Unsloth 发布 Qwen-Image-2.1 的 GGUF 量化版，12GB 显存可本地运行
Qwen-Image-2.1 can now run locally on 12GB VRAM with Unsloth GGUFs! 🖼️
Unsloth 发布 Qwen-Image-2.1 的 GGUF 量化版本，可在 12GB 显存下本地运行，该 7B 模型表现与 Nano Banana 2.0 相当。若追求更高质量，还可通过 offloading 在仅 6GB 显存上运行 Dynamic FP8。Unsloth 同时表示 FP8 与 GGUF 量化版已可在 Unsloth Desktop 中正常运行，支持图像生成与编辑。
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Unsloth 给出 Qwen-Image-2.1 的本地量化版本与显存门槛，读者可据此判断自己的设备能否跑图像生成与编辑。
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查看原帖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…
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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://t.co/tVntKOi7jy - GitHub: https://t.co/cRj66wCrWr - Model Scope: https://t.co/64d7Ix6YFR - Hugging Face: https://t.co/njHSBXUbVS
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Qwen-Image-2.1 can now run locally on 12GB VRAM with Unsloth GGUFs! 🖼️
The 7B model performs on par with Nano Banana 2.0.
For higher quality, you can also run Dynamic FP8 on just 6GB of VRAM via offloading.