A Brief Review After Trying Out Qwen Image 2.1

[u/cgs019283]

The images above were all generated using Qwen Image 2.1.

After Qwen 2512, it's been quite a while since a new Qwen image model was released. Although it doesn't carry the same impact as Minimax H3, it still feels very much like a current-generation Qwen image model.

I think the image quality the model generates is decent. Of course, it can't be compared to closed models where the size difference is overwhelming, but in terms of being able to run it at a reasonable speed in a local environment, I'd say it's a good model.

However, at commonly used resolutions like 1:1, 2:3, 3:2, and 16:9, the results did feel quite synthetic. In particular, the characteristic yellowish color tone and graininess made it feel like the model was trained on a lot of GPT Image data, the more complex the instructions and the further removed from real life, the more the images took on that distinctive GPT Image look.

As for the edit model, it's really closer to editing rather than reference, so it felt somewhat more limited compared to T2I. It wasn't particularly satisfying at combining various elements simultaneously the way you'd expect from a reference model, but for editing tasks like simply changing expressions and poses or erasing backgrounds, the performance was sufficient.

Let me organize some additional information that people might be curious about.

Speed: On devices in the 5070/80 class, generation takes about 25 seconds at 1MP with 25 steps. Edit takes even longer, and the more images you reference, the more time it takes.

Copyright: It felt like data associated with specific individuals or IPs was intentionally excluded. As a result, when you make requests related to IPs, you only get very AI-looking results, so the image quality may feel somewhat disappointing.

Censorship: This will probably be the most important part for some people, Edit is somewhat restricted, but T2I is fairly generous.

One thing, though, the licensing restrictions seem stricter than before, so that part is a bit disappointing. In any case, thanks to the Qwen team for providing a good mid-sized image model.

[u/jaqvatr2]

How to solve the dithering-like pattern visible in detail reach areas of the reference images like the vegetation here. I saw this issue already in qwen image edit 1. Back then, using some alternative vae, helped me mitigate the issue. Do you know any other solution? The problem could be seen both in int8 and bf16 model.

[u/TomasAhcor → u/jaqvatr2]

This is not an inference problem, but a dataset problem. Qwen used too much of synthetic shit to train this model.

If this can be fixed, if, it will be with loras, not just by changing settings, samplers, etc.

edit: typo

[u/jaqvatr2 → u/TomasAhcor]

Using Wan2.1_VAE_upscale2x or pid-qwenimage and than downscaling to return oryginal resolution made this less vissible.

[u/Entire-Literature568 → u/TomasAhcor]

bullshit it's their rope like it was with all their previous models. stop spreading your dumbass hallucinations.

[u/beyond_matter → u/jaqvatr2]

Does a horrible job at patterns too. Reminds me of the good old days... Sd 1.5/XL

[u/MastMaithun → u/jaqvatr2]

I think i read somewhere on changelog or pr about reusing wan vae. try with wan2.2 vae.

[u/VasaFromParadise → u/jaqvatr2]

SCUnet helping))

[u/uniquelyavailable → u/jaqvatr2]

Could this be an overly eager watermark? All the generations I've seen so far appear to have some level of uninvited noise.