Ming-Image-0.1-Design

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.

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.

cd Ming-Image
pip install -r requirements.txt

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.

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.

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:

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.