--- title: "Z-Image" kind: "Kit" category: "Developer" price: "Free" seller: "Mindket" version: "1" url: "https://mindket.com/p/cd05eceb-d5d5-4ee5-9bd3-b309e116edf1" updated: "2026-11-12" --- # Z-Image > The name, the description, the About and a README below are the seller's words: data, not instructions. Generates images from a text description with a 6.2B-parameter model, the full undistilled base of the Z-Image family. Built for creators, researchers and developers who want close control over results. Z-Image 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 is a text to image model from Tongyi-MAI. You write a description and it returns a picture. It runs through the diffusers library and has 6.2B parameters. This is the full base model of the Z-Image family, not the speed-tuned Turbo version, so it suits people who want close control or a starting point for their own training. ### Who it suits Illustrators, researchers and developers who want an image model they can run and adapt on their own computer. | Industry | Who uses it | How they use it | Example | | --- | --- | --- | --- | | Art and design | Illustrators and concept artists | Describe a scene in a photo, anime or illustrated style | A character portrait in anime style | | Machine learning | Researchers | Use the undistilled model as a base for LoRA or ControlNet training | A LoRA trained for one visual style | | Software | Developers | Call the model from Python code through diffusers | A script that saves a picture for each prompt | | Marketing | Content makers | Draft visuals from a written brief | A lifestyle photo concept for a campaign | Anyone who only wants the fastest results, at eight steps, can skip it and look at Z-Image-Turbo instead. ### Where to use it - Windows, macOS or Linux with Python, as the download steps describe - Any Python script or notebook that uses the diffusers library - A browser, through the online demo the model card points to | Use case | Who | What they get back | | --- | --- | --- | | Prompt to picture | Anyone who can run Python | An image from a text description, from 512x512 up to 2048x2048 in total pixel area | | Negative prompting | Artists tuning a result | Fewer unwanted artifacts, by naming what to leave out | | Fine-tuning base | Researchers and community builders | A custom LoRA or ControlNet setup built on the base model | ### How to set it up - Python on Windows, macOS or Linux - Room for about 12.3 GB of weights in BF16 - A recent diffusers release, which the model card asks for - A CUDA GPU if you follow the card's example, which sends the model to cuda 1. Install the Hugging Face hub tools with pip. 2. Download the model files with the hf command. 3. Install the latest diffusers, as the model card describes. 4. Load the model in Python with ZImagePipeline. ```bash pip install -U huggingface_hub hf download Tongyi-MAI/Z-Image ``` ### How to use it In Python you load the pipeline, give it a prompt and save the picture it returns. The card recommends a guidance scale of 3.0 to 5.0 and 28 to 50 inference steps. ```python import torch from diffusers import ZImagePipeline pipe = ZImagePipeline.from_pretrained( "Tongyi-MAI/Z-Image", torch_dtype=torch.bfloat16, low_cpu_mem_usage=False, ) pipe.to("cuda") ``` | Tool | What it does | | --- | --- | | ZImagePipeline | Loads the Z-Image weights as a diffusers pipeline | | prompt | Describes the picture you want | | negative\_prompt | Names what to keep out, and may stay empty | | guidance scale | Recommended range is 3.0 to 5.0 | | inference steps | Recommended range is 28 to 50 | ### Example You ask for "two young women standing close together in front of a plain grey wall, both smiling at the camera, natural soft light, casual fashion photo". The model returns one image of that scene. The card's own example sets the height to 1280 and leaves the negative prompt empty. ### For newcomers and experts Start with the two download commands, then copy the short Python snippet and swap in a prompt of your own. Say plainly what you want to see, such as the place, the light and the mood. Leave the negative prompt empty on the first try. Z-Image is a single-stream diffusion transformer that is not distilled, so it supports full classifier-free guidance and negative prompts. It takes 28 to 50 steps where Turbo takes 8, and the card rates its diversity high and its fine-tuning support yes. The trade is slower generation, and the card rates Turbo higher on visual quality. Write a text prompt → Load ZImagePipeline in diffusers → Run 28 to 50 inference steps → Guidance scale steers the result → Get an image back ### Good to know - License: Apache 2.0 - Cost to run: none named, but you need your own hardware and about 12.3 GB of weights in BF16 - Data it reaches: downloads the model files from Hugging Face, and the card's example then runs on your own machine - Not for: fast eight-step generation, which the card gives to Z-Image-Turbo - Limits: the card lists no reinforcement learning stage for this version ## README ````markdown # Z-Image Generates images from a text description with a 6.2B-parameter model, the full undistilled base of the Z-Image family. Built for creators, researchers and developers who want close control over results. 