inclusionAI/Ming-Image-0.1-Design-Layer
image-text-to-image model by inclusionAI
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Model card & documentation
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🧩 ModelScope · 🤗 Hugging Face · 📄 Blog · 🖥️ Demo 🎨 Design Skill · 📊 PPT Skill 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. Use the companion Ming-Image repository for installation and inference: This runs the released six-layer card-making example. See the full 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. We recommend the following inference frameworks to serve the model: and installation guide. layer decomposition. The output preserves the input image's aspect ratio. Provide --input-image plus either a detailed layer specification through --prompt, or omit --prompt and set --num-layers N to create the…
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Model overview
inclusionAI/Ming-Image-0.1-Design-Layer is a image-text-to-image model repository published by inclusionAI on Hugging Face. The source reports the custom library.
This page summarizes Hub metadata. For intended use, training data, evaluation results and limitations, consult the original model card.
Model facts
- Task
- image-text-to-image
- Library
- custom
- Recent downloads (30 days)
- 0
- Cumulative likes
- 84
- Hugging Face trending score
- 83
- Reported safetensors parameters
- 6,154,908,736
- Architecture
- Not reported
- License
- mit
- Access
- Not gated by Hugging Face
- Created
- 2026-09-17T11:36:00.000Z
- Last modified
- 2026-09-23T15:44:37.000Z
Compatibility and lineage
Language coverage was not reported.
Base-model lineage was not included.
Parameter count is not a RAM/VRAM requirement. Check precision, quantization, context length and runtime compatibility in the model card; no hardware or API-cost claim is inferred here.
Use and evaluate
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Source and freshness
Source: Hugging Face Hub. Metadata observed 2026-09-29T06:38:06.650Z. Daily imports are snapshots, not real-time monitoring.
Popularity and source listings do not establish security, suitability, licensing rights or benchmark performance.