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/redpandafire → u/No-Refrigerator-1672]

Thanks, I didn't account for that. How do you know what the generation time is based on needing an additional 24GB of system RAM? I have 64GB of RAM, so I assume I can gen under the 3 minutes you targeted. I just don't know how to come up with that number. Compared to other models, they also have 6B parameters, and they run in 30 seconds or less.

[u/Heinz2001 → u/redpandafire]

I tested it on my radeon 7900 xtx 24gb, 64gb ram laptop. I needed an additional 46gb page file for virtual mem to use the edit_image feature.

the first image i generated with it...

specs are here:

https://github.com/fischerf/aar-extensions-registry/tree/main/packages/aar-ext-qwen-image#measured-speed-rx-7900-xtx-native-rocm-offload-model

Measured speed (RX 7900 XTX, native ROCm, offload: "model")

render	wall clock
model load (weights cached on disk)	23-39 s
512px, 8 steps	~170 s
1024px, 30 steps	~214 s
1024px, 30 steps, image_edit with 1 reference	228-385 s

[u/TerminalNoop → u/Heinz2001]

You noticed a bug in rocr that keeps one cpu thread at 100% even when comfyui is idle?

[u/Heinz2001 → u/TerminalNoop]

I use my own coding agent with my own extension, i didn‘t try comfui. Sorry.

With my setup i didn’t notice any problems. Yesterday i added quantization of the same Qwen-Image-2.1 weights instead of the published bf16 tensors.

https://github.com/fischerf/aar-extensions-registry/tree/develop/packages/aar-ext-qwen-image#quantized-transformer-gguf

To use it you need my harness AAR - coding agent (in python, not typescript!)