Ming-Image 0.1 Design: 6B UI and Poster Model in ComfyUI

Design examples from the official model card.

What Ming-Image 0.1 Design is

Ming-Image 0.1 Design comes from inclusionAI, the team behind the Ling and Bailing model families, and it is aimed at a job general image models handle badly: laying out a clean, readable design rather than a photograph. Screens, dashboards, responsive card layouts, infographic panels, product posters and logo sheets are the target content, and the official samples lean heavily on rendered text being legible and typographically sane.

• 6B diffusion transformer. The DiT is a 30-layer, 3840-dimension design with 16 input channels and 2x2 patching, paired with a separate vision-language encoder and its own connector. Kijai describes the base as Z-Image derived with a new text encoder.
• Design-first training data rather than a general photo corpus, which is why the outputs read as composed layouts with real text blocks.
• Two native output buckets, 1024 and 2048, with 2048 recommended for full layouts.
• Sampling defaults are short: 12 steps at CFG 1.0, so a design comes out in a small number of passes.
• Prompt enhancement is part of the intended flow. The official pipeline recommends rewriting the prompt with Ling-3.0-flash-VL or qwen3.8-27B before generation, which matters most for dense layouts with a lot of text to place.

A responsive card layout, one of the official text-to-image samples.

Transparent backgrounds as a first-class output

Alongside ordinary images, Ming-Image 0.1 Design can emit an RGBA image with a real alpha channel, so a generated poster element, product cutout or UI asset arrives ready to composite instead of needing a separate matting pass. The model card ships a set of recommended trigger phrases and asks you to prepend exactly one of them, which is the same pattern Qwen-Image 2.1 uses for its transparency mode.

Transparent-background outputs. The checkerboard previews the alpha channel and is not part of the generated image.

The Layer sibling: design to editable layers

The series has a second 6B checkpoint, Ming-Image 0.1 Design Layer, that runs the problem in reverse: it takes a flattened design image and decomposes it into separately editable transparent layers. That is the step that turns a generated mockup into something you can actually work on in a design tool, and inclusionAI pairs it with two published agent workflows, a Ling UI Design skill that uses generated references plus layer decomposition to build and visually check UI code, and an image-to-editable-PPT skill that recreates a generated page as native PowerPoint elements.

The Layer checkpoint uses different sampling defaults from the design model: 12 steps at CFG 2.0, with 512 and 1024 working buckets.

Where it sits on the UI/UX leaderboard

inclusionAI published the model on the Artificial Analysis UI/UX Design leaderboard together with the release, which is the clearest signal of who the model is competing with.

Ming-Image 0.1 Design's placement on the UI/UX Design leaderboard, from the official repository.

ComfyUI availability

ComfyUI support for Ming-Image is in progress, so there is no merged core implementation or official workflow template yet. Two things make it usable today:

• Kijai published a ComfyUI repack of the weights at Kijai/Ming-Image-ComfyUI with BF16, int8_convrot and w4a8 variants of the transformer and the text encoder, plus the matching VAE, laid out in the usual diffusion_models / text_encoders / vae folders.
• The core implementation is in ComfyUI PR #16482, where Kijai states the node already handles RGBA output. The pull request is still open, so an unmodified ComfyUI install cannot load the model yet.

Early testing in the Banodoco #ming-image channel suggests the samplers matter more than usual here: Kijai notes that LCM output is clean but overly smooth and that random text failures are still common at INT8, while RuneX reports the model responds to bounding-box style prompts and edits quickly at its small size. The Layer checkpoint is not being packaged yet, since Kijai considers the extra text-encoder and projector handling a significant amount of complexity for a narrower use case.