Instructions to use pinecoresystems/Ming-Image-0.1-Design with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use pinecoresystems/Ming-Image-0.1-Design with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("pinecoresystems/Ming-Image-0.1-Design", dtype=torch.bfloat16, device_map="cuda") prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
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---
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license: mit
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library_name: custom
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pipeline_tag: text-to-image
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inference: false
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tags:
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- text-to-image
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- image-generation
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- graphic-design
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- text-rendering
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- rgba
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---
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# Ming-Image-0.1-Design
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[🧩 ModelScope](https://www.modelscope.cn/models/inclusionAI/Ming-Image-0.1-Design) · [🤗 Hugging Face](https://huggingface.co/inclusionAI/Ming-Image-0.1-Design) · [📄 Blog](https://mp.weixin.qq.com/s/VGdtxfM8kbHIQJw50VD_Sw) · [🖥️ Demo](https://huggingface.co/spaces/hugging-apps/ming-image-0-1-design-demo)<br>
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[🎨 Design Skill](https://github.com/inclusionAI/ling-cookbook/tree/main/resources/recommended-skills/ling-ui-design) · [📊 PPT Skill](https://github.com/inclusionAI/ling-cookbook/tree/main/resources/recommended-skills/image-to-editable-ppt)
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Ming-Image-0.1-Design is a 6B text-to-image model for UI, infographics,
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posters, and other text-rich visual designs. It generates complete visual
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compositions and supports RGBA output with transparent backgrounds.
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## UI/UX Design leaderboard
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<p align="center">
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<img src="./assets/uiux_leaderboard.webp" width="100%" alt="Ming-Image-0.1-Design UI/UX Design leaderboard">
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</p>
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## Quick Start
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Use the companion [Ming-Image repository](https://github.com/inclusionAI/Ming-Image)
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for installation and inference:
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```bash
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git clone https://github.com/inclusionAI/Ming-Image
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cd Ming-Image
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pip install -r requirements.txt
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python infer.py \
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--model inclusionAI/Ming-Image-0.1-Design \
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--task text-to-image \
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--prompt assets/t2i_four_seasons_cabin_prompt.json \
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--resolution 2048 \
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--output-dir outputs/t2i
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```
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Prompt enhancement (PE) can use `Ling-3.0-flash-VL` or `qwen3.8-27B`; see
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[text-to-image prompt rewriting](https://github.com/inclusionAI/Ming-Image#text-to-image-prompt-rewriting).
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### Transparent-background generation
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For transparent-background generation, prepend exactly one of the recommended
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RGBA phrases. See the
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[transparent-background generation tip](https://github.com/inclusionAI/Ming-Image#transparent-background-generation-tip).
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## Deployment
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We recommend the following inference frameworks to serve the model:
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- vLLM-Omni: see the [recipes](https://github.com/vllm-project/vllm-omni/blob/main/recipes/inclusionAI/Ming-Image.md)
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and [installation guide](https://docs.vllm.ai/projects/vllm-omni/en/latest/getting_started/quickstart/).
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## Recommended settings
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- Resolution: **2048 x 2048** (recommended), or **1024 x 1024** for faster
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generation.
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- Sampling steps: **12**.
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- CFG scale: **1.0**.
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- Precision: **BF16**.
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- Hardware: **one CUDA GPU with 80 GiB VRAM** (validated configuration).
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The public inference code maps text-to-image resolution requests to the
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supported 1024 or 2048 bucket.
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## Gallery
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### Text-to-image
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<p align="center">
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<img src="./assets/showcase.webp" width="100%" alt="Ming-Image-0.1-Design generated examples">
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</p>
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### Transparent-background text-to-image
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<p align="center">
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<img src="./assets/transparency_showcase.webp" width="100%" alt="Ming-Image-0.1-Design transparent-background examples">
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</p>
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The checkerboard is used only to preview transparency; it is not part of the
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generated RGBA images.
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## License
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This model is released under the [MIT License](./LICENSE).
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---
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license: mit
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library_name: custom
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pipeline_tag: text-to-image
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inference: false
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tags:
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- text-to-image
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- image-generation
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- graphic-design
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- text-rendering
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- rgba
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---
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# Ming-Image-0.1-Design
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[🧩 ModelScope](https://www.modelscope.cn/models/inclusionAI/Ming-Image-0.1-Design) · [🤗 Hugging Face](https://huggingface.co/inclusionAI/Ming-Image-0.1-Design) · [📄 Blog](https://mp.weixin.qq.com/s/VGdtxfM8kbHIQJw50VD_Sw) · [🖥️ Demo](https://huggingface.co/spaces/hugging-apps/ming-image-0-1-design-demo)<br>
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[🎨 Design Skill](https://github.com/inclusionAI/ling-cookbook/tree/main/resources/recommended-skills/ling-ui-design) · [📊 PPT Skill](https://github.com/inclusionAI/ling-cookbook/tree/main/resources/recommended-skills/image-to-editable-ppt)
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Ming-Image-0.1-Design is a 6B text-to-image model for UI, infographics,
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posters, and other text-rich visual designs. It generates complete visual
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compositions and supports RGBA output with transparent backgrounds.
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## UI/UX Design leaderboard
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<p align="center">
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<img src="./assets/uiux_leaderboard.webp" width="100%" alt="Ming-Image-0.1-Design UI/UX Design leaderboard">
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</p>
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## Quick Start
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Use the companion [Ming-Image repository](https://github.com/inclusionAI/Ming-Image)
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for installation and inference:
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```bash
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git clone https://github.com/inclusionAI/Ming-Image
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cd Ming-Image
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pip install -r requirements.txt
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python infer.py \
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--model inclusionAI/Ming-Image-0.1-Design \
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--task text-to-image \
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--prompt assets/t2i_four_seasons_cabin_prompt.json \
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--resolution 2048 \
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--output-dir outputs/t2i
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```
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Prompt enhancement (PE) can use `Ling-3.0-flash-VL` or `qwen3.8-27B`; see
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[text-to-image prompt rewriting](https://github.com/inclusionAI/Ming-Image#text-to-image-prompt-rewriting).
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### Transparent-background generation
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+
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For transparent-background generation, prepend exactly one of the recommended
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RGBA phrases. See the
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[transparent-background generation tip](https://github.com/inclusionAI/Ming-Image#transparent-background-generation-tip).
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## Deployment
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+
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We recommend the following inference frameworks to serve the model:
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- vLLM-Omni: see the [recipes](https://github.com/vllm-project/vllm-omni/blob/main/recipes/inclusionAI/Ming-Image.md)
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and [installation guide](https://docs.vllm.ai/projects/vllm-omni/en/latest/getting_started/quickstart/).
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## Recommended settings
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- Resolution: **2048 x 2048** (recommended), or **1024 x 1024** for faster
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generation.
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- Sampling steps: **12**.
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- CFG scale: **1.0**.
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- Precision: **BF16**.
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- Hardware: **one CUDA GPU with 80 GiB VRAM** (validated configuration).
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+
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The public inference code maps text-to-image resolution requests to the
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supported 1024 or 2048 bucket.
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+
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## Gallery
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+
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### Text-to-image
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<p align="center">
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<img src="./assets/showcase.webp" width="100%" alt="Ming-Image-0.1-Design generated examples">
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</p>
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### Transparent-background text-to-image
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<p align="center">
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<img src="./assets/transparency_showcase.webp" width="100%" alt="Ming-Image-0.1-Design transparent-background examples">
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</p>
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+
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The checkerboard is used only to preview transparency; it is not part of the
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generated RGBA images.
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## License
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This model is released under the [MIT License](./LICENSE).
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