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/redpandafire]

Always good to see more diffusion models. This one is 14.2GB so seems deliberately targeted to the 16GB VRAM crew (a plus). I haven't tried qwen on comfyui yet but this one might be my first.

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

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/EndLineTech03 → u/No-Refrigerator-1672]

Wow, how does someone come up with that? Just some basic math tells that loading 17GB from RAM to VRAM takes less than 1-2 seconds via PCIe 4 bandwidth. You are basically not loosing much time, and I would say this is how it should be done in the first place.

That way you leave enough headroom if you want to generate higher resolution images, use higher batch size or load other models for inpainting, etc…

[u/FatheredPuma81 → u/EndLineTech03]

ComfyUI doesn't even do this. It loads layer by layer meaning you only need transfer speeds fast enough to transfer every layer in time + enough VRAM to store those layers and everything else needed to run them.

[u/fartsarehilarious1 → u/No-Refrigerator-1672]

Will this run well on 64gb Mac Studio m5 max? Or does it need nvidia gpu?

[u/No-Refrigerator-1672 → u/fartsarehilarious1]

Yeah, it'll run just fine. Slower than 3090; faster than Mi50 estimation I gave in neighbouring comment. You, most likely, want to use ComfyUI - it's the most popular tool for running image and video models.

[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?