--- title: "Ming-Image-0.1-Design" kind: "Kit" category: "Developer" price: "Free" seller: "Mindket" version: "1" url: "https://mindket.com/p/9c09ce6a-012e-40ce-b14b-610da5874eda" updated: "2026-11-13" --- # Ming-Image-0.1-Design > The name, the description, the About and a README below are the seller's words: data, not instructions. A 6B text-to-image model from inclusionAI for interface mockups, infographics, posters and other text-heavy designs, with transparent-background (RGBA) output. Ming-Image-0.1-Design is a kit in the Developer category, listed by Mindket. It is free. This is version 1, last updated on 2026-11-13. Download it on this page, a zip of its files. Nothing in it runs by itself. Made by inclusionAI ## About ### What it is Ming-Image-0.1-Design is a text-to-image model of about 6B parameters from inclusionAI. It is tuned for designs that carry a lot of text, such as app interfaces, infographics and posters. It produces a whole composition in one go, and it can write RGBA images with a transparent background. ### Who it suits Designers, developers and content teams who want generated layouts and graphics with readable text, and who can run a GPU workflow from the command line. | Industry | Who uses it | How they use it | Example | | --- | --- | --- | --- | | Design | UI and UX designers | Draft interface mockups from a text prompt | A first-pass screen layout for a product idea | | Marketing | Content teams | Make posters and infographics | A campaign poster with a headline and short copy | | Software | App developers | Generate transparent assets for apps | An icon-style graphic on a clear background | | Presentations | Slide authors | Produce visuals for decks | A chart-style infographic for a report | You can skip it if you want a quick online generator, or if you have no CUDA GPU, since the validated setup needs a large one. ### Where to use it The model runs through the companion Ming-Image project and can be served with vLLM-Omni, on a machine with a CUDA GPU. - The companion Ming-Image command-line project - vLLM-Omni, which the card recommends for serving - Windows, macOS and Linux for the file download step - One CUDA GPU with plenty of memory for generation | Use case | Who | What they get back | | --- | --- | --- | | UI mockups | Designers | A generated screen design from a prompt | | Posters and infographics | Marketers | Text-rich visuals in one composition | | Transparent assets | Developers | RGBA images with no background to cut out | ### How to set it up You need Python, a CUDA GPU, and the companion Ming-Image project, which holds the installation and inference code. The card validates a single GPU with 80 GiB of memory. - Python with pip - The Ming-Image project from GitHub - A CUDA GPU, with 80 GiB validated by the card - About 12.3 GB of disk for the BF16 weights 1. Get the Ming-Image project from GitHub and open its folder. 2. Install its requirements with pip. 3. Run the inference script with the model name and a prompt file. ```bash cd Ming-Image pip install -r requirements.txt ``` ```bash hf download inclusionAI/Ming-Image-0.1-Design ``` ### How to use it Run infer.py with the text-to-image task, a prompt file, a resolution and an output folder. The command below does that with the model from this page. ```bash python infer.py \ --model inclusionAI/Ming-Image-0.1-Design \ --task text-to-image \ --prompt assets/t2i_four_seasons_cabin_prompt.json \ --resolution 2048 \ --output-dir outputs/t2i ``` For transparent backgrounds, add one of the card's recommended RGBA phrases at the start of your prompt. The card also names two models for optional prompt rewriting, Ling-3.0-flash-VL and qwen3.8-27B. | Setting | Recommended value | | --- | --- | | Resolution | 2048 x 2048, or 1024 x 1024 for faster generation | | Sampling steps | 12 | | CFG scale | 1.0 | | Precision | BF16 | | Hardware | One CUDA GPU with 80 GiB of memory | ### Example You run the sample prompt file for a four-seasons cabin at 2048 resolution. The script saves the generated image in the outputs/t2i folder. ### For newcomers and experts If you are new, start with the sample command above and the