| --- |
| license: mit |
| library_name: custom |
| pipeline_tag: image-text-to-image |
| inference: false |
| tags: |
| - image-text-to-image |
| - layer-decomposition |
| - rgba |
| - graphic-design |
| --- |
| |
| # Ming-Image-0.1-Design-Layer |
|
|
| [🧩 ModelScope](https://www.modelscope.cn/models/inclusionAI/Ming-Image-0.1-Design-Layer) · [🤗 Hugging Face](https://huggingface.co/inclusionAI/Ming-Image-0.1-Design-Layer) · [📄 Blog](https://mp.weixin.qq.com/s/VGdtxfM8kbHIQJw50VD_Sw) · [🖥️ Demo](https://huggingface.co/spaces/Xiaolong-Wang/Ming-Image-0.1-Design-Layer)<br> |
| [🎨 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) |
|
|
| Ming-Image-0.1-Design-Layer decomposes a flattened design image into a |
| requested number of RGBA layers using an image and a layer plan. |
|
|
| ## Quick Start |
|
|
| Use the companion [Ming-Image repository](https://github.com/inclusionAI/Ming-Image) |
| for installation and inference: |
|
|
| ```bash |
| git clone https://github.com/inclusionAI/Ming-Image |
| cd Ming-Image |
| pip install -r requirements.txt |
| |
| python infer.py \ |
| --model inclusionAI/Ming-Image-0.1-Design-Layer \ |
| --task layer-decompose \ |
| --input-image assets/layer_samples/card_making_input.png \ |
| --prompt assets/layer_samples/card_making_prompt.txt \ |
| --attn-implementation flash_attention_2 \ |
| --resolution 1024 \ |
| --output-dir outputs/layers |
| ``` |
|
|
| This runs the released six-layer card-making example. See the full |
| [layer-decomposition demo](https://github.com/inclusionAI/Ming-Image#layer-decomposition-demo) |
| for the output structure and layer-count behavior. |
|
|
| Prompt enhancement (PE) can use `Ling-3.0-flash-VL` or `qwen3.8-27B`; see |
| [layer-decomposition prompt rewriting](https://github.com/inclusionAI/Ming-Image#layer-decomposition-prompt-rewriting). |
|
|
| ## Deployment |
|
|
| We recommend the following inference frameworks to serve the model: |
|
|
| - vLLM-Omni: see the [recipes](https://github.com/vllm-project/vllm-omni/blob/main/recipes/inclusionAI/Ming-Image.md) |
| and [installation guide](https://docs.vllm.ai/projects/vllm-omni/en/latest/getting_started/quickstart/). |
|
|
| ## Recommended settings |
|
|
| - Working-resolution bucket: **1024** (recommended), or **512** for faster |
| layer decomposition. The output preserves the input image's aspect ratio. |
| - Sampling steps: **12**. |
| - CFG scale: **2.0**. |
| - Precision: **BF16**. |
| - Hardware: **one CUDA GPU with 80 GiB VRAM** (validated configuration). |
|
|
| Provide `--input-image` plus either a detailed layer specification through |
| `--prompt`, or omit `--prompt` and set `--num-layers N` to create the default |
| request. When `--prompt` is supplied, the layer count declared in that prompt |
| controls the output count. The standalone outputs are saved as RGBA PNG files. |
|
|
| ## Gallery |
|
|
| <p align="center"> |
| <img src="./assets/showcase.webp" width="100%" alt="Ming-Image-0.1-Design-Layer decomposition example"> |
| </p> |
|
|
| The example shows the input design, six decomposed layers, and the recomposed |
| result. |
|
|
| <p align="center"> |
| <img src="./assets/gallery.webp" width="100%" alt="Ming-Image-0.1-Design-Layer gallery"> |
| </p> |
|
|
| The gallery shows additional flattened designs, their transparent layers, and |
| the corresponding recomposed results. |
|
|
| ## Performance |
|
|
| <p align="center"> |
| <img src="./assets/performance.webp" width="100%" alt="Layer-decomposition results on the Crello test set"> |
| </p> |
|
|
| The table reports quantitative layer-decomposition results on the Crello test |
| set; lower RGB L1 and higher Alpha soft IoU are better. |
|
|
| ## License |
|
|
| This model is released under the [MIT License](./LICENSE). |
|
|
