An Open Model That Makes Editable, Layered Designs

Most image models a design team has tried this year produce the same thing: a flat picture. It may be a good picture, but the moment a designer wants to move the headline, swap the product shot or change the background, the picture has to be rebuilt or traced. Two open-weight models from inclusionAI, published on Hugging Face on September 17, 2026 and updated on September 23, go at that problem from both ends. One generates design images with transparent backgrounds. The other takes a flat design and splits it into editable layers.

Both are released under the MIT licence, which permits commercial use, and both were validated by the authors on a single 80 GiB GPU. That last fact is the price of admission, and this post is honest about it. What follows is what the two model cards say, why editable layers change the hand-off between a model and a design tool, the settings and hardware the cards specify, a short path to trying them, and where they sit next to the closed image models we track. No quality claim in this post goes beyond the cards' own.

The two Design and Design-Layer cards share a GitHub repository for installation and inference, demo spaces (a separate one per model), and links to a Design Skill and a PPT Skill in inclusionAI's ling-cookbook. The Design card also shows a UI and UX design leaderboard image. It is the vendor's image with no numbers in the text, so we reproduce neither the ranking nor a claim about it. The Hugging Face pipeline tags are text-to-image for the first model and image-text-to-image for the second, which is the clearest one-line description of the pair.

A design tool works in layers. A headline is a text object, a product shot is an image object with its own mask, a background is a fill. A flat raster from an image model collapses all of that into pixels, and the hand-off from model to designer becomes tracing: rebuild the layout by hand so that it can be changed. The better the flat image, the more it costs to throw it away when the client wants the price moved.

Transparent output addresses half of this. A generated element with an alpha channel drops into an existing layout as its own layer, so the model can be asked for a component rather than a finished page. Layer decomposition addresses the other half: a finished flat design, whether generated or inherited, comes back as separable parts a designer can edit. Together they make the model a participant in a layered workflow instead of a source of reference pictures. That is a different job from the one closed models such as Google's Nano Banana family are sold for, which we describe in our post on Nano Banana 2 Lite.

What the cards do not tell you is how clean the layers are. A decomposition that splits a card into background, image, headline and button is useful. One that leaves halos on the cut edges or merges two text blocks into one layer is a starting point that still needs hand work. The gallery shows recomposed results on a public dataset; it reports RGB L1 and Alpha soft IoU on the Crello test set as an image only, with no edge-halo measure, and we have not measured one on client designs.

The hardware line deserves plain language. An 80 GiB accelerator is a data-centre part. A design team will not run this on a laptop or a typical workstation card, and the cards give no quantised or reduced-memory configuration. The practical options are a rented GPU by the hour for a batch of jobs, or a hosted endpoint. Two aggregator routes for the models appeared on September 22 and 23, which we observed on September 25 at 08:01 UTC; both showed zero catalog prices, and a zero in a catalog is a missing price, not a free tier. Check the serving provider's own terms before sending client work through either route.

September's image-model news has mostly been about closed models and their calendars: Google's Gemini 2.5 Flash Image retirement on October 2, its successors priced per image, and the aggregator catalog adding three image-output routes between September 22 and 23, of which these two are the open-weight pair. Our September release tracker carries the dated list.

The Ming pair is not a competitor to those models on the axis they compete on. Nano Banana 2 produces a flat image at up to 4K for a per-image price; Ming-Image-0.1-Design produces a transparent design element at 2K on hardware you rent. What the pair offers that none of the closed models on our list offers is the second model: a way back from a flat design to layers, under a licence that lets an agency build it into a client pipeline without a usage agreement. Whether the layers are good enough is the trial in section 04. Our content engine practice runs that trial on a client's own design archive before recommending any generation model.