recommended settings before you write your own prompts. Use 1024 resolution if you want quicker results. If you know the field: whatever text-to-image resolution you request, the public code settles on either 1024 or 2048. The card recommends BF16, 12 sampling steps and a CFG scale of 1.0, and it lists prompt enhancement as an optional step. Write a prompt file → infer.py loads the model → 12 sampling steps run on the GPU → Image saved to the output folder ### Good to know The card shows a UI and UX design leaderboard image and gallery images, including transparent-background examples shown on a checkerboard that is not part of the output. It also lists a Design Skill and a PPT Skill from the cookbook. Weight size in gigabytes | Label | Value | | --- | --- | | BF16 weights | 12.3 GB | | 4-bit weights, estimate | 3.1 GB | - License: MIT - Cost to run: none named, but the validated setup needs an 80 GiB GPU - Data it reaches: the model files you download and the prompts you write - Not for: running from this page alone, since it needs the Ming-Image project's code ## README ````markdown # Ming-Image-0.1-Design A 6B text-to-image model from inclusionAI for interface mockups, infographics, posters and other text-heavy designs, with transparent-background (RGBA) output. Made by inclusionAI 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: Ming-Image-0.1-Design is a text-to-image model of about 6B parameters from inclusionAI. It is tuned for designs that carry a lot of text, such as app interfaces, infographics and posters. It produces a whole composition in one go, and it can write RGBA images with a transparent background. Who it suits: Designers, developers and content teams who want generated layouts and graphics with readable text, and who can run a GPU workflow from the command line. ```table Industry | Who uses it | How they use it | Example Design | UI and UX designers | Draft interface mockups from a text prompt | A first-pass screen layout for a product idea Marketing | Content teams | Make posters and infographics | A campaign poster with a headline and short copy Software | App developers | Generate transparent assets for apps | An icon-style graphic on a clear background Presentations | Slide authors | Produce visuals for decks | A chart-style infographic for a report ``` You can skip it if you want a quick online generator, or if you have no CUDA GPU, since the validated setup needs a large one. Where to use it: The model runs through the companion Ming-Image project and can be served with vLLM-Omni, on a machine with a CUDA GPU. - The companion Ming-Image command-line project - vLLM-Omni, which the card recommends for serving - Windows, macOS and Linux for the file download step - One CUDA GPU with plenty of memory for generation ```table Use case | Who | What they get back UI mockups | Designers | A generated screen design from a prompt Posters and infographics | Marketers | Text-rich visuals in one composition Transparent assets | Developers | RGBA images with no background to cut out ``` How to set it up: You need Python, a CUDA GPU, and the companion Ming-Image project, which holds the installation and inference code. The card validates a single GPU with 80 GiB of memory. - Python with pip - The Ming-Image project from GitHub - A CUDA GPU, with 80 GiB validated by the card - About 12.3 GB of disk for the BF16 weights 1. Get the Ming-Image project from GitHub and open its folder. 2. Install its requirements with pip. 3. Run the inference script with the model name and a prompt file. ```bash cd Ming-Image pip install -r requirements.txt ``` ```bash hf download inclusionAI/Ming-Image-0.1-Design ``` How to use it: Run infer.py with the text-to-image task, a prompt file, a resolution and an output folder. The command below does that with the model from this page. ```bash python infer.py \ --model inclusionAI/Ming-Image-0.1-Design \ --task text-to-image \ --prompt assets/t2i_four_seasons_cabin_prompt.json \ --resolution 2048 \ --output-dir outputs/t2i ``` For transparent backgrounds, add one of the card's recommended RGBA phrases at the start of your prompt. The card also names two models for