Question 1

What's new in Qwen-Image-2512 versus the original Qwen-Image?

Accepted Answer

Per the model card: markedly better human realism (less 'AI look', better skin, hair, and aged features), finer natural detail in landscapes and fur, and more accurate in-image text rendering and layout. The card reports it as the top open-source model in 10,000+ blind AI Arena comparisons at release.

Question 2

What settings should I use?

Accepted Answer

The chute defaults to 30 steps and true_cfg_scale 4 at 1024x1024, which is a reasonable speed/quality balance. The model card's reference example uses 50 steps, true_cfg_scale 4.0, and larger aspect-ratio presets like 1328x1328 or 1664x928; move toward those for final-quality renders.

Question 3

Does it support negative prompts?

Accepted Answer

Yes. The chute exposes negative_prompt (default empty). The model card itself uses a negative prompt targeting low resolution, deformed hands, over-saturation, waxy skin, and blurry text. Keep yours short and specific.

Question 4

Can it render text inside images?

Accepted Answer

Yes, improved text rendering is one of the release's three headline upgrades, covering accuracy, layout, and combined text+image composition. Quote the exact wording you want in the prompt.

Question 5

What license applies and can I use outputs commercially?

Accepted Answer

Apache-2.0, per the Hugging Face repo Qwen/Qwen-Image-2512. That permits commercial use, modification, and redistribution with attribution and license notice.

Question 6

How do I call this chute from code?

Accepted Answer

POST a flat JSON body to https://vonkaiser-qwen-image-2512.chutes.ai/generate with an Authorization: Bearer header carrying your Chutes API key. The response body is the JPEG image itself; write it straight to a file.

Question 7

Can I use it to edit an existing image?

Accepted Answer

No, this chute is text-to-image only and accepts no image input. For instruction-based editing with reference images, use the Qwen-Image-Edit-2511 chute.

Question 8

Does it understand Chinese prompts?

Accepted Answer

Yes. The model is tagged for English and Chinese, and the model card's own examples include Chinese negative prompts. Both prompt and negative_prompt can be written in either language.

Qwen Image 2512

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Qwen Image 2512

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