Those simple prompts produce nearly the exact same layout in the 2 different models?
replyMy 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.
replyThat 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.
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.
replyEqually confused with this. They must be using a lot more guidance than just the provided prompt.
replyQwen is trained off of gpt’s outputs. This is both a positive and negative
replyQwen's latest image models have a ton of distillation from gpt-image, same with Grok Imagine.
replyEven the artifacts are getting picked up.
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.
replyA 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.
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reply> The text rendering definitely is much, much better than anything else on the open weights market right now
replyReally? 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.
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.
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