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UPSTREAM-README.md

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+---
+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).