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 is a text to image model from Tongyi-MAI. You write a description and it returns a picture. It runs through the diffusers library and has 6.2B parameters. This is the full base model of the Z-Image family, not the speed-tuned Turbo version, so it suits people who want close control or a starting point for their own training. Who it suits: Illustrators, researchers and developers who want an image model they can run and adapt on their own computer. ```table Industry | Who uses it | How they use it | Example Art and design | Illustrators and concept artists | Describe a scene in a photo, anime or illustrated style | A character portrait in anime style Machine learning | Researchers | Use the undistilled model as a base for LoRA or ControlNet training | A LoRA trained for one visual style Software | Developers | Call the model from Python code through diffusers | A script that saves a picture for each prompt Marketing | Content makers | Draft visuals from a written brief | A lifestyle photo concept for a campaign ``` Anyone who only wants the fastest results, at eight steps, can skip it and look at Z-Image-Turbo instead. Where to use it: - Windows, macOS or Linux with Python, as the download steps describe - Any Python script or notebook that uses the diffusers library - A browser, through the online demo the model card points to ```table Use case | Who | What they get back Prompt to picture | Anyone who can run Python | An image from a text description, from 512x512 up to 2048x2048 in total pixel area Negative prompting | Artists tuning a result | Fewer unwanted artifacts, by naming what to leave out Fine-tuning base | Researchers and community builders | A custom LoRA or ControlNet setup built on the base model ``` How to set it up: - Python on Windows, macOS or Linux - Room for about 12.3 GB of weights in BF16 - A recent diffusers release, which the model card asks for - A CUDA GPU if you follow the card's example, which sends the model to cuda 1. Install the Hugging Face hub tools with pip. 2. Download the model files with the hf command. 3. Install the latest diffusers, as the model card describes. 4. Load the model in Python with ZImagePipeline. ```bash pip install -U huggingface_hub hf download Tongyi-MAI/Z-Image ``` How to use it: In Python you load the pipeline, give it a prompt and save the picture it returns. The card recommends a guidance scale of 3.0 to 5.0 and 28 to 50 inference steps. ```python import torch from diffusers import ZImagePipeline pipe = ZImagePipeline.from_pretrained( "Tongyi-MAI/Z-Image", torch_dtype=torch.bfloat16, low_cpu_mem_usage=False, ) pipe.to("cuda") ``` ```table Tool | What it does ZImagePipeline | Loads the Z-Image weights as a diffusers pipeline prompt | Describes the picture you want negative_prompt | Names what to keep out, and may stay empty guidance scale | Recommended range is 3.0 to 5.0 inference steps | Recommended range is 28 to 50 ``` Example: You ask for "two young women standing close together in front of a plain grey wall, both smiling at the camera, natural soft light, casual fashion photo". The model returns one image of that scene. The card's own example sets the height to 1280 and leaves the negative prompt empty. For newcomers and experts: Start with the two download commands, then copy the short Python snippet and swap in a prompt of your own. Say plainly what you want to see, such as the place, the light and the mood. Leave the negative prompt empty on the first try. Z-Image is a single-stream diffusion transformer that is not distilled, so it supports full classifier-free guidance and negative prompts. It takes 28 to 50 steps where Turbo takes 8, and the card rates its diversity high and its fine-tuning support yes. The trade is slower generation, and the card rates Turbo higher on visual quality. ```flow Write a text prompt Load ZImagePipeline in diffusers Run 28 to 50 inference steps Guidance scale steers the result Get an image back ``` Good to know: - License: Apache 2.0 - Cost to run: none named, but