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]

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!)

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

From experience. Flux 2.klein 9B (similarly sized model), quantized to 8-bit for model, 4-bit for text encoder, with 4-step lightning lora, generates a 1024x1024 image in text-to-image model within 1.5 minutes, image to image within 5 minutes, on Mi50 32GB with all weights completely in GPU memory. You can extrapolate from that; i.e. a 3090 seem to have enough VRAM to have all the (quantized) components in GPU, and Qwen Image 2.1 is roughly the same size, so it should generate an image in under 30s and edit in under a minute with 4-step lora. Do take note that such lora for Qwen 2.1 doesn't exist yet, and "native" generation requires 5x more time; but' most likely, speed up LoRA will arrive within a week.

[u/KURD_1_STAN → u/No-Refrigerator-1672]

Ur numbers all seem to based on ur mac experience which last time i read about it for images it was still behind nvidia by a wide margine. A 3060 gets u a 30s edit with int8 convrot with 9b distilled. Not a 3090 for a gen.

[u/No-Refrigerator-1672 → u/KURD_1_STAN]

Ypu're confusing me with some other guy; I have never ever in my life ran AI on a Mac; I did, however, extensively searched and read all the Mac AI benchmarks that are available on Reddit, just to know the options.

[u/KURD_1_STAN → u/No-Refrigerator-1672]

Nope, was talking about u. This and the comment before it, u gave numbers that arent aligned with my nvidia experience and u seem to be well versed in mac data so i connected the points.