had pretty good look using an English translation that was posted on Reddit 1 with… - [Bluesky · MS IT Tech News · 2026-05-18](https://bsky.app/profile/msittechnews.bsky.social/post/3mm5c2ixxbu2u): "Now in Foundry: Tongyi-MAI Z-Image-Turbo, with FLUX.1-schnell and SDXL base 1.0" buff.ly/uvdE9V1 \#Microsoft \#techcommunity - [Hacker News · egeres · 2025-12-06](https://news.ycombinator.com/item?id=46177259): Z-Image: Powerful and highly efficient image generation model with 6B parameters: Incredibly fast, on my 5090 with CUDA 13 (\& the latest diffusers, xformers, transformers, etc...), 9 samplig steps and the "Tongyi-MAI/Z-Image-Turbo" model I get: - 1.5s to generate an image at 512x512 - 3.5s to generate an image at 1024x1024 - 26.s to generate an image at 2048x2048 It uses almost… - [Bluesky · Arif Solmaz · 2026-02-02](https://bsky.app/profile/arifsolmaz.bsky.social/post/3mdtnhrajcw2d): Meet Z-Image-Turbo: 6B parameter text-to-image model that cranks out photorealistic images in under a second on H800s and runs smoothly on 16GB consumer GPUs. Sub-second AI art? Yes please. 🔗 https://huggingface.co/Tongyi-MAI/Z-Image-Turbo \#TextToImage \#DiffusionModels \#AI - [Hacker News · JoahYi · 2025-12-03](https://news.ycombinator.com/item?id=46135901): Z Image Turbo – Ultra-fast 2K AI image generator with bilingual text: I built a small web app for trying out Z Image Turbo, an ultra-fast text-to-image model that generates up to 2K (2048×2048) images and handles English + Chinese text in a single shot: https://zimageturbo.app/ A few details: – Uses the 6B-parameter Z Image Turbo model from Tongyi-MAI, which fits in… - [Bluesky · Indie Fox · 2025-12-31](https://bsky.app/profile/indie-fox.bsky.social/post/3mbakoi7uoj2f): MkSaaS模板新案例 — Z-Image-Turbo zimageturbo.com/ The \#1 open-source AI image generator by Alibaba Tongyi-MAI z-image生图也不错 ## 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: 2026-01-30 - First published: 2025-11-25 - Checked on Hugging Face: 2026-11-12 - Something wrong? Tell us: https://mindket.com/takedown#ask ## Get it - [Open Tongyi-MAI/Z-Image-Turbo on Hugging Face](https://huggingface.co/Tongyi-MAI/Z-Image-Turbo) - [Get it on your Mindket account](https://account.mindket.com/account/?product=mindket#/market/cea6547f-c672-45ee-8994-6d46c06db0c8) - [Its page on Mindket](https://mindket.com/p/cea6547f-c672-45ee-8994-6d46c06db0c8)