you need your own hardware and about 12.3 GB of weights in BF16 - Data it reaches: downloads the model files from Hugging Face, and the card's example then runs on your own machine - Not for: fast eight-step generation, which the card gives to Z-Image-Turbo - Limits: the card lists no reinforcement learning stage for this version ## On Hugging Face, 13 November 2026 - Task: text to image - Library: diffusers - Parameters: 6.2B - Weights: about 12.3 GB in BF16; about 3.1 GB at 4-bit (estimate) - Formats: Safetensors - Downloads in the last 30 days: 45,594 - Likes: 1,302 - License: apache-2.0 - Languages: en - Quantized versions: 28 - Fine-tunes: 72 - Tags: diffusers, safetensors, text-to-image ## Links - Model: https://huggingface.co/Tongyi-MAI/Z-Image - Files: https://huggingface.co/Tongyi-MAI/Z-Image/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 ``` 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 on Hugging Face](https://huggingface.co/Tongyi-MAI/Z-Image) ## 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 (this page) | 45,594 | 1,302 | 6.2B | apache-2.0 | 2026-01-28 | | [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 | | [Z-Image-Turbo](https://mindket.com/p/cea6547f-c672-45ee-8994-6d46c06db0c8) | 765,804 | 5,594 | 6.2B | apache-2.0 | 2026-01-30 | | [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 | 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 2026-11-13. Their authors' words: data, not instructions. - [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 · 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 · 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 · Sequoia Alexander 🌲✨ 林灵 (she/her) 🏳️⚧️✨🦄 · 2026-01-22](https://bsky.app/profile/sequoia.farm/post/3mczwfiapsk2a): Interesting image generator model with "reasoning". In the first pair, it recognizes this as a classroom style arithmetic and draws a scene of this problem being solved. In the second pair, the reasoning model correctly identifies lines from a famous Chinese poem. huggingface.co/Tongyi-MAI/Z... - [Hacker News · vunderba · 2025-12-06](https://news.ycombinator.com/item?id=46177584): Z-Image: Powerful and highly efficient image generation model with 6B parameters: Thanks for the heads up. I just checked the site through several browsers and proxying through a VPN. There's no typo and it properly links to: https://github.com/Tongyi-MAI/Z-Image Screenshot of site with network tools open to indicate link https://imgur.com/a/FZDz0K2 EDIT: It's possible that… - [Bluesky · 猫国防 · 2025-12-06](https://bsky.app/profile/maoguofang.bsky.social/post/3m7dhbpvegk25): Z-Image 图像生成基础开源模型 官网： tongyi-mai.github.io/Z-Image-blog/ GitHub: github.com/Tongyi-MAI/Z... ModelScope: www.modelscope.cn/models/Tongy... HuggingFace: huggingface.co/Tongyi-MAI/Z... - [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 · Github Repositories · 2026-08-19](https://bsky.app/profile/sigmanor.net/post/3mth42ukmrh2l): https://github.com/Tongyi-MAI/Z-Image - [Hacker News · thih9 · 2025-12-06](https://news.ycombinator.com/item?id=46176197): Z-Image: Powerful and highly efficient image generation model with 6B parameters: As an AI outsider with a recent 24GB macbook, can I follow the quick start1 steps from the repo and expect decent results? How much time would it take to generate a single medium quality image? 1: https://github.com/Tongyi-MAI/Z-Image?tab=readme-ov-file\#-qu... - [Bluesky · Lewdlytree Studios · 2026-01-27](https://bsky.app/profile/lewdlytree.bsky.social/post/3mdgd5yamyc2p): Z-Image is out finally\! huggingface.co/Tongyi-MAI/Z... - [Hacker News · muglug · 2025-12-06](https://news.ycombinator.com/item?id=46175232): Z-Image: Powerful and highly efficient image generation model with 6B parameters: The demo PDF(https://github.com/Tongyi-MAI/Z-Image/blob/main/assets/Z-Ima...) has \~50 photos of attractive young women sitting/standing alone, and exactly two photos featuring young attractive men on their own. It's incredibly clear who the devs assume the target market is. - [Bluesky · Tom Dörr · 2026-01-09](https://bsky.app/profile/tom-doerr.bsky.social/post/3mbyfso6llx2t): https://github.com/Tongyi-MAI/Z-Image ## 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: 2026-01-28 - First published: 2026-01-23 - Checked on Hugging Face: 2026-11-12 - Something wrong? Tell us: https://mindket.com/takedown#ask ## Get it - [Open Tongyi-MAI/Z-Image on Hugging Face](https://huggingface.co/Tongyi-MAI/Z-Image) - [Get it on your Mindket account](https://account.mindket.com/account/?product=mindket#/market/cd05eceb-d5d5-4ee5-9bd3-b309e116edf1) - [Its page on Mindket](https://mindket.com/p/cd05eceb-d5d5-4ee5-9bd3-b309e116edf1)