I run a prompt-to-ui design site that uses image models for the design process[1]. The text rendering especially makes this model deeply interesting to me, despite the license. Here are some tests using my harness comparing the outputs of gpt-image-2 and qwen 2.1:

https://html.non.io/qwen-comparison/

The text rendering definitely is much, much better than anything else on the open weights market right now. Small text fidelity is quite good. It seems like the text encoder however gets a little bit overloaded with larger prompts - note the presence of hex codes in the design output, those were inputs from the expanded prompt.

I'll be trying a post-training run on this for web design, it has some serious potential.

[1] diffui.ai

▲

cloudking 6 hours ago | parent | next [-]

Those simple prompts produce nearly the exact same layout in the 2 different models?

▲

jjcm 4 hours ago | parent | next [-]

My harness expands the prompt into a json representation that specifies layout much more rigorously, which is why you see such that amount of alignment between the two.

That internal json backing helps significantly when you want to maintain consistent design system components/patterns across multiple pages. The aligned layout is it working as intended.

▲

orbital-decay 5 hours ago | parent | prev | next [-]

Totally normal for modern models due to training on the same datasets supplied by third parties, dataset contamination, and mode collapse, especially for simple prompts that don't have enough semantic capacity. -isms are often very similar even without distillation, and tend to come and go in waves along with model generations.

▲

supermatt 6 hours ago | parent | prev | next [-]

Equally confused with this. They must be using a lot more guidance than just the provided prompt.

▲

howdareme 5 hours ago | parent | prev | next [-]

Qwen is trained off of gpt’s outputs. This is both a positive and negative

▲

BoorishBears 5 hours ago | parent | prev [-]

Qwen's latest image models have a ton of distillation from gpt-image, same with Grok Imagine.

Even the artifacts are getting picked up.

	▲	vunderba 2 hours ago \| parent [-]
		Agreed. There's also a lot of bad tinging/yellow saturation that very much reminds me of early gpt-image outputs on a lot of the non-cherry picked stuff I've been seeing on Twitter/Reddit. A lot of people were putting ZiT as a refiner downstream in early Qwen-Image 1.0 workflows, so I'm wondering if we're going to see something similar with 2.1.

▲

6 hours ago | parent | prev | next [-]

[deleted]

▲

xienze 6 hours ago | parent | prev [-]

> The text rendering definitely is much, much better than anything else on the open weights market right now

Really? Because basically everything in those screenshots is completely garbled. I didn't follow it super closely but I thought Ideogram or whatever was really good for this particular use, with actual clear text.

	▲	vunderba 6 hours ago \| parent [-]
		This is my experience as well. Ideogram4 (assuming you are willing to put in the work to use the proper structured JSON input) is very accurate when it comes to text rendering in an image.