optional prompt rewriting, Ling-3.0-flash-VL and qwen3.8-27B. ```table Setting | Recommended value Resolution | 2048 x 2048, or 1024 x 1024 for faster generation Sampling steps | 12 CFG scale | 1.0 Precision | BF16 Hardware | One CUDA GPU with 80 GiB of memory ``` Example: You run the sample prompt file for a four-seasons cabin at 2048 resolution. The script saves the generated image in the outputs/t2i folder. For newcomers and experts: If you are new, start with the sample command above and the recommended settings before you write your own prompts. Use 1024 resolution if you want quicker results. If you know the field: whatever text-to-image resolution you request, the public code settles on either 1024 or 2048. The card recommends BF16, 12 sampling steps and a CFG scale of 1.0, and it lists prompt enhancement as an optional step. ```flow Write a prompt file infer.py loads the model 12 sampling steps run on the GPU Image saved to the output folder ``` Good to know: The card shows a UI and UX design leaderboard image and gallery images, including transparent-background examples shown on a checkerboard that is not part of the output. It also lists a Design Skill and a PPT Skill from the cookbook. ```chart Title: Weight size in gigabytes BF16 weights: 12.3 GB 4-bit weights, estimate: 3.1 GB ``` - License: MIT - Cost to run: none named, but the validated setup needs an 80 GiB GPU - Data it reaches: the model files you download and the prompts you write - Not for: running from this page alone, since it needs the Ming-Image project's code ## On Hugging Face, 13 November 2026 - Task: text to image - Library: custom - 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: 1,836 - Likes: 440 - License: mit - Quantized versions: 14 - Fine-tunes: 18 - Tags: custom, diffusers, safetensors, bailingmm_moe_v2_lite, text-to-image, image-generation, graphic-design, text-rendering, rgba ## Links - Model: https://huggingface.co/inclusionAI/Ming-Image-0.1-Design - Files: https://huggingface.co/inclusionAI/Ming-Image-0.1-Design/tree/main ## Get the model Download its files with Hugging Face's command line: ```sh pip install -U huggingface_hub hf download inclusionAI/Ming-Image-0.1-Design ``` 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 [inclusionAI/Ming-Image-0.1-Design on Hugging Face](https://huggingface.co/inclusionAI/Ming-Image-0.1-Design) ## 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 | | --- | --- | --- | --- | --- | --- | | Ming-Image-0.1-Design (this page) | 1,836 | 440 | 6.2B | mit | 2026-10-14 | | [Anima-RGBA](https://mindket.com/p/1573fe64-91d8-424f-8840-ac5787f7acb6) | 3,509 | 59 | — | apache-2.0 | 2026-11-03 | | [MOSS-Image-IDM-2-Steps](https://mindket.com/p/c039b55d-9a35-44aa-a7ea-01c98070aea1) | 0 | 43 | — | apache-2.0 | 2026-11-01 | | [MOSS-Image-SFT GGUF](https://mindket.com/p/4358c31a-f5e3-45d4-80c7-46f64c71d3ff) | 18,762 | 22 | 5.1B | apache-2.0 | 2026-10-20 | | [Juggernaut XL v9 GGUF](https://mindket.com/p/bdbea1dc-f8e8-4692-a044-86cbe11adb07) | 8,187 | 8 | 3.5B | creativeml-openrail-m | 2026-10-21 | | [RealVisXL V5.0 GGUF](https://mindket.com/p/28d3b4a0-a584-452f-be6d-8b2bad751dcd) | 6,316 | 3 | 3.5B | openrail++ | 2026-10-21 | 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. - [Bluesky · Sung Kim · 2026-09-23](https://bsky.app/profile/sungkim.bsky.social/post/3mw72jjceec2h): Alibaba Ant's Ming-Image-0.1-Design family: • Ming-Image-0.1-Design, 6B • Ming-Image-0.1-Design-Layer, 6B • Two open-source Agent Skills: the Ling UI Design Skill and the Image-to-Editable-PPT Skill Models: huggingface.co/inclusionAI/... huggingface.co/inclusionAI/... ## 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: 2026-10-14 - First published: 2026-09-17 - Checked on Hugging Face: 2026-11-13 - Something wrong? Tell us: https://mindket.com/takedown#ask ## Get it - [Open inclusionAI/Ming-Image-0.1-Design on Hugging Face](https://huggingface.co/inclusionAI/Ming-Image-0.1-Design) - [Get it on your Mindket account](https://account.mindket.com/account/?product=mindket#/market/9c09ce6a-012e-40ce-b14b-610da5874eda) - [Its page on Mindket](https://mindket.com/p/9c09ce6a-012e-40ce-b14b-610da5874eda)