Ming Image 0.1 Design: 6B Open Model Makes Posters + Editable Layers (I Tested It)
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Author: Prompt Engineer 48 · Watch on YouTube
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Original language: English
Original video: Ming Image 0.1 Design: 6B Open Model Makes Posters + Editable Layers (I Tested It) · Channel: Prompt Engineer 48 · 03:08 · Watch on YouTube
Topics: AI content generation, Model inference and optimization, Local and open-source models
Summary
Section titled “Summary”The video tests Ming Image 0.1 Design and Design Layer, two 6-billion-parameter models from Ant Group that generate complete compositions and separate images into editable layers. The creator runs them on a rented 80 GB A100 GPU after setup fixes, since their laptop GPU is too small. The Design model produces strong posters and a landing page, but text it was not given can become gibberish, and it can make small errors in specified text. The Layer model creates transparent PNG layers but may invent elements that were not in the source image; the full test cost about $3.
Key points
Section titled “Key points”- Ming Image 0.1 Design and Design Layer are both 6-billion-parameter models and MIT licensed.
- Design generates a full composition, including text, from a prompt; Design Layer separates an image into transparent layers.
- The creator says Design ranks first among open-weights models on the Artificial Analysis UI UX design leaderboard.
- The model weights are about 49 GB, and the model card recommends a 180 GB GPU.
- The creator used an 80 GB A100 rented on RunPod for $1.59 an hour and needed two setup fixes.
- The landing page test took about 36 seconds at 12 steps and 2048-pixel square resolution; requested text was accurate, while unspecified text was gibberish.
- The jazz poster looked polished, but the model wrote “tickets $25” instead of dollars.
- The robot scene produced a cinematic result, though a label was nonsense.
- Design Layer took about three minutes to produce five transparent PNGs and invented a holographic panel that was not in the source image.
- The creator reports a total test cost of about $3 and advises checking the API availability before relying on it.
Chapters
Section titled “Chapters”| Start | Chapter | Summary |
|---|---|---|
| 00:00 | Introducing Ming Image 0.1 Design | The video introduces the two models, their capabilities, licensing, and the creator’s interest in testing them. |
| 01:02 | GPU setup and first test | The creator explains the hardware requirements, rents an A100, makes setup fixes, and tests a landing page. |
| 01:34 | Poster and robot image tests | The model creates a jazz poster and a cinematic robot scene, with some errors in text. |
| 02:36 | Editable layers, API, and cost | The layer model’s invented element is noted, followed by API availability caveats and the overall test cost. |
Quotes
Section titled “Quotes”One sentence, one image, five layers. — 00:31
So, it is really good at the text you give it, and it makes up the rest. — 01:34
That is a really great cinematic result for a 6 billion parameter model. — 02:06
So, describe only what is really there. — 02:36
Concepts and entities
Section titled “Concepts and entities”Ming Image 0.1 Design, Ming Image 0.1 Design Layer, InclusionAI, Ant Group, RunPod, OpenRouter, A100, Artificial Analysis
Full transcript (original language)
Section titled “Full transcript (original language)”00:00 All right, in this video we will see Ming Image 0.1 Design, a 6 billion parameter model from Ant Group, and you can see that it draws full posters and web pages with the text actually readable. Now, mind you, every image you will see here, I generated myself on a rented 80 GB GPU. Let’s go ahead and test this out. So, this is Ming Image 0.1 Design from Inclusion AI, the Ant Group team. It came out on September 22nd. There are two models, both 6 billion parameters, and both are MIT
00:31 licensed. The first one, Design, takes a prompt and draws the whole composition, text included. The second one is called Design Layer, and it takes a flat image and splits it into separate transparent layers. And on the artificial analysis UI UX design leaderboard, it sits first among the open weights models. This is the clip that got me interested. It is from Leo Wang on X, so credit to them. One sentence, one image, five layers. Now, let’s see what it does on my own prompts. Now, the catch, the weights are about 49 GB, and the model card says to
01:02 use 180 GB GPU. Mind you, my laptop has an 8 GB GPU. So, that is a no. I tried OpenRouter first, but the provider came back with not enough balance. So, what I did is rent an A100 with 80 GB on RunPod at $1.59 an hour. Setting it up needed two small fixes, the right flash attention version, and a one-line patch, because the repo is pinned to an older torch. First test, the landing page for an app called Flow, straight from the model card. 12 steps at 2048 pixels square, and it took about 36 seconds. If
01:34 you look here, the logo, the nav links, the headline, and the button are exactly the text I asked for, but the subheading and the card text, which I did not specify, are gibberish. So, it is really good at the text you give it, and it makes up the rest. Second test, a jazz poster, title, date, venue, price, and a footer line. And you can see that it It really good. The serif title is clean, the layout is balanced, and the saxophone looks great. One slip, it wrote tickets $25 instead of dollars, so proofread your text before you print.
02:06 Third test, the robot from the post, a humanoid in an abandoned factory with a drone and an orange toolbox. That is a really great cinematic result for a 6 billion parameter model. The label under toolbox is nonsense again, same pattern as before. Now, the layer model, I give it this image and a plan for five layers. It took about 3 minutes and gave me back five transparent PNG files, the drone, the robot, the toolbox, and the background with the hidden parts filled in. Now, the honest part, I asked for a holographic panel, which is not in this
02:36 picture, and it invented one. So, describe only what is really there. Now, if renting a GPU feels like a lot, what you can do is use the API on Open Router. The model ID is inclusion.ai/mingimage0.1design. The post said there is a free window, but when I tried, the provider was out of balance, so check it works before you plan around it. My total cost for this whole test was about $3. So, in this video, we have seen how we can run Ming Image Design and Design Layer on a rented 80 GB GPU and make
03:08 pages, posters, and editable layers. If you’re interested, you can go ahead and try this out on your own system as well, and let me know how it goes. Credit to Inclusion.ai and the end group team, and to Lawang for the clip. I’ll see you in another one. Check out the next video.