Ming-Image-0.1 Design and Design-Layer: RGBA Design Generation Explained

Two related models solve different design tasks

inclusionAI publishes Ming-Image-0.1-Design and Ming-Image-0.1-Design-Layer as separate 6B models. Design is a text-to-image model for UI, infographics, posters and other text-rich visual compositions. Design-Layer takes a flattened design image plus a layer plan and decomposes it into requested RGBA layers. PromptToVisual does not currently host either model.

What the Design model produces

The Design model card describes complete visual compositions and optional transparent-background RGBA output. The documented quick start uses the companion Ming-Image repository and supports 1024 or 2048 output buckets, with 2048 recommended for the validated setup. Treat those settings as upstream guidance, not as PromptToVisual controls or a guarantee that every local GPU will match the published behavior.

What Design-Layer actually means

Design-Layer can split a flattened design into a requested number of RGBA PNG layers using either a detailed layer specification or a requested layer count. That can be useful for separating visual components, but the outputs are raster layers. The model card does not say it reconstructs original vector objects, editable text boxes, fonts, constraints or a native PSD/Figma project. Do not turn “layer decomposition” into a stronger editability claim.

A practical acceptance check

For a design-generation test, inspect text spelling, hierarchy, alignment, object count, transparency and whether important elements are clipped. For layer decomposition, recompose the exported RGBA layers and compare them with the flattened input. Also inspect halos, missing details, layer overlap and whether an intended component was split across several layers. A plausible decomposition is not proof that it recovered the authoring structure.

Local hardware guidance is demanding

The current model cards document BF16-oriented local inference and a validated single-CUDA-GPU setup with 80 GiB VRAM. Design recommends 12 sampling steps and CFG 1.0; Design-Layer documents its own settings. Those are reference configurations, not minimum requirements established by PromptToVisual. We have not benchmarked either model on consumer GPUs.

License and deployment boundary

Both Hugging Face model cards identify the models as MIT licensed. License permissiveness does not remove the need to review the repository dependencies, input rights, generated-content requirements and any separate service terms around a deployment stack. If you use a hosted third-party endpoint rather than the released weights, evaluate that provider’s terms separately.

When this is relevant to a hosted image user

Ming is most interesting when your task is text-rich design generation, transparent design assets or experimental raster layer decomposition. If you only need to add exact final wording to an already useful image, a deterministic text overlay may be simpler. If you need to revise a flat poster semantically, use an editing model and inspect the whole regenerated result.

Sources and next steps

Source check: September 26, 2026. The inclusionAI model cards identify both Design and Design-Layer as 6B MIT-licensed models. PromptToVisual has not run or integrated them; RGBA layer decomposition is not described here as recovery of native PSD, vector or editable-text structure.

Hugging Face: Ming-Image-0.1-Design →

Hugging Face: Ming-Image-0.1-Design-Layer →

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