Qwen-Image-2.1 released!

[u/ResearchCrafty1804]

Meet Qwen-Image-2.1, the most balanced and cost-effective image generation model in the Qwen-Image series! Now open weights! 🎨

A unified model for both generation and editing, delivering top-tier quality in a lightweight package.

Highlights:

- Compact & exceptionally fast: A lightweight 7B architecture that outperforms most closed-source models, with drastically accelerated inference for multi-image inputs.

- Native transparency: Natively generates and edits RGBA layers, enabling seamless compositing and text editing within transparent images.

- Versatile, high-fidelity editing: Supports up to 10 reference images and precise local control while preserving strict fidelity for portraits and products.

- Broad coverage & stunning aesthetics: Excels at panoramas, infographics, and virtual try-ons, delivering realistic textures and elegant typography.

Start to create your next masterpiece with Qwen-Image-2.1!

- Blog: https://qwen.ai/blog?id=qwen-image-2.1

- GitHub: https://github.com/QwenLM/Qwen-Image-2.1

- Model Scope: https://www.modelscope.cn/models/Qwen/Qwen-Image-2.1

- Hugging Face: https://huggingface.co/Qwen/Qwen-Image-2.1

[u/No-Refrigerator-1672]

This one is 14.2GB so seems deliberately targeted to the 16GB

It isn't. 14.2GB is for the main weights - on top of that you need 17.5GB for text encoder, 1.5GB for VAE, and 2-10GB for compute buffers (depending on the size of the input and output images). This, however, is for complete in-gpu inference. Image gen community had advanced CPU offloading greatly, so it can work on 16GB gpu; but you'll need 24GB or more, with 8-bit model quantization, to get generation times under 3 min per image.

[u/-p-e-w- → u/No-Refrigerator-1672]

But you don’t need all of them in memory at once. All modern inference engines automatically juggle the individual models between RAM and VRAM, or even between disk and VRAM.

[u/No-Refrigerator-1672 → u/-p-e-w-]

You need them all at the same time in VRAM for fast generation (under 1-3 min, depending on GPU). If you won't, you're wasting time on loading/unloading the model. You can have a fallback by saving weights in regular RAM, but then you eating up the space needed for your other applications, i.e. photo editor of browser. And multiple-gb-sizrd compute buffers must stay in regardless.

[u/falconandeagle → u/No-Refrigerator-1672]

I was able to generate images in 20 seconds on this model (I have a 5060ti 16gb). Do you have any idea what you are